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"summary.longiPenal"<-
function(object,level=.95, len=6, d=2, lab=c("coef","hr"), ...)
{
x <- object
if (!inherits(x, "longiPenal"))
stop("Object must be of class 'longiPenal'")
z<-abs(qnorm((1-level)/2))
co <- x$coef
se <- sqrt(diag(x$varH))
or <- exp(co)
li <- exp(co-z * se)
ls <- exp(co+z * se)
p <- signif(1 - pchisq((co/se)^2, 1), 5)
rl <- cbind(co, se, p)
dimnames(rl) <- list(names(co), c(lab[1], "SE","p"))
ddl <- dim(rl)
al <- formatC(rl, d, len,format="f")
dim(al) <- ddl
if(length(ddl) == 1){
ddl<-c(1,ddl)
dim(al)<-ddl
labl<-" "
}
else
labl <- dimnames(rl)[[1]]
mxl <- max(nchar(labl)) + 1
al[which(al[,3]==formatC(0, d, len,format="f")),3]<-formatC("<1e-16", d, len,format="f")
if(x$TwoPart==0){
cat("Longitudinal outcome:\n")
cat("------------- \n")
cat(paste(rep(" ",mxl),collapse=""),paste(" ",dimnames(rl)[[2]]),"\n")
for(i in (x$nvar[1]+1):ddl[1])
{
labl[i] <- paste(c(rep(" ", mxl - nchar(labl[i])), labl[i]),collapse = "")
cat(labl[i], al[i, 1:3]," \n")
}
r <- cbind(or, li, ls)
dimnames(r) <- list(names(co), c(lab[2], paste(level*100,"%",sep=""), "C.I."))
n<-r
dd <- dim(n)
n[n > 999.99] <- Inf
a <- formatC(n, d, len,format="f")
dim(a) <- dd
if(length(dd) == 1){
dd<-c(1,dd)
dim(a)<-dd
lab<-" "
}
else
lab <- dimnames(n)[[1]]
mx <- max(nchar(lab)) + 1
}
else{
cat("Binary outcome:\n")
cat("------------- \n")
cat(paste(rep(" ",mxl),collapse=""),paste(" ",dimnames(rl)[[2]]),"\n")
for(i in (x$nvar[1]+x$nvarY+1):(ddl[1]))
{
labl[i] <- paste(c(rep(" ", mxl - nchar(labl[i])), labl[i]),collapse = "")
cat(labl[i], al[i, 1:3]," \n")
}
cat("\n")
cat("Continuous outcome:\n")
cat("------------- \n")
cat(paste(rep(" ",mxl),collapse=""),paste(" ",dimnames(rl)[[2]]),"\n")
for(i in (x$nvar[1]+1):(ddl[1]-x$nvarB))
{
labl[i] <- paste(c(rep(" ", mxl - nchar(labl[i])), labl[i]),collapse = "")
cat(labl[i], al[i, 1:3]," \n")
}
r <- cbind(or, li, ls)
dimnames(r) <- list(names(co), c(lab[2], paste(level*100,"%",sep=""), "C.I."))
n<-r
dd <- dim(n)
n[n > 999.99] <- Inf
a <- formatC(n, d, len,format="f")
dim(a) <- dd
if(length(dd) == 1){
dd<-c(1,dd)
dim(a)<-dd
lab<-" "
}
else
lab <- dimnames(n)[[1]]
mx <- max(nchar(lab)) + 1
}
cat("\n")
cat("Terminal event:\n")
cat("--------------- \n")
cat(paste(rep(" ",mx),collapse=""),paste(" ",dimnames(n)[[2]]),"\n")
for(i in 1:x$nvar[1])
{
lab[i] <- paste(c(rep(" ", mx - nchar(lab[i])), lab[i]),collapse = "")
cat(lab[i], a[i, 1], "(", a[i, 2], "-", a[i, 3], ") \n")
}
}
|
test_that("bpc returns a bpc object the bt-U-ordereffect model", {
skip_on_cran()
test_btU <- load_testdata('test_btUordereffect')
m1 <- bpc(
data = test_btU,
player0 = 'player0',
player1 = 'player1',
result_column = 'y',
cluster = 'cluster',
z_player1 = 'z1',
model_type = 'bt-U-ordereffect',
solve_ties = 'random',
win_score = 'higher',
iter = 2000,
warmup = 600,
show_chain_messages = F,
seed = 8484
)
expect_s3_class(m1, 'bpc')
expect_no_error(summary(m1))
})
|
library(survival)
library(BART)
data(lung)
N <- length(lung$status)
table(lung$ph.karno, lung$pat.karno)
h <- which(is.na(lung$ph.karno))
lung$ph.karno[h] <- lung$pat.karno[h]
times <- lung$time
delta <- lung$status-1
times <- ceiling(times/30)
table(times)
table(delta)
x.train <- cbind(lung$age, lung$sex, lung$ph.karno)
dimnames(x.train)[[2]] <- c('age(yr)', 'M(1):F(2)', 'ph.karno(0:100:10)')
post <- mc.surv.bart(x.train=x.train, times=times, delta=delta, x.test=x.train,
mc.cores=8, seed=99)
K <- post$K
h <- seq(1, N*K, floor(N/10)*K)
for(i in h) {
post.mcmc <- post$yhat.test[ , (i-1)+1:K]
z <- gewekediag(post.mcmc)$z
y <- max(c(4, abs(z)))
if(i==1) plot(post$times, z, ylim=c(-y, y), type='l',
xlab='t', ylab='z')
else lines(post$times, z, type='l')
}
lines(post$times, rep(-1.96, K), type='l', lty=2)
lines(post$times, rep( 1.96, K), type='l', lty=2)
|
test_that("convertMassUnitsSI", {
expect_equal(convertMassUnitsSI(2, 'mg', 'kg'), 2e-6)
expect_equal(convertMassUnitsSI(2, 'mg', 'ug'), 2e3)
expect_equal(convertMassUnitsSI(2, 'g', 'ug'), 2e6)
})
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reconstruct_attrs <- poorman:::reconstruct_attrs
reconstruct_attrs.data.frame <- poorman:::reconstruct_attrs.data.frame
expect_identical(
reconstruct_attrs(data.frame(), data.frame()),
data.frame(),
info = "classes are restored for data.frames"
)
expect_identical(
reconstruct_attrs(data.frame(), structure(data.frame(), class = c("foo", "data.frame"))),
structure(data.frame(), class = c("foo", "data.frame")),
info = "classes are restored for additional classes"
)
expect_identical(
reconstruct_attrs(
structure(list(), .Names = character(0), row.names = c(NA, -1L), class = "data.frame"),
structure(list(), class = "data.frame", row.names = integer(0), .Names = character(0), foo = 1)
),
structure(list(), class = "data.frame", row.names = c(NA, -1L), .Names = character(0), foo = 1),
info = "attributes of `template` are kept"
)
data <- data.frame(a = c(1, 2))
template <- data.frame()
x <- reconstruct_attrs(data, template)
expect <- data.frame(a = c(1, 2))
expect_identical(x, expect, info = "compact row names are retained")
expect_identical(
.row_names_info(x, type = 0L),
.row_names_info(expect, type = 0L),
info = "Explicitly ensure internal row name structure is identical"
)
df <- structure(list(x = 1), class = c("tmp", "data.frame"), row.names = 1L, foo = "bar")
out <- poorman:::remove_attributes(df)
expect_identical(
out,
data.frame(x = 1),
info = "remove_attributes() strips attributes before dispatch"
)
df <- structure(list(x = 1), class = c("tmp", "data.frame"), row.names = "a", foo = "bar")
out <- poorman:::remove_attributes(df)
expect_identical(
out,
data.frame(x = 1, row.names = "a"),
info = "remove_attributes() strips attributes before dispatch"
)
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dOFV.vs.cov <-
function(xpdb1,
xpdb2,
covariates=xvardef("covariates",xpdb1),
ylb=expression(paste(Delta, OFV[i])),
main="Default",
smooth=TRUE,
abline=c(0,0),
ablcol="grey",
abllwd=2,
abllty="dashed",
max.plots.per.page=1,
...)
{
if(is.null(xpdb2)){
cat("Comparison database needed for this plot!")
return(NULL)
}
iv1 <- [email protected]
iv2 <- [email protected]
if(!all(iv1$ID == iv2$ID)){
cat("All ID labels for both databases must match\n")
return(NULL)
}
comp.frame <- data.frame(id=iv1$ID,obj1=iv1$OBJ,obj2=iv2$OBJ)
comp.frame$d.obj <- comp.frame$obj2 - comp.frame$obj1
xpdb1@Data[!duplicated(xpdb1@Data[,xvardef("id",xpdb1)]),"d.obj"] <- comp.frame$d.obj
xpdb1@Prefs@Labels$d.obj <- "Change in individual OFV"
cat.covs <- c()
cont.covs <- c()
for (i in covariates) {
if(is.factor(xpdb1@Data[[i]])){
cat.covs <- c(cat.covs,i)
}else{
cont.covs <- c(cont.covs,i)
}
}
cov.list <- list(cont.covs=cont.covs,cat.covs=cat.covs)
if(is.null(cat.covs)) cov.list$cat.covs <- NULL
if(is.null(cont.covs)) cov.list$cont.covs <- NULL
plotList <- vector("list",length(cov.list))
plot.num <- 0
for (j in 1:length(cov.list)) {
xplot <- xpose.plot.default(cov.list[[j]],
"d.obj",
xpdb1,
onlyfirst=T,
inclZeroWRES=T,
smooth=smooth,
abline=c(0,0),
ablcol=ablcol,
abllwd=abllwd,
abllty=abllty,
ylb=ylb,
main=NULL,
pass.plot.list=TRUE,
...)
plot.num <- plot.num+1
plotList[[plot.num]] <- xplot
}
default.plot.title <- paste("Individual change in OFV vs. Covariate(s)\n",
"(Run",xpdb2@Runno," - Run",xpdb1@Runno,")",sep="" )
plotTitle <- xpose.multiple.plot.title(object=xpdb1,
plot.text = default.plot.title,
main=main,
no.runno=T,
...)
obj <- xpose.multiple.plot(plotList,plotTitle,max.plots.per.page=max.plots.per.page,
...)
return(obj)
}
|
knitr::opts_chunk$set(fig.width=6, fig.height=4, warning=FALSE)
library(VWPre)
data(VWdat)
library(VWPre)
load("aligndat.rda")
check_msg_time(data = aligndat, Msg = "VowelOnset")
check_all_msgs(data = aligndat)
aligned1 <- align_msg(data = aligndat, Msg = "VowelOnset")
check_msg_time(data = aligned1, Msg = "VowelOnset")
aligned2 <- create_time_series(data = aligned1, Adjust = 0)
check_time_series(data = aligned2)
check_msg_time(data = aligned2, Msg = "VowelOnset")
|
library(rpf)
lmp.item<-rpf.lmp(q=2)
par<-c(.69,.71,-.5,-8.48,.52,-3.32)
theta<-seq(-3,3,.1)
P<-rpf.prob(lmp.item, par, theta)
plot(theta, P[2,], type="l", ylim=c(0,1), xlab="Theta", ylab="P(Theta)")
round(rpf.dLL(lmp.item, par, theta[1], weight=c(5,7)),2)
rpf.dTheta(lmp.item, par, where=-.5, dir=1)
library(numDeriv)
dTheta.wrap<-function(theta, spec, par, cat=1){
rpf.prob(spec, par, theta)[cat]
}
grad(dTheta.wrap, -.5, spec=lmp.item, par=par)
hessian(dTheta.wrap, -.5, spec=lmp.item, par=par)
|
cchs <- function(formula, data=parent.frame(), inSubcohort, stratum,
samplingFractions, cohortStratumSizes, precision=NULL,
returnAdjustedTimes=FALSE,
swap=TRUE, dropNeverAtRiskRows=TRUE, dropSubcohEventsDfbeta=FALSE,
adjustSampFracIfAnyNAs=FALSE, keepAllCoxphElements=FALSE,
confidenceLevel=0.95, verbose=FALSE, annotateErrors=TRUE,
coxphControl, ...) {
originalCall <- match.call()
checkCchsArgumentsNotMissing(formula, inSubcohort, stratum)
result <- getExtraVariables(data, originalCall,
samplingFractions, cohortStratumSizes, verbose)
if (length(result) > 0)
for (varName in names(result))
assign(varName, result[[varName]])
checkCchsArguments(formula, data, inSubcohort, stratum, confidenceLevel)
inSubcohort <- as.logical(inSubcohort)
if (annotateErrors) {
tryCatch(
modelFrame <- model.frame(formula, data, na.action=na.omit),
error=function(e) {
if (e$call[[1]] == "Surv") {
errorReason <- "the Surv object being illegal"
} else if (grepl("^object .* not found$", e$message)) {
errorReason <- "a variable in the model-formula\nnot existing"
} else if (grepl("^variable lengths differ", e$message)) {
errorReason <-
"variables in the model-formula having\ndifferent lengths"
} else {
errorReason <- "a problem with the model-formula"
}
stop(e$message, "\n[This error is caused by ", errorReason, ".]")
}
)
} else {
modelFrame <- model.frame(formula, data, na.action=na.omit)
}
rowsToDrop <- attr(modelFrame, "na.action")
if (is.null(rowsToDrop)) {
rowsToDrop <- numeric(0)
} else {
rowsToDrop <- as.vector(rowsToDrop)
}
rowsToDrop <- union(rowsToDrop, which(is.na(inSubcohort)))
rowsToDrop <- union(rowsToDrop, which(is.na(stratum)))
nBeforeDroppingRows <- length(inSubcohort)
rowsToKeep <- setdiff(1:nBeforeDroppingRows, rowsToDrop)
nRowsToDrop <- length(rowsToDrop)
droppedRowsMessage <- { if (nRowsToDrop==0) "" else
paste0(nRowsToDrop, " observation", { if (nRowsToDrop==1)
" was" else "s were" }, " dropped because of NAs.") }
if (!is.factor(stratum)) {
stratum <- tryCatch(
as.factor(stratum),
error=function(e) stop("stratum cannot be converted to a factor")
)
}
stratum <- droplevels(stratum)
checkSubcohortStrataNotEmpty(stratum, inSubcohort,
message="before dropping NA-containing rows")
coxphControl <- getCoxphControl(coxphControl, ...)
samplingFractionInfo <- checkSamplingFractions(samplingFractions,
cohortStratumSizes, stratum, inSubcohort, verbose)
samplingFractions <- getSamplingFractions(samplingFractions,
cohortStratumSizes, stratum, inSubcohort, samplingFractionInfo)
checkCohortStratumSizes(samplingFractions, cohortStratumSizes,
stratum, inSubcohort, verbose=FALSE)
if (adjustSampFracIfAnyNAs && nRowsToDrop > 0)
samplingFractions <- adjustSamplingFractions(samplingFractions,
stratum, inSubcohort, rowsToKeep, verbose)
modelFrame <- model.frame(formula, data, na.action=na.pass)
survObject <- model.response(modelFrame)
if (!inherits(survObject, "Surv"))
stop("the response has to be a Surv object")
censoringType <- attr(survObject, "type")
if (censoringType == "right") {
if (!identical(colnames(survObject), c("time", "status")))
stop("INTERNAL ERROR: unable to process Surv object")
allExitTimes <- survObject[,"time"]
} else if (censoringType == "counting") {
if (!identical(colnames(survObject), c("start", "stop", "status")))
stop("INTERNAL ERROR: unable to process Surv object")
allExitTimes <- survObject[,"stop"]
} else {
stop("censoring type \"", censoringType, "\" is not allowed")
}
modelFrame <- modelFrame[rowsToKeep,]
if (nrow(modelFrame) == 0) stop("no observations")
stratum <- stratum[rowsToKeep]
inSubcohort <- inSubcohort[rowsToKeep]
samplingFractions <- samplingFractions[rowsToKeep]
stratum <- droplevels(stratum)
checkSubcohortStrataNotEmpty(stratum, inSubcohort,
message="after dropping NA-containing rows")
modelMatrix <- model.matrix(attr(modelFrame, "terms"), modelFrame)
modelMatrix <- modelMatrix[, -1, drop=FALSE]
prohibitedSpecialNames <- c("cluster", "strata")
prohibitedSpecialTermsList <-
attr(terms(formula, specials=prohibitedSpecialNames), "specials")
for (i in prohibitedSpecialNames)
if (!is.null(prohibitedSpecialTermsList[[i]]))
stop("formula is not allowed to contain these special terms: ",
paste(prohibitedSpecialNames, collapse=", "))
survObject <- model.response(modelFrame)
if (censoringType == "right") {
timeAtEntry <- rep(0, nrow(survObject))
timeAtExit <- survObject[,"time"]
} else {
timeAtEntry <- survObject[,"start"]
timeAtExit <- survObject[,"stop"]
}
isCase <- survObject[,"status"]
isCase <- as.logical(isCase)
isNonsubcohortNoncase <- !inSubcohort & !isCase
if (any(isNonsubcohortNoncase)) {
nNonsubNoncase <- sum(isNonsubcohortNoncase)
if (nNonsubNoncase == 1) {
stop("there is 1 non-subcohort non-case")
} else {
stop("there are ", nNonsubNoncase, " non-subcohort non-cases")
}
}
n <- nrow(modelMatrix)
nEachStatus <- c(
subcohortNoncases=sum(inSubcohort & !isCase),
subcohortCases=sum(inSubcohort & isCase),
nonsubcohortCases=sum(!inSubcohort)
)
adjustedEntryAndExitTimes <-
adjustTiedEventTimes(timeAtEntry, timeAtExit, isCase, precision)
timeAtEntry <- adjustedEntryAndExitTimes$timeAtEntry
timeAtExit <- adjustedEntryAndExitTimes$timeAtExit
tiedTimesMessage <- adjustedEntryAndExitTimes$message
entryAndExitTimes <- c(timeAtEntry, timeAtExit)
epsilon <- minDifference(entryAndExitTimes) / 10
if (verbose) cat("
if (epsilon == 0)
stop("epsilon has underflowed to zero, because some entry and \n",
"exit times were too close together")
stratumSizes <- tapply(stratum, stratum, length)
subcohortStratumSizes <- sapply(X=levels(stratum),
FUN=function(x) sum(inSubcohort[stratum==x]))
if (verbose) {
sf <- round(samplingFractions[match(levels(stratum), stratum)], 6)
cat("\n
"\n
"\n
}
modelMatrixColumnNames <- colnames(modelMatrix)
colnames(modelMatrix) <- paste0("dummyName", 1:ncol(modelMatrix))
modelMatrixPlus <- modelMatrix
rm(modelMatrix)
extraVariables <- c("timeAtEntry", "timeAtExit", "isCase", "inSubcohort",
"stratum", "samplingFractions", "id")
id <- 1:n
modelMatrixPlus <- as.data.frame(modelMatrixPlus)
for (varName in extraVariables)
modelMatrixPlus[[varName]] <- get(varName)
if (swap)
modelMatrixPlus <- doSplitting(modelMatrixPlus, epsilon,verbose=verbose)
nonSubcohortRows <- which(!modelMatrixPlus$inSubcohort)
modelMatrixPlus$timeAtEntry[nonSubcohortRows] <-
modelMatrixPlus$timeAtExit[nonSubcohortRows] - epsilon
modelMatrixPlus <- modelMatrixPlus[
modelMatrixPlus$timeAtEntry != modelMatrixPlus$timeAtExit,]
modelMatrixPlus$useForDfbeta <- modelMatrixPlus$inSubcohort
if (dropSubcohEventsDfbeta)
modelMatrixPlus <-
splitSubcohortCasesJustBeforeEvent(modelMatrixPlus, epsilon)
if (dropNeverAtRiskRows)
modelMatrixPlus <-
dropRowsThatAreNeverAtRisk(modelMatrixPlus, verbose=FALSE)
useForDfbeta <- NULL
varNames <- c(extraVariables, "useForDfbeta")
for (varName in varNames)
assign(varName, modelMatrixPlus[[varName]])
modelMatrix <- modelMatrixPlus[,setdiff(names(modelMatrixPlus), varNames)]
rm(modelMatrixPlus)
modelMatrix <- as.matrix(modelMatrix)
colnames(modelMatrix) <- modelMatrixColumnNames
if (annotateErrors) {
result <- withCallingHandlers(
tryCatch(
coxph(Surv(timeAtEntry, timeAtExit, isCase) ~ modelMatrix +
offset(-log(samplingFractions)), control=coxphControl),
error=function(e) stop(e$message,
"\n[This error was thrown by coxph, which was called by cchs.]")
),
warning=function(w) {
warning(conditionMessage(w),
"\n[This warning was thrown by coxph, which was called by cchs.]")
invokeRestart("muffleWarning")
}
)
} else {
result <- coxph(Surv(timeAtEntry, timeAtExit, isCase) ~ modelMatrix +
offset(-log(samplingFractions)), control=coxphControl)
}
names(result$coefficients) <- modelMatrixColumnNames
dimnames(result$var) <- list(modelMatrixColumnNames, modelMatrixColumnNames)
dfbetaUncombined <- residuals(result, type="dfbeta")
if (is.matrix(dfbetaUncombined)) {
dfbetaUncombined <- dfbetaUncombined[useForDfbeta, , drop=FALSE]
} else {
dfbetaUncombined <- matrix(dfbetaUncombined[useForDfbeta], ncol=1)
}
dfbetaCombined <- aggregate(dfbetaUncombined,
list(stra=stratum[useForDfbeta], id=id[useForDfbeta]), sum)
dfbetaStrata <- dfbetaCombined$stra
dfbetaCombined <- dfbetaCombined[, !(names(dfbetaCombined) %in%
c("stra","id")), drop=FALSE]
nParameters <- ncol(dfbetaUncombined)
samplingFractions <- samplingFractions[match(levels(stratum), stratum)]
stratumWeights <- subcohortStratumSizes * (1 - samplingFractions)
varAdjustment <- matrix(0, nParameters, nParameters)
for (i in levels(stratum)) {
result$var <- result$var + stratumWeights[i] *
var(dfbetaCombined[dfbetaStrata==i,])
}
beta <- result$coefficients
se <- sqrt(diag(result$var))
z <- qnorm((1 + confidenceLevel) / 2)
result$coeffsTable <- cbind(exp(beta), exp(beta-z*se), exp(beta+z*se),
1-pchisq((beta/se)^2, 1), beta, se)
colnames(result$coeffsTable) <-
c("HR", "CIlower", "CIupper", "p", "logHR", "SElogHR")
result$call <- originalCall
result$n <- n
result$nEachStatus <- nEachStatus
result$nevent <- n - unname(result$nEachStatus["subcohortNoncases"])
result$nStrata <- nlevels(stratum)
message <- droppedRowsMessage
if (message != "" && tiedTimesMessage != "")
message <- paste0(message, "\n")
if (tiedTimesMessage != "")
message <- paste0(message, tiedTimesMessage)
result$message <- message
if (confidenceLevel != 0.95)
result$confidenceLevel <- confidenceLevel
varsToKeep <- c("coefficients","var","iter","n","nevent","nStrata","call",
"coeffsTable","nEachStatus","message","confidenceLevel","loglik")
if (!keepAllCoxphElements)
for (varName in names(result))
if (!(varName %in% varsToKeep))
result[varName] <- NULL
if (returnAdjustedTimes) {
adjustedTimes <- unname(allExitTimes)
adjustedTimes[rowsToKeep] <- adjustedEntryAndExitTimes$timeAtExit
result$adjustedTimes <- adjustedTimes
}
result <- setS3class(result, "cchs")
return(result)
}
|
norm_test_fun <- function(x,
method = "anderson",
pval = 0.05,
xn = 'x',
bin = FALSE) {
if (!method %in% c("shapiro", "anderson", "ks")) {
warning("Method should only be 'ks', 'shapiro' or 'anderson'.
Setting method as 'anderson'")
method <- "anderson"
}
if (length(x) > 5000) {
warning(paste0(xn, " is very large (>5000), normality test may be accurate.
Consider changing method to 'Anderson-Darling'"))
}
if (length(x) < 3) {
warning(paste0(xn," is very small (<3), normality test can't be performed."))
return(0)
}
if (method == "shapiro") {
out <- shapiro.test(x)
} else if (method == "anderson") {
out <- anderson.test(x)
} else {
out <- ks.test(x, "pnorm", mean = mean(x), sd = sd(x))
}
if (bin) {
return(out$p.value > pval)
} else {
return(out)
}
}
anderson.test <- function (x){
DNAME <- deparse(substitute(x))
x <- sort(x[complete.cases(x)])
n <- length(x)
if (n < 8)
stop("sample size must be greater than 7")
logp1 <- pnorm( (x-mean(x))/sd(x), log.p=TRUE)
logp2 <- pnorm( -(x-mean(x))/sd(x), log.p=TRUE )
h <- (2 * seq(1:n) - 1) * (logp1 + rev(logp2))
A <- -n - mean(h)
AA <- (1 + 0.75/n + 2.25/n^2) * A
if (AA < 0.2) {
pval <- 1 - exp(-13.436 + 101.14 * AA - 223.73 * AA^2)
}
else if (AA < 0.34) {
pval <- 1 - exp(-8.318 + 42.796 * AA - 59.938 * AA^2)
}
else if (AA < 0.6) {
pval <- exp(0.9177 - 4.279 * AA - 1.38 * AA^2)
}
else if (AA < 10) {
pval <- exp(1.2937 - 5.709 * AA + 0.0186 * AA^2)
}
else pval <- 3.7e-24
out <- list(statistic = c(A = A), p.value = pval,
method = "Anderson-Darling normality test",
data.name = DNAME)
class(out) <- "htest"
return(out)
}
|
test_that("P", {
data("Train", package = "mlogit")
data = prepare(form = choice ~ price | 0 | time + comfort + change,
choice_data = Train)
expect_snapshot(print(data))
expect_snapshot(summary(data))
})
test_that("MMNP", {
data("Train", package = "mlogit")
data = prepare(form = choice ~ price | 0 | time + comfort + change,
choice_data = Train,
re = c("price","time"))
expect_snapshot(print(data))
expect_snapshot(summary(data))
})
test_that("without choice variable", {
data("Train", package = "mlogit")
Train[["choice"]] = NULL
data = prepare(form = choice ~ price | 0 | time + comfort + change,
choice_data = Train,
re = c("price","time"),
alternatives = c("A","B"))
expect_error(mcmc(data))
expect_snapshot(print(data))
expect_snapshot(summary(data))
})
test_that("train and test data", {
data("Train", package = "mlogit")
data = prepare(form = choice ~ price | 0 | time + comfort + change,
choice_data = Train,
re = c("price","time"),
test_prop = 0.2)
expect_type(data, "list")
expect_snapshot(print(data))
for(i in 1:2){
expect_snapshot(print(data[[i]]))
expect_snapshot(summary(data[[i]]))
}
})
|
unstrip <- function(x)
{
dd <- dim(x)
y <- x
if(length(dd)==2){
dd2 <- dd[2]
if(dd2==1) y<- c(x[,1])
if(dd2==2) y<- cbind(c(x[,1]),c(x[,2]))
if(dd2>2) y<- cbind(c(x[,1]),c(x[,2]),c(x[,3]))
if(dd2>3)for(i in 4:dd2) y <- cbind(y,c(x[,i]))
y
}
if(length(dd)==1 || length(dd)==0){
y <- c(unlist(c(unlist(x))))
names(y) <- NULL
}
y
}
hare <- function(data, delta, cov, penalty, maxdim, exclude,
include, prophaz = FALSE, additive = FALSE, linear, fit, silent = TRUE)
{
call <- match.call()
if(!missing(data)) data <- unstrip(data)
if(!missing(delta)) delta <- unstrip(delta)
if(!missing(cov)) cov <- unstrip(cov)
if(!missing(exclude)) exclude <- unstrip(exclude)
if(!missing(include)) include <- unstrip(include)
MAXKNOTS <- -3
MAXSPACE <- -3
z <- .C("sharex",
mk = as.integer(MAXKNOTS),
ms = as.integer(MAXSPACE),
PACKAGE = "polspline")
MAXKNOTS <- z$mk
MAXSPACE <- z$ms
if(missing(data))
stop("there has to be data")
if(length(data) < 25)
stop("not enough data")
if(min(data) < 0)
stop("negative data")
if(missing(delta))
delta <- data - data + 1
if(length(data) != length(delta))
stop("data and delta have different length")
dd <- abs(delta - 0.5)
if(min(dd) < 0.5 || max(dd) > 0.5)
stop("delta not all 0 or 1")
ndata <- length(data)
if(missing(cov)) {
ncov <- 0
cov <- 0
iia <- order(data)
delta <- delta[iia]
data <- data[iia]
}
else {
if(length(cov) == ndata)
cov <- matrix(cov, ncol = 1, nrow = ndata)
if(length(cov[, 1]) != ndata)
stop("covariates not ndata * ncov matrix")
ncov <- length(cov[1, ])
cov <- cbind(cov, 1)
y <- cbind(data, cov)
keys <- 1:ndata
for(i in (ncov + 2):1)
keys <- keys[sort.list(y[keys, i])]
data <- data[keys]
delta <- delta[keys]
cov <- cov[keys, 1:ncov]
}
if(additive) {
if(!missing(exclude))
stop("cannot have exclude and additive")
if(!missing(include))
stop("cannot have include and additive")
prophaz <- FALSE
include <- c(0, 0)
}
if(missing(exclude) + missing(include) == 0)
stop("only 1 from exclude and include allowed")
vexclude <- 0
if(missing(exclude) == FALSE) {
if(length(exclude) == 2)
exclude <- matrix(exclude, ncol = 2, nrow = 1)
if(length(exclude[1, ]) != 2)
stop("exclude has wrong shape")
if(min(exclude) < 0 || max(exclude) > ncov)
stop("exclude has wrong values")
vexclude <- as.vector(t(exclude))
vexclude <- c(length(vexclude)/2, vexclude)
if(prophaz && ncov > 0) {
vexclude <- c(vexclude, as.vector(rbind(1:ncov, 0)))
vexclude[1] <- vexclude[1] + ncov
}
}
if(missing(include) == FALSE || additive) {
if(length(include) == 2)
include <- matrix(include, ncol = 2, nrow = 1)
if(length(include[1, ]) != 2)
stop("include has wrong shape")
if(min(include) < 0 || max(include) > ncov)
stop("include has wrong values")
include <- t(apply(include, 1, sort))
if(length(include) == 2)
include <- matrix(include, ncol = 2, nrow = 1)
if(prophaz)
include <- include[include[, 1] > 0, ]
vexclude <- as.vector(t(include))
vexclude <- c( - length(vexclude)/2, vexclude)
}
if(missing(include) && missing(exclude) && prophaz && ncov > 0)
vexclude <- c(ncov, as.vector(rbind(1:ncov, 0)))
mindist <- 5
if(missing(penalty))
penalty <- log(ndata)
if(missing(maxdim)) {
maxdim <- floor(6 * (ndata)^0.2)
if(maxdim > MAXSPACE - 1)
maxdim <- MAXSPACE - 1
maxdim <- - maxdim
}
if(maxdim > MAXSPACE - 1) {
maxdim <- MAXSPACE - 1
print(paste("maximum dimension reduced to", maxdim))
}
lins <- rep(0, MAXSPACE)
if(!missing(linear)) {
linear[linear <= 0] <- ncov + 1
linear[linear > ncov + 1] <- ncov + 1
lins[linear] <- 1
}
if(additive)
vexclude <- c(-1, 0, 0)
fitter <- 0
bbtt <- matrix(0, ncol = 6, nrow = abs(maxdim))
cckk <- matrix(0, ncol = (MAXKNOTS + 1), nrow = (ncov + 1))
if(!missing(fit)) {
if(class(fit)!="hare")
stop("fit is not a hare object")
fitter <- fit$ndim
if(fit$ncov != ncov)
stop("ncov and fit's ncov are different")
bbtt[1:fit$ndim, ] <- fit$fcts
bbtt <- as.vector(t(bbtt))
bbtt[is.na(bbtt)] <- -1
a1 <- length(fit$knots[1, ])
cckk[, 1:a1] <- fit$knots
cckk <- as.vector(cckk)
cckk[is.na(cckk)] <- -1
}
z <- .C("share",
as.integer(ncov),
ndim = as.integer(ndata),
as.double(data),
as.integer(delta),
as.double(cov),
as.double(penalty),
as.integer(mindist),
as.integer(maxdim),
bbtt = as.double(bbtt),
cckk = as.double(cckk),
as.integer(vexclude),
as.integer(lins),
as.integer(silent),
logl = as.double(rep(0, MAXSPACE)),
as.integer(fitter),
ad = as.integer(rep(0, MAXSPACE)),
as.integer(0),
PACKAGE = "polspline")
maxdim <- abs(maxdim)
z$bbtt <- matrix(z$bbtt, nrow = maxdim, ncol = 6, byrow = TRUE)[1:z$ndim,
]
z$cckk <- matrix(z$cckk, nrow = ncov + 1, ncol = MAXKNOTS + 1, byrow =
TRUE)
z$cckk <- z$cckk[, 1:(1 + max(z$cckk[, 1]))]
z$cckk <- matrix(z$cckk, nrow = ncov + 1)
l1 <- max(z$cckk[, 1])
for(i in 1:(ncov + 1))
if(z$cckk[i, 1] != l1) z$cckk[i, (z$cckk[i, 1] + 2):(l1 + 1)] <-
NA
if(l1 > 0 && ncov > 0)
dimnames(z$cckk) <- list(c("T", 1:ncov), c("K", 1:l1))
if(l1 > 0 && ncov == 0)
dimnames(z$cckk) <- list(c("T"), c("K", 1:l1))
if(l1 == 0 && ncov > 0)
dimnames(z$cckk) <- list(c("T", 1:ncov), "K")
if(l1 == 0 && ncov == 0)
dimnames(z$cckk) <- list(c("T"), "K")
l1 <- max((1:MAXSPACE)[z$logl > -1e+100])
z$bbtt <- matrix(z$bbtt, ncol = 6)
dimnames(z$bbtt) <- list(1:(z$ndim), c("dim1", "knot1", "dim2", "knot2",
"beta", "SE"))
z$bbtt[z$bbtt[, 3] == -1, 3:4] <- NA
if(is.na(l1)) {
z$logl <- z$logl[1]
z$ad <- z$ad[1]
}
else {
z$logl <- z$logl[1:l1]
z$ad <- z$ad[1:l1]
}
z$ad[z$logl < -1e+100] <- NA
z$logl[z$logl < -1e+100] <- NA
z$logl <- cbind(z$logl, z$ad)
dimnames(z$logl) <- list(NULL, c("log-lik", "A/D"))
ranges <- NA
if(ncov == 1)
ranges <- matrix(range(cov), ncol = 1, nrow = 2)
if(ncov > 1)
ranges <- apply(cov, 2, range)
fit <- list(call = call, ncov = ncov, ndim = z$ndim, fcts = z$bbtt, knots
= z$cckk, penalty = penalty, max = max(data), ranges = ranges,
logl = z$logl, sample = ndata)
class(fit) <- "hare"
fit
}
plot.hare <- function(x, cov, n = 100, which = 0, what = "d", time, add = FALSE,
xlim, xlab, ylab, type, ...)
{
if(class(x)!="hare")
stop("x is not a hare object")
if(!missing(cov))cov <- unstrip(cov)
if(!missing(time))time <- unstrip(time)
fit <- x
nocov <- 0
if(fit$ncov == 0)
nocov <- 1
else {
if(length(cov) != fit$ncov)
stop("covariates are wrong")
}
if(which == 0) {
if(missing(xlim)) {
if(nocov == 0) {
u1 <- qhare(0.01, cov, fit)
u2 <- qhare(0.99, cov, fit)
}
else {
u1 <- qhare(0.01, fit = fit)
u2 <- qhare(0.99, fit = fit)
}
u3 <- 1.1 * u1 - 0.1 * u2
u2 <- min(u2, fit$max)
u4 <- 1.1 * u2 - 0.1 * u1
if(u3 < 0)
u3 <- 0
else if(u4/u3 > 5)
u3 <- 0
}
else {
u3 <- xlim[1]
u4 <- xlim[2]
}
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
if(fit$ncov > 0)
yy <- cov
}
else {
if(which < 0 || which > fit$ncov)
stop("which is wrong")
if(missing(time))
stop("time is missing")
if(missing(xlim)) {
u3 <- fit$ranges[1, which]
u4 <- fit$ranges[2, which]
}
else {
u3 <- xlim[1]
u4 <- xlim[2]
}
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
yy <- matrix(cov, ncol = fit$ncov, nrow = n, byrow = TRUE)
yy[, which] <- xx
xx <- time
}
iwhat <- 0
if(what == "d" || what == "D")
iwhat <- 3
if(what == "h" || what == "H")
iwhat <- 2
if(nocov == 0)
yy <- xhare(iwhat, xx, yy, fit)
else yy <- xhare(iwhat, xx, arg4 = fit)
if(what == "s" || what == "S")
yy <- 1 - yy
if(missing(xlab))
xlab <- ""
if(missing(ylab))
ylab <- ""
if(missing(type))
type <- "l"
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
if(!add)
plot(xx, yy, xlab = xlab, ylab = ylab, type = type, ...)
else lines(xx, yy, type = type, ...)
}
print.hare <- function(x,...)
{
summary.hare(x)
}
summary.hare <- function(object,...)
{
if(class(object)!="hare")
stop("object is not a hare object")
fit <- object
s3 <- as.vector(t(fit$logl))
s3[is.na(s3)] <- 0
s1 <- as.vector(t(fit$fcts))
s2 <- as.vector(fit$knots)
s1[is.na(s1)] <- -1
s2[is.na(s2)] <- -1
.C("ssumm",
as.double(fit$penalty),
as.integer(fit$sample),
as.double(s3),
as.integer(length(s3)/2),
as.double(s2),
as.double(s1),
as.integer(fit$ndim),
as.integer(fit$ncov),
PACKAGE = "polspline")
invisible()
}
dhare <- function(q,cov,fit)
{
if(class(fit)!="hare")
stop("fit is not a hare object")
xhare(3, q,cov,fit)
}
hhare <- function(q,cov,fit)
{
if(class(fit)!="hare")
stop("fit is not a hare object")
xhare(2, q,cov,fit)
}
phare <- function(q,cov,fit)
{
if(class(fit)!="hare")
stop("fit is not a hare object")
xhare(0, q,cov,fit)
}
qhare <- function(p,cov,fit)
{
if(class(fit)!="hare")
stop("fit is not a hare object")
xhare(1, p,cov,fit)
}
rhare <- function(n, cov,fit)
{
if(class(fit)!="hare")
stop("fit is not a hare object")
xhare(1, runif(n), cov,fit)
}
xhare <- function(arg1,arg2,arg3,arg4)
{
iwhat <- arg1
if(!missing(arg2))arg2 <- unstrip(arg2)
if(!missing(arg3))arg3 <- unstrip(arg3)
q <- arg2
cov <- arg3
fit <- arg4
if(class(fit)!="hare")
stop("fit is not a hare object")
zz <- 0
if(missing(arg4)) {
zz <- 7
fit <- cov
if(is.null(fit$ncov))
stop("fit missing")
}
if(fit$ncov == 0) {
if(!missing(arg3) && zz==0)
stop("there should be no covariates")
else cov <- 0
}
else {
if(is.matrix(cov) == FALSE)
cov <- matrix(cov, ncol = fit$ncov)
nd <- length(cov[, 1])
nc <- length(cov[1, ])
nq <- length(q)
if(nc != fit$ncov)
stop("not the right number of covariates")
if(nd != 1 && nq != 1 && nd != nq)
stop("no matching number of cases")
if(nq == 1)
q <- rep(q, nd)
if(nd == 1 && nq != 1)
cov <- matrix(cov, nrow = nq, ncol = nc, byrow = TRUE)
}
fit$fcts <- as.vector(t(fit$fcts))
fit$fcts[is.na(fit$fcts)] <- -1
fit$knots <- as.vector(fit$knots)
fit$knots[is.na(fit$knots)] <- 0
z <- .C("sphare",
as.integer(fit$ncov),
as.integer(fit$ndim),
as.integer(length(q)),
as.double(cov),
as.integer(iwhat),
q = as.double(q),
as.double(fit$knots),
as.double(fit$fcts),
PACKAGE = "polspline")
z$q
}
heft <- function(data, delta, penalty, knots, leftlin, shift,
leftlog, rightlog, maxknots, mindist, silent = TRUE)
{
call <- match.call()
if(!missing(data))data <- unstrip(data)
if(!missing(delta))delta <- unstrip(delta)
if(!missing(knots))knots<- unstrip(knots)
if(missing(leftlin))leftlin<-2
leftlin<-leftlin*1
nx <- -1
z <- .C("sheftx",
z = as.integer(nx),
PACKAGE = "polspline")
lgth <- z$z
lgth <- 40
if(missing(mindist))
mindist <- 5
if(mindist < 2) {
warning("mindist reset to 2")
mindist <- 2
}
if(missing(delta))
delta <- data - data + 1
if(length(data) != length(delta))
stop("data and delta have different length")
if(min(data) < 0)
stop("negative data")
if(min(data) == 0) {
if(!missing(leftlog)){
if(leftlog != 0)
stop("** hard-zeros, leftlog has to be 0 **")
}
else{
leftlog <- 0
warning("*** hard zeros: leftlog set to 0 ***")
}
if(leftlin==2){
warning("*** hard zeros: leftlin set to TRUE ***")
leftlin <- 1
}
}
leftlin <- (leftlin==1)
dd <- abs(delta - 0.5)
if(min(dd) < 0.5 || max(dd) > 0.5)
stop("delta not all 0 or 1")
delta <- delta[order(data)]
data <- sort(data)
nx <- length(data)
if(!missing(maxknots) && !missing(knots) && maxknots < length(knots))
stop("maxknots is smaller than length(knots)")
if(missing(maxknots))
maxknots <- 0
if(missing(penalty))
penalty <- log(nx)
if(maxknots > lgth - 5) {
maxknots <- lgth - 5
warning(paste("maxknots reduced to", maxknots))
}
if(!missing(shift))
if(shift <= - min(data))
stop("shift too small")
if(missing(shift))
shift <- quantile(data[delta==1], 0.75)
nknots <- 0
iauto <- 0
if(!missing(knots)) {
nknots <- length(knots)
if(nknots > lgth - 5)
stop(paste("nknots can be at most", lgth - 5))
iauto <- 2
uu <- knots[2:nknots] - knots[1:(nknots - 1)]
if(min(uu) < 0)
stop("knots not in sequence")
if(knots[1] < 0)
stop("knot 1 is negative")
knots <- c(knots, rep(0, lgth - nknots))
}
if(iauto < 2)
knots <- rep(0, lgth)
error <- c(1, rep(0, 20))
if(silent != TRUE)
error[7] <- 37
tails <- c(0, 0, 0, 0, 1)
if(!missing(leftlog) || min(data) == 0) {
if(leftlog <= -1)
stop("leftlog should be smaller than -1")
tails[1] <- 1
tails[2] <- leftlog
}
if(!missing(rightlog)) {
if(rightlog < -1)
stop("rightlog should be at least -1")
tails[3] <- 1
tails[4] <- rightlog
}
if(leftlin)
tails[5] <- 0
z <- .C("sheft",
as.integer(nx),
as.double(data),
as.integer(delta),
nk = as.integer(nknots),
knots = as.double(knots),
as.double(penalty),
tails = as.double(tails),
as.integer(iauto),
logl = as.double(rep(0, lgth)),
theta = as.double(rep(0, lgth)),
iknots = as.integer(rep(0, lgth)),
error = as.integer(error),
as.double(shift),
as.integer(maxknots),
ad = as.integer(rep(0, lgth)),
as.integer(mindist),
PACKAGE = "polspline")
error <- z$error
if(z$nk < -100) error[2] <- 1
z$logl[abs(z$logl) < 1e-100] <- 0
z$logl[z$ad == 2] <- 0
if(error[2] == 0){
fit <-list(call = call, knots = z$knots[1:z$nk], logl = z$logl[2:(z$nk
+ 1)], thetak = z$theta[1:z$nk], thetap = z$theta[z$nk +
(1:2)], thetal = z$theta[z$nk + (3:4)], penalty =
penalty, shift = shift, sample = length(data), logse =
z$tails[c(2, 4)], max = max(data), adddel = z$ad[2:(z$nk
+ 1)])
class(fit) <- "heft"
fit
}
else {
print("sorry......")
invisible()
}
}
plot.heft <- function(x, n = 100, what = "d", add = FALSE, xlim, xlab, ylab, type,
...)
{
if(class(x)!="heft")
stop("x is not a heft object")
fit <- x
if(missing(xlim)) {
u2 <- min(qheft(0.99, fit), fit$max)
u3 <- 0
u4 <- 1.1 * u2
xlim <- c(u3, u4)
}
u3 <- xlim[1]
u4 <- xlim[2]
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
if(u3 == 0)
xx <- (1:n)/n * u4
yy <- c(-10, -10)
if(what == "d" || what == "D")
yy <- dheft(xx, fit)
if(what == "h" || what == "H")
yy <- hheft(xx, fit)
if(what == "f" || what == "F" || what == "p" || what == "P")
yy <- pheft(xx, fit)
if(what == "s" || what == "S")
yy <- 1-pheft(xx, fit)
if(yy[1] < -8)
stop("What is wrong? Well: what is wrong.")
if(missing(xlab))
xlab <- ""
if(missing(ylab))
ylab <- ""
if(missing(type))
type <- "l"
if(!add)
plot(xx, yy, xlim = xlim, xlab = xlab, ylab = ylab, type = type,
...)
else lines(xx, yy, type = type, ...)
}
print.heft <- function(x,...)
{
summary.heft(x)
}
summary.heft <- function(object,...)
{
if(class(object)!="heft")
stop("object is not a heft object")
fit <- object
ul <- fit$penalty
um <- fit$sample
ll <- fit$logl
kk <- (1:length(ll))
kk <- kk[fit$ad != 2]
ll <- ll[fit$ad != 2]
ad <- fit$ad[fit$ad != 2]
bb <- -2 * ll + ul * (kk-2)
if(fit$thetal[1]!=0) bb <- bb+ul
if(fit$thetal[2]!=0) bb <- bb+ul
if(fit$thetap[2]!=0) bb <- bb+ul
if(fit$thetap[2]==0 && min(kk)==2) bb <- bb+ul
cc1 <- bb
cc2 <- bb
cc2[1] <- Inf
cc1[length(bb)] <- 0
if(length(bb) > 1) {
for(i in 1:(length(bb) - 1)) {
cc1[i] <- max((ll[(i + 1):(length(bb))] - ll[i])/(kk[(i +
1):(length(bb))] - kk[i]))
cc2[i + 1] <- min((ll[1:i] - ll[i + 1])/(kk[1:i] - kk[i +
1]))
}
}
c3 <- cc2 - cc1
cc1[c3 < 0] <- NA
cc2[c3 < 0] <- NA
uu <- cbind(kk, ad, ll, bb, 2 * cc1, 2 * cc2)
ww <- rep("", length(bb))
dimnames(uu) <- list(ww, c("knots", "A(0)/D(1)", "loglik", "AIC",
"minimum penalty", "maximum penalty"))
print(round(uu, 2))
cat(paste("the present optimal number of knots is ", kk[bb == min(bb)],
"\n"))
if(ul == log(um))
cat(paste("penalty(AIC) was the default: BIC=log(samplesize): log(",
um, ")=", round(ul, 2), "\n"))
else cat(paste("penalty(AIC) was ", round(ul, 2),
", the default (BIC) ", "would have been", round(log(um
), 2), "\n"))
if(min(kk) == 3 && fit$thetap[2] != 0) {
cat(paste("models with fewer than", kk[1], "knots",
"can be fitted, but they are not optimal for the\n"))
cat(paste("present choice of penalty - choose penalty in",
"heft larger to see these fits\n"))
}
if(min(kk) > 3) {
cat(paste("models with fewer than", kk[1], "knots",
"can be fitted, but they are not optimal for the\n"))
cat(paste("present choice of penalty - choose penalty in",
"heft larger to see these fits\n"))
}
uuu <- matrix(NA, ncol = 3, nrow = 2, dimnames = list(c("left tail",
"right tail"), c("theta", "SE", "t")))
uuu[, 1] <- fit$thetal
if(fit$logse[1] > 0) {
uuu[1, 2] <- fit$logse[1]
uuu[1, 3] <- abs(fit$thetal[1]/fit$logse[1])
}
if(fit$logse[2] > 0) {
uuu[2, 2] <- fit$logse[2]
uuu[2, 3] <- abs(fit$thetal[2]/fit$logse[2])
}
print(round(uuu, 2))
invisible()
}
dheft <- function(q, fit)
{
if(class(fit)!="heft")
stop("fit is not a heft object")
y <- hheft(q, fit)
z <- 1 - pheft(q, fit)
y * z
}
hheft <- function(q, fit)
{
if(class(fit)!="heft")
stop("fit is not a heft object")
q <- unstrip(q)
y <- fit$thetap[1] + q * fit$thetap[2] + fit$thetal[1] * log(q/(q + fit$
shift)) + fit$thetal[2] * log(q + fit$shift)
for(i in 1:length(fit$knots)) {
if(fit$thetak[i] != 0)
y <- y + fit$thetak[i] * ((abs(q - fit$knots[i]) + q -
fit$knots[i])/2)^3
}
exp(y)
}
pheft <- function(q, fit)
{
if(class(fit)!="heft")
stop("fit is not a heft object")
q <- unstrip(q)
sq <- rank(q)
q <- sort(q)
z <- .C("heftpq",
as.double(c(fit$knots,rep(0,100))),
as.double(c(fit$shift,rep(0,100))),
as.double(c(fit$thetak,rep(0,100))),
as.double(c(fit$thetal,rep(0,100))),
as.double(c(fit$thetap,rep(0,100))),
as.integer(1),
pp = as.double(q),
as.double(q),
as.integer(length(fit$knots)),
as.integer(length(q)),
PACKAGE = "polspline")
zz <- z$pp[sq]
zz[q < 0] <- 0
zz
}
qheft <- function(p, fit)
{
if(class(fit)!="heft")
stop("fit is not a heft object")
p <- unstrip(p)
sp <- rank(p)
p <- sort(p)
z <- .C("heftpq",
as.double(c(fit$knots,rep(0,100))),
as.double(c(fit$shift,rep(0,100))),
as.double(c(fit$thetak,rep(0,100))),
as.double(c(fit$thetal,rep(0,100))),
as.double(c(fit$thetap,rep(0,100))),
as.integer(0),
as.double(p),
qq = as.double(p),
as.integer(length(fit$knots)),
as.integer(length(p)),
PACKAGE = "polspline")
zz <- z$qq[sp]
zz[p < 0] <- NA
zz[p == 0] <- 0
zz[p == 1] <- Inf
zz[p > 1] <- NA
zz
}
rheft <- function(n, fit)
{
if(class(fit)!="heft")
stop("fit is not a heft object")
pp <- runif(n)
qheft(pp, fit)
}
oldlogspline.to.logspline <- function(obj,data)
{
nobj <- list()
nobj$call <- obj$call
if(is.null(obj$call))nobj$call <- "translated from oldlogspline"
nobj$knots <- sum(obj$coef[-(1:2)]!=0)
nobj$coef.pol <- obj$coef[1:2]
nobj$coef.kts <- obj$coef[-(1:2)]
nobj$coef.kts <- nobj$coef.kts[nobj$coef.kts!=0]
nobj$knots <- obj$knots[obj$coef[-(1:2)]!=0]
nobj$maxknots <- length(obj$coef)-2
nobj$penalty <- obj$penalty
nobj$bound <- obj$bound
nobj$samples <- obj$sample
nobj$logl <- obj$logl[obj$logl!=0]
lx <- length(nobj$logl)
nobj$logl <- cbind(nobj$maxknots+1-(lx:1),c(rep(2,lx-1),1),nobj$logl)
class(nobj) <- "logspline"
if(!missing(data))nobj$range <- obj$range
else {
lx <- 1/(nobj$samples+1)
nobj$range <- qlogspline(c(lx,1-lx),nobj)
}
nobj$mind
nobj
}
poldlogspline <- function(q, fit)
{
fitx <- oldlogspline.to.logspline(fit)
plogspline(q,fitx)
}
qoldlogspline <- function(p, fit)
{
fitx <- oldlogspline.to.logspline(fit)
qlogspline(p,fitx)
}
roldlogspline <- function(n, fit)
{
if(class(fit)!="oldlogspline")
stop("fit is not an oldlogspline object")
pp <- runif(n)
qoldlogspline(pp, fit)
}
doldlogspline <- function(q, fit)
{
x <- q
if(class(fit)!="oldlogspline")
stop("fit is not an oldlogspline object")
q <- unstrip(q)
y <- fit$coef[1] + x * fit$coef[2]
for(i in 1:length(fit$knots)) {
if(fit$coef[i+2] != 0)
y <- y + fit$coef[i+2] * ((abs(x - fit$knots[i]) +
x - fit$knots[i])/2)^3
}
y <- exp(y)
if(fit$bound[1] > 0)
y[x < fit$bound[2]] <- 0
if(fit$bound[3] > 0)
y[x > fit$bound[4]] <- 0
y
}
plot.oldlogspline <- function(x, n = 100, what = "d", xlim, xlab = "", ylab = "", type = "l", add = FALSE, ...)
{
fit <- x
if(class(fit)!="oldlogspline")
stop("fit is not an oldlogspline object")
if(missing(xlim)) {
u1 <- qoldlogspline(0.01, fit)
u2 <- qoldlogspline(0.99, fit)
u3 <- 1.1 * u1 - 0.1 * u2
u4 <- 1.1 * u2 - 0.1 * u1
}
else {
u3 <- xlim[1]
u4 <- xlim[2]
}
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
if(what == "d" || what == "D")
yy <- doldlogspline(xx, fit)
if(what == "f" || what == "F" || what == "p" || what == "P")
yy <- poldlogspline(xx, fit)
if(what == "s" || what == "S")
yy <- 1 - poldlogspline(xx, fit)
if(what == "h" || what == "H")
yy <- doldlogspline(xx, fit)/(1 - poldlogspline(xx, fit))
if(missing(xlab))
xlab <- ""
if(missing(ylab))
ylab <- ""
if(missing(type))
type <- "l"
if(add==FALSE)plot(xx, yy, xlab = xlab, ylab = ylab, type = type, ...)
else lines(xx,yy, type = type, ...)
}
print.oldlogspline <- function(x,...)
{
summary.oldlogspline(x)
}
summary.oldlogspline <- function(object,...)
{
if(class(object)!="oldlogspline")
stop("fit is not an oldlogspline object")
fit <- object
if(fit$delete==FALSE)stop(paste("summary.oldlogspline can only provide",
"information if delete in oldlogspline is TRUE"))
ul <- fit$penalty
um <- fit$sample
ll <- fit$logl
kk <- (1:length(ll))
kk <- kk[ll != 0] + 2
ll <- ll[ll != 0]
error<-FALSE
rr <- ll[1:(length(ll)-1)]-ll[2:length(ll)]
if(length(ll)>1 && max(rr)>0)error<-TRUE
bb <- -2 * ll + ul * kk
cc1 <- bb
cc2 <- bb
cc2[1] <- Inf
cc1[length(bb)] <- 0
if(length(bb) > 1) {
for(i in 1:(length(bb) - 1)) {
cc1[i] <- max((ll[(i + 1):(length(bb))] - ll[i])/(
kk[(i + 1):(length(bb))] - kk[i]))
cc2[i + 1] <- min((ll[1:i] - ll[i + 1])/(kk[1:i] - kk[i + 1]))
}
}
c3 <- cc2 - cc1
cc1[c3 < 0] <- NA
cc2[c3 < 0] <- NA
uu <- cbind(kk, ll, bb, 2 * cc1, 2 * cc2)
ww <- rep("", length(bb))
if(error){
cat("Warning - imprecision in loglikelihood (possibly due to heavy tails)\n")
cat("the output of summary.oldlogspline might not be correct\n")
}
dimnames(uu) <- list(ww, c("knots", "loglik", "AIC", "minimum penalty",
"maximum penalty"))
print(round(uu, 2))
cat(paste("the present optimal number of knots is ", kk[bb== min(bb)],"\n"))
if(ul == log(um))
cat(paste("penalty(AIC) was the default: BIC=log(samplesize): log(",
um, ")=", round(ul, 2),"\n"))
else
cat(paste("penalty(AIC) was ", round(ul, 2),", the default (BIC) ",
"would have been", round(log(um), 2),"\n"))
if(min(kk) > 3 && fit$delete==TRUE){
cat(paste( "models with fewer than", kk[1],"knots ",
"can be fitted, but they are not optimal for\n"))
cat(paste("the present choice of penalty - choose penalty in",
"oldlogspline larger\nto see these fits\n"))
}
if(min(kk) > 3 && fit$delete==3)
cat(paste("models with fewer than", kk[1],"knots ",
"were not fitted because of convergence problems\n"))
invisible()
}
oldlogspline <- function(uncensored, right, left, interval, lbound, ubound,
nknots, knots, penalty, delete = TRUE)
{
nsample <- rep(0, 6)
if(!missing(uncensored))uncensored <- unstrip(uncensored)
if(!missing(right))right <- unstrip(right)
if(!missing(left))left <- unstrip(left)
if(!missing(interval))interval <- unstrip(interval)
if(!missing(knots))knots <- unstrip(knots)
if(!missing(interval)) {
if(length(interval[1, ]) != 2)
stop("interval must have two columns")
if(min(abs(interval[, 1] - interval[, 2])) < 0) stop(
"not all lower bounds smaller than upper bounds")
nsample[3] <- length(interval)/2
nsample[1] <- length(interval)/2
if(!missing(lbound))
interval[interval[, 1] < lbound, 1] <- lbound
if(!missing(ubound))
interval[interval[, 2] > ubound, 2] <- ubound
sample <- as.vector(t(interval))
ror <- order(interval[,1],interval[,2])
if(nsample[3]>1){
ro1 <- interval[ror[(1:(nsample[3]-1))],1]==interval[ror[2:nsample[3]],1]
ro2 <- interval[ror[(1:(nsample[3]-1))],2]==interval[ror[2:nsample[3]],2]
nsample[6] <- nsample[3]-sum(ro1+ro2==2)
}
else nsample[6] <- 1
}
if(!missing(uncensored)) {
uncensored2 <- uncensored[!is.na(uncensored)]
u2 <- length(uncensored) - length(uncensored2)
if(u2 > 0)
print(paste("***", u2, " NAs ignored in uncensored"))
uncensored <- uncensored2
if(nsample[1] > 0)
sample <- c(uncensored, sample)
if(nsample[1] == 0)
sample <- uncensored
nsample[1] <- length(uncensored) + nsample[1]
nsample[2] <- length(uncensored)
uncensored <- sort(uncensored)
if(nsample[2]>1)
nsample[6] <- sum(uncensored[2:nsample[2]] !=
uncensored[1:(nsample[2]-1)]) + 1 + nsample[6]
else
nsample[6] <- nsample[6]+1
}
if(nsample[1] == 0) stop("you either need uncensored or interval censored data")
if(!missing(right)) {
if(nsample[1] > 0)
sample <- c(sample, right)
if(nsample[1] == 0)
sample <- right
nsample[1] <- length(right) + nsample[1]
nsample[4] <- length(right)
right <- sort(right)
if(nsample[4]>1){
nsample[6] <- sum(right[2:nsample[4]]!=right[1:(nsample[4]-1)])+
1 + nsample[6]
}
else nsample[6] <- nsample[6]+1
}
if(!missing(left)) {
if(nsample[1] > 0)
sample <- c(sample, left)
if(nsample[1] == 0)
sample <- left
nsample[1] <- length(left) + nsample[1]
nsample[5] <- length(left)
left <- sort(left)
if(nsample[5]>1){
nsample[6] <- sum(left[2:nsample[5]]!=left[1:(nsample[5]-1)])+
1 + nsample[6]
}
else nsample[6] <- nsample[6]+1
}
if(missing(penalty)) penalty <- log(nsample[1])
n1 <- 4 * nsample[1]^0.2 + 1
if(!missing(nknots))
n1 <- nknots + 1
if(!missing(knots)) n1 <- length(knots) + 1
if(!missing(knots)) {
nknots <- length(knots)
knots <- sort(knots)
iautoknot <- 0
if(knots[1] > min(sample))
stop("first knot must be smaller than smallest sample")
if(knots[nknots] < max(sample))
stop("last knot should be larger than largest sample")
}
else {
if(missing(nknots))
nknots <- 0
knots <- vector(mode = "double", length = max(nknots, 50))
iautoknot <- 1
}
xbound <- c(1, 0, 0, 0, 0)
if(!missing(lbound)) {
xbound[2] <- 1
xbound[3] <- lbound
if(lbound > min(sample))
stop("lbound should be smaller than smallest sample")
}
if(!missing(ubound)) {
xbound[4] <- 1
xbound[5] <- ubound
if(ubound < max(sample))
stop("ubound should be larger than largest sample")
}
SorC <- vector(mode = "integer", length = 35)
SorC[1] <- 1
SorC[17] <- 0
nsample[6] <- nsample[6]-1
if(length(table(sample))<3)stop("Not enough unique values")
z <- .C("logcensor",
as.integer(delete),
as.integer(iautoknot),
as.double(sample),
as.integer(nsample),
bd = as.double(xbound),
SorC = as.integer(SorC),
nk = as.integer(nknots),
kt = as.double(knots),
cf = as.double(c(knots, 0, 0)),
as.double(penalty),
as.double(sample),
as.double(sample),
logl = as.double(rep(0, n1 + 1)),
PACKAGE = "polspline")
SorC <- z$SorC
if(SorC[1] == -1 && SorC[28] == 0 && nsample[1]!=nsample[2] && nsample[2]>15){
SorC <- vector(mode = "integer", length = 35)
SorC[1] <- 1
SorC[17] <- 1
z <- .C("logcensor",
as.integer(delete),
as.integer(iautoknot),
as.double(sample),
as.integer(nsample),
bd = as.double(xbound),
SorC = as.integer(SorC),
nk = as.integer(nknots),
kt = as.double(knots),
cf = as.double(c(knots, 0, 0)),
as.double(penalty),
as.double(sample),
as.double(sample),
logl = as.double(rep(0, n1 + 1)),
PACKAGE = "polspline")
}
bound <- c(z$bd[2], z$bd[3], z$bd[4], z$bd[5])
SorC <- z$SorC
if(abs(SorC[1]) > 2) {
for(i in 3:abs(SorC[1]))
cat(paste("===> warning: knot ", SorC[i - 1],
" removed - double knot\n"))
if(SorC[1] < 0)
SorC[1] <- -1
if(SorC[1] == 23)
SorC[1] <- -3
}
if(abs(SorC[1]) > 3) {
cat("* several double knots suggests that your data is *\n")
cat("* strongly rounded, attention might be required *\n")
SorC[1] <- 1
}
if(SorC[1] == -3)
stop("* too many double knots")
if(SorC[1] == -1 && SorC[28] == 0)
stop("* no convergence")
if(SorC[28] > 0)
cat(paste("* convergence problems, smallest number of knots",
" tried is ", SorC[28] + 1," *\n"))
if(SorC[1] == 2)
stop("* sample is too small")
if(SorC[1] == -2)
stop(paste("* too many knots, at most ", SorC[2],
"knots possible"))
if(SorC[22] == 1) {
cat("possible discontinuity at lower end\n")
cat(paste("consider rerunning with lbound=", z$kt[1],
"\n"))
}
if(SorC[22] == 3) {
cat("possible infinite density at lower end\n")
cat("running program with fewer knots\n")
}
if(SorC[21] == 1)
cat("running with maximum degrees of freedom\n")
if(SorC[25] >0)
cat("* problems are possibly due to a very heavy right tail *\n")
if(SorC[24] >0)
cat("* problems are possibly due to a very heavy left tail *\n")
if(SorC[23] == 3) {
cat("possible infinite density at upper end\n")
cat("running program with fewer knots\n")
}
if(SorC[23] == 1) {
cat("possible discontinuity at upper end\n")
cat(paste("consider rerunning with ubound=", z$kt[z$nk],
"\n"))
}
if(delete && SorC[28]>0)delete<-3
coef <- z$cf[1:(z$nk + 2)]
uu <- 3:z$nk
if(delete == FALSE)uu <- 1
fit <- list(coef = coef, knots = z$kt[1:z$nk], bound = bound, logl = z$logl[
uu], penalty = penalty, sample = nsample[1], delete = delete)
class(fit) <- "oldlogspline"
fit
}
lspec <- function(data, period, penalty, minmass, knots, maxknots, atoms,
maxatoms, maxdim, odd = FALSE, updown = 3,silent=TRUE)
{
call <- match.call()
if(!missing(data))data <- unstrip(data)
if(!missing(period))period <- unstrip(period)
if(!missing(knots))knots <- unstrip(knots)
if(!missing(atoms))atoms <- unstrip(atoms)
if(missing(period) && missing(data))
stop(" either data or period should be specified ")
if(!missing(period) && !missing(data))
stop(" only one of data or period should be specified ")
if(!missing(period))
ny <- 2 * length(period)
if(missing(period)) {
ny <- length(data)
period <- Mod(fft(data))^2/(ny * 2 * pi)
period <- period[1:floor((length(period) + 2)/2)]
odd <- TRUE
if(floor(ny/2) == ny/2)
odd <- FALSE
}
else{
if(odd) ny <- ny + 1
period <- c(1,period)
}
if(min(period) <= 0)
stop(" all period elements should be larger than 0 ")
if(length(period) < 10)
stop("too few observations")
z <- .C("tspspsx",
z = as.integer(rep(-1, 12)),
PACKAGE = "polspline")
lgth <- z$z[1]
nx <- length(period)
if(missing(penalty))
penalty <- log(nx - 1)
dimatt <- 0
ktsatt <- 1
spkatt <- 1
nknots <- 0
natoms <- 0
if(!missing(maxknots)){
maxknots <- max(1, maxknots)
}
else {
maxknots <- -1
ktsatt <- 0
}
if(!missing(maxatoms)){
maxatoms <- max(0, maxatoms)
}
else {
maxatoms <- -1
spkatt <- 0
}
if(missing(minmass)){
if(!missing(data))
minmass <- var(data)*(-log(1-0.95^(1/nx))-1)/ny
else{
minmass <- mean(period[2:length(period)])*2*pi
minmass <- minmass*(-log(1-0.95^(1/nx))-1)/ny
}
}
minmass <- minmass * ny /(2*pi)
if(!missing(knots)) {
nknots <- length(knots)
if(nknots>1){
uu <- knots[2:nknots] - knots[1:(nknots - 1)]
if(min(uu) <= 0)
stop("knots not in sequence")
}
if(knots[1] < 0)
stop("knot 1 too small")
if(knots[nknots] > pi)
stop("last knot too large")
knots <- c(knots, rep(0, lgth - nknots))
if(ktsatt * maxknots < ktsatt * nknots)
stop("more knots than maxknots")
}
else{
knots <- rep(0, lgth)
}
if(!missing(atoms)) {
natoms <- length(atoms)
atoms <- round((atoms * ny)/(2 * pi))
if(natoms>1){
uu <- atoms[2:natoms] - knots[1:(natoms - 1)]
if(min(uu) <= 0)
stop("atoms not in sequence or too close")
}
if(atoms[1] < 1)
stop("atom 1 too small")
if(atoms[natoms] > ny/2)
stop("last atom too large")
atoms <- c(atoms, rep(0, lgth - natoms))
if(spkatt * maxatoms < spkatt * natoms)
stop("more atoms than maxatoms")
}
else{
atoms <- rep(0, lgth)
}
u1 <- max(nknots, 1, maxknots) + max(natoms, maxatoms)
if(u1 > lgth - 5)
stop("too many dimensions")
if(!missing(maxdim)) {
dimatt <- 1
if(u1 > maxdim)
stop("maxdim too small for other specifications")
if(maxdim > lgth - 5)
stop(paste("maxdim can be at most", lgth - 5))
}
else{
maxdim <- max(4 * nx^0.2, 15, u1)
}
dims <- c(nx, maxdim, dimatt, maxknots, ktsatt, nknots, maxatoms,
spkatt, natoms, odd, updown, 1*silent, 0)
z <- .C("tspsps",
dims = as.integer(dims),
as.double(period),
knots = as.double(c(knots,rep(0,nx))),
atoms = as.integer(c(atoms,rep(0,nx))),
as.double(penalty),
logl = as.double(rep(0, lgth)),
theta = as.double(rep(0, lgth)),
ad = as.integer(rep(0, lgth)),
minmass = as.double(minmass),
PACKAGE = "polspline")
dims <- z$dims
minmass <- minmass /( ny /(2*pi))
if(dims[12] == 1)
stop(paste("numerical problems -\n",
"probably too many knots or knots too close together",
" or a very sharp atom"))
if(dims[12] == 2)
stop("no convergence")
z$logl[abs(z$logl) < 1e-100] <- 0
z$logl[z$ad == 2] <- 0
mass <- ((z$theta[(dims[6] + 4) + (1:dims[9])]) * 2 * pi)/ny
atoms <- (z$atoms[1:dims[9]] * 2 * pi)/ny
if(dims[9] == 0) {
mass <- 0
atoms <- 0
}
thetak <- z$theta[5:(dims[6] + 4)]
knots <- z$knots[1:dims[6]]
if(dims[6] == 0) {
thetak <- 0
knots <- 0
}
logl <- z$logl[dims[6]+dims[9]]
fit <- list(call = call, thetap = z$theta[1:4], nknots = dims[6], knots =
knots, thetak = thetak, natoms = dims[9], atoms = atoms,
mass = mass, penalty = penalty, minmass = minmass,
sample = ny, logl = logl, updown = dims[11])
class(fit) <- "lspec"
fit
}
clspec <- function(lag, fit, cov = TRUE, mm)
{
if(class(fit)!="lspec")
stop("fit is not an lspec object")
if(!missing(lag))lag <- unstrip(lag)
llag <- abs(lag)
if(max(abs(round(llag) - llag)) > 0.01)
stop("some lags are not integer")
if(missing(mm)) {
mm <- max(c(1024, fit$sample, max(llag + 1)))
mm <- 2^(1 + floor(log(mm - 0.1)/log(2)))
}
if(mm < max(llag + 1))
stop("mm too small")
rr <- dlspec(((0:mm) * pi)/mm, fit)$d
rr <- c(rr, rr[mm:2])
rr <- (Re(fft(rr)) * pi)/mm
rr <- rr[llag + 1]
if(fit$natoms>0){
for(i in 1:fit$natoms) {
rr <- rr + 2 * cos(lag * fit$atoms[i]) * fit$mass[i]
}
}
if(cov == FALSE)
rr <- rr/rr[1]
rr
}
dlspec <- function(freq, fit)
{
if(class(fit)!="lspec")
stop("fit is not an lspec object")
if(!missing(freq))freq <- unstrip(freq)
freq <- freq - floor(freq/(2 * pi)) * 2 * pi
freq[freq > pi] <- 2 * pi - freq[freq > pi]
y <- rep(fit$thetap[1], length(freq)) + freq * fit$thetap[2]
y <- y + freq^2 * fit$thetap[3] + freq^3 * fit$thetap[4]
if(fit$nknots > 0) {
for(i in 1:fit$nknots) {
z <- freq - fit$knots[i]
y[z > 0] <- y[z > 0] + z[z > 0]^3 * fit$thetak[i]
}
}
d1 <- exp(y)
modfreq <- round((freq * fit$sample)/(2 * pi))
modmatch <- round((fit$atoms * fit$sample)/(2 * pi))
uu <- rep(0, round(fit$sample/2) + 2)
uu[modmatch] <- fit$mass
uu <- c(NA, uu)
l1 <- uu[modfreq+1]
modfreq <- ((2 * pi)/fit$sample) * modfreq
list(d = d1, modfreq = modfreq, m = l1)
}
plspec <- function(freq, fit, mm)
{
if(class(fit)!="lspec")
stop("fit is not an lspec object")
if(!missing(freq))freq <- unstrip(freq)
if(missing(mm)) {
mm <- max(c(4096, fit$sample))
mm <- 2^floor(log(mm - 0.1)/log(2))
}
ff <- freq[freq >= - pi]
ff <- ff[ff <= pi]
gg <- c(abs(ff), pi)
uu <- (c((1:mm) - 0.5) * pi)/mm
tt <- dlspec(uu, fit)$d
ss <- cumsum(tt)/mm
ss <- (c(0, 0, ss, ss[mm]) * pi)
tt <- (gg * mm)/pi
vv <- floor(tt)
tt <- tt - vv
tt <- (1 - tt) * ss[vv + 2] + tt * ss[vv + 3]
if(fit$natoms > 0) {
for(i in 1:fit$natoms)
tt[gg >= fit$atoms[i]] <- tt[gg >= fit$atoms[i]] + fit$
mass[i]
}
if(length(gg) < length(freq) + 1 || is.na(gg[1]))
warning("plspec is only valid for frequencies between -pi and pi")
ss <- rep(NA, length(freq))
ss[abs(freq) <= pi] <- tt[ - length(tt)] + tt[length(tt)]
ss[freq < 0] <- 2 * tt[length(tt)] - ss[freq < 0]
if(fit$natoms > 0) {
for(i in 1:fit$natoms)
ss[freq == - fit$atoms[i]] <- ss[freq == - fit$atoms[
i]] + fit$mass[i]
}
ss
}
rlspec <- function(n, fit, mean = 0, cosmodel = FALSE, mm)
{
if(class(fit)!="lspec")
stop("fit is not an lspec object")
if(missing(mm)) {
mm <- max(c(1024, fit$sample, n))
mm <- 2^(1 + floor(log(mm - 0.1)/log(2)))
}
if(mm < max(n/2 + 1))
stop("mm too small")
rr <- (dlspec(((0:mm) * pi)/mm, fit)$d * pi)/(2 * mm)
rr[1] <- rr[1] * 2
rr[mm + 1] <- rr[mm + 1] * 2
rr <- sqrt(rr)
uu <- rnorm(rr, 0, rr)
uu <- c(uu, uu[mm:2])
vv <- rnorm(rr, 0, rr)
vv <- c(vv, - vv[mm:2])
vv[c(1, (mm + 1))] <- 0
uu <- uu + vv * (1i)
uu <- Re(fft(uu))
uu <- uu[1:n] + mean
if(fit$natoms > 0) {
cc <- runif(1)*2*pi-pi
if(cosmodel) aa <- 2*sqrt(fit$mass)
else
aa <- 2 * rnorm(fit$natoms, 0, sqrt(fit$mass))
aa[fit$atoms == pi] <- 2 * aa[fit$atoms == pi]
for(i in 1:fit$natoms)
uu <- uu + aa[i] * cos((1:n) * fit$atoms[i] + pi * cc)
}
uu
}
plot.lspec <- function(x, what = "b", n, add = FALSE, xlim, ylim, xlab, ylab, type, ...)
{
fit <- x
if(class(fit)!="lspec")
stop("fit is not an lspec object")
if(add) {
plim <- (par()$usr)[1:2]
if(!missing(xlim)) {
plim[1] <- max(xlim[1], plim[1])
plim[2] <- min(xlim[2], plim[2])
}
}
else {
plim <- c(0, pi)
if(what =="p"||what=="P"||what=="f"||what=="F")plim[1]<- -pi
if(!missing(xlim)) {
plim[1] <- xlim[1]
plim[2] <- xlim[2]
}
}
if(missing(xlab))
xlab <- ""
if(missing(ylab))
ylab <- ""
if(what == "l" || what == "L") {
if(missing(type))
type <- "h"
if(fit$natoms>0){
x5 <- c(-fit$atoms,fit$atoms)
tt <- round(plim[2]/(2*pi))+1
vv <- round(plim[1]/(2*pi))-1
x1 <- x5
for(i in vv:tt)
if(i!=0)x1 <- c(x1, x5+i*2*pi)
y1 <- dlspec(x1,fit)$m
y1 <- y1[x1 <= plim[2]]
x1 <- x1[x1 <= plim[2]]
y1 <- y1[x1 > plim[1]]
x1 <- x1[x1 > plim[1]]
x1 <- c(x1[1], x1)
y1 <- c(0, y1)
if(!add)
plot(x1, y1, xlim = plim, xlab = xlab, ylab = ylab,
type = type, ...)
else lines(x1, y1, type = type, ...)
abline(h = 0)}
else{
if(add) abline(h=0)
else plot(plim,c(0,0),xlab = xlab, ylab = ylab,type="l",...)
}
}
if(what == "d" || what == "D" || what == "b" || what == "B") {
if(missing(type))
type <- "l"
if(missing(n))
n <- max(100, fit$sample + 1)
xx <- (0:(n - 1))/(n - 1) * (plim[2] - plim[1]) + plim[1]
yy <- dlspec(xx, fit)$d
if(fit$natoms == 0)
what <- "d"
if(missing(ylim))ylim<-range(yy)
}
if(what == "b" || what == "B") {
type <- "l"
x5 <- c(-fit$atoms,fit$atoms)
tt <- round(plim[2]/(2*pi))+1
vv <- round(plim[1]/(2*pi))-1
x3 <- x5
for(i in vv:tt)
if(i!=0)x3 <- c(x3, x5+i*2*pi)
y3 <- dlspec(x3, fit)
y3 <- max(yy)*1.1
if(fit$nknots==1)y3 <- 2*y3
if(!missing(ylim))y3 <- ylim[2]
x2 <- x3
y2 <- dlspec(x2, fit)$d
x4 <- x3
y4 <- y2
for(i in 1:length(x3)) {
yy <- c(yy[xx < x2[i]], y2[i], y3, y4[i], yy[xx > x4[
i]])
xx <- c(xx[xx < x2[i]], x2[i], x3[i], x4[i], xx[xx > x4[
i]])
}
if(missing(ylim))ylim<-range(yy)
yy <- yy[xx >= plim[1]]
xx <- xx[xx >= plim[1]]
yy <- yy[xx <= plim[2]]
xx <- xx[xx <= plim[2]]
y2 <- y2[x2 >= plim[1]]
x2 <- x2[x2 >= plim[1]]
y2 <- y2[x2 <= plim[2]]
x2 <- x2[x2 <= plim[2]]
}
if(what == "f" || what == "F" || what == "p" || what == "P") {
if(!missing(xlim)){
if(xlim[1]< -pi || xlim[2]>pi)
stop("for this plot the range cannot strecth beyond (-pi,pi)")
}
if(missing(xlim)){
plim[1] <- max(plim[1],-pi)
plim[2] <- min(plim[2],pi)
}
if(missing(type))
type <- "l"
if(missing(n))
n <- max(100, fit$sample + 1)
xx <- (0:(n - 1))/(n - 1) * (plim[2] - plim[1]) + plim[1]
yy <- plspec(xx, fit)
if(missing(ylim))ylim<-range(yy)
if(fit$natoms > 0) {
x2 <- fit$atoms
y3 <- plspec(x2, fit)
y2 <- y3 - fit$mass
for(i in 1:fit$natoms) {
yy <- c(yy[xx < x2[i]], y2[i], y3[i], yy[xx >
x2[i]])
xx <- c(xx[xx < x2[i]], x2[i], x2[i], xx[xx >
x2[i]])
}
x2 <- -fit$atoms
y3 <- plspec(x2, fit)
y2 <- y3 - fit$mass
for(i in 1:fit$natoms) {
yy <- c(yy[xx < x2[i]], y2[i], y3[i], yy[xx >
x2[i]])
xx <- c(xx[xx < x2[i]], x2[i], x2[i], xx[xx >
x2[i]])
}
yy <- yy[xx >= plim[1]]
xx <- xx[xx >= plim[1]]
yy <- yy[xx <= plim[2]]
xx <- xx[xx <= plim[2]]
}
}
if(what != "l" && what != "L") {
if(!add)
plot(xx, yy, xlim = plim, xlab = xlab, ylab = ylab,
type = type, ylim = ylim, ...)
else lines(xx, yy, type = type, ...)
if(what =="b" || what=="B")points(x2,y2)
}
invisible()
}
print.lspec <- function(x,...)
{
summary.lspec(x)
}
summary.lspec <- function(object,...)
{
fit <- object
if(class(fit)!="lspec")
stop("fit is not an lspec object")
aa <- " Logspline Spectral Estimation\n"
aa <- paste(aa,"=============================\n")
aa <- paste(aa,"The fit was obtained by the command:\n ")
cat(aa)
print(fit$call)
aic <- round(-2*fit$logl+fit$penalty*(fit$nknots+fit$natoms),2)
logl <- round(fit$logl,2)
ns <- fit$natoms
nk <- fit$nknots
nd <- ns + nk
if(ns==0 && nk==1)
aa <- paste(" Only 1 basis function, a constant, was fitted.\n")
if(ns==0 && nk>1)
aa <- paste(" A spline with",nk,"knots, was fitted;",
"there were no lines in the model.\n")
if(ns>0 && nk>1)
aa <- paste(" A spline with",nk,"knots, was fitted;",
"there were also",ns,"lines in the model.\n")
if(ns>0 && nk==1)
aa <- paste(" There were",nd,"basisfunctions, a constant and",
ns,"lines, in the model.\n")
aa <- paste(aa,"The log-likelihood of the model was",logl,
"which corresponds to an AIC\n value of",aic,".\n\n")
aa <- paste(aa,"The program went though",abs(fit$updown))
if(fit$updown>0)
aa <-paste(aa,"updown cycles, and reached a stable solution.\n")
if(fit$updown<0)
aa <-paste(aa,"updown cycles, and did not reach a stable solution.\n")
p1 <- round(fit$penalty,2)
n1 <- round(fit$minmass,4)
nn <- floor(fit$sample/2)
p2 <- round(log(nn),2)
uu <- plspec(pi,fit)
n2 <- round(uu*(-log(1-0.95^(1/nn))-1)/fit$sample,4)
p3 <- (p1==p2)
p4 <- TRUE
if(n1/n2 > 1.2 || n2/n1 > 1.2) p4 <- FALSE
if(p3==TRUE && p4==TRUE)aa<-paste(aa,
"Both penalty (AIC) and minmass were the default values. For penalty this\n",
"was log(n)=log(",nn,")=",p1," (as in BIC) and for minmass this was",n1,".\n")
if(p3==TRUE && p4==FALSE)aa<-paste(aa,
"Penalty (AIC) had the default values",
"log(n)=log(",nn,")=",p1," (as in BIC).\n Minmass was",n1,
", the default would have been",n2,".\n")
if(p3==FALSE && p4==FALSE)aa<-paste(aa,
"Penalty was",p1,", the default would have been",
"log(n)=log(",nn,")=",p2,"\n(as in BIC). Minmass was",n1,
", the default would have been",n2,".\n")
if(p3==FALSE && p4==TRUE)aa<-paste(aa,
"Penalty was",p1,", the default would have been, log(n)=log(",nn,")=",
p2,"\n (as in BIC). Minmass was the default",n1,".\n\n")
if(nk>1){aa<-paste(aa,"The locations of the knots were:")
for(i in 1:nk)aa<-paste(aa,round(fit$knots[i],3))
aa<-paste(aa,"\n")
}
if(ns>0){
aa<-paste(aa,"The locations and the mass in each line were:\n")
bb <- matrix(0,ncol=4,nrow=ns)
for(i in 1:ns){
bb[i,1]<-round(fit$atoms[i],3)
bb[i,2]<-2*pi/(fit$atoms[i])
bb[i,2]<-round(bb[i,2],2)
bb[i,3]<-round(fit$mass[i],5)
bb[i,4]<- round(100*fit$mass[i]/uu,2)
}
dimnames(bb) <- list(rep("",ns),c("angular frequency","period","mass",
"% of total mass"))
}
cat(aa)
if(ns>0)print(bb)
invisible()
}
polymars <- function(responses, predictors, maxsize, gcv = 4., additive = FALSE, startmodel,
weights, no.interact, knots, knot.space = 3, ts.resp, ts.pred,
ts.weights, classify, factors, tolerance = 1e-06, verbose = FALSE)
{
call <- match.call()
ism0 <- missing(classify)
ism1 <- missing(ts.resp)
ism2 <- missing(maxsize)
ism3 <- missing(ts.pred)
ism4 <- missing(ts.weights)
ism5 <- missing(knots)
ism6 <- missing(factors)
ism7 <- missing(startmodel)
ism8 <- missing(weights)
ism9 <- missing(no.interact)
if(!missing(responses))
responses <- unstrip(responses)
if(!missing(predictors))
predictors <- unstrip(predictors)
if(!missing(weights))
weights <- unstrip(weights)
if(!missing(no.interact))
no.interact <- unstrip(no.interact)
if(!missing(knots))
knots <- unstrip(knots)
if(!missing(ts.resp))
ts.resp <- unstrip(ts.resp)
if(!missing(ts.pred))
ts.pred <- unstrip(ts.pred)
if(!missing(ts.weights))
ts.weights <- unstrip(ts.weights)
if(!missing(factors))
factors <- unstrip(factors)
responses <- as.matrix(responses)
predictors <- data.matrix(predictors)
nresponses <- ncol(responses)
npredictors <- ncol(predictors)
ncases <- nrow(predictors)
if(ism0)
classify <- FALSE
if(mode(responses) == "character" || classify == TRUE) {
if(ncol(responses) > 1) {
stop("When using character responses or classify = TRUE only 1 response per case is allowed\n"
)
}
char.responses <- responses
int.responses <- as.integer(as.factor(responses))
nresponses <- length(unique(responses))
responses <- matrix(ncol = nresponses, nrow = ncases, data =
int.responses)
for(i in 1:nresponses) {
responses[, i] <- (responses[, i] == (unique(
int.responses)[i]))
}
conversion <- matrix(ncol = 2, nrow = nresponses, c(unique(
char.responses), unique(int.responses)))
classify <- TRUE
if(!ism1) {
char.responses.test <- ts.resp
ts.resp <- matrix(ncol = nresponses, nrow = length(
char.responses.test), data = 0)
for(i in 1:nresponses) {
ts.resp[, i] <- as.integer(char.responses.test ==
conversion[i, 1])
}
}
}
else {
conversion <- FALSE
classify <- FALSE
}
if(ism2) maxsize <- ceiling(min(6 * (ncases^(1/3)), ncases/4, 100))
if(!ism1 || !ism3) {
if(ism1 || ism3) {
stop("Both ts.resp (testsets responses) and ts.pred (testset predictors) should be specified\n"
)
}
if(!is.matrix(ts.resp))
ts.resp <- as.matrix(ts.resp)
if(!is.matrix(ts.pred))
ts.pred <- as.matrix(ts.pred)
if(ncol(ts.resp) != nresponses) {
stop("Testset should have the same number of responses as the training set\n "
)
}
if(ncol(ts.pred) != npredictors) {
stop("Testset should have the same number of predictors as the training set\n "
)
}
if(nrow(ts.resp) != nrow(ts.pred)) {
stop("Testset ts.pred and ts.resp should have the same number of cases (rows)"
)
}
testsetmatrix <- cbind(ts.resp, ts.pred)
testsetcases <- nrow(testsetmatrix)
testset <- TRUE
if(!ism4) {
if(length(ts.weights) != testsetcases) {
stop("length of testset weights misspecified\n"
)
}
testset.weighted <- TRUE
testsetmatrix <- cbind(ts.resp * ts.weights, ts.pred)
}
else {
testset.weighted <- FALSE
ts.weights <- 0
}
}
else {
testsetmatrix <- 0
testsetcases <- 0
testset <- FALSE
testset.weighted <- FALSE
ts.weights <- 0
}
mesh.specified <- FALSE
mesh.vector <- 0
if(nrow(responses) != nrow(predictors)) {
stop("The number of rows (cases) of the response and predictor matricies should be the same"
)
}
if(!ism5 && !is.matrix(knots) && length(knots) != npredictors && length(
knots) != 1) {
stop("Length of vector of `knots per predictor' should be equal to number of predictors or 1\n"
)
}
if(!ism5) {
if(!is.matrix(knots)) {
if(length(knots) == 1) {
knots <- rep(knots, npredictors)
if(!ism6) {
for(i in 1:length(factors)) {
if(!is.vector(factors)) {
stop("`factors' should be a vector whose elements are indicies of predictors that are factors\n"
)
}
knots[factors[i]] <- -1
}
}
}
}
else {
mesh <- knots
mesh.vector <- vector(length = ncol(mesh) * nrow(mesh),
mode = "double")
knots <- vector(length = npredictors, mode = "integer")
k <- 0
for(i in 1:npredictors) {
knots[i] <- length(unique(mesh[is.na(mesh[
, i]) == FALSE, i]))
for(j in 1:knots[i]) {
k <- k + 1
mesh.vector[k] <- unique(mesh[!is.na(
mesh[, i]), i])[j]
}
}
if(!ism6) {
for(i in 1:length(factors)) {
if(!is.vector(factors)) {
stop("`factors' should be a vector whose elements are indicies of predictors that are factors\n"
)
}
knots[factors[i]] <- -1
}
}
mesh.specified <- TRUE
}
}
if(ism5) {
knots <- rep(min(20, round(ncases/4)), npredictors)
if(!ism6) {
for(i in 1:length(factors)) {
if(!is.vector(factors)) {
stop("`factors' should be a vector whose elements are indicies of predictors that are factors\n"
)
}
knots[factors[i]] <- -1
}
}
}
startmodelsize <- 1
no.remove <- 0
no.remove.size <- 0
if(!ism7) {
if(is.vector(startmodel))
startmodel <- t(as.matrix(startmodel))
v1 <- (class(startmodel) == "polymars")
if(length(v1) == 0)
v1 <- FALSE
if(!(is.matrix(startmodel) || v1) || (is.matrix(startmodel) &&
(ncol(startmodel) != 4 && (ncol(startmodel) != 5)))) {
stop(paste(
"startmodel should be a matrix with each row corresponding to",
"a function with number of columns = 4 (or 5 for extra boolean\n",
"column specifying predictors which cannot be removed)",
"or startmodel should be a polymars object\n"))
}
if(is.matrix(startmodel)) {
if(ncol(startmodel) == 5) {
no.remove <- vector(length = (nrow(startmodel))
)
j <- 0
for(i in 1:nrow(startmodel)) {
if(startmodel[i, 5] == TRUE) {
j <- j + 1
no.remove[j] <- i
}
}
no.remove.size <- j
}
startknots <- as.vector(t(cbind(startmodel[, 2],
startmodel[, 4])))
startknots[is.na(startknots)] <- 0.
startmodel <- matrix(startmodel[, 1:4], ncol = 4)
startmodel[!is.na(startmodel[, 2]), 2] <- 1
startmodel[is.na(startmodel[, 2]), 2] <- 0
startmodel[is.na(startmodel[, 3]), 3] <- 0
startmodel[startmodel[, 3] == 0, 4] <- 0
for(i in 1:nrow(startmodel)) {
if((!is.na(startmodel[i, 4])) && startmodel[
i, 3] != 0)
startmodel[i, 4] <- 1
}
startmodel[is.na(startmodel[, 4]), 4] <- 0
startmodelsize <- nrow(startmodel) + 1
}
else {
startmodelsize <- startmodel$model.size
startmodel <- startmodel$model[-1, ]
startknots1 <- startmodel$knot1
startknots2 <- startmodel$knot2
L1 <- FALSE
if(!is.null(startmodel$level1)) {
L1 <- TRUE
level1 <- startmodel$level1
}
if(L1) {
startmodel$knot1[!is.na(level1)] <- 1
startknots1[!is.na(level1)] <- level1[!is.na(
level1)]
}
startknots <- cbind(startknots1, startknots2)
startknots <- as.vector(t(startknots))
startknots[is.na(startknots)] <- 0.
startmodel <- cbind(startmodel[, "pred1"], startmodel[
, "knot1"], startmodel[, "pred2"], startmodel[
, "knot2"])
startmodel[, 2] <- !is.na(startmodel[, 2])
startmodel[, 4] <- !is.na(startmodel[, 4])
}
}
else {
startmodel <- 0
startknots <- 0
}
if(!ism8) {
if(length(weights) != ncases) {
stop("Number of weights not equal to the numnber of cases\n"
)
}
weighted <- TRUE
responses <- responses * weights
}
else {
weighted <- FALSE
weights <- 0
}
datamatrix <- cbind(responses, predictors)
if(!ism9) {
if(!is.matrix(no.interact) || ncol(no.interact) != 2) {
stop("list of interactions disallowed has been misspecified,must be a 2*n matrix"
)
}
no.interact <- t(no.interact)
no.interact.size <- ncol(no.interact)
}
else {
no.interact.size <- 0
no.interact <- 0
}
if(startmodelsize > maxsize) {
stop("start model should not be of greater size than the max model size\n"
)
}
if(startmodelsize != 1) {
for(i in 1:(startmodelsize - 1)) {
if(startmodel[i, 1] == 0) {
stop("first column of startmodel cannot be zero\n"
)
}
if(startmodel[i, 2] == 1) {
if(startknots[(i * 2) - 1] < min(predictors[
, startmodel[i, 1]]) || startknots[
(i * 2) - 1] > max(predictors[,
startmodel[i, 1]])) {
stop("Knot out of range of its predictor \n"
)
}
}
if(startmodel[i, 4] == 1) {
if(startknots[(i * 2)] <= min(predictors[,
startmodel[i, 3]]) || startknots[(
i * 2)] >= max(predictors[, startmodel[
i, 3]])) {
stop("Knot out of range of its predictor\n"
)
}
}
}
if(max(startmodel[, c(1, 3)] > npredictors)) {
stop("Initial model misspecified on input\n")
}
}
startmodel <- t(startmodel)
resultmodelsize <- 0
end.state <- 0
step.count <- 0
z <- .C("polymarsF",
as.integer(npredictors),
as.integer(nresponses),
as.integer(ncases),
as.double(datamatrix),
as.integer(knots),
as.double(mesh.vector),
as.integer(mesh.specified),
as.integer(maxsize),
as.double(gcv),
as.integer(additive),
as.integer(startmodelsize),
start.model = as.integer(startmodel),
start.knots = as.double(startknots),
as.integer(weighted),
as.double(weights),
as.integer(no.interact.size),
as.integer(no.interact),
as.integer(no.remove.size),
as.integer(no.remove),
as.integer(knot.space),
as.integer(testset),
as.double(testsetmatrix),
as.integer(testsetcases),
as.integer(testset.weighted),
as.double(ts.weights),
as.integer(classify),
as.double(tolerance),
as.integer(verbose),
best.model = as.integer(matrix(nrow = maxsize, ncol = 4, data
= rep(0, maxsize * 4))),
coefficients = as.double(matrix(nrow = maxsize, ncol =
nresponses, data = rep(0., maxsize * nresponses))),
steps = as.integer(matrix(nrow = maxsize * 2, ncol = 2,
data = rep(0, maxsize * 4))),
rss.gcv = as.double(matrix(nrow = maxsize * 2, ncol =
nresponses + 1, data = rep(0., maxsize * 2 * (
nresponses + 1)))),
modelsize = as.integer(resultmodelsize),
modelknots = as.double(matrix(nrow = maxsize, ncol = 2, data =
rep(0., maxsize * 2))),
coefficient.se.term = as.double(rep(0., maxsize)),
end.state = as.integer(end.state),
step.count = as.integer(step.count),
PACKAGE = "polspline")
if(z$end.state != 0 && z$end.state != 5) {
switch(z$end.state,
stop("Mis-specification of initial model\n"),
stop("Initial model with non-linear function must contain the corresponding linear function\n"
),
stop("Initial model contains two-predictor functions that require prerequisite functions\n"
))
}
else {
model <- matrix(z$best.model[1:((z$modelsize - 1) * 4)], ncol
= 4, byrow = TRUE)
knot.values <- matrix(z$modelknots[1:((z$modelsize - 1) * 2)],
ncol = 2, byrow = TRUE)
for(i in 1:nrow(model)) {
if(model[i, 2] != 0) {
model[i, 2] <- knot.values[i, 1]
}
else {
model[i, 2] <- NA
}
if(model[i, 4] != 0) {
model[i, 4] <- knot.values[i, 2]
}
else {
model[i, 4] <- NA
}
}
if(length(knots[model[, 1]]) != 0 && min(knots[model[, 1]]) <
0) {
factor1 <- TRUE
levels1 <- rep(NA, z$modelsize - 1)
factor.variables <- unique(model[knots[model[, 1]] <
0, 1])
for(i in 1:length(factor.variables)) {
for(j in 1:length(model[, 1])) {
if(model[j, 1] == factor.variables[
i]) {
levels1[j] <- model[j, 2]
}
}
model[model[, 1] == factor.variables[i], 2] <-
NA
}
levels1 <- c(NA, levels1)
}
else {
factor1 <- FALSE
}
coefs <- matrix(z$coefficients[1:(z$modelsize * nresponses)],
ncol = nresponses)
if(z$modelsize > 1) {
if(factor1 == FALSE) {
model <- rbind(c(0, NA, 0, NA), model)
model <- data.frame(model, coefs)
if(nresponses == 1) {
dimnames(model) <- list(1:z$modelsize,
c("pred1", "knot1", "pred2",
"knot2", "coefs"))
}
else {
dimnames(model) <- list(1:z$modelsize,
c("pred1", "knot1", "pred2",
"knot2", paste("Coefs", 1:
nresponses)))
}
}
if(factor1 == TRUE) {
model[(knots[model[, 1]] < 0), 2] <- NA
model <- rbind(c(0, NA, 0, NA), model)
model <- data.frame(model[, 1:2], levels1,
model[, 3:4], coefs)
if(nresponses == 1) {
dimnames(model) <- list(1:z$modelsize,
c("pred1", "knot1", "level1",
"pred2", "knot2", "coefs"))
}
else {
dimnames(model) <- list(1:z$modelsize,
c("pred1", "knot1", "level1",
"pred2", "knot2", paste("Coefs",
1:nresponses)))
}
}
}
else {
model <- data.frame(0, NA, 0, NA, coefs)
if(nresponses == 1) {
dimnames(model) <- list(1:z$modelsize, c(
"pred1", "knot1", "pred2", "knot2",
"coefs"))
}
else {
dimnames(model) <- list(1:z$modelsize, c(
"pred1", "knot1", "pred2", "knot2",
paste("Coefs", 1:nresponses)))
}
}
ranges.and.medians <- matrix(ncol = npredictors, nrow = 3,
data = 0)
for(i in 1:npredictors) {
ranges.and.medians[1, i] <- min(predictors[, i])
}
for(i in 1:npredictors) {
ranges.and.medians[2, i] <- max(predictors[, i])
}
for(i in 1:npredictors) {
ranges.and.medians[3, i] <- median(predictors[, i])
}
steps <- matrix(z$steps[1:(2 * (z$step.count + 1))], ncol = 2,
byrow = TRUE)
rss.gcv <- matrix(z$rss.gcv[1:((nresponses + 1) * (z$step.count +
1))], ncol = nresponses + 1, byrow = TRUE)
fitting <- data.frame(steps, rss.gcv)
if(testset == FALSE) {
if(nresponses == 1) {
dimnames(fitting) <- list(1:(nrow(fitting)),
c("0/1", "size", "RSS", "GCV"))
}
else {
dimnames(fitting) <- list(1:nrow(fitting),
c("0/1", "size", paste("RSS", 1:
nresponses), "GCV"))
}
}
else {
if(classify == FALSE) {
if(nresponses == 1) {
dimnames(fitting) <- list(1:(nrow(
fitting)), c("0/1", "size",
"RSS", "T.S. RSS"))
}
else {
dimnames(fitting) <- list(1:nrow(
fitting), c("0/1", "size",
paste("RSS", 1:nresponses),
"T.S. RSS"))
}
}
else {
if(nresponses == 1) {
dimnames(fitting) <- list(1:(nrow(
fitting)), c("0/1", "size",
"RSS", "T.S.M.C."))
}
else {
dimnames(fitting) <- list(1:nrow(
fitting), c("0/1", "size",
paste("RSS", 1:nresponses),
"T.S.M.C."))
}
}
}
if(factor1 == TRUE) {
model2 <- model[-1, ]
factors.in.model <- unique(model2[knots[model2[, 1]] <
0, 1])
maxfactors <- 0
for(i in 1:length(factors.in.model)) {
maxfactors <- max(maxfactors, length(unique(
predictors[, factors.in.model[i]])))
}
factor.matrix <- matrix(ncol = length(factors.in.model),
nrow = maxfactors + 2, data = NA)
for(i in 1:length(factors.in.model)) {
factor.matrix[1, i] <- factors.in.model[i]
factor.matrix[2, i] <- length(unique(predictors[
, factors.in.model[i]]))
for(j in 3:(length(unique(predictors[,
factors.in.model[i]])) + 2)) {
factor.matrix[j, i] <- unique(
predictors[, factors.in.model[
i]])[j - 2]
}
}
}
else {
factor.matrix <- 0
}
if(nresponses == 1) {
model <- cbind(model, model[,1])
dimnames(model)[[2]][length(dimnames(model)[[2]])] <-
"SE"
}
else {
for(i in 1:nresponses) {
model <- cbind(model, model[,1])
dimnames(model)[[2]][length(dimnames(model)[[
2]])] <- paste("SE", i)
}
}
result <- list(model = model, fitting = fitting, model.size = z$
modelsize, responses = nresponses, ranges.and.medians =
ranges.and.medians, call = call, conversion =
conversion, factor.matrix = factor.matrix)
class(result) <- "polymars"
dd <- design.polymars(result,predictors)
model2 <- result$model
rsquared2 <- rep(0,nresponses)
for(i in 1:nresponses){
if(z$modelsize>1) mm <- summary(lm(responses[, i] ~ dd[, -1]))
else mm <- summary(lm(responses[, i] ~ 1 ))
rsquared2[i] <- mm$r.squared
mm <- mm$coefficients
model2[,i+factor1+4] <- mm[,1]
model2[,i+factor1+4+nresponses] <- mm[,2]
}
result$model <- model2
result$Rsquared <- rsquared2
if(z$modelsize > 1) {
fitted <- predict.polymars(result, x = predictors)
residuals <- responses - fitted
}
else {
fitted <- matrix(ncol = nresponses, nrow = ncases,
data = coefs[1, 1])
residuals <- matrix(ncol = nresponses, nrow = ncases,
data = responses - coefs[1, 1])
}
result$residuals = residuals
result$fitted = fitted
return(result)
}
}
predict.polymars<-function(object,x,classify=FALSE,intercept,...)
{
if(missing(intercept))
{
intercept<-TRUE
}
if(!missing(x))x <- unstrip(x)
if(class(object)!="polymars")
stop("object is not a polymars object")
pmars.model <- object
if(!(is.matrix(x)))
{
if(length(unique(pmars.model$model[, "pred1"]))== 1 || ncol(pmars.model$ranges.and.medians)== 1 )
{
x<-matrix(data=x,ncol=1)
}
}
if((is.matrix(x) && ncol(x)
!= length(unique(pmars.model$model[,"pred1"]))))
{
if(ncol(x) != ncol(pmars.model$ranges.and.medians))
{
stop("Input should be a matrix with number of columns equal to either number of original predictors or number of predictors in model\n")
}
}
if(is.matrix(x) && ncol(x) == length(unique(pmars.model$model[, "pred1"])) && ncol(x) != ncol(pmars.model$ranges.and.medians))
{
tempmatrix<-x
x<-matrix(nrow=nrow(tempmatrix),ncol=ncol(pmars.model$ranges.and.medians),data = 0)
for(i in 1:length(unique(pmars.model$model[, "pred1"])))
{
for(j in 1:nrow(tempmatrix))
{
x[j,sort(unique(pmars.model$model[,"pred1"]))[i]]<-x[j]
}
}
}
if(!(is.matrix(x)))
{
if(!(length(x) == ncol(pmars.model$ranges.and.medians) || length(x) == unique(pmars.model$model[, "pred1"])))
{
stop("The vector of values must be equal in length to either the number of original predictors or predictors in the model\n")
}
if(length(x) == unique(pmars.model$model[, "pred1"]) && length(x) != ncol(pmars.model$ranges.and.medians))
{
x <- rep(0, ncol(pmars.model$ranges.and.medians))
for(i in 1:length(unique(pmars.model$model[, "pred1"])))
{
x[sort(unique(pmars.model$model[, "pred1"]))[i]]<-x[i]
}
}
x <- t(as.matrix(x))
}
if(dimnames(pmars.model$model)[[2]][3] == "level1")
{
level1<-TRUE
pmars.model$model<-pmars.model$model[,c(1:(5+pmars.model$responses))]
}
else
{
level1<-FALSE
pmars.model$model<-pmars.model$model[,c(1:(4+pmars.model$responses))]
}
responses<-pmars.model$responses
Y <- matrix(ncol = responses, nrow = nrow(x), data = rep(0, nrow(x)))
Y1 <- matrix(ncol = 1, nrow = nrow(x), data = rep(0, nrow(x)))
Y2 <- matrix(ncol = 1, nrow = nrow(x), data = rep(0, nrow(x)))
if(is.logical(intercept))
{
if(intercept==TRUE)
{
for(i in 1:responses)Y[,i] <- pmars.model$model[1,ncol(pmars.model$model)-responses+i]
}
else
{
if(intercept==FALSE)
{
for(i in 1:responses)Y[,i] <- 0.0
}
}
}
else
{
if(is.numeric(intercept))
{
if(length(intercept)==responses)
{
for(i in 1:responses)Y[,i] <- intercept[i]
}
else
{
if(length(intercept) != 1)
{
stop("Intercept arguement mispecified \n")
}
for(i in 1:responses)Y[,i] <- intercept
}
}
}
if(pmars.model$model.size>1)
{
for(i in 2:pmars.model$model.size)
{
Y2[] <- 1
Y1[] <- x[,pmars.model$model[i, "pred1"]]
if(!is.na(pmars.model$model[i, "knot1"]))
{
Y1 <- Y1 - pmars.model$model[i,"knot1"]
Y1[Y1 < 0,] <- 0
}
if(level1)
{
if(!is.na(pmars.model$model[i, "level1"]))
{
Y1<- (Y1 == pmars.model$model[i, "level1"])
}
}
if(!is.na(pmars.model$model[i, "pred2"]) & pmars.model$model[i, "pred2"] != 0)
{
Y2[] <- x[,pmars.model$model[i,"pred2"]]
if(!is.na(pmars.model$model[i,"knot2" ]))
{
Y2 <- Y2 - pmars.model$model[i,"knot2"]
Y2[Y2 < 0,] <- 0
}
}
for(j in 1:responses){Y[,j]<-Y[,j]+(Y1 * Y2 * pmars.model$model[i,ncol(pmars.model$model)-responses+j])}
}
}
if(classify == TRUE)
{
for(i in 1:nrow(Y))
{
Y[i,]<-Y[i,]==max(Y[i,])
}
if(is.matrix(pmars.model$conversion))
Z<-Y
Y<-matrix(ncol=1,nrow=nrow(Z))
for(i in 1:nrow(Y))
{
for(j in 1:ncol(Z))
{
if(Z[i,j] == 1) Y[i,] <- pmars.model$conversion[j]
}
}
}
return(Y)
}
print.polymars<-function(x,...)
{
summary.polymars(x)
}
summary.polymars<-function(object,...)
{
if(class(object)!="polymars")
stop("object is not a polymars object")
pmars.model <- object
cat("Call:\n")
print(pmars.model$call)
cat("\nModel fitting\n\n")
print(pmars.model$fitting)
cat("\n\nModel produced\n\n")
print(pmars.model$model)
if(pmars.model$responses != 1)
cat("\nRESPONSES :", pmars.model$responses, "\n")
if(!is.null(pmars.model$Rsquared))
cat("\nRsquared :",round(pmars.model$Rsquared,3),"\n")
invisible()
}
plot.polymars<-function(x,predictor1,response,predictor2,xx,add=FALSE,n,xyz=FALSE,contour.polymars=FALSE,xlim,ylim,intercept,...)
{
if(class(x)!="polymars")
stop("x is not a polymars object")
pmars.model <- x
if(missing(xx))xx<-pmars.model$ranges.and.medians[3,]
if(length(xx) != ncol(pmars.model$ranges.and.medians))
{
stop("xx should be of length equal to the number of predictors in original data\n")
}
x <- xx
if(!missing(predictor2))xyz <- TRUE
if(missing(predictor2) && (!missing(response)) && pmars.model$responses == 1)
{
if(missing(predictor1) && xyz == TRUE)
{
stop("You must specify 2 predictor numbers")
}
xyz<-TRUE
predictor2<-response
response<-1
}
if(contour.polymars == TRUE)
{
xyz<-TRUE
}
if(missing(intercept))
{
intercept<-TRUE
}
if(xyz==TRUE)
{
if(missing(n))n<-33
if(missing(response))
{
if(missing(xlim))
{
persp.polymars(pmars.model,
predictor1,
predictor2,
n=n,
contour.polymars=contour.polymars,
intercept=intercept,
...)
}
else
{
persp.polymars(pmars.model,
predictor1,
predictor2,
n=n,
xlim=xlim,
contour.polymars=contour.polymars,
intercept=intercept,
...)
}
}
else
{
if(missing(xlim))
{
persp.polymars(pmars.model,
predictor1,
predictor2,
response,
n=n,
contour.polymars=contour.polymars,
intercept=intercept,
...)
}
else
{
persp.polymars(pmars.model,
predictor1,
predictor2,
response,
n=n,
xlim=xlim,
contour.polymars=contour.polymars,
intercept=intercept,
...)
}
}
invisible(return())
}
else
{
if(missing(predictor1))
{
cat("predictor should be specified \n")
}
if(pmars.model$responses != 1 && missing(response)&& missing(predictor2))
{
cat("Response should be specified (default: response =1)\n")
}
inmodel<-FALSE
for(i in 2:pmars.model$model.size)
{
if(pmars.model$model[i,"pred1"] == predictor1)inmodel<-TRUE
}
if(is.matrix(pmars.model$factor.matrix))
{
if(length(pmars.model$factor.matrix[1,pmars.model$factor.matrix[1,]== predictor1]) != 0)
{
isfactor<-TRUE
}
else
{
isfactor<-FALSE
}
}
else
{
isfactor<-FALSE
}
if(isfactor == TRUE)
{
pred.values <- matrix(nrow = pmars.model$factor.matrix[2,pmars.model$factor.matrix[1,]==predictor1], ncol = ncol(pmars.model$ranges.and.medians),data = x, byrow = TRUE)
factors<-pmars.model$factor.matrix[-c(1,2),pmars.model$factor.matrix[1,]==predictor1]
pred.values[,predictor1]<- factors[!is.na(factors)]
mesh<-factors[!is.na(factors)]
}
else
{
if(missing(n))n<-100
if(missing(xlim))xlim<-c(pmars.model$ranges.and.medians[1,predictor1],pmars.model$ranges.and.medians[2,predictor1])
pred.values <- matrix(nrow = n, ncol = ncol(pmars.model$ranges.and.medians),
data = x, byrow = TRUE)
mesh <- matrix(seq(xlim[1],xlim[2],(xlim[2]-xlim[1])/(n-1)),nrow=1)
pred.values[,predictor1]<-mesh
}
if(missing(response) && missing(predictor2))response<-1
if(missing(response))response <- 1
if(response > pmars.model$responses || response < 0)
{
stop("response arguement = ",response,"is out of range\n")
}
model<-pmars.model$model
Y<-predict.polymars(pmars.model,pred.values,intercept=intercept)
if(isfactor == FALSE)
{
if(add == FALSE)
{
if(pmars.model$responses == 1)
{
plot(mesh,Y,...,type="l",xlab=paste("Predictor ",predictor1),ylab="Response")
}
else
{
plot(mesh,
Y[,response],
type="l",
xlab=paste("Predictor ",predictor1),
ylab=paste("Response ",response),
...)
}
}
else
{
points(mesh,
Y,
type="l")
}
}
if(isfactor == TRUE)
{
if(add == FALSE)
{
if(pmars.model$responses == 1)
{
plot(mesh,Y,...,xlab=paste("Predictor ",predictor1),ylab="Response")
}
else
{
plot(mesh,
Y[,response],
type="l",
xlab=paste("Predictor ",predictor1),
ylab=paste("Response ",response),
...)
}
}
else
{
points(mesh,
Y,
type="l")
}
}
invisible()
}
}
persp.polymars<-function(x, predictor1, predictor2, response, n= 33,xlim,ylim,xx,contour.polymars,main,intercept,...)
{
if(class(x)!="polymars")
stop("x is not a polymars object")
pmars.model <- x
if(missing(xx))xx<-pmars.model$ranges.and.medians[3,]
if(missing(xlim))xlim<-c(pmars.model$ranges.and.medians[1,predictor1],pmars.model$ranges.and.medians[2,predictor1])
if(missing(ylim))ylim<-c(pmars.model$ranges.and.medians[1,predictor2],pmars.model$ranges.and.medians[2,predictor2])
if(missing(predictor1) || missing(predictor2))
{
stop("You must specify 2 predictor numbers\n")
}
if(pmars.model$responses != 1 && missing(response))
{
cat("Response should be specified (default: response =1)\n")
}
if(missing(response))response <- 1
if(response > pmars.model$responses || response < 0)
{
stop("response arguement = ",response,"is out of range\n")
}
if(sum(as.integer(predictor1==pmars.model$model[,1])) == 0)
{
stop("Predictor 1 not in model\n")
}
if(sum(as.integer(predictor2==pmars.model$model[,1])) == 0)
{
stop("Predictor 2 not in model\n")
}
X <- seq(xlim[1],xlim[2],(xlim[2] - xlim[1])/(n-1))
y <- seq(ylim[1],ylim[2],(ylim[2] - ylim[1])/(n-1))
meshX <- rep(X, n)
meshY <- rep(y, n)
meshY <- sort(meshY)
pred.values <- matrix(nrow = n^2, ncol = ncol(pmars.model$ranges.and.medians),
data = xx, byrow = TRUE)
for(i in 1:(n^2))pred.values[i, predictor1] <- meshX[i]
for(i in 1:(n^2))pred.values[i, predictor2] <- meshY[i]
Z <- predict.polymars(pmars.model, pred.values,intercept=intercept)[,response]
Z <- matrix(Z, ncol = n, byrow = FALSE)
xtitle<-paste("Predictor", predictor1)
ytitle<-paste("Predictor", predictor2)
if(pmars.model$responses > 1)
{
if(missing(main) && (!contour.polymars))
{
ztitle <- paste("Response", response)
}
if(missing(main) && (contour.polymars))
{
ztitle <- paste("Contour of response",response)
}
}
else
{
if(missing(main) && (!contour.polymars))ztitle <- "Response"
if(missing(main) && contour.polymars)ztitle <- paste("Contour of response")
}
if(!contour.polymars)
{
persp(X, y, Z, xlab = xtitle, ylab= ytitle, zlab = ztitle, ...)
}
else
{
contour(X, y, Z, xlab = xtitle, ylab = ytitle , main = ztitle, ...)
}
invisible()
}
design.polymars<-function(object,x)
{
if(!missing(x))x <- unstrip(x)
if(class(object)!="polymars")
stop("object is not a polymars object")
pmars.model <- object
if(!(is.matrix(x)))
{
if(length(unique(pmars.model$model[, "pred1"]))== 1 || ncol(pmars.model$ranges.and.medians)== 1 )
{
x<-matrix(data=x,ncol=1)
}
}
if((is.matrix(x) && ncol(x)
!= length(unique(pmars.model$model[,"pred1"]))))
{
if(ncol(x) != ncol(pmars.model$ranges.and.medians))
{
stop("Input should be a matrix with number of columns equal to either number of original predictors or number of predictors in model\n")
}
}
if(is.matrix(x) && ncol(x) == length(unique(pmars.model$model[, "pred1"])) && ncol(x) != ncol(pmars.model$ranges.and.medians))
{
tempmatrix<-x
x<-matrix(nrow=nrow(tempmatrix),ncol=ncol(pmars.model$ranges.and.medians),data = 0)
for(i in 1:length(unique(pmars.model$model[, "pred1"])))
{
for(j in 1:nrow(tempmatrix))
{
x[j,sort(unique(pmars.model$model[,"pred1"]))[i]]<-x[j]
}
}
}
if(!(is.matrix(x)))
{
if(!(length(x) == ncol(pmars.model$ranges.and.medians) || length(x) == unique(pmars.model$model[, "pred1"])))
{
stop("The vector of values must be equal in length to either the number of original predictors or predictors in the model\n")
}
if(length(x) == unique(pmars.model$model[, "pred1"]) && length(x) != ncol(pmars.model$ranges.and.medians))
{
x <- rep(0, ncol(pmars.model$ranges.and.medians))
for(i in 1:length(unique(pmars.model$model[, "pred1"])))
{
x[sort(unique(pmars.model$model[, "pred1"]))[i]]<-x[i]
}
}
x <- t(as.matrix(x))
}
if(dimnames(pmars.model$model)[[2]][3] == "level1")
{
level1<-TRUE
pmars.model$model<-pmars.model$model[,c(1:(5+pmars.model$responses))]
}
else
{
level1<-FALSE
pmars.model$model<-pmars.model$model[,c(1:(4+pmars.model$responses))]
}
responses<-pmars.model$responses
Y <- matrix(ncol = 1, nrow = nrow(x), data = rep(1, nrow(x)))
Y1 <- matrix(ncol = 1, nrow = nrow(x), data = rep(0, nrow(x)))
Y2 <- matrix(ncol = 1, nrow = nrow(x), data = rep(0, nrow(x)))
if(pmars.model$model.size>1)
{
for(i in 2:pmars.model$model.size)
{
Y2[] <- 1
Y1[] <- x[,pmars.model$model[i, "pred1"]]
if(!is.na(pmars.model$model[i, "knot1"]))
{
Y1 <- Y1 - pmars.model$model[i,"knot1"]
Y1[Y1 < 0,] <- 0
}
if(level1)
{
if(!is.na(pmars.model$model[i, "level1"]))
{
Y1<- (Y1 == pmars.model$model[i, "level1"])
}
}
if(!is.na(pmars.model$model[i, "pred2"]) & pmars.model$model[i, "pred2"] != 0)
{
Y2[] <- x[,pmars.model$model[i,"pred2"]]
if(!is.na(pmars.model$model[i,"knot2" ]))
{
Y2 <- Y2 - pmars.model$model[i,"knot2"]
Y2[Y2 < 0,] <- 0
}
}
Y<-cbind(Y,Y1 * Y2)
}
}
return(Y)
}
logspline <- function(x, lbound, ubound, maxknots=0, knots, nknots=0,
penalty= -1, silent = TRUE,mind= -1, error.action=2)
{
call <- match.call()
if(!missing(x))x <- unstrip(x)
data <- x
if(length(table(data))<3)stop("Not enough unique values")
ilx <- 0; iux <- 0
if(!missing(lbound)){ilx <- 1;jlx <- lbound}
if(!missing(ubound)){iux <- 1;jux <- ubound}
u2 <- length(data)
data <- data[!is.na(data)]
nsample <- length(data)
if(nsample<10)stop("not enough data")
if(u2 !=nsample) print(paste("***", u2-nsample, " NAs ignored in data"))
data <- sort(data)
if(!missing(lbound)) if(data[1] < lbound) stop("data below lbound")
if(!missing(ubound)) if(data[nsample] > ubound) stop("data above ubound")
mm <- range(data)
if(!missing(lbound)) mm <- range(c(mm, lbound))
if(!missing(ubound)) mm <- range(c(mm, ubound))
ilow <- (!missing(lbound)) * 1
iupp <- (!missing(ubound)) * 1
low <- 0
upp <- 0
if(ilow == 1) low <- lbound
if(iupp == 1) upp <- ubound
intpars <- c(-100, rep(0, 9))
z <- .C("nlogcensorx", z = as.integer(intpars),
PACKAGE = "polspline")
maxp <- z$z[1]
kts <- vector(mode = "double", length = max(maxp))
if(maxknots > maxp - 5) warning(paste("maxknots reduced to", maxp))
nknots <- -nknots
if(!missing(knots)) {
nknots <- length(knots)
knots <- sort(knots)
if(!missing(lbound)) if(min(knots) < lbound)
stop("data (knots) below lbound")
if(!missing(ubound)) if(max(knots) > ubound)
stop("data (knots) above ubound")
if(nknots < 3) stop("need at least three starting knots")
if(nknots > maxp - 5) stop(paste("at most", maxp - 5, "knots possible"))
kts[1:nknots] <- knots
}
silent <- (silent == FALSE)
intpars <- c(nsample, maxknots, nknots, silent, 1-ilow, 1-iupp,mind)
dpars <- c(penalty, low, upp)
data <- c(data, rep(0, maxp))
z <- .C("nlogcensor",
ip = as.integer(intpars),
coef = as.double(data),
dp = as.double(dpars),
logl = as.double(rep(0, maxp)),
ad = as.integer(rep(0, maxp)),
kts = as.double(kts),
PACKAGE = "polspline")
if(z$ip[1] != 0 && z$ip[1]<100) {
if(z$ip[1] == 17) warning("too many knots beyond data")
if(z$ip[1] == 18) warning("too many knots before data")
if(z$ip[1] == 39) warning("too much data close together")
if(z$ip[1] == 40) warning("no model could be fitted")
if(z$ip[1] == 2) warning("error while solving system")
if(z$ip[1] == 8) warning("too much step-halving")
if(z$ip[1] == 5) warning("too much step-halving")
if(z$ip[1] == 7)
warning("numerical problems, likely tail related. Try lbound/ubound")
if(z$ip[1] == 1) warning("no convergence")
i <- 0
if(missing(knots))i<- 1
if(z$ip[1] == 3 && i==1)
warning("right tail extremely heavy, try running with ubound")
if(z$ip[1] == 4 && i==1)
warning("left tail extremely heavy, try running with lbound")
if(z$ip[1] == 6 && i==1)
warning("both tails extremely heavy, try running with lbound and ubound")
if(z$ip[1] == 3 && i==0)
warning("right tail too heavy or not enough knots in right tail")
if(z$ip[1] == 4 && i==0)
warning("left tail too heavy or not enough knots in left tail")
if(z$ip[1] == 6 && i==0)
warning("both tails too heavy or not enough knots in both tail")
if(error.action==0) stop("fatal error")
if(error.action==1) {
print("no object returned")
invisible()
}
if(error.action==2) {
if(ilx==0 && iux==0)z <- oldlogspline(x)
if(ilx==0 && iux==1)z <- oldlogspline(x,ubound=jux)
if(ilx==1 && iux==0)z <- oldlogspline(x,lbound=jlx)
if(ilx==1 && iux==1)z <- oldlogspline(x,lbound=jlx,ubound=jux)
z <- oldlogspline.to.logspline(z,x)
z$call <- call
warning("re-ran with oldlogspline")
z
}
}
else{
if(z$ip[1]>100) {
warning(" Not all models could be fitted")
}
logl <- cbind(z$ad, z$logl)
logl <- cbind(2+(1:z$ip[3]),logl[1+(1:z$ip[3]), ])
kk <- (1:length(logl[,1]))
kk <- kk[logl[, 2] == 0 ]
if(length(kk)>0)logl <- logl[-kk,]
fit <- list(call = call, nknots = z$ip[2], coef.pol = z$coef[1:2], coef.kts =
z$coef[2 + (1:z$ip[2])], knots = z$kts[1:z$ip[2]], maxknots = z$ip[3]+2,
penalty = z$dp[1], bound = c(ilow, low, iupp, upp), samples = nsample,
logl = logl, range = mm, mind = z$ip[7])
class(fit) <- "logspline"
fit}
}
plogspline <- function(q, fit)
{
if(class(fit)!="logspline")
stop("fit is not a logspline object")
if(!missing(q))q <- unstrip(q)
sq <- rank(q)
q <- sort(q)
z <- .C("rpqlsd",
as.double(c(fit$coef.pol, fit$coef.kts)),
as.double(fit$knots),
as.double(fit$bound),
as.integer(1),
pp = as.double(q),
as.integer(length(fit$knots)),
as.integer(length(q)),
PACKAGE = "polspline")
zz <- z$pp[sq]
if(fit$bound[1] > 0) zz[q<fit$bound[2]] <- 0
if(fit$bound[3] > 0) zz[q>fit$bound[4]] <- 1
zz
}
qlogspline <- function(p, fit)
{
if(class(fit)!="logspline")
stop("fit is not a logspline object")
if(!missing(p))p <- unstrip(p)
sp <- rank(p)
p <- sort(p)
z <- .C("rpqlsd",
as.double(c(fit$coef.pol, fit$coef.kts)),
as.double(fit$knots),
as.double(fit$bound),
as.integer(0),
qq = as.double(p),
as.integer(length(fit$knots)),
as.integer(length(p)),
PACKAGE = "polspline")
zz <- z$qq[sp]
zz[p<0] <- NA
zz[p>1] <- NA
zz
}
rlogspline <- function(n, fit)
{
if(class(fit)!="logspline")
stop("fit is not a logspline object")
pp <- runif(n)
qlogspline(pp, fit)
}
dlogspline <- function(q, fit)
{
if(class(fit)!="logspline")
stop("fit is not a logspline object")
if(!missing(q))q <- unstrip(q)
x <- q
y <- fit$coef.pol[1] + x * fit$coef.pol[2]
for(i in 1:length(fit$knots))
y <- y + fit$coef.kts[i] * ((abs(x - fit$knots[i]) +x- fit$knots[i])/2)^3
y <- exp(y)
if(fit$bound[1] > 0) y[x < fit$bound[2]] <- 0
if(fit$bound[3] > 0) y[x > fit$bound[4]] <- 0
y
}
plot.logspline <-function(x, n = 100, what = "d", add = FALSE, xlim, xlab = "", ylab = "", type = "l", ...)
{
fit <- x
if(class(fit)!="logspline")
stop("fit is not a logspline object")
if(add){
plim <- (par()$usr)[1:2]
u4 <- plim[1]
u3 <- plim[2]
if(!missing(xlim)) {
u4 <- max(xlim[1], plim[1])
u3 <- min(xlim[2], plim[2])
}
}
else{
if(missing(xlim)) {
u1 <- qlogspline(0.01, fit)
u2 <- qlogspline(0.99, fit)
u3 <- 1.1 * u1 - 0.1 * u2
u4 <- 1.1 * u2 - 0.1 * u1
}
else {
u3 <- xlim[1]
u4 <- xlim[2]
}}
xx <- (0:(n - 1))/(n - 1) * (u4 - u3) + u3
if(what == "d" || what == "D") yy <- dlogspline(xx, fit)
if(what == "f" || what == "F" || what == "p" || what == "P")
yy <- plogspline(xx, fit)
if(what == "s" || what == "S") yy <- 1 - plogspline(xx, fit)
if(what == "h" || what == "H") yy <- dlogspline(xx, fit)/(1 - plogspline(xx, fit))
if(missing(xlab)) xlab <- ""
if(missing(ylab)) ylab <- ""
if(missing(type)) type <- "l"
if(add)lines(xx,yy, ...)
else plot(xx, yy, xlab = xlab, ylab = ylab, type = type, ...)
invisible()
}
print.logspline <- function(x,...)
{
summary.logspline(x)
}
summary.logspline <- function(object,...)
{
fit <- object
if(class(fit)!="logspline")
stop("fit is not a logspline object")
ul <- fit$penalty
um <- fit$samples[1]
if(length(fit$samples)>1)
um <- fit$samples[1]+ fit$samples[4]
else
um <- fit$samples
kk <- fit$logl[fit$logl[,2] != 0,1]
ad <- fit$logl[fit$logl[,2] != 0,2]
ll <- fit$logl[fit$logl[,2] != 0,3]
bb <- -2 * ll + ul * (kk-1)
cc1 <- bb
cc2 <- bb
cc2[1] <- Inf
cc1[length(bb)] <- 0
if(length(bb) > 1) {
for(i in 1:(length(bb) - 1)) {
cc1[i] <- max((ll[(i + 1):(length(bb))] - ll[i])/(kk[(i + 1):
(length(bb))] - kk[i]))
cc2[i + 1] <- min((ll[1:i] - ll[i + 1])/(kk[1:i] - kk[i + 1]))
}
}
c3 <- cc2 - cc1
cc1[c3 < 0] <- NA
cc2[c3 < 0] <- NA
uu <- cbind(kk, ad, ll, bb, 2 * cc1, 2 * cc2)
ww <- rep("", length(bb))
dimnames(uu) <- list(ww, c("knots", "A(1)/D(2)", "loglik", "AIC",
"minimum penalty", "maximum penalty"))
print(round(uu, 2))
cat(paste("the present optimal number of knots is ",kk[bb== min(bb)],"\n"))
if(ul == log(um))
cat(paste("penalty(AIC) was the default: BIC=log(samplesize): log(",
um, ")=", round(ul, 2), "\n"))
else cat(paste("penalty(AIC) was ", round(ul, 2),
", the default (BIC) ", "would have been", round(log(um), 2), "\n"))
invisible()
}
polyclass <- function(data, cov, weight, penalty, maxdim, exclude, include,
additive = FALSE, linear, delete=2, fit, silent = TRUE, normweight = TRUE, tdata,
tcov, tweight, cv, select=0, loss, seed)
{
call <- match.call()
if(!missing(cov))cov <- unstrip(cov)
if(!missing(exclude))exclude <- unstrip(exclude)
if(!missing(include))include <- unstrip(include)
if(!missing(data))data <- unstrip(data)
if(!missing(weight))weight <- unstrip(weight)
if(!missing(tdata))tdata <- unstrip(tdata)
if(!missing(tweight))tweight <- unstrip(tweight)
if(!missing(tcov))tcov <- unstrip(tcov)
it <- 0
ntdata <- 0
if(!missing(cv)) it <- 2
if(!missing(tdata))it <- 1
if(!missing(tdata))if(is.factor(tdata)) tdata <- as.integer(tdata)
if(missing(cv)) cv <- 0
if(it==1||it==0) cv <- 0
if(it==2){
if(!missing(seed)){
if(sum(seed)!=0){
if(length(seed)>11) assign(".Random.seed", seed[1:12], envir=.GlobalEnv)
else set.seed(seed[1])
seed <- get(".Random.seed", envir=.GlobalEnv, inherits = FALSE)
}
}
else{
if(!missing(fit)){
if(fit$method==2) assign(".Random.seed", fit$seef, envir=.GlobalEnv)
}
seed <- get(".Random.seed", envir=.GlobalEnv, inherits = FALSE)
}
}
z <- .C("spolyx", mk = as.integer(rep(-3,13)),
PACKAGE = "polspline")
MAXKNOTS <- z$mk[1]
MAXSPACE <- z$mk[2]
if(missing(data)) stop("there has to be data")
if(length(data) < 25) stop("not enough data")
if(is.factor(data)) data <- as.integer(data)
if(is.integer(data) == FALSE){
if(max(abs(as.integer(data) - data)) < 0.001)
data <- as.integer(data)
else stop("not-integer data")
}
if(it == 1) {
if(is.integer(tdata) == FALSE){
if(max(abs(as.integer(tdata) - tdata)) < 0.001)
tdata <- as.integer(tdata)
else stop("not-integer test data")
}
alldata <- c(data,tdata)
if(min(alldata)<0) stop("negative data")
clss <- min(alldata):max(alldata)
if(min(alldata) == 1){
data <- data - 1
tdata <- tdata - 1
}
ntdata <- length(tdata)
if(missing(tweight)) tweight <- rep(1,ntdata)
if(length(tweight)!=ntdata)stop("length tweight is incorrect")
if(normweight == TRUE)tweight <- tweight*ntdata/sum(tweight)
}
else{
if(min(data)<0) stop("negative data")
clss <- min(data):max(data)
if(min(data) == 1) data <- data - 1
}
nclass <- length(clss)
ndata <- length(data)
nu <- exists(".Random.seed", envir=.GlobalEnv, inherits = FALSE)
if(nu) xx <- get(".Random.seed", envir=.GlobalEnv, inherits = FALSE)
yy <- sample(ndata)
if(nu)assign(".Random.seed", xx, envir=.GlobalEnv)
if(missing(weight)) weight <- rep(1,ndata)
if(it==2){
if(sum(abs(seed[1]))==0) myord <- 1:ndata
else myord <- sample(ndata)
data <- data[myord]
weight <- weight[myord]
}
if(length(weight)!=ndata)stop("length weight is incorrect")
if(normweight == TRUE)weight <- weight*ndata/sum(weight)
if(missing(cov)) {
stop("covariates required")
}
else {
if(length(cov) == ndata)
cov <- matrix(cov, ncol = 1, nrow = ndata)
if(length(cov[, 1]) != ndata)
stop("covariates not ndata * ncov matrix")
if(it==2)cov <- cov[myord,]
ncov <- length(cov[1, ])
nms <- 1:ncov
if(is.matrix(cov))
nms <- dimnames(cov)[[2]]
if(length(nms) != ncov)
nms <- 1:ncov
}
if(missing(penalty) && it ==0)
penalty <- log(ndata)
if(missing(penalty) && it >0)
penalty <- 0
il <- 1
if(select==1) il <- 0
if(select==2) il <- 2
if(delete!=0 && delete !=1) delete <- 2
iml <- missing(loss)
if(iml) loss <- 1 - diag(rep(1,nclass))
if(il!=1 && !iml)
stop("if loss is specified, select has to be 0")
if((it == 0) && !iml)
warning("loss only has effect when there is a test-set or CV is used")
if(it == 1){
if(missing(tcov)) {
if(ncov!=0)stop("missing tcov")
tcov <- 0
}
else {
if(length(tcov) == ntdata)
tcov <- matrix(tcov, ncol = 1, nrow = ntdata)
if(length(tcov[, 1]) != ntdata)
stop("test-covariates not ntdata * ncov matrix")
ntcov <- length(cov[1, ])
if(ntcov!=ncov) stop("wrong number of test-covariates")
}
}
naction <- nclass
if(it>0){
if(is.matrix(loss)==FALSE)stop("loss is not a matrix")
if(length(loss[1,])!=nclass)stop("loss has not nclass columns")
naction <- length(loss[,1])
}
if(additive) {
if(!missing(exclude)) stop("cannot have exclude and additive")
if(!missing(include)) stop("cannot have include and additive")
include <- c(0, 0)
}
if(missing(exclude) + missing(include) == 0)
stop("only 1 from exclude and include allowed")
vexclude <- 0
if(missing(exclude) == FALSE) {
if(length(exclude) == 2)
exclude <- matrix(exclude, ncol = 2, nrow = 1)
if(length(exclude[1, ]) != 2) stop("exclude has wrong shape")
if(min(exclude) < 0 || max(exclude) > ncov)
stop("exclude has wrong values")
vexclude <- as.vector(t(exclude))
vexclude <- c(length(vexclude)/2, vexclude)
}
if(missing(include) == FALSE || additive) {
if(length(include) == 2)
include <- matrix(include, ncol = 2, nrow = 1)
if(length(include[1, ]) != 2)
stop("include has wrong shape")
if(min(include) < 0 || max(include) > ncov)
stop("include has wrong values")
include <- t(apply(include, 1, sort))
if(length(include) == 2)
include <- matrix(include, ncol = 2, nrow = 1)
vexclude <- as.vector(t(include))
vexclude <- c( - length(vexclude)/2, vexclude)
}
if(missing(maxdim)) {
maxdim <- floor(4 * (ndata)^(1/3))+1
maxdim <- min(ndata/2, MAXSPACE-1, (nclass-1)*maxdim)
maxdim <- - maxdim
}
if(maxdim > MAXSPACE - 1) {
maxdim <- MAXSPACE - 1
print(paste("maximum dimension reduced to", maxdim))
}
lins <- rep(0, MAXSPACE)
if(!missing(linear)) {
linear[linear <= 0] <- ncov + 1
linear[linear > ncov + 1] <- ncov + 1
lins[linear] <- 1
}
if(additive)
vexclude <- c(-1, 0, 0)
fitter <- 0
bbtt <- matrix(0, ncol = 4 + max(data), nrow = abs(maxdim))
cckk <- matrix(0, ncol = (MAXKNOTS + 1), nrow = ncov+1)
if(!missing(fit)) {
if(class(fit)!="polyclass")stop("fit is not a polyclass object")
fitter <- (fit$nclass-1)*(fit$nbas)
if(fit$ncov != ncov)
stop("ncov and fit's ncov are different")
if(fit$nclass != nclass)
stop("nclass and fit's nclass are different")
a1 <- length(fit$fcts[1,])
bbtt[1:fit$nbas, ] <- fit$fcts[,-a1]
bbtt <- as.vector(t(bbtt))
bbtt[is.na(bbtt)] <- -1
a1 <- length(fit$knots[1, ])
a2 <- as.vector(t(fit$knots))
cckk <- as.vector(cckk)
cckk <- c(a1,a2,cckk)
cckk[is.na(cckk)] <- -1
}
mindist <- 3*nclass
if(missing(tdata)){
tdata<-0
tcov <-0
tweight <- 0
}
ranges <- NA
if(ncov == 1)
ranges <- matrix(range(cov), ncol = 1, nrow = 2)
if(ncov > 1)
ranges <- apply(cov, 2, range)
cov <- as.single(t(cov))
aicx <- as.single(rep(0,1000))
intpars <-c(ndata,nclass,ncov,mindist,maxdim,silent,fitter,cv,it,ntdata,
naction,il,delete)
anova <- loss
if(length(anova)<abs(maxdim)*4)anova<-c(anova,rep(0,abs(4*maxdim)))
z <- .C("spoly",
intpars = as.integer(intpars),
as.integer(data),
as.single(cov),
anova = as.double(anova),
as.double(penalty),
bbtt = as.double(bbtt),
cckk = as.double(cckk),
as.integer(vexclude),
as.integer(lins),
logl = as.double(rep(0, 11*MAXSPACE+1)),
as.double(weight),
as.integer(tdata),
as.single(t(tcov)),
as.double(tweight),
bbb = as.double(rep(0, MAXSPACE*nclass)),
aicx=as.single(aicx),
PACKAGE = "polspline")
ndim <- z$intpars[1]
aicx <- z$aicx[1:4]
aicy <- 0
if(it==2){
aicy <- z$aicx[6:(z$aicx[5])]
aicy <- matrix(aicy,ncol=3,byrow=TRUE)
if(z$aicx[5]<995)aicy[length(aicy[,1]),2]<- Inf
dimnames(aicy) <- list(NULL,c("pen-min","pen-max","cv-loss"))
}
nclass <- z$intpars[2]+1
nbas <- z$intpars[3]
maxdim <- abs(maxdim)
z$bbtt <- matrix(z$bbtt, nrow = maxdim, ncol = 3 + nclass, byrow = TRUE)
z$bbtt <- z$bbtt[1:nbas, ]
z$cckk <- matrix(z$cckk, nrow = ncov+1, ncol = MAXKNOTS + 1, byrow = TRUE)
z$cckk <- z$cckk[1:ncov,]
z$cckk <- matrix(z$cckk, nrow = ncov)
z$cckk <- z$cckk[, 1:(1 + max(z$cckk[, 1]))]
z$cckk <- matrix(z$cckk, nrow = ncov)
l1 <- max(z$cckk[, 1])
for(i in 1:(ncov))
if(z$cckk[i, 1] != l1) z$cckk[i, (z$cckk[i, 1] + 2):(l1 + 1)] <-
NA
if(l1 > 0)
dimnames(z$cckk) <- list(nms, c("K", 1:l1))
if(l1 == 0)
dimnames(z$cckk) <- list(nms, "K")
z$bbtt <- matrix(z$bbtt, ncol = 3 + nclass)
z$bbtt <- cbind(z$bbtt, 0)
dimnames(z$bbtt) <- list(1:nbas, c("dim1", "knot1",
"dim2", "knot2", as.character(clss)))
z$bbtt[z$bbtt[, 3] == -1, 3:4] <- NA
z$bbtt[z$bbtt[, 4] == 0, 4] <- NA
z$bbtt[1,1] <- NA
i <- z$logl[1]
z$logl <- matrix(z$logl[2:(11*i+1)],ncol=11,byrow=TRUE)
z$logl[z$logl[,10]<0,10] <- NA
z$logl[z$logl[,11]<0,11] <- NA
z$logl[1,11] <- Inf
dimnames(z$logl) <- list(NULL, c("dim","loss","l-lik-trn","loss-trn",
"sq-err-trn","l-lik-test","loss-tst","sq-err-tst"
, "A/D","pen-min","pen-max"))
if(it!=1){
dimnames(z$logl)[[2]][2] <- "AIC"
z$logl <- z$logl[,-(6:8)]
}
anova <- z$anova[2:(1+z$anova[1])]
anova[anova<0] <- NA
anova <- matrix(anova,ncol=3,byrow=TRUE)
dimnames(ranges) <- list(c("min", "max"), nms)
z$bbtt[0, 0] <- NA
z$bbtt[z$bbtt[, 2] == 0, 2] <- NA
z$bbtt[z$bbtt[, 2] == 0, 4] <- NA
if(nclass==naction)
yyy <- clss
else
yyy <- 1:naction
if(it!=0)dimnames(loss) <- list(as.character(yyy),clss)
if(il!=1)loss <- -1
bbb <- z$bbb
bbb <- bbb[1:(nbas*nclass)]
bbb <- matrix(bbb,nrow=nbas,byrow=TRUE)
if(it==0){
nfit <- list(call = call, ncov = ncov, ndim = ndim, nclass = nclass,
nbas = nbas, fcts = z$bbtt, knots = z$cckk, penalty = penalty,
method = it, ranges = ranges, logl= z$logl,
sample = ndata, wgtsum = sum(weight), covnames = nms,
classnames = clss, beta = bbb, delete = delete, anova = anova)
}
else{
if(it==1)
nfit <- list(call = call, ncov = ncov, ndim = ndim, nclass = nclass,
nbas = nbas, naction = naction, fcts = z$bbtt, knots = z$cckk,
loss = loss, penalty = penalty, method = it, ranges = ranges,
logl= z$logl, sample = ndata, tsample = ntdata,
wgtsum = sum(weight), covnames = nms, classnames = clss, beta = bbb,
delete = delete, anova = anova,
select = select, twgtsum = sum(tweight))
else
nfit <- list(call = call, ncov = ncov, ndim = ndim, nclass = nclass,
nbas = nbas, naction = naction, fcts = z$bbtt, knots = z$cckk,
cv = cv, loss = loss, penalty = penalty, method = it, ranges = ranges,
logl= z$logl, sample = ndata, wgtsum = sum(weight), covnames = nms,
classnames = clss, cv.aic = aicx, cv.tab = aicy, seed = seed,
beta = bbb, delete = delete, anova = anova, select = select)
}
class(nfit) <- "polyclass"
nfit
}
cpolyclass <- function(cov, fit)
{
if(class(fit)!="polyclass")stop("fit is not a polyclass object")
if(!missing(cov))cov <- unstrip(cov)
xxx <- ppolyclass(cov, fit)
yyy <- fit$classnames
if(fit$method!=0){
if(length(fit$loss)==1)
fit$loss <- 1 - diag(rep(1,fit$nclass))
xxx <- t(-fit$loss%*%t(xxx))
if(fit$nclass==fit$naction)
yyy <- fit$classnames
else
yyy <- 1:fit$naction
}
zzz <- xxx[, 1]
www <- rep(yyy[1], length(zzz))
for(i in 2:length(yyy)) {
www[zzz < xxx[, i]] <- yyy[i]
zzz[zzz < xxx[, i]] <- xxx[zzz < xxx[, i], i]
}
www
}
ppolyclass <- function(data, cov, fit)
{
imf <- missing(fit)
if(imf) {
fit <- cov
cov <- data
}
if(!missing(cov))cov <- unstrip(cov)
if(!missing(data))data <- unstrip(data)
if(!missing(data) && is.factor(data)) data <- as.integer(data)
if(class(fit)!="polyclass")stop("fit is not a polyclass object")
if(is.matrix(cov) == FALSE)
cov <- matrix(cov, ncol = 1)
if(length(cov[1, ]) != fit$ncov) {
if(length(cov[1, ]) == 1 && length(cov[, 1]) == fit$ncov)
cov <- t(cov)
else stop("incorrect number of covariates")
}
ncase <- length(cov[, 1])
nclass <- fit$nclass
nbas <- length(fit$fcts[, 1])
if(imf || missing(data))
data <- rep(-1, ncase)
if(length(data) == 1)
data <- rep(data, ncase)
if(is.integer(data) == FALSE)
if(max(abs(as.integer(data) - data)) < 0.001)
data <- as.integer(data)
else stop("not-integer data")
w2 <- fit$classnames
if(data[1] != -1 && (min(w2) > min(data) || max(w2) < max(data)))
stop("data has wrong range")
if(min(data) == 0)
data <- data + 1
ppp <- matrix(0, ncol = nclass, nrow = ncase)
for(i in 1:(nclass - 1))
ppp[, i] <- (fit$fcts[1, (4 + i)])
if(nbas > 1)
for(j in 2:nbas) {
uuu <- cov[, fit$fcts[j, 1]]
if(is.na(fit$fcts[j, 2]) == FALSE) {
uuu <- uuu - fit$knots[fit$fcts[j, 1], fit$fcts[
j, 2] + 1]
uuu[uuu < 0] <- 0
}
vvv <- rep(1, ncase)
if(is.na(fit$fcts[j, 3]) == FALSE) {
vvv <- cov[, fit$fcts[j, 3]]
if(is.na(fit$fcts[j, 4]) == FALSE) {
vvv <- vvv - fit$knots[fit$fcts[j, 3], fit$
fcts[j, 4] + 1]
vvv[vvv < 0] <- 0
}
}
uuu <- uuu * vvv
for(i in 1:(nclass - 1))
ppp[, i] <- ppp[, i] + uuu * fit$fcts[j, (4 + i
)]
}
ppp <- ppp-apply(ppp,1,max)
ppp <- exp(ppp)
zzz <- ppp[, nclass]
for(i in 1:(nclass - 1))
zzz <- zzz + ppp[, i]
for(i in 1:nclass)
ppp[, i] <- ppp[, i]/zzz
if(data[1] == -1)
dimnames(ppp) <- list(NULL, fit$classnames)
else ppp <- ppp[cbind(1:ncase, data)]
ppp
}
plot.polyclass <- function(x,cov, which, lims, what, data, n, xlab, ylab, zlab, ...)
{
if(class(x)!="polyclass")stop("x is not a polyclass object")
if(!missing(cov))cov <- unstrip(cov)
fit <- x
here <- c(-1, -1)
if(length(which) == 1 || length(which) == 2)
here[1] <- length(which)
here[2] <- as.integer(what)
if(here[2]< 1||here[2]>8) stop("what is wrong")
if(min(here) < 0)
stop("which is wrong")
if(here[2] < 5.5 && here[1] == 1)
stop("which and what contradict")
if(here[2] > 5.5 && here[1] == 2)
stop("which and what contradict")
if(length(cov) != fit$ncov)
stop("length of cov is wrong")
clbs <- fit$covnames
ww <- fit$classnames
w1 <- (1:fit$ncov)
if(missing(lims))
lims <- NULL
if(length(lims) != 0 && length(lims) != (here[1] * 2))
stop("lims is wrong")
wa <- 0
for(i in 1:length(which)){
if(is.numeric(which) == FALSE)
wa <- c(wa,w1[which[i] == clbs])
else wa <- c(wa,w1[w1 == which[i]])
}
wa <- wa[-1]
if(length(wa) != here[1])
stop("which is wrong")
wb <- clbs[wa]
if(here[2] < 3.5 || here[2] > 7.5) {
if(missing(data))
stop("data is missing")
if(length(data) > 1)
stop("only one class (data) allowed")
}
if(length(lims) == 0) {
if(here[1] == 1)
lims <- fit$ranges[, wa]
else lims <- c(fit$ranges[, wa[1]], fit$ranges[, wa[2]])
}
if(missing(xlab))
xlab <- as.character(wb[1])
if(missing(ylab)) {
if(here[1] == 2)
ylab <- as.character(wb[2])
if(here[2] > 6.5)
ylab <- "probability"
if(here[2] == 6)
ylab <- "class"
}
if(missing(zlab) && here[2] == 2)
zlab <- "probability"
if(missing(n) && here[1] == 1)
n <- 250
if(missing(n) && here[1] == 2)
n <- 50
if(here[1] == 1) {
cov <- matrix(cov, byrow = TRUE, nrow = n, ncol = fit$ncov)
c1 <- lims[1] + ((lims[2] - lims[1]) * (0:(n - 1)))/(n - 1)
cov[, wa] <- c1
}
if(here[1] == 2) {
cov <- matrix(cov, byrow = TRUE, nrow = n * n, ncol = fit$ncov)
c1 <- lims[1] + ((lims[2] - lims[1]) * (0:(n - 1)))/(n - 1)
c11 <- (rep(c1, n))
cov[, wa[1]] <- c11
c2 <- lims[3] + ((lims[4] - lims[3]) * (0:(n - 1)))/(n - 1)
c22 <- sort(rep(c2, n))
cov[, wa[2]] <- c22
}
if(here[2] <= 3) {
v1 <- ppolyclass(data, cov, fit)
v1 <- matrix(v1, n, n)
if(here[2] == 1)
contour(c1, c2, v1, xlab = xlab, ylab = ylab, ...)
if(here[2] == 2)
persp(c1, c2, v1, xlab = xlab, ylab = ylab, zlab = zlab,
...)
if(here[2] == 3)
image(c1, c2, v1, xlab = xlab, ylab = ylab, ...)
}
if(here[2] == 6) {
v1 <- cpolyclass(cov, fit)
plot(c1, v1, type = "l", ylim = range(ww), xlab = xlab, ylab =
ylab, ...)
}
if(here[2] == 8) {
v1 <- ppolyclass(data, cov, fit)
plot(c1, v1, type = "l", xlab = xlab, ylab = ylab, ...)
}
if(here[2] == 4 || here[2] == 5) {
v1 <- cpolyclass(cov, fit)
v1 <- matrix(v1, n, n)
if(here[2] == 5)
image(c1, c2, v1, xlab = xlab, ylab = ylab, ...)
}
if(here[2] == 4) {
zz <- range(v1)
z1 <- 1 * (v1 < zz[1] + 0.5)
contour(c1, c2, z1, xlab = xlab, ylab = ylab, levels = 0.5,
labex = 0, ...)
if(zz[2] - zz[1] > 1)
for(i in (zz[1] + 1):(zz[2] - 1)) {
z1 <- 1 * (v1 < i + 0.5)
contour(c1, c2, z1, labex = 0, levels = 0.5,
add = TRUE, ...)
}
}
if(here[2] == 7) {
v1 <- ppolyclass(cov, fit)
plot(c1, v1[, 1], type = "l", xlab = xlab, ylab = ylab, ylim =
c(0, 1), ...)
abline(h = c(0, 1))
zz <- length(v1[1, ])
if(zz > 2)
for(i in 2:zz) {
v1[, 1] <- v1[, 1] + v1[, i]
lines(c1, v1[, 1])
}
}
invisible()
}
rpolyclass <- function(n, cov, fit)
{
if(class(fit)!="polyclass")stop("fit is not a polyclass object")
if(!missing(cov))cov <- unstrip(cov)
if(n < 1)
stop("n is wrong")
if(is.matrix(cov) == FALSE)
cov <- matrix(cov, nrow = 1)
if(length(cov[1, ]) != fit$ncov)
stop("wrong number of covariates")
if(n > 1 && length(cov[, 1]) == 1)
cov <- matrix(cov, nrow = n, ncol = fit$ncov, byrow = TRUE)
if(n != length(cov[, 1]))
stop("cov has wrong number of rows")
vv <- ppolyclass(cov, fit)
ww <- runif(n)
zz <- rep(fit$nclass, n)
for(i in 2:fit$nclass)
vv[, i] <- vv[, i] + vv[, (i - 1)]
for(i in fit$nclass:1)
zz[ww < vv[, i]] <- i
if(min(fit$classnames) == 0)
zz <- zz - 1
zz
}
print.polyclass <- function(x,...)
{
summary.polyclass(x)
}
summary.polyclass <- function(object,...)
{
if(class(object)!="polyclass")stop("object is not a polyclass object")
fit <- object
it <- fit$method
cat("========================POLYCLASS summary=======================\n")
cat(paste("The fit was obtained with\n "))
cat("\b\b")
print(fit$call)
cat(paste("There were",fit$nclass,"classes and", fit$ncov,"covariates.\n"))
if(it == 1) cat(paste("There were", fit$sample, "trial cases and",
fit$tsample, "test cases.\n\n"))
else cat(paste("There were",fit$sample,"cases.\n\n"))
if(it == 0) {
cat("The model selection was carried out using AIC.\n")
if(0.99 < fit$penalty/log(fit$wgtsum)&&fit$penalty/log(fit$wgtsum) <1.01){
cat(paste("The penalty was the default, log("))
cat(paste(round(fit$wgtsum), "\b)="))
cat(paste(round(log(fit$wgtsum), 2), "\b.\n"))
}
else{
cat(paste("The penalty was", round(fit$penalty, 2),
"\b, the default would have been log("))
cat(paste("\b",round(fit$wgtsum), "\b)="))
cat(paste(round(log(fit$wgtsum), 2), "\b.\n"))
}
}
if(it == 1) cat("The model selection was carried out using a test set.\n")
if(it == 2) cat(paste("The model selection was carried out using", fit$cv,
"\b-fold cross-validation.\n"))
if((it == 1 || it == 2) && fit$select==0) {
a2 <- range(fit$loss + diag(rep(1, fit$nclass)))
if(a2[2] == 1 && a2[1] == 1) cat("The standard loss-matrix was used.\n")
else cat("A loss matrix was provided.\n")
}
if((it == 1 || it == 2) && fit$select ==2) {
cat("The sum of squared probabilities was used for the loss.\n")
}
if((it == 1 || it == 2) && fit$select ==1) {
cat("Minus the test set log likelihood was used for the loss.\n")
}
if(length(fit$logl)<12)fit$logl<-t(as.matrix(fit$logl))
a2 <- fit$logl[fit$logl[,1]==fit$ndim, ]
cat(paste("The model had dimension", fit$ndim,
"\b, log-likelihood",round(a2[3], 2)))
if(it == 0) cat(paste(" and AIC", round( a2[2], 2), "\b.\n\n"))
if(it == 1) cat(paste(" and loss", round( a2[2], 2), "\b.\n\n"))
if(it == 2) cat(paste(" and AIC", round( a2[2], 2), "\b.\n\n"))
if(it == 2){
cat(paste("The penalty was cross-validated between",
round(fit$cv.aic[1],2)))
if(fit$cv.aic[2]<0) cat(paste(" and Inf to",round(fit$cv.aic[4],2)))
else cat(paste(" and",round(fit$cv.aic[2],2),"to",
round(fit$cv.aic[4],2)))
cat(paste(" (loss",round(fit$cv.aic[3],2),"\b).\n"))
cat(paste("The default penalty would have been log(",
round(fit$wgtsum), "\b)=", round(log(fit$wgtsum), 2), "\b.\n"))
}
cat("The locations of the knots:\n")
dimnames(fit$knots)[[2]][1] <- "Number"
print(round(fit$knots, 3))
cat(paste("\n There are", fit$nbas, "basis functions, summarized below:\n"))
a3 <- length(dimnames(fit$fcts)[[2]])
for(i in 5:a3)
dimnames(fit$fcts)[[2]][i] <- paste("Class",dimnames(fit$fcts)[[2]][i])
print(round(fit$fcts, 3))
cat("The first basis function is the constant function. For all others,\n")
cat("the first column and the third column indicate on which covariates\n")
cat("that basis function depends. If the third column is NA, the basis\n")
cat("function depends on only one covariate.\n")
cat("For the nonconstant basis functions the second and the fourth column\n")
cat("indicate on which knot the function depend. If these columns are NA,\n")
cat("the basis function is linear in this covariate.\n")
cat("The remaining columns give the coefficients.\n")
cat("\n")
cat("================================================================\n")
if(fit$method==0)
cat("The influence of the penalty parameter is summarized below:\n")
if(fit$method==1)
cat("The effect of the penalty in the final run is summarized below:\n")
if(fit$method==2)
cat("The equivalence of the penalty parameter is summarized below:\n")
dimnames(fit$logl)[[1]] <- rep("",length(fit$logl[,1]))
fit$logl[,3:5] <- fit$logl[,3:5]/fit$wgtsum
if(fit$method==1)fit$logl[,6:8] <- fit$logl[,6:8]/fit$twgtsum
fit$logl[,"AIC"] <- (-fit$logl[,"AIC"])
print(round(fit$logl, 3))
fit$logl[,"AIC"] <- (-fit$logl[,"AIC"])
if(fit$method==2){
cat("The relation between the CV-loss and the penalty is summarized below:\n")
dimnames(fit$cv.tab)[[1]] <- rep("",length(fit$cv.tab[,1]))
print(round(fit$cv.tab, 3))}
cat("================================================================\n")
cat("The importance-anova decomposition is:\n")
anova <- fit$anova
anova[,3] <- anova[,3]*100
dimnames(anova) <- list(rep("",length(anova[,1])),
c("Cov-1","Cov-2","Percentage"))
print(round(anova,2))
cat("================================================================\n")
invisible()
}
beta.polyclass <- function(fit, which, xsp = 0.4, cex)
{
if(class(fit)!="polyclass")stop("fit is not a polyclass object")
plot(c(0, 1), c(0, 1), axes = FALSE, xlab = "", xlim = c(0.1, 0.9), ylim =
c(0.1, 0.9), ylab = "", type = "n")
lines(c(0, 1, 1, 0, 0), c(1, 1, 0, 0, 1))
xsp <- xsp/4
if(missing(which))
which <- fit$classnames
if(fit$classnames[1] == 0)
which <- which + 1
if(missing(cex))
cex <- par()$cex
nb <- fit$nbas
lines( c(4 * xsp,4*xsp),c(0,1))
b11 <- fit$beta
for(i in 1:nb) {
b1 <- fit$fcts[i, ]
y1 <- 1 - (i - 0.5)/nb
y0 <- 1 - (i - 1)/nb
y2 <- 1 - (i - 0)/nb
lines(c(0, 4 * xsp), c(y0, y0))
lines(c(2 * xsp, 2 * xsp), c(y0, y2))
aa <- fit$covnames[b1[1]]
if(is.na(b1[2])) aa <- paste(aa, "linear")
else aa <- paste(aa, "at", signif(fit$knots[b1[1], b1[2] + 1], 2))
if(i==1)aa <- "constant"
text(xsp, y1, aa, cex = cex)
if(is.na(b1[3]) == FALSE) {
aa <- fit$covnames[b1[3]]
if(is.na(b1[4])) aa <- paste(aa, "linear")
else aa <- paste(aa, "at", signif(fit$knots[b1[3], b1[4] + 1], 2))
text(xsp * 3, y1, aa, cex = cex)
}
lines(c(4 * xsp + 0.03, 0.97), c(1 - (i - 0.1)/nb, 1 - (i - 0.1)/nb))
}
b2 <- range(b11)
for(i in 1:nb) {
b1 <- round(((0.92-4*xsp)*(b11[i,]-b2[1]))/(b2[2]-b2[1])+4*xsp+0.04,2)
aa <- rep(1, length(b1))
for(j in 1:length(b1)) aa[j] <- sum(abs(b1[1:j] - b1[j]) < 0.01)
bb <- max(aa)
if(bb > 1) bb <- 0.7/(nb * bb)
for(j in which) text(b1[j], 1 - (i - 0.2)/nb + bb * (aa[j] - 1),
as.character(fit$classnames[j]))
}
invisible()
}
testhare <- c(4.974595958,0,1,2.456985,8,38,5.229125,1,3.422498434,0,0,2.177377,7,49,5.277500,0
,4.290693972,1,0,4.381446,20,54,5.485566,0,11.301950208,0,0,3.526174,10,65,4.621450,0
,10.683645663,0,1,1.150400,5,34,4.766442,1,3.741203855,1,0,5.087841,13,52,6.405083,0
,7.141522554,0,1,1.056958,8,46,4.682535,1,2.563535609,1,1,1.278860,6,25,4.556451,1
,3.701746380,0,1,3.999343,10,54,6.084539,0,6.395697579,0,1,1.336799,7,59,4.631800,0
,0.275924575,1,0,4.426891,10,45,5.141796,1,7.993160854,0,0,1.512389,8,53,4.976703,0
,10.650698724,0,1,2.227674,3,56,4.770898,0,1.015110143,1,0,5.693455,13,71,4.648958,0
,3.805403838,1,0,2.315779,5,45,4.921255,1,8.068892808,1,1,3.921555,14,29,4.820110,1
,0.944656017,1,0,6.564750,10,46,4.194352,1,1.320377850,1,1,2.505369,30,53,5.652503,0
,4.858707158,0,1,3.818449,8,24,5.283514,1,12.207398556,0,1,3.033311,3,50,4.506939,0
,10.981959783,0,0,2.896733,17,62,4.637291,0,3.407096607,0,1,1.175291,13,58,5.060192,0
,7.508765234,0,0,3.347511,9,44,5.096031,1,5.665519855,0,1,1.961776,3,30,5.067910,1
,11.655373133,0,0,3.555977,3,44,4.810457,0,1.961668982,1,1,7.169299,5,28,5.025885,1
,9.198057574,1,1,2.027242,7,40,5.077524,1,8.781112429,0,0,3.017898,19,61,4.660392,0
,1.093624486,1,1,6.143122,8,39,4.956558,1,2.924913855,1,1,4.724365,13,67,4.627196,0
,10.315301712,0,0,3.139070,15,70,6.654374,0,3.512635454,0,1,1.635826,3,50,6.034860,0
,1.883750176,1,1,2.651898,7,51,5.034317,1,4.690818787,1,1,5.931697,13,29,5.220239,0
,4.525531470,0,1,1.882305,4,48,6.011017,0,1.643812226,1,1,4.041397,17,39,5.052686,1
,4.777447362,0,1,1.906920,5,34,5.695211,1,2.127848760,0,0,2.436000,7,42,4.745345,1
,8.193705169,0,0,5.602968,15,39,5.161291,1,10.871217535,0,1,3.307412,3,50,4.506939,0
,3.646383136,0,1,3.742517,4,42,5.357143,1,1.809633167,1,1,3.949245,5,28,5.015292,1
,9.403031955,0,1,1.650917,9,66,4.374088,0,2.598629459,0,0,5.005954,20,54,5.270361,0
,0.716941135,0,0,2.367035,15,29,5.151093,1,1.149237689,1,0,3.917028,3,66,5.229125,0
,6.634921268,1,0,3.619424,2,39,4.753973,1,6.677475551,0,0,2.680901,3,59,4.619330,0
,1.853423959,1,0,7.280685,13,39,5.374839,1,1.977927943,1,1,1.479475,5,40,5.329045,1
,3.071427712,1,1,6.376802,17,61,4.761905,0,5.729473555,0,1,2.209530,4,70,5.251685,0
,12.999040825,0,1,3.899504,7,45,4.800717,1,4.059714382,0,1,3.377176,5,32,4.535342,1
,0.744850674,1,0,4.984910,20,55,6.196016,0,0.855878893,1,0,7.315716,7,47,5.851493,1
,9.801334057,0,0,1.829557,5,54,5.921052,1,8.941585984,0,0,3.592722,4,33,4.594660,1
,2.148160196,1,0,2.452756,7,41,4.230605,1,3.481338340,1,1,4.809930,7,34,5.247021,1
,6.696794779,0,1,5.234634,17,46,4.693797,0,3.835564836,1,0,4.091902,13,32,5.455447,1
,1.005947051,1,0,8.155644,17,56,5.352583,0,3.254906373,0,0,2.894001,13,38,4.860499,1
,3.673054257,0,1,3.269990,8,56,6.070261,0,6.852679664,0,0,2.728159,3,27,4.902511,1
,3.766346315,0,0,1.107997,6,52,4.599488,0,0.722085805,1,1,2.561833,13,31,4.705882,1
,5.808865352,0,1,4.302919,12,48,4.778376,0,0.758610999,1,1,4.873266,13,68,4.781478,0
,8.604434532,0,0,3.738226,10,50,4.980119,0,0.649993805,1,0,1.755723,5,62,5.077524,0
,8.846562896,0,0,2.704749,7,33,5.344762,1,8.872918703,0,1,1.049267,13,57,4.848485,0
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testhare <- matrix(testhare,ncol=8,byrow=TRUE)
|
library(hexSticker)
library(ggplot2)
library(gridExtra)
library(grid)
library(gtable)
library(sysfonts)
sysfonts::font_add_google(name = "Roboto", family = "Roboto")
col1 <- c(rep(" ", 4))
df <- data.frame(col1, col1, col1, col1)
print(df)
colnames(df) <- NULL
print(df)
myt <- ttheme_default(
core = list(bg_params=list(fill = NA, col="gray99" ) ),
colhead = list(bg_params=list(fill = NA, col="gray99"))
)
g <- tableGrob(df, rows = NULL, theme = myt)
g <- gtable_add_grob(g, grobs = rectGrob(gp = gpar(fill = NA, col="gray99",lwd = 9)),
t = 1, b = nrow(g), l = 1, r = ncol(g))
g <- gtable_add_grob(g, grobs = rectGrob(gp = gpar(fill = NA, col="gray99", lwd = 8)),
t = 1, l = 1, r = ncol(g))
grid.draw(g)
png(filename = "./man/figures/table.png", width=400, height=260, bg = "transparent", res=200)
grid.draw(g)
dev.off()
img_address <- "./man/figures/table.png"
sticker( img_address,
package="gtfs2gps", p_color="gray99", p_size=22, p_family= "Roboto",
s_x=1, s_y=.8, s_width=.6, s_height=.6,
h_fill="
spotlight=TRUE, l_x=20, l_y=4, l_width=2, l_height=2,
filename="./man/figures/gtfs2gps_logo.png", dpi=300)
|
orderly_graph_src <- function(name, config, direction = "downstream",
max_depth = Inf, recursion_limit = 100,
show_all = FALSE) {
nms <- orderly_list(config)
if (!(name %in% nms)) {
stop(sprintf("Unknown source report '%s'", name), call. = FALSE)
}
src <- lapply(nms, orderly_recipe$new, config, develop = TRUE)
names(src) <- nms
deps <- lapply(src, function(x) unique(x$depends[c("name", "id")]))
if (direction == "downstream") {
len <- viapply(deps, NROW, USE.NAMES = FALSE)
parent <- unlist(lapply(deps, "[[", "name"), FALSE, FALSE)
deps <- split(data_frame(
name = rep(names(deps), len),
id = unlist(lapply(deps, "[[", "id"), FALSE, FALSE)), parent)
}
root <- report_vertex$new(NULL, name, "latest", FALSE)
seen <- new.env()
build_tree_src(root, deps, max_depth, recursion_limit, NULL)
report_tree$new(root, direction)
}
build_tree_src <- function(parent, deps, depth, limit, seen = NULL) {
if (limit < 0) {
stop("The tree is very large or degenerate.")
}
name <- parent$name
if (any(seen == name)) {
loop <- c(seen[which(seen == name):length(seen)], name)
stop(paste("Detected circular dependency:",
paste(squote(loop), collapse = " -> ")),
call. = FALSE)
}
if (is.null(deps[[name]]) || depth == 0) {
return(NULL)
}
d <- deps[[name]]
for (i in seq_len(NROW(d))) {
child <- report_vertex$new(parent, d$name[[i]], d$id[[i]], FALSE)
parent$add_child(child)
build_tree_src(child, deps, depth - 1, limit - 1, c(seen, name))
}
child
}
|
context("Making a new dataset")
test_that("write.csv.gz gzips a csv", {
df <- data.frame(a = 1:1000)
f <- tempfile()
f2 <- tempfile()
write.csv.gz(df, file = f)
write.csv(df, file = f2, row.names = FALSE)
expect_true(file.exists(f))
expect_true(file.exists(f2))
expect_true(file.size(f) < file.size(f2))
expect_equal(read.csv(f), df)
expect_equal(read.csv(f2), df)
})
test_that("newDataset input validation", {
expect_error(
newDataset(NULL),
"Can only make a Crunch dataset from a two-dimensional data"
)
expect_error(
newDataset(1:5),
"Can only make a Crunch dataset from a two-dimensional data"
)
})
with_mock_crunch({
test_that("Basic exercise of turning data.frame to Crunch payload", {
expect_POST(
newDataset(data.frame(a = 1), name = "Testing"),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"Testing",',
'"table":{"element":"crunch:table",',
'"metadata":{"a":{"type":"numeric","name":"a","alias":"a"}},',
'"order":["a"]}}}'
)
})
test_that("Turning data.frame to Crunch payload without a name", {
expect_POST(
newDataset(data.frame(a = 1)),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"data.frame(a = 1)",',
'"table":{"element":"crunch:table",',
'"metadata":{"a":{"type":"numeric","name":"a","alias":"a"}},',
'"order":["a"]}}}'
)
})
test_that("Turning data.frame to Crunch payload with a long name", {
expect_POST(
newDataset(data.frame(a = 1, really_really_long_name = 2)),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"data.frame(a = 1, ',
'really_really_long_nam","table":{"element":"crunch:table",',
'"metadata":{"a":{"type":"numeric","name":"a","alias":"a"},',
'"really_really_long_name":{"type":"numeric",',
'"name":"really_really_long_name","alias":"really_really_long_name"}},',
'"order":["a","really_really_long_name"]}}}'
)
})
test_that("uploadData writes out a gzipped file", {
ds <- cachedLoadDataset("test ds")
with_DELETE(NULL, {
expect_POST(
uploadData(ds, data.frame(a = 1)),
"https://app.crunch.io/api/sources/",
"list(uploaded_file"
)
})
})
test_that("createDataset with named args", {
expect_POST(
createDataset(name = "Foo", description = "Bar."),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"Foo",',
'"description":"Bar."}}'
)
})
test_that("createDataset returns a dataset", {
with_POST(
"https://app.crunch.io/api/datasets/1/",
expect_true(is.dataset(createDataset(name = "Foo")))
)
})
test_that("newDataset calls newDatasetFromFile if given a string", {
expect_POST(
newDataset("helper.R"),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"helper.R"}}'
)
})
test_that("newDataset with a schema posts to sources", {
expect_POST(
newDataset(x = "helper.R", schema = "helper.R"),
"https://app.crunch.io/api/sources/",
"list\\(uploaded_file = list\\(path = .*helper.R",
fixed = FALSE
)
})
test_that("newDataset with schema and data posts, adds to batches and appends", {
with_POST("https://app.crunch.io/api/datasets/1/", {
ds <- newDataset(x = "teardown.R", schema = "setup.R")
})
})
test_that("newDataset(FromFile) cleans up the dataset entity if the file is invalid", {
with_POST("https://app.crunch.io/api/datasets/1/", {
expect_DELETE(
newDataset("NOTAFILE.exe"),
"https://app.crunch.io/api/datasets/1/"
)
with_DELETE(NULL, {
expect_error(
newDataset("NOTAFILE.exe"),
"File not found"
)
})
})
})
test_that("newDataset(FromFile) can take an s3 URL", {
with_DELETE(NULL, {
expect_POST(
newDataset("s3://httpbin.org/get"),
"https://app.crunch.io/api/datasets/1/batches/",
'{"element":"shoji:entity",',
'"body":{"url":"s3://httpbin.org/get"}}'
)
})
})
test_that("newDataset(FromFile) can take an http(s) URL", {
with_DELETE(NULL, {
expect_POST(
newDataset("https://httpbin.org/get"),
"https://app.crunch.io/api/sources/",
'{"element":"shoji:entity",',
'"body":{"location":"https://httpbin.org/get"}}'
)
})
})
test_that("newDatasetByColumn", {
expect_POST(
newDatasetByColumn(data.frame(a = 1), name = "Bam!"),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"Bam!"}}'
)
})
test_that("createSource validation", {
expect_error(
createSource("File not found"),
"File not found"
)
expect_error(
createSource(name = "x"),
"Must provide a file or url to createSource"
)
})
test_that("newExampleDataset", {
expect_POST(
newExampleDataset(),
"https://app.crunch.io/api/datasets/",
'{"element":"shoji:entity","body":{"name":"Example dataset",'
)
})
})
with_test_authentication({
whereas("The two methods for sending data", {
testfile.csv <- "fake.csv"
testfile.df <- read.csv(testfile.csv)
test_that("fake.csv is what we expect", {
expect_identical(dim(testfile.df), c(20L, 6L))
})
test_that("newDataset creates a dataset if given a file", {
ds <- newDataset(testfile.csv)
expect_true(is.dataset(ds))
expect_identical(nrow(ds), 20L)
expect_identical(ncol(ds), 6L)
expect_equivalent(mean(ds[[2]]), mean(testfile.df[[2]]))
})
test_that("Dataset-by-column variable types get set correctly", {
ds <- newDatasetByColumn(df)
expect_valid_df_import(ds)
expect_equivalent(mean(ds$v3), mean(df$v3))
expect_true(setequal(names(df), names(ds)))
expect_identical(names(df), names(ds))
})
})
m <- fromJSON(system.file("example-datasets", "pets.json", package = "crunch"),
simplifyVector = FALSE
)
whereas("Creating with metadata and csv", {
test_that("createWithMetadataAndFile using docs example", {
ds <- newDatasetFromFixture("apidocs")
expect_valid_apidocs_import(ds)
})
test_that("data.frame with spaces in column names", {
input <- data.frame(a = factor("A"), b = 4)
names(input) <- c("var one", "var two")
expect_identical(names(input), c("var one", "var two"))
ds <- newDataset(input)
expect_identical(names(ds), c("var one", "var two"))
})
test_that("Can create dataset with data in S3", {
ds <- createWithMetadataAndFile(m,
file = "s3://testing-crunch-io/example-dataset.csv"
)
expect_valid_apidocs_import(ds)
ds2 <- newDatasetFromFixture("apidocs")
expect_identical(dim(ds), dim(ds2))
expect_identical(as.vector(ds$q1), as.vector(ds2$q1))
})
})
test_that("Duplicate subvariables are forbidden", {
m2 <- m
dupe <- list(name = "Another", alias = "allpets_1")
m2$body$table$metadata$allpets$subvariables[[4]] <- dupe
expect_error(createWithMetadataAndFile(
m2,
system.file("example-datasets", "pets.csv", package = "crunch")
))
})
test_that("newDataset without specifying name grabs object name", {
dsz <- newDataset(df)
expect_true(is.dataset(dsz))
expect_identical(name(dsz), "df")
expect_valid_df_import(dsz)
})
test_that("data.frame with missing data in datetime & date columns", {
input <- data.frame(
date = c(as.Date("2020-01-01"), NA),
datetime = as.POSIXlt(c(NA, "2020-01-01 01:05:00"), tz = "UTC")
)
ds <- newDataset(input, "missing vals datetimes")
expect_is(ds$date, "DatetimeVariable")
expect_equal(as.vector(ds$date), input$date)
expect_is(ds$datetime, "DatetimeVariable")
expect_equal(as.vector(ds$datetime), input$datetime)
})
})
|
expected <- eval(parse(text="structure(c(1L, 1L, 2L, 5L, 6L, 6L), .Label = c(\"1:1\", \"1:2\", \"1:3\", \"2:1\", \"2:2\", \"2:3\"), class = \"factor\")"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(1L, 1L, 1L, 2L, 2L, 2L), .Label = c(\"1\", \"2\"), class = \"factor\"), structure(c(1L, 1L, 2L, 2L, 3L, 3L), .Label = c(\"1\", \"2\", \"3\"), class = \"factor\"))"));
do.call(`:`, argv);
}, o=expected);
|
context("Export summary statistics table to a flextable")
library(flextable)
library(officer)
test_that("A summary table with a row variable is correctly exported to flextable", {
summaryTable <- data.frame(
PARAM = factor(c("A", "B"), levels = c("B", "A")),
n = c(9, 10)
)
expect_silent(
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "PARAM"
)
)
expect_s3_class(ft, "flextable")
expect_identical(
ft$body$dataset[, 1],
c("B", "A")
)
})
test_that("Multiple row variables are nested correctly in a flextable summary table", {
summaryTable <- data.frame(
PARAM = rep(c("Actual Value", "Change from baseline"), each = 3),
COHORT = rep(c("I", "I", "II"), times = 2),
TRT = factor(rep(c("A", "B", "A"), times = 2), levels = c("B", "A")),
n = seq_len(6)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("PARAM", "COHORT", "TRT")
)
dataRef <- data.frame(
c(
"Actual Value", "I", "B", "A", "II", "A",
"Change from baseline", "I", "B", "A", "II", "A"
),
c(
NA_character_, NA_character_, "2", "1", NA_character_, "3",
NA_character_, NA_character_, "5", "4", NA_character_, "6"
),
stringsAsFactors = FALSE
)
expect_equal(
unname(ft$body$dataset),
unname(dataRef),
check.attributes = FALSE
)
})
test_that("Padding is correctly set for nested row variables in a flextable summary table", {
summaryTable <- data.frame(
PARAM = rep(c("Actual Value", "Change from baseline"), each = 3),
COHORT = rep(c("I", "I", "II"), times = 2),
TRT = factor(rep(c("A", "B", "A"), times = 2), levels = c("B", "A")),
n = seq_len(6)
)
rowPadBase <- 50
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("PARAM", "COHORT", "TRT"),
rowPadBase = rowPadBase
)
expect_equal(
ft$body$styles$pars$padding.left$data[-c(1, 7), 1],
rowPadBase * rep(c(1, 2, 2, 1, 2), 2)
)
})
test_that("Horizontal lines are correctly set for nested row variables in a flextable summary table", {
summaryTable <- data.frame(
PARAM = rep(c("Actual Value", "Change from baseline"), each = 3),
COHORT = rep(c("I", "I", "II"), times = 2),
TRT = factor(rep(c("A", "B", "A"), times = 2), levels = c("B", "A")),
n = seq_len(6)
)
colorTable <- getColorPaletteTable()
colorTable["line"] <- "red"
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("PARAM", "COHORT", "TRT"),
colorTable = colorTable
)
ftDataBd <- ft$body$styles$cells$border.color.top$data
expect_setequal(
object = ftDataBd[7, ],
expected = "red"
)
expect_false(unique(c(ftDataBd[-7, ])) == "red")
})
test_that("The specified labels of the row variables are correctly set in a flextable summary table", {
summaryTable <- data.frame(
PARAM = factor(c("A", "B"), levels = c("B", "A")),
n = c(9, 10)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "PARAM",
rowVarLab = c(PARAM = "Parameter")
)
expect_identical(
object = ft$header$dataset[, 1],
expected = "Parameter"
)
})
test_that("The labels of the row variables, extracted from the labels of all variables, are correctly set in a flextable summary table", {
summaryTable <- data.frame(
PARAM = factor(c("A", "B"), levels = c("B", "A")),
n = c(9, 10)
)
expect_identical(
object = {
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "PARAM",
labelVars = c(PARAM = "Parameter")
)
ft$header$dataset[, 1]
},
expected = "Parameter"
)
})
test_that("Row variables in a separated column are merged correctly in a flextable summary table", {
summaryTable <- data.frame(
PARAM = rep(c("Actual Value", "Change from baseline"), each = 3),
COHORT = rep(c("I", "I", "II"), times = 2),
TRT = factor(rep(c("A", "B", "A"), times = 2), levels = c("B", "A")),
n = seq_len(6)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("PARAM", "COHORT", "TRT"),
rowVarInSepCol = c("COHORT", "TRT")
)
expect_identical(
object = as.character(as.vector(ft$header$dataset[1, 1:3])),
expected = c("PARAM", "COHORT", "TRT")
)
dataRef <- data.frame(
rep(c("Actual Value", "Change from baseline"), each = 3),
rep(c("I", "I", "II"), times = 2),
rep(c("B", "A", "A"), times = 2),
c("2", "1", "3", "5", "4", "6"),
stringsAsFactors = FALSE
)
expect_equal(
object = unname(ft$body$dataset),
expected = unname(dataRef),
check.attributes = FALSE
)
expect_identical(
ft$body$spans$columns[, 1:2],
cbind(
c(3, 0, 0, 3, 0, 0),
c(2, 0, 1, 2, 0, 1)
)
)
expect_setequal(
c(ft$body$spans$columns[, -(1:2)]),
1
)
})
test_that("Nested and merged row variables are displayed correctly in a flextable summary table", {
summaryTable <- data.frame(
AESOC = c("A", "A", "A"),
AEDECOD = factor(c("a", "b", "b"), levels = c("b", "a")),
WORSTINT = factor(
c("Moderate", "Severe", "Moderate"),
levels = c("Moderate", "Severe")
),
n = c(2, 4, 7)
)
rowPadBase <- 50
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("AESOC", "AEDECOD", "WORSTINT"),
rowVarInSepCol = "WORSTINT",
rowPadBase = rowPadBase
)
dataRef <- data.frame(
c("A", "b", "b", "a"),
c(NA_character_, "Moderate", "Severe", "Moderate"),
c(NA_character_, "7", "4", "2"),
stringsAsFactors = FALSE
)
expect_equal(
object = unname(ft$body$dataset),
expected = unname(dataRef),
check.attributes = FALSE
)
expect_setequal(
ft$body$styles$pars$padding.left$data[-1, 1],
rowPadBase
)
})
test_that("Horizontal lines are correctly set for multiple row variables in a separated column in a flextable summary table", {
summaryTable <- data.frame(
PARAM = rep(c("Actual Value", "Change from baseline"), each = 3),
COHORT = rep(c("I", "I", "II"), times = 2),
TRT = factor(rep(c("A", "B", "A"), times = 2), levels = c("B", "A")),
n = seq_len(6)
)
expect_warning(
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("PARAM", "COHORT", "TRT"),
rowVarInSepCol = c("COHORT", "TRT"),
colorTable = c(line = "red")
)
)
ftDataBd <- ft$body$styles$cells$border.color.top$data
expect_setequal(
object = ftDataBd[c(3, 4, 6), 2:4],
expected = "red"
)
expect_false(unique(c(ftDataBd[-c(3, 4, 6), 2:4])) == "red")
})
test_that("Custom formatting is correctly set to a row variable in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "B"),
n = 1:2
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "TRT",
rowVarFormat = list(TRT = "bold")
)
expect_identical(
object = unname(ft$body$styles$text$bold$data),
expected = cbind(
rep(TRUE, nrow(summaryTable)),
rep(FALSE, nrow(summaryTable))
)
)
})
test_that("Custom formatting is correctly set to a nested row variable in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = c("a", "b", "a", "b"),
n = 1:4
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("TRT", "PARAM"),
rowVarFormat = list(PARAM = "bold")
)
expect_identical(
object = unname(ft$body$styles$text$bold$data),
expected = cbind(
c(FALSE, TRUE, TRUE, FALSE, TRUE, TRUE),
rep(FALSE, 6)
)
)
})
test_that("Custom formatting is correctly set to a row variable in a different column in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = c("a", "b", "a", "b"),
n = 1:4
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("TRT", "PARAM"),
rowVarInSepCol = "PARAM",
rowVarFormat = list(PARAM = "bold")
)
expect_identical(
object = unname(ft$body$styles$text$bold$data),
expected = cbind(
rep(FALSE, nrow(summaryTable)),
rep(TRUE, nrow(summaryTable)),
rep(FALSE, nrow(summaryTable))
)
)
})
test_that("Row totals are correctly included in the header row in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = factor(c("a", "Total", "b", "Total"), levels = c("Total", "a", "b")),
n = c("2", "1", "4", "3")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("TRT", "PARAM"),
rowVarTotalInclude = "PARAM"
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("A", "a", "B", "b"),
c("1", "2", "3", "4"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("Row totals are correctly included in separated rows in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = factor(c("a", "Total", "b", "Total"), levels = c("Total", "a", "b")),
n = c("2", "1", "4", "3")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("TRT", "PARAM"),
rowVarTotalInclude = "PARAM",
rowVarTotalInSepRow = "PARAM"
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("A", "Total", "a", "B", "Total", "b"),
c(NA_character_, "1", "2", NA_character_, "3", "4"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("The variable group and name are correctly merged in a flextable summary table in case of unique group", {
summaryTable <- data.frame(
variable = c("A", "B", "B"),
variableGroup = c("a", "b1", "b2"),
n = c("1", "2", "3")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
rowAutoMerge = TRUE
)
expect_equal(
object = ft$body$dataset[, 1],
expected = c("A a", "B", "b1", "b2"),
check.attributes = FALSE
)
})
test_that("The variable group and name are correctly included in different rows when requested in a flextable summary table in case of unique group", {
summaryTable <- data.frame(
variable = c("A", "B", "B"),
variableGroup = c("a", "b1", "b2"),
n = c("1", "2", "3")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
rowAutoMerge = FALSE
)
expect_equal(
object = ft$body$dataset[, 1],
expected = c("A", "a", "B", "b1", "b2"),
check.attributes = FALSE
)
})
test_that("The variable name and the statistic are correctly merged in a flextable summary table in case of unique statistic", {
summaryTable <- data.frame(
variable = c("A", "B"),
n = c("1", "2"),
Mean = c(NA_character_, "0.56"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "variable",
statsVar = c("n", "Mean"),
rowAutoMerge = TRUE
)
expect_equal(
object = ft$body$dataset[, 1],
expected = c("A n", "B", "n", "Mean"),
check.attributes = FALSE
)
})
test_that("The variable name and the statistic are correctly included in different rows when requested in a flextable summary table in case of unique statistic", {
summaryTable <- data.frame(
variable = c("A", "B"),
n = c("1", "2"),
Mean = c(NA_character_, "0.56"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "variable",
statsVar = c("n", "Mean"),
rowAutoMerge = FALSE
)
expect_equal(
object = ft$body$dataset[, 1],
expected = c("A", "n", "B", "n", "Mean"),
check.attributes = FALSE
)
})
test_that("A summary table with a continuous and a categorical variables is correctly exported to flextable", {
summaryTable <- data.frame(
variable = factor(
c("SEX", "SEX", "AGE"),
levels = c("SEX", "AGE")
),
variableGroup = factor(
c("Female", "Male", NA_character_),
levels = c("Male", "Female")
),
n = c(3, 4, NA_real_),
mean = c(NA_real_, NA_real_, 3.33)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = c("n", "mean")
)
dataRef <- data.frame(
c("SEX", "Male n", "Female n", "AGE mean"),
c(NA_character_, "4", "3", "3.33"),
stringsAsFactors = FALSE
)
expect_equal(
object = unname(ft$body$dataset),
expected = unname(dataRef),
check.attributes = FALSE
)
})
test_that("A summary table with a continuous and a categorical variables is correctly exported without row merging to flextable", {
summaryTable <- data.frame(
variable = factor(
c("SEX", "SEX", "AGE"),
levels = c("SEX", "AGE")
),
variableGroup = factor(
c("Female", "Male", NA_character_),
levels = c("Male", "Female")
),
n = c(3, 4, NA_real_),
mean = c(NA_real_, NA_real_, 3.33)
)
rowPadBase <- 50
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = c("n", "mean"),
rowPadBase = rowPadBase,
rowAutoMerge = FALSE
)
dataRef <- data.frame(
c("SEX", "Male", "n", "Female", "n",
"AGE", "mean"),
c(NA_character_, NA_character_, "4", NA_character_, "3",
NA_character_, "3.33"),
stringsAsFactors = FALSE
)
expect_equal(
object = unname(ft$body$dataset),
expected = unname(dataRef),
check.attributes = FALSE
)
expect_equal(
ft$body$styles$pars$padding.left$data[-c(1, 6), 1],
rowPadBase * c(1, 2, 1, 2, 1)
)
})
test_that("A summary table with a continuous and a categorical variable, with and without named statistic, is correctly exported to flextable", {
summaryTable <- data.frame(
variable = factor(
c("SEX", "SEX", "AGE"),
levels = c("SEX", "AGE")
),
variableGroup = factor(
c("Female", "Male", NA_character_),
levels = c("Male", "Female")
),
Statistic = c(3, 4, NA_real_),
Mean = c(NA_real_, NA_real_, 3.33)
)
rowPadBase <- 50
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
rowPadBase = rowPadBase
)
dataRef <- data.frame(
c("SEX", "Male", "Female", "AGE Mean"),
c(NA_character_, "4", "3", "3.33"),
stringsAsFactors = FALSE
)
expect_equal(
object = unname(ft$body$dataset),
expected = unname(dataRef),
check.attributes = FALSE
)
expect_setequal(
ft$body$styles$pars$padding.left$data[-c(1, 4), 1],
rowPadBase
)
})
test_that("A summary table with a column variable is correctly exported to flextable", {
summaryTable <- data.frame(
TRT = factor(c("A", "B"), levels = c("B", "A")),
n = c(9, 10)
)
expect_silent(
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
colVar = "TRT"
)
)
expect_s3_class(ft, "flextable")
expect_identical(
unname(unlist(ft$header$dataset[1, ])),
c("B", "A")
)
})
test_that("An error is generated if a flextable summary table contains multiple records for the same variable", {
summaryTable <- data.frame(
TRT = c("A", "A", "B"),
n = c(9, 8, 10)
)
expect_error(
exportSummaryStatisticsTable(
summaryTable = summaryTable,
colVar = "TRT"
),
"Table formatting to multiple columns failed because of duplicated records for each row/col"
)
})
test_that("A summary table without totals in the header is correctly exported to flextable", {
summaryTable <- data.frame(
TRT = factor(c("A", "A", "B", "B"), levels = c("B", "A")),
statN = c(1, 4, 2, 5),
isTotal = c(FALSE, TRUE, FALSE, TRUE)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
colVar = "TRT",
colHeaderTotalInclude = TRUE
)
expect_identical(
object = unname(unlist(ft$header$dataset)),,
expected = c("B\n(N=5)", "A\n(N=4)")
)
})
test_that("A summary table without totals in the header is correctly exported to flextable", {
summaryTable <- data.frame(
TRT = factor(c("A", "A", "B", "B"), levels = c("B", "A")),
statN = c(1, 4, 2, 5),
isTotal = c(FALSE, TRUE, FALSE, TRUE)
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
colVar = "TRT",
colHeaderTotalInclude = FALSE
)
expect_identical(
object = unname(unlist(ft$header$dataset)),
expected = c("B", "A")
)
})
test_that("A summary table with statistics in rows is correctly exported to flextable", {
summaryTable <- data.frame(
variable = c("A", "B"),
n = c("1", "2"),
Mean = c("0.34", "0.56"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "variable",
statsVar = c("n", "Mean"),
statsLayout = "row"
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("A", "n", "Mean", "B", "n", 'Mean'),
c(NA_character_, "1", "0.34", NA_character_, "2", "0.56"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("A summary table with statistics in columns is correctly exported to flextable", {
summaryTable <- data.frame(
variable = c("A", "B"),
n = c("1", "2"),
Mean = c("0.34", "0.56"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "variable",
statsVar = c("n", "Mean"),
statsLayout = "col"
)
expect_identical(
object = unname(unlist(ft$header$dataset[1, ])),
expected = c("variable", "n", "Mean")
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("A", "B"),
c("1", "2"),
c("0.34", "0.56"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("A summary table with statistics in a separated column is correctly exported to flextable", {
summaryTable <- data.frame(
variable = c("A", "B"),
n = c("1", "2"),
Mean = c("0.34", "0.56"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = "variable",
statsVar = c("n", "Mean"),
statsLayout = "rowInSepCol"
)
expect_identical(
object = unname(unlist(ft$header$dataset[1:2])),
expected = c("variable", "Statistic")
)
expect_equal(
object = as.data.frame(sapply(ft$body$dataset, as.character)),
expected = data.frame(
c("A", "A", "B", "B"),
c("n", "Mean", "n", "Mean"),
c("1", "0.34", "2", "0.56")
),
check.attributes = FALSE
)
})
test_that("The label for the statistic value is correctly set in a flextable summary table", {
summaryTable <- data.frame(
variable = c("A", "B"),
Statistic = c("1", "2"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable, rowVar = "variable",
statsVar = "Statistic",
statsValueLab = "Number of subjects"
)
expect_match(
object = unname(unlist(ft$header$dataset[, 2])),
regexp = "Number of subjects.*"
)
})
test_that("An error is generated if the label for the statistic value is set to the default name in a flextable summary table", {
summaryTable <- data.frame(
variable = c("A", "B"),
Statistic = c("1", "2"),
stringsAsFactors = FALSE
)
expect_error(
exportSummaryStatisticsTable(summaryTable, rowVar = "variable",
statsVar = "Statistic", statsValueLab = "Statistic"
),
"'statsValueLab' should be different than 'Statistic'."
)
})
test_that("A summary table with one statistic is correctly exported to flextable with the statistic name", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
variable = c("a", "b", "a", "b"),
n = c("1", "2", "3", "4"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "variable", colVar = "TRT",
statsVar = "n",
statsLabInclude = TRUE
)
expect_identical(
object = ft$body$dataset[, 1],
expected = c("a", "n", "b", "n")
)
})
test_that("A summary table with one statistic is correctly exported to flextable without the statistic name", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
variable = c("a", "b", "a", "b"),
n = c("1", "2", "3", "4"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "variable", colVar = "TRT",
statsVar = "n",
statsLabInclude = FALSE
)
expect_identical(
object = ft$body$dataset[, 1],
expected = c("a", "b")
)
})
test_that("A warning is generated if a flextable summary table contain multiple statistics but the names are specified to be not included", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
variable = c("a", "b", "a", "b"),
n = c("1", "2", "3", "4"),
Mean = c("0.34", "0.56"),
stringsAsFactors = FALSE
)
expect_warning(
exportSummaryStatisticsTable(
summaryTable,
rowVar = "variable", colVar = "TRT",
statsVar = c("n", "Mean"),
statsLabInclude = FALSE
),
"Statistic label is included.*because more than one statistic variable.*"
)
})
test_that("A placeholder for empty value is correctly included by default in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = c("a", "b", "a", "b"),
n = c("1", "2", "3", NA_character_),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "PARAM", colVar = "TRT",
statsVar = "n"
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("a", "b"),
c("1", "2"),
c("3", "-"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("A specified placeholder for empty value is correctly included in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
PARAM = c("a", "b", "a", "b"),
n = c("1", "2", "3", NA_character_),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "PARAM", colVar = "TRT",
statsVar = "n",
emptyValue = "0"
)
expect_equal(
object = ft$body$dataset,
expected = data.frame(
c("a", "b"),
c("1", "2"),
c("3", "0"),
stringsAsFactors = FALSE
),
check.attributes = FALSE
)
})
test_that("A list of summary tables is correctly exported to flextable", {
summaryTables <- list(
`PARAM 2` = data.frame(n = 10),
`PARAM 1` = data.frame(n = 2)
)
fts <- exportSummaryStatisticsTable(
summaryTables,
outputType = "flextable"
)
expect_type(fts, "list")
expect_named(fts, names(summaryTables))
for(group in names(summaryTables)){
expect_identical({
ft <- exportSummaryStatisticsTable(summaryTables[[!!group]])
ft$body$dataset
},
expected = fts[[!!group]]$body$dataset
)
expect_equal(fts[[!!group]]$header$dataset[1, ], !!group)
}
})
test_that("A list of summary tables with different titles is correctly exported to flextable", {
summaryTables <- list(
`PAR2` = data.frame(n = 10),
`PAR1` = data.frame(n = 2)
)
titles <- c("PARAMETER 2", "PARAMETER 1")
fts <- exportSummaryStatisticsTable(
summaryTables,
outputType = "flextable",
title = titles
)
for(i in seq_along(summaryTables)){
expect_equal(
fts[[!!i]]$header$dataset[1, 1],
titles[!!i]
)
}
})
test_that("A warning is generated if the variable group with totals is not formatted correctly in a flextable summary table", {
summaryTable <- data.frame(
variable = factor(c("RACE", "SEX", "SEX", "SEX")),
variableGroup = factor(
c("White", "Female", "Male", "Total"),
levels = c("White", "Female", "Male", "Total")
),
n = c("9", "3", "7", "10")
)
expect_warning(
exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = "n",
rowVarTotalInclude = "variableGroup"
),
"variable.*with total.*should be formatted.*with 'Total' as the first level"
)
})
test_that("A flextable summary table is correctly formatted if totals are included only for a subset of the categorical variables", {
summaryTable <- data.frame(
variable = factor(c("RACE", "SEX", "SEX", "SEX")),
variableGroup = factor(
c("White", "Female", "Male", "Total"),
levels = c("Total", "White", "Female", "Male")
),
n = c("9", "3", "7", "10")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = "n",
rowVarTotalInclude = "variableGroup"
)
refData <- data.frame(
c("RACE", "White", "SEX", "Female", "Male"),
c(NA_character_, "9", "10", "3", "7"),
stringsAsFactors = FALSE
)
expect_equal(
unname(ft$body$dataset),
unname(refData),
check.attributes = FALSE
)
})
test_that("A flextable summary table is correctly formatted if totals are included only for a subset of the categorical variables in separated rows", {
summaryTable <- data.frame(
variable = factor(c("RACE", "SEX", "SEX", "SEX")),
variableGroup = factor(
c("White", "Female", "Male", "Total"),
levels = c("Total", "White", "Female", "Male")
),
n = c("9", "3", "7", "10")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = "n",
rowVarTotalInclude = "variableGroup",
rowVarTotalInSepRow = "variableGroup"
)
refData <- data.frame(
c("RACE White", "SEX", "Total", "Female", "Male"),
c("9", NA_character_, "10", "3", "7"),
stringsAsFactors = FALSE
)
expect_equal(
unname(ft$body$dataset),
unname(refData),
check.attributes = FALSE
)
})
test_that("Missing values in nested row variables are correctly represented in a flextable summary table", {
summaryTable <- data.frame(
variable = factor(c("SEX", "SEX", "SEX")),
variableGroup = factor(
c(NA_character_, "Male", "Female"),
levels = c("Male", "Female")
),
n = c("3", "7", "10")
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = "n"
)
refData <- data.frame(
c("SEX", "Male", "Female", NA_character_),
c(NA_character_, "7", "10", "3"),
stringsAsFactors = FALSE
)
expect_equal(
unname(ft$body$dataset),
unname(refData),
check.attributes = FALSE
)
})
test_that("A summary table with only one element in nested row variable in a row variable is correctly exported to flextable", {
summaryTable <- data.frame(
variable = "SEX",
variableGroup = factor("Total"),
n = "10"
)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
rowVar = c("variable", "variableGroup"),
statsVar = "n",
rowVarTotalInclude = "variableGroup"
)
expect_equal(
unname(ft$body$dataset),
unname(data.frame("SEX", "10", stringsAsFactors = FALSE)),
check.attributes = FALSE
)
})
test_that("Page dimension are correctly set in a flextable summary table", {
summaryTable <- data.frame(n = 10)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
pageDim = c(10, 3),
margin = 0
)
expect_equal(
object = ft$body$colwidths,
expected = 10
)
})
test_that("Margins are correctly set in a flextable summary table", {
summaryTable <- data.frame(n = 10)
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
pageDim = c(10, 3),
margin = 1
)
expect_equal(
object = ft$body$colwidths,
expected = 8
)
})
test_that("A summary table is correctly exported to flextable in landscape mode", {
summaryTable <- data.frame(n = 10)
ftLandscape <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
landscape = TRUE
)
ftPortrait <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
landscape = FALSE
)
for(part in c("body", "header", "footer"))
expect_gt(
object = ftLandscape[[!!part]]$colwidths,
expected = ftPortrait[[!!part]]$colwidths
)
})
test_that("Multiple titles are correctly included in a flextable summary table", {
summaryTable <- data.frame(n = 10)
titles <- c("Title A", "Title B")
expect_identical(
object = {
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
title = titles
)
ft$header$dataset[seq_along(titles), 1]
}, titles
)
})
test_that("Footers are correctly included in a flextable summary table", {
summaryTable <- data.frame(n = 10)
footers <- c("Explanation 1", "Explanation 2")
ft <- exportSummaryStatisticsTable(
summaryTable = summaryTable,
footer = footers
)
expect_identical(
object = ft$footer$dataset[seq_along(footers), 1],
expected = footers
)
})
test_that("Colors are correctly set in a flextable summary table", {
summaryTable <- data.frame(n = 10)
colorTable <- c(
header = "
body = "
footerBackground = "
line = "
)
expect_silent(
ft <- exportSummaryStatisticsTable(
summaryTable,
colorTable = colorTable,
footer = "test"
)
)
expect_setequal(ft$body$styles$text$color$data, colorTable["body"])
expect_setequal(ft$body$styles$cells$background.color$data, colorTable["bodyBackground"])
expect_setequal(ft$header$styles$text$color$data, colorTable["header"])
expect_setequal(ft$header$styles$cells$background.color$data, colorTable["headerBackground"])
expect_setequal(ft$footer$styles$text$color$data, colorTable["footer"])
expect_setequal(ft$footer$styles$cells$background.color$data, colorTable["footerBackground"])
expect_setequal(ft$body$styles$cells$border.color.bottom$data, colorTable["line"])
})
test_that("Fontsize is correctly set in a flextable summary table", {
summaryTable <- data.frame(n = 10)
ft <- exportSummaryStatisticsTable(
summaryTable,
fontsize = 25
)
expect_identical(
object = c(ft$body$styles$text$font.size$data),
expected = 25
)
})
test_that("Fontname is correctly set in a flextable summary table", {
summaryTable <- data.frame(n = 10)
ft <- exportSummaryStatisticsTable(
summaryTable,
fontname = "Arial"
)
expect_identical(
object = c(ft$body$styles$text$font.family$data),
expected = "Arial"
)
})
test_that("Vertical lines are not included in a flextable summary table when requested", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
AVISIT = c("a", "b", "a", "b"),
n = c("1", "2", "3", "4"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
colVar = c("TRT", "AVISIT"),
statsVar = "n",
vline = "none"
)
expect_setequal(
object = ft$body$styles$cells$border.width.left$data,
expected = 0
)
})
test_that("Vertical lines are correctly set in a flextable summary table", {
summaryTable <- data.frame(
TRT = c("A", "A", "B", "B"),
AVISIT = c("a", "b", "a", "b"),
n = c("1", "2", "3", "4"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
colVar = c("TRT", "AVISIT"),
statsVar = "n",
vline = "auto"
)
expect_setequal(
object = ft$body$styles$cells$border.width.left$data[, c(1, 3)],
expected = 1
)
})
test_that("Horizontal lines are not included in a flextable summary table when requested", {
summaryTable <- data.frame(
PARAM = c("a", "b"),
n = c("1", "2"),
Mean = c("1", "2"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "PARAM",
statsVar = c("n", "Mean"),
hline = "none"
)
expect_setequal(
object = ft$body$styles$cells$border.width.top$data,
expected = 0
)
})
test_that("Horizontal lines are correctly set in a flextable summary table", {
summaryTable <- data.frame(
PARAM = c("a", "b"),
n = c("1", "2"),
Mean = c("1", "2"),
stringsAsFactors = FALSE
)
ft <- exportSummaryStatisticsTable(
summaryTable,
rowVar = "PARAM",
statsVar = c("n", "Mean"),
hline = "auto"
)
expect_setequal(
object = ft$body$styles$cells$border.width.top$data[4, ],
expected = 1
)
})
test_that("A warning is generated if no data remain after filtering of the column totals in a flextable summary table", {
data <- data.frame(
isTotal = rep(TRUE, 2),
n = c(1, 2)
)
expect_warning(
exportSummaryStatisticsTable(data),
regexp = "No data remain after filtering of total rows."
)
})
test_that("An error is generated if a flextable summary table contains multiple values for the column total but no column variables are specified", {
data <- data.frame(
isTotal = c(FALSE, TRUE, TRUE),
statN = c(1, 2, 3)
)
expect_error(
exportSummaryStatisticsTable(data),
regexp = "Multiple values for the header total .*"
)
})
test_that("A superscript is correctly formatted in a flextable summary table", {
xSps <- "<0.001^{*}"
data <- data.frame(
pValue = c("0.05", xSps, "1", "0.89"),
TRT = rep(c("A", "B"), each = 2),
PARAM = rep(c("Actual Value", "Change from Baseline"), times = 2)
)
ft <- exportSummaryStatisticsTable(
data,
rowVar = "PARAM", colVar = "TRT",
statsVar = "pValue"
)
idxSps <- which(ft$body$dataset == xSps)
ftBodyCnt <- ft$body$content$content$data
cntDataSps <- ftBodyCnt[idxSps][[1]]
expect_equal(nrow(cntDataSps), 2)
expect_equal(cntDataSps[, "txt"], c("<0.001", "*"))
expect_equal(cntDataSps[, "vertical.align"], c(NA_character_, "superscript"))
alignDataOther <- unlist(lapply(ftBodyCnt[-idxSps], "[", "vertical.align"))
expect_setequal(alignDataOther, NA_character_)
})
test_that("A subscript is correctly formatted in a flextable summary table", {
xSbs <- "<0.001_{(significative)}"
data <- data.frame(
pValue = c("0.05", xSbs, "1", "0.89"),
TRT = rep(c("A", "B"), each = 2),
PARAM = rep(c("Actual Value", "Change from Baseline"), times = 2)
)
ft <- exportSummaryStatisticsTable(
data,
rowVar = "PARAM", colVar = "TRT",
statsVar = "pValue"
)
idxSbs <- which(ft$body$dataset == xSbs)
ftBodyCnt <- ft$body$content$content$data
cntDataSbs <- ftBodyCnt[idxSbs][[1]]
expect_equal(nrow(cntDataSbs), 2)
expect_equal(cntDataSbs[, "txt"], c("<0.001", "(significative)"))
expect_equal(cntDataSbs[, "vertical.align"], c(NA_character_, "subscript"))
alignDataOther <- unlist(lapply(ftBodyCnt[-idxSbs], "[", "vertical.align"))
expect_setequal(alignDataOther, NA_character_)
})
test_that("A cell is correctly formatted in bold in a flextable summary table", {
xBold <- "bold{<0.001}"
data <- data.frame(
pValue = c("0.05", xBold, "1", "0.89"),
TRT = rep(c("A", "B"), each = 2),
PARAM = rep(c("Actual Value", "Change from Baseline"), times = 2)
)
ft <- exportSummaryStatisticsTable(
data,
rowVar = "PARAM", colVar = "TRT",
statsVar = "pValue"
)
isBold <- apply(
ft$body$content$content$data,
2, function(x) sapply(x, `[[`, "bold")
)
idxBold <- which(ft$body$dataset == xBold)
expect_setequal(isBold[idxBold], TRUE)
expect_setequal(isBold[-idxBold], NA)
})
test_that("A summary table is correctly exported to a docx file", {
data <- data.frame(
pValue = c("0.05", "<0.001", "1", "0.89"),
TRT = rep(c("A", "B"), each = 2),
PARAM = rep(c("Actual Value", "Change from Baseline"), times = 2)
)
file <- tempfile(pattern = "table", fileext = ".docx")
expect_silent(
ft <- exportSummaryStatisticsTable(
data,
rowVar = "PARAM", colVar = "TRT",
statsVar = "pValue",
file = file
)
)
expect_true(file.exists(file))
doc <- officer::read_docx(file)
docTable <- subset(docx_summary(doc), `content_type` == "table cell")
docTableData <- with(
subset(docTable, !`is_header`),
tapply(text, list(`row_id`, `cell_id`), I)
)
docTableHeader <- with(
subset(docTable, is_header),
tapply(text, `cell_id`, I)
)
colnames(docTableData) <- docTableHeader
dataRef <- cbind(
PARAM = c("Actual Value", "Change from Baseline"),
A = c("0.05", "<0.001"),
B = c("1", "0.89")
)
expect_equal(object = docTableData, expected = dataRef, check.attributes = FALSE)
})
test_that("A summary table is correctly exported to a docx file in landscape format", {
file <- tempfile(pattern = "table", fileext = ".docx")
ft <- exportSummaryStatisticsTable(
summaryTable = data.frame(n = 10),
file = file,
landscape = TRUE
)
doc <- officer::read_docx(file)
docCnt <- officer::docx_body_xml(doc)
expect_match(
object = as.character(docCnt),
regexp = 'orient=\"landscape\"',
fixed = TRUE
)
})
test_that("A list of summary tables is correctly exported to docx files", {
summaryTables <- list(
`PARAM 2` = data.frame(n = 10),
`PARAM 1` = data.frame(n = 2)
)
file <- tempfile(pattern = "table", fileext = ".docx")
dts <- exportSummaryStatisticsTable(
summaryTables,
file = file,
colHeaderTotalInclude = FALSE
)
fileTableOutput <- paste0(
tools::file_path_sans_ext(file),
"_", c("1", "2"), ".docx"
)
expect_true(all(file.exists(fileTableOutput)))
for(iTable in seq_along(summaryTables)){
expect_equal(
object = {
doc <- officer::read_docx(fileTableOutput[!!iTable])
docTables <- subset(officer::docx_summary(doc), `content_type` == "table cell")
docTables$text
},
expected = {
table <- summaryTables[[!!iTable]]
tableCnt <- c(names(summaryTables)[!!iTable], colnames(table), unlist(table))
unname(tableCnt)
}
)
}
})
test_that("A list of summary tables is correctly exported to a single docx file", {
summaryTables <- list(
`PARAM 2` = flextable(data.frame(n = "10")),
`PARAM 1` = flextable(data.frame(n = "2"))
)
file <- tempfile(pattern = "table", fileext = ".docx")
exportFlextableToDocx(object = summaryTables, file = file)
doc <- officer::read_docx(file)
docTables <- subset(officer::docx_summary(doc), `content_type` == "table cell")
expect_equal(docTables$text, c("n", "10", "n", "2"))
})
test_that("A flextable summary table is correctly styled for a report", {
summaryTable <- data.frame(n = 9)
expect_identical(
object = exportSummaryStatisticsTable(
summaryTable = summaryTable,
style = "report"
),
expected = exportSummaryStatisticsTable(
summaryTable = summaryTable,
fontname = "Times", fontsize = 8,
landscape = FALSE,
pageDim = getDimPage(style = "report", margin = 0),
colorTable = getColorPaletteTable(style = "report")
)
)
})
test_that("A flextable summary table is correctly styled for a presentation", {
summaryTable <- data.frame(n = 9)
expect_identical(
object = exportSummaryStatisticsTable(
summaryTable = summaryTable,
style = "presentation"
),
expected = exportSummaryStatisticsTable(
summaryTable = summaryTable,
fontname = "Tahoma", fontsize = 10,
landscape = TRUE,
pageDim = getDimPage(
style = "presentation", margin = 0,
landscape = FALSE
),
colorTable = getColorPaletteTable(style = "presentation")
)
)
})
|
SODAS.to.RSDA <- function(XMLPath, labels = T) {
parsed.xml <- XML::xmlInternalTreeParse(XMLPath)
containsNode <- XML::getNodeSet(parsed.xml, "/assofile/contains")
if (length(containsNode) == 0) {
stop("No 'contains' tag is present in the XML file")
}
containsNode <- containsNode[[1]]
if (XML::xmlGetAttr(containsNode, "INDIVIDUALS") != "YES" || XML::xmlGetAttr(
containsNode,
"VARIABLES"
) != "YES" || XML::xmlGetAttr(containsNode, "RECTANGLE_MATRIX") != "YES") {
stop("Insufficient data in XML file")
}
if (labels) {
sym.obj.names <- XML::xpathSApply(
parsed.xml, "/assofile/individus/stindiv/label",
XML::xmlValue
)
variables.names <- XML::xpathSApply(
parsed.xml, "/assofile/variables/stvar/ident/label",
XML::xmlValue
)
} else {
sym.obj.names <- XML::xpathSApply(
parsed.xml, "/assofile/individus/stindiv/name",
XML::xmlValue
)
variables.names <- XML::xpathSApply(
parsed.xml, "/assofile/variables/stvar/ident/name",
XML::xmlValue
)
}
variables.types <- XML::xpathSApply(parsed.xml, "/assofile/variables/stvar/*[2]", XML::xmlName)
result <- data.frame(row.names = sym.obj.names)
number.of.rows <- nrow(result)
for (i in 1:length(variables.types)) {
cat(paste0("Processing variable ", i, ": ", variables.names[[i]], "\n"))
switch(variables.types[[i]], `inter-cont` = {
result <- cbind(result, process.inter.cont.variable(
number.of.rows, parsed.xml,
i, variables.names[[i]]
))
}, continue = {
result <- cbind(result, process.continue.variable(
number.of.rows, parsed.xml,
i, variables.names[[i]]
))
}, nominal = {
result <- cbind(result, process.nominal.variable(
labels, number.of.rows,
parsed.xml, i, variables.names[[i]]
))
}, mult_nominal = {
result <- cbind(result, process.mult.nominal.variable(
labels, number.of.rows,
parsed.xml, i, variables.names[[i]]
))
}, mult_nominal_Modif = {
type.modif <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
i, "]/mult_nominal_Modif/type_modif"
), XML::xmlValue)
if (type.modif != "proba") {
cat(
paste0("Unsupported type.modif in mult_nominal_Modif variable: "),
type.modif, "\n"
)
} else {
result <- cbind(result, process.mult.nominal.modif.variable(
labels,
number.of.rows, parsed.xml, i, variables.names[[i]]
))
}
}, cat(paste0("Variable type not supported:"), variables.types[[i]], "\n"))
}
out <- newSobject(result)
class(out) <- "sym.data.table"
out <- to.v3(out)
return(out)
}
process.nominal.variable <- function(labels, number.of.rows, parsed.xml, variable.index,
variable.name) {
aux <- list()
aux[[1]] <- rep("$S", number.of.rows)
if (labels) {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/nominal/nominal-desc/list-nom/label"
), XML::xmlValue)
} else {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/nominal/nominal-desc/list-nom/name"
), XML::xmlValue)
}
aux[[2]] <- rep(length(categories), number.of.rows)
nodes <- XML::getNodeSet(parsed.xml, paste0(
"/assofile/indiv_mat/ligmat/valmat[", variable.index,
"]"
))
after.evaluator <- function(node) {
if (length(node["val_nomina"]) == 0) {
return(rep(NA, length(categories)))
} else {
category <- as.numeric(XML::xmlValue(node))
return(append(rep(0, length(categories) - 1), 1, category - 1))
}
}
node.categories <- t(XML::xmlSApply(nodes, after.evaluator))
aux <- data.frame(c(aux, as.data.frame(node.categories)))
colnames(aux) <- c("$S", variable.name, categories)
return(aux)
}
process.mult.nominal.variable <- function(labels, number.of.rows, parsed.xml, variable.index,
variable.name) {
aux <- list()
aux[[1]] <- rep("$S", number.of.rows)
if (labels) {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/mult_nominal/nominal-desc/list-nom/label"
), xmlValue)
} else {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/mult_nominal/nominal-desc/list-nom/name"
), xmlValue)
}
aux[[2]] <- rep(length(categories), number.of.rows)
nodes <- XML::getNodeSet(parsed.xml, paste0(
"/assofile/indiv_mat/ligmat/valmat[", variable.index,
"]"
))
after.evaluator <- function(node) {
if (length(node["val_modal"]) == 0) {
return(NA)
} else {
present.mods <- as.numeric(XML::xmlSApply(node, XML::xmlValue))
modals.vector <- rep(0, length(categories) - length(present.mods))
for (present.mod in present.mods) {
modals.vector <- append(modals.vector, 1, present.mod - 1)
}
return(modals.vector)
}
}
node.categories <- t(XML::xmlSApply(nodes, after.evaluator))
aux <- data.frame(c(aux, as.data.frame(node.categories)))
colnames(aux) <- c("$S", variable.name, categories)
return(aux)
}
process.mult.nominal.modif.variable <- function(labels, number.of.rows, parsed.xml,
variable.index, variable.name) {
aux <- list()
aux[[1]] <- rep("$M", number.of.rows)
if (labels) {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/mult_nominal_Modif/nominal-desc/list-nom/label"
), XML::xmlValue)
} else {
categories <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/variables/stvar[",
variable.index, "]/mult_nominal_Modif/nominal-desc/list-nom/name"
), XML::xmlValue)
}
aux[[2]] <- rep(length(categories), number.of.rows)
nodes <- XML::getNodeSet(parsed.xml, paste0(
"/assofile/indiv_mat/ligmat/valmat[", variable.index,
"]"
))
get.distributions <- function(node) {
if (length(node["val_list_modal"]) == 0) {
return(rep(NA, length(categories)))
} else {
moda.nodes <- as.numeric(sapply(
XML::xmlSApply(node, function(x) x["no_moda"]),
XML::xmlValue
))
frequencies <- as.numeric(sapply(
XML::xmlSApply(node, function(x) x["frequency"]),
XML::xmlValue
))
missing.categories.indexes <- setdiff(1:length(categories), moda.nodes)
for (missing.cat.index in missing.categories.indexes) {
frequencies <- append(frequencies, 0, after = missing.cat.index - 1)
}
return(frequencies)
}
}
all.frequencies <- t(round(sapply(nodes, get.distributions), 3))
aux <- data.frame(c(aux, as.data.frame(all.frequencies)))
colnames(aux) <- c("$M", variable.name, categories)
return(aux)
}
process.inter.cont.variable <- function(number.of.rows, parsed.xml, variable.index,
variable.name) {
aux <- list()
aux[[1]] <- rep("$I", number.of.rows)
after.evaluator <- function(node, element.to.retrieve) {
if (length(node["val_interv"]) == 0) {
return(NA)
} else {
return(as.numeric(XML::xmlValue(XML::xmlElementsByTagName(node[[1]], element.to.retrieve)[[1]])))
}
}
nodes <- XML::getNodeSet(parsed.xml, paste0(
"/assofile/indiv_mat/ligmat/valmat[", variable.index,
"]"
))
aux[[2]] <- sapply(nodes, after.evaluator, element.to.retrieve = "pmin")
aux[[3]] <- sapply(nodes, after.evaluator, element.to.retrieve = "pmax")
aux <- data.frame(aux)
colnames(aux) <- c("$I", variable.name, variable.name)
return(aux)
}
process.continue.variable <- function(number.of.rows, parsed.xml, variable.index, variable.name) {
aux <- list()
aux[[1]] <- rep("$C", number.of.rows)
after.evaluator <- function(node) {
if (length(node["val_conti"]) == 0) {
return(NA)
} else {
return(as.numeric(XML::xmlValue(node[[1]])))
}
}
aux[[2]] <- XML::xpathSApply(parsed.xml, paste0(
"/assofile/indiv_mat/ligmat/valmat[",
variable.index, "]"
), after.evaluator)
aux <- data.frame(aux)
colnames(aux) <- c("$C", variable.name)
return(aux)
}
|
"as.mmglm0" <-
function (object)
{
x <- list(Pi=object$Pi, delta=object$delta,
family=object$distn[2],
link=object$distn[3],
beta=rbind(object$pm$beta0, object$pm$beta1),
sigma=object$pm$sigma,
nonstat=object$nonstat)
x$x <- object$glmdata
x$glmformula <- object$glmformula
class(x) <- c("mmglm0")
return(x)
}
|
varim <-
function(A){
conv=.000001
T=matrix(0,ncol(A),ncol(A))
r=ncol(A)
m=nrow(A)
for (i in 1:r){
T[i,i]=1
}
B=A
f=sum(((A*A)-matrix(1,m,1)%*%colSums(A*A)/m)^2)
fold=f-2*conv*f
if (f==0){
fold=-conv
}
iter=0
while ((f-fold)>(f*conv)){
fold=f
iter=iter+1
for (i in 1:(r-1)){
for (j in (i+1):r){
x=B[,i]
y=B[,j]
xx=T[,i]
yy=T[,j]
u=x^2-y^2
v=2*x*y
u=u-matrix(1,m,1)*sum(u)/m
v=v-matrix(1,m,1)*sum(v)/m
a=2*sum(u*v)
b=sum(u^2)-sum(v^2)
c=(a^2+b^2)^.5
if (a>=0){
sign=1
}
if (a<0){
sign=-1
}
o=1
if (c<.00000000001){
cos=1
sin=0
o=0
}
if (c>=.00000000001){
vvv=-sign*((b+c)/(2*c))^.5
sin=(.5-.5*vvv)^.5
cos=(.5+.5*vvv)^.5
}
v=cos*x-sin*y
w=cos*y+sin*x
vv=cos*xx-sin*yy
ww=cos*yy+sin*xx
if (o==1){
if (vvv>=0){
B[,i]=v
B[,j]=w
T[,i]=vv
T[,j]=ww
}
if (vvv<0){
B[,i]=w
B[,j]=v
T[,i]=ww
T[,j]=vv
}
}
}
}
f=sum(((B*B)-matrix(1,nrow(B),1)%*%colSums(B*B)/nrow(B))^2)
}
out=list()
out$f=f
out$B=B
out$T=T
return(out)
}
|
ftu <- function(tu, data){
ord <- order(data[, 1], -data[, 2])
size <- length(data[, 1])
prob <- numeric(size)
surv <- 1
for (i in 1:size){
if (data[ord[i], 2]==1){ prob[ord[i]] <- surv / (size - i + 1) }
surv <- surv - prob[ord[i]]
}
cdf <- sapply(1:NROW(tu), function(j){
logical.mark <- sapply(2:NCOL(tu), function(col){ data[, col + 1] <= tu[j, col] })
return(sum(prob[data[, 1] <= tu[j, 1] & Reduce("&", as.list(as.data.frame(logical.mark)))], na.rm=TRUE))
})
return(cdf)
}
g <- function(tu, data){
if (is.vector(tu)) tu <- t(as.matrix(tu))
tu <- na.omit(tu)
return(abs(ftu(tu, data) - ftu(cbind(tu[, 1], matrix(Inf, NROW(tu), NCOL(tu) - 1)), data) * ftu(cbind(Inf, tu[, -1]), data[data[, 2]==1, ])))
}
testIndepTimeMark <- function(data, iter=1000){
D <- max(g(data[data[, 2]==1, -2], data))
n <- NROW(data)
resamp <- matrix(sample(1:n, n*iter, replace=TRUE), n, iter)
obs.mark <- data[data[, 2]==1, -(1:2)]
if (is.vector(obs.mark)){ obs.mark <- as.matrix(obs.mark) }
bD <- sapply(1:iter, function(j){
bdata <- data[resamp[, j], 1:2]
if (sum(bdata[, 2]) > 1){
m <- sum(bdata[, 2])
resamp.mark <- sample(NROW(obs.mark), m, replace=TRUE)
bmark <- as.data.frame(matrix(0, n, NCOL(data) - 2))
bmark[bdata[, 2]==1, ] <- obs.mark[resamp.mark, ]
bdata <- cbind(bdata, bmark)
tstat <- max(g(bdata[bdata[, 2]==1, -2], bdata))
} else {
tstat <- NULL
}
return(tstat)
})
if (is.list(bD)){ bD <- do.call("c", lapply(bD, "[", 1)) }
return(mean(bD >= D))
}
|
getExif <- function(api_key, photo_id){
url <- paste0("https://api.flickr.com/services/rest/?method=flickr.photos.getExif&api_key=", api_key, "&photo_id=",
photo_id, "&format=json&nojsoncallback=1")
raw_data <- RCurl::getURL(url, ssl.verifypeer = FALSE)
data <- jsonlite::fromJSON(raw_data)
as.data.frame(data)
}
|
if(interactive()) library(testthat)
test_that("widths work", {
ff <- tempfile()
cat(file = ff, "123456", "987654", sep="\n")
rest <- structure(list(
V1 = c(1, 9),
V2 = c(23, 87),
V3 = c(456, 654)),
.Names = c("V1", "V2", "V3"),
row.names = c(NA, -2L),
class="data.frame"
)
test <- fast.read.fwf(ff, widths = c(1, 2, 3))
expect_equal(rest, test)
expect_s3_class(test, "data.frame")
unlink(ff)
})
test_that("colclasses work",{
ff <- tempfile()
cat(file=ff, "abc123def1010", "ghi456jkl1100", sep="\n")
test <- fast.read.fwf(file=ff, widths=c(3, 3, 3, 1, 1, 1, 1),
colClasses=c("character", "numeric", "character", rep("logical", 4)))
rest <- structure(list(
V1 = c("abc", "ghi"),
V2 = c(123, 456),
V3 = c("def", "jkl"),
V4 = c(TRUE, TRUE),
V5 = c(FALSE, TRUE),
V6 = c(TRUE, FALSE),
V7 = c(FALSE, FALSE)),
.Names = c("V1", "V2", "V3", "V4", "V5", "V6", "V7"),
row.names = c(NA, -2L), class = "data.frame")
unlink(ff)
expect_equal(test, rest)
})
|
mapdeckGridDependency <- function() {
list(
createHtmlDependency(
name = "grid",
version = "1.0.0",
src = system.file("htmlwidgets/lib/grid", package = "mapdeck"),
script = c("grid.js"),
all_files = FALSE
)
)
}
add_grid <- function(
map,
data = get_map_data(map),
lon = NULL,
lat = NULL,
polyline = NULL,
cell_size = 1000,
extruded = TRUE,
elevation = NULL,
elevation_function = c("sum","mean","min","max"),
colour = NULL,
colour_function = c("sum","mean","min","max"),
elevation_scale = 1,
colour_range = NULL,
legend = FALSE,
legend_options = NULL,
auto_highlight = FALSE,
highlight_colour = "
layer_id = NULL,
update_view = TRUE,
focus_layer = FALSE,
digits = 6,
transitions = NULL,
brush_radius = NULL
) {
l <- list()
l[["lon"]] <- force( lon )
l[["lat"]] <- force( lat )
l[["polyline"]] <- force( polyline )
l[["elevation"]] <- force( elevation )
l[["colour"]] <- force( colour )
colour_function <- match.arg( colour_function )
colour_function <- toupper( colour_function )
elevation_function <- match.arg( elevation_function )
elevation_function <- toupper( elevation_function )
legend <- force( legend )
legend <- aggregation_legend( legend, legend_options )
use_weight <- FALSE
if(!is.null(elevation)) use_weight <- TRUE
use_colour <- FALSE
if(!is.null(colour)) use_colour <- TRUE
l <- resolve_data( data, l, c("POINT") )
bbox <- init_bbox()
update_view <- force( update_view )
focus_layer <- force( focus_layer )
if ( !is.null(l[["data"]]) ) {
data <- l[["data"]]
l[["data"]] <- NULL
}
if( !is.null(l[["bbox"]] ) ) {
bbox <- l[["bbox"]]
l[["bbox"]] <- NULL
}
checkNumeric(elevation_scale)
checkNumeric(cell_size)
if( is.null( colour_range ) ) {
colour_range <- colourvalues::colour_values(1:6, palette = "viridis")
}
if(length(colour_range) != 6)
stop("mapdeck - colour_range must have 6 hex colours")
checkHex(colour_range)
checkHexAlpha(highlight_colour)
layer_id <- layerId(layer_id, "grid")
map <- addDependency(map, mapdeckGridDependency())
tp <- l[["data_type"]]
l[["data_type"]] <- NULL
jsfunc <- "add_grid_geo"
if ( tp == "sf" ) {
geometry_column <- c( "geometry" )
shape <- rcpp_aggregate_geojson( data, l, geometry_column, digits, "grid" )
} else if ( tp == "df" ) {
geometry_column <- list( geometry = c("lon", "lat") )
shape <- rcpp_aggregate_geojson_df( data, l, geometry_column, digits, "grid" )
} else if ( tp == "sfencoded" ) {
geometry_column <- "polyline"
shape <- rcpp_aggregate_polyline( data, l, geometry_column, "grid" )
jsfunc <- "add_grid_polyline"
}
js_transitions <- resolve_transitions( transitions, "grid" )
invoke_method(
map, jsfunc, map_type( map ), shape[["data"]], layer_id, cell_size,
jsonify::to_json(extruded, unbox = TRUE), elevation_scale,
colour_range, auto_highlight, highlight_colour, bbox, update_view, focus_layer,
js_transitions, use_weight, use_colour, elevation_function, colour_function, legend,
brush_radius
)
}
clear_grid <- function( map, layer_id = NULL) {
layer_id <- layerId(layer_id, "grid")
invoke_method(map, "md_layer_clear", map_type( map ), layer_id, "grid" )
}
|
library(testthat)
library(parsnip)
library(dplyr)
library(rlang)
context("changing arguments and engine")
test_that('pipe arguments', {
mod_1 <- rand_forest() %>%
set_args(mtry = 1)
expect_equal(
quo_get_expr(mod_1$args$mtry),
1
)
expect_equal(
quo_get_env(mod_1$args$mtry),
empty_env()
)
mod_2 <- rand_forest(mtry = 2) %>%
set_args(mtry = 1)
var_env <- rlang::current_env()
expect_equal(
quo_get_expr(mod_2$args$mtry),
1
)
expect_equal(
quo_get_env(mod_2$args$mtry),
empty_env()
)
expect_error(rand_forest() %>% set_args())
})
test_that('pipe engine', {
mod_1 <- rand_forest() %>%
set_mode("regression")
expect_equal(mod_1$mode, "regression")
expect_error(rand_forest() %>% set_mode())
expect_error(rand_forest() %>% set_mode(2))
expect_error(rand_forest() %>% set_mode("haberdashery"))
})
test_that("can't set a mode that isn't allowed by the model spec", {
expect_error(
set_mode(linear_reg(), "classification"),
"'classification' is not a known mode"
)
})
test_that("unavailable modes for an engine and vice-versa", {
expect_error(
decision_tree() %>%
set_mode("regression") %>%
set_engine("C5.0"),
"Available modes for engine C5"
)
expect_error(
decision_tree() %>%
set_engine("C5.0") %>%
set_mode("regression"),
"Available modes for engine C5"
)
expect_error(
decision_tree(engine = NULL) %>%
set_engine("C5.0") %>%
set_mode("regression"),
"Available modes for engine C5"
)
expect_error(
decision_tree(engine = NULL)%>%
set_mode("regression") %>%
set_engine("C5.0"),
"Available modes for engine C5"
)
expect_error(
proportional_hazards() %>% set_mode("regression"),
"'regression' is not a known mode"
)
expect_error(
linear_reg() %>% set_mode(),
"Available modes for model type linear_reg"
)
expect_error(
linear_reg() %>% set_engine(),
"Missing engine"
)
expect_error(
proportional_hazards() %>% set_engine(),
"No known engines for"
)
})
|
optimal_rerandomization_exact = function(
W_base_object,
estimator = "linear",
q = 0.95,
skip_search_length = 1,
smoothing_degree = 1,
smoothing_span = 0.1,
z_sim_fun,
N_z = 1000,
dot_every_x_iters = 100
){
optimal_rerandomization_argument_checks(W_base_object, estimator, q)
n = W_base_object$n
X = W_base_object$X
W_base_sort = W_base_object$W_base_sort
max_designs = W_base_object$max_designs
imbalance_by_w_sorted = W_base_object$imbalance_by_w_sorted
if (estimator == "linear"){
Xt = t(X)
XtXinv = solve(Xt %*% X)
P = X %*% XtXinv %*% Xt
I = diag(n)
I_min_P = I - P
}
s_star = NULL
Q_star = Inf
Q_primes = array(NA, max_designs)
rel_mse_zs = matrix(NA, nrow = max_designs, ncol = N_z)
w_w_T_running_sum = matrix(0, n, n)
if (estimator == "linear"){
w_w_T_P_w_w_T_running_sum = matrix(0, n, n)
}
ss = seq(from = 1, to = max_designs, by = skip_search_length)
for (i in 1 : length(ss)){
s = ss[i]
if (!is.null(dot_every_x_iters)){
if (i %% dot_every_x_iters == 0){
cat(".")
}
}
w_s = W_base_sort[s, , drop = FALSE]
w_s_w_s_T = t(w_s) %*% w_s
w_w_T_running_sum = w_w_T_running_sum + w_s_w_s_T
Sigma_W = 1 / i * w_w_T_running_sum
if (estimator == "linear"){
w_w_T_P_w_w_T_running_sum = w_w_T_P_w_w_T_running_sum + w_s_w_s_T %*% P %*% w_s_w_s_T
D = 1 / i * w_w_T_P_w_w_T_running_sum
G = I_min_P %*% Sigma_W %*% I_min_P
for (n_z in 1 : N_z){
z = z_sim_fun()
rel_mse_zs[s, n_z] = t(z) %*% (G + 2 / n * D) %*% z
}
Q_primes[s] = quantile(rel_mse_zs[s, ], q)
}
if (Q_primes[s] < Q_star){
Q_star = Q_primes[s]
s_star = s
}
}
cat("\n")
smoothing_fit = loess(Q_primes ~ imbalance_by_w_sorted, degree = smoothing_degree, span = smoothing_span)
Q_primes_smoothed = predict(smoothing_fit, data.frame(X = imbalance_by_w_sorted))
s_star_smoothed = NULL
Q_star_smoothed = Inf
for (s in seq(from = 1, to = max_designs, by = skip_search_length)){
if (Q_primes_smoothed[s] < Q_star_smoothed){
Q_star_smoothed = Q_primes_smoothed[s]
s_star_smoothed = s
}
}
all_data_from_run = data.frame(
imbalance_by_w_sorted = imbalance_by_w_sorted,
Q_primes = Q_primes,
Q_primes_smoothed = Q_primes_smoothed
)
ll = list(
type = "exact",
q = q,
estimator = estimator,
z_sim_fun = z_sim_fun,
N_z = N_z,
W_base_object = W_base_object,
W_star = W_base_sort[1 : s_star, ],
W_star_size = s_star,
a_star = imbalance_by_w_sorted[s_star],
a_stars = imbalance_by_w_sorted[1 : s_star],
W_star_size_smoothed = s_star_smoothed,
a_star_smoothed = imbalance_by_w_sorted[s_star_smoothed],
a_stars_smoothed = imbalance_by_w_sorted[1 : s_star_smoothed],
all_data_from_run = all_data_from_run,
Q_star = Q_star,
Q_star_smoothed = Q_star_smoothed
)
class(ll) = "optimal_rerandomization_obj"
ll
}
|
setup_LC_geometry <- function(horizon = list(PLC = 1, FLC = 0),
speed = 1, space.dim = 1, shape = "cone") {
if (is.null(horizon$FLC)) {
horizon$FLC <- 0
}
out <- list(horizon = horizon,
speed = speed,
space.dim = space.dim,
shape = shape)
out$coordinates <-
list(PLC = compute_LC_coordinates(horizon = horizon$PLC, speed = speed,
space.dim = space.dim, shape = shape,
type = "PLC"),
FLC = compute_LC_coordinates(horizon = horizon$FLC, speed = speed,
space.dim = space.dim, shape = shape,
type = "FLC"))
out$n.p <- nrow(out$coordinates$PLC)
out$n.f <- nrow(out$coordinates$FLC)
class(out) <- "LC"
return(out)
}
|
pdfsq<-function(s, dist, p1, p2)
{
d.sample(s, dist, p1,p2)^2
}
|
l_image_import_files <- function(paths) {
if (!.withTclImg) {
if (any(vapply(paths, function(p) {
!(toupper(tools::file_ext(p)) %in% c("PPM", "PGM", "PNG", "GIF"))
}, logical(1)))) {
stop("All file formats other than png require the IMG Tcl extension.")
}
}
unique_paths <- unique(paths)
unique_ids <- sapply(unique_paths, function(path) {
tclvalue(tkimage.create('photo', file = path))
})
ii <- match(paths,unique_paths)
return(unique_ids[ii])
}
|
print.R0.sR <- function
(x,
...
)
{
if (class(x)!="R0.sR") {
stop("'x' must be of class 'R0.sR'")
}
if (exists("EG", where = x$estimates)) {
print(x$estimates$EG, ...)
}
if (exists("ML", where = x$estimates)) {
print(x$estimates$ML, ...)
}
if (exists("AR", where = x$estimates)) {
print(x$estimates$AR, ...)
}
if (exists("TD", where = x$estimates)) {
print(x$estimates$TD, ...)
}
if (exists("SB", where = x$estimates)) {
print(x$estimates$SB, ...)
}
}
|
render_gt <- function(expr,
width = NULL,
height = NULL,
align = NULL,
env = parent.frame(),
quoted = FALSE,
outputArgs = list()) {
check_shiny()
func <-
shiny::installExprFunction(
expr = expr,
name = "func",
eval.env = env,
quoted = quoted
)
shiny::createRenderFunction(
func = func,
function(result, shinysession, name, ...) {
if (is.null(result)) {
return(NULL)
}
if (!inherits(result, "gt_tbl")) {
result <- result %>% gt()
}
result <-
result %>%
tab_options(
container.width = width,
container.height = height,
table.align = align
)
html_tbl <- as.tags.gt_tbl(result)
dependencies <-
lapply(
htmltools::resolveDependencies(htmltools::findDependencies(html_tbl)),
shiny::createWebDependency
)
names(dependencies) <- NULL
list(
html = htmltools::doRenderTags(html_tbl),
deps = dependencies
)
},
gt_output, outputArgs
)
}
gt_output <- function(outputId) {
check_shiny()
shiny::htmlOutput(outputId)
}
check_shiny <- function() {
if (!requireNamespace("shiny", quietly = TRUE)) {
stop("Please install the *shiny* package before using this function:\n",
"* Use `install.packages(\"shiny\")`",
call. = FALSE)
}
}
|
test_that("proj_set() errors on non-existent path", {
expect_usethis_error(
proj_set("abcedefgihklmnopqrstuv"),
"does not exist"
)
})
test_that("proj_set() errors if no criteria are fulfilled", {
tmpdir <- withr::local_tempdir(pattern = "i-am-not-a-project")
expect_usethis_error(
proj_set(tmpdir),
"does not appear to be inside a project or package"
)
})
test_that("proj_set() can be forced, even if no criteria are fulfilled", {
tmpdir <- withr::local_tempdir(pattern = "i-am-not-a-project")
expect_error_free(old <- proj_set(tmpdir, force = TRUE))
withr::defer(proj_set(old))
expect_identical(proj_get(), proj_path_prep(tmpdir))
})
test_that("is_package() detects package-hood", {
create_local_package()
expect_true(is_package())
create_local_project()
expect_false(is_package())
})
test_that("check_is_package() errors for non-package", {
create_local_project()
expect_usethis_error(check_is_package(), "not an R package")
})
test_that("check_is_package() can reveal who's asking", {
create_local_project()
expect_usethis_error(check_is_package("foo"), "foo")
})
test_that("proj_path() appends to the project path", {
create_local_project()
expect_equal(
proj_path("a", "b", "c"),
path(proj_get(), "a/b/c")
)
expect_identical(proj_path("a", "b", "c"), proj_path("a/b/c"))
})
test_that("proj_rel_path() returns path part below the project", {
create_local_project()
expect_equal(proj_rel_path(proj_path("a/b/c")), "a/b/c")
})
test_that("proj_rel_path() returns path 'as is' if not in project", {
create_local_project()
expect_identical(proj_rel_path(path_temp()), path_temp())
})
test_that("proj_set() enforces proj path preparation policy", {
t <- withr::local_tempdir("proj-set-path-prep")
a <- path_real(dir_create(path(t, "a")))
b <- dir_create(path(a, "b"))
b2 <- link_create(b, path(a, "b2"))
d <- dir_create(path(b, "d"))
path_with_symlinks <- path(b2, "d")
expect_equal(path_rel(path_with_symlinks, a), path("b2/d"))
local_project(path_with_symlinks, force = TRUE)
expect_equal(path_rel(proj_get(), a), path("b/d"))
file_create(path(b, "d", ".here"))
proj_set(path_with_symlinks, force = FALSE)
expect_equal(path_rel(proj_get(), a), path("b/d"))
})
test_that("proj_path_prep() passes NULL through", {
expect_null(proj_path_prep(NULL))
})
test_that("is_in_proj() detects whether files are (or would be) in project", {
create_local_package()
expect_true(is_in_proj(proj_path("fiction")))
expect_true(is_in_proj(proj_path("DESCRIPTION")))
expect_false(is_in_proj(file_temp()))
expect_false(is_in_proj(path_temp()))
})
test_that("is_in_proj() does not activate a project", {
pkg <- create_local_package()
path <- proj_path("DESCRIPTION")
expect_true(is_in_proj(path))
local_project(NULL)
expect_false(is_in_proj(path))
expect_false(proj_active())
})
test_that("proj_sitrep() reports current working/project state", {
pkg <- create_local_package()
x <- proj_sitrep()
expect_s3_class(x, "sitrep")
expect_false(is.null(x[["working_directory"]]))
expect_identical(
fs::path_file(pkg),
fs::path_file(x[["active_usethis_proj"]])
)
})
test_that("with_project() runs code in temp proj, restores (lack of) proj", {
old_project <- proj_get_()
withr::defer(proj_set_(old_project))
temp_proj <- create_project(
file_temp(pattern = "TEMPPROJ"),
rstudio = FALSE, open = FALSE
)
proj_set_(NULL)
expect_identical(proj_get_(), NULL)
res <- with_project(path = temp_proj, proj_get_())
expect_identical(res, temp_proj)
expect_identical(proj_get_(), NULL)
})
test_that("with_project() runs code in temp proj, restores original proj", {
old_project <- proj_get_()
withr::defer(proj_set_(old_project))
host <- create_project(
file_temp(pattern = "host"),
rstudio = FALSE, open = FALSE
)
guest <- create_project(
file_temp(pattern = "guest"),
rstudio = FALSE, open = FALSE
)
proj_set(host)
expect_identical(proj_get_(), host)
res <- with_project(path = guest, proj_get_())
expect_identical(res, guest)
expect_identical(proj_get(), host)
})
test_that("with_project() works when temp proj == original proj", {
old_project <- proj_get_()
withr::defer(proj_set_(old_project))
host <- create_project(
file_temp(pattern = "host"),
rstudio = FALSE, open = FALSE
)
proj_set(host)
expect_identical(proj_get_(), host)
res <- with_project(path = host, proj_get_())
expect_identical(res, host)
expect_identical(proj_get(), host)
})
test_that("local_project() activates proj til scope ends", {
old_project <- proj_get_()
withr::defer(proj_set_(old_project))
new_proj <- file_temp(pattern = "localprojtest")
create_project(new_proj, rstudio = FALSE, open = FALSE)
proj_set_(NULL)
foo <- function() {
local_project(new_proj)
proj_sitrep()
}
res <- foo()
expect_identical(
res[["active_usethis_proj"]],
as.character(proj_path_prep(new_proj))
)
expect_null(proj_get_())
})
test_that("proj_activate() works with relative path when RStudio is not detected", {
sandbox <- path_real(dir_create(file_temp("sandbox")))
withr::defer(dir_delete(sandbox))
orig_proj <- proj_get_()
withr::defer(proj_set(orig_proj, force = TRUE))
withr::local_dir(sandbox)
rel_path_proj <- path_file(file_temp(pattern = "mno"))
out_path <- create_project(rel_path_proj, rstudio = FALSE, open = FALSE)
with_mock(
rstudio_available = function(...) FALSE,
expect_error_free(
result <- proj_activate(rel_path_proj)
)
)
expect_true(result)
expect_equal(path_wd(), out_path)
expect_equal(proj_get(), out_path)
})
test_that("local_project()'s `quiet` argument works", {
temp_proj <- create_project(
file_temp(pattern = "TEMPPROJ"),
rstudio = FALSE, open = FALSE
)
withr::defer(dir_delete(temp_proj))
local_project(path = temp_proj, quiet = TRUE, force = TRUE, setwd = FALSE)
expect_true(getOption("usethis.quiet"))
})
|
knitr::opts_chunk$set(comment = "")
options(width = 100, max.print = 100)
set.seed(123456)
library(simTool)
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
library(broom)
library(boot)
regData <- function(n, SD) {
x <- seq(0, 1, length = n)
y <- 10 + 2 * x + rnorm(n, sd = SD)
tibble(x = x, y = y)
}
eval_tibbles(
expand_tibble(fun = "regData", n = 5L, SD = 1:2),
expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
post_analyze = broom::tidy,
summary_fun = list(mean = mean, sd = sd),
group_for_summary = "term",
replications = 3
)
print(dg <- dplyr::bind_rows(
expand_tibble(fun = "rexp", n = c(10L, 20L), rate = 1:2),
expand_tibble(fun = "rnorm", n = c(10L, 20L), mean = 1:2)
))
print(pg <- dplyr::bind_rows(
expand_tibble(proc = "min"),
expand_tibble(proc = "mean", trim = c(0.1, 0.2))
))
dg <- expand_tibble(fun = "rnorm", n = 10, mean = 1:2)
pg <- expand_tibble(proc = "min")
eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 2)
eg
eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 3)
eg
eg <- eval_tibbles(data_grid = dg, proc_grid = pg, replications = 1)
eg$simulation
eg$generated_data
dg <- expand_tibble(fun = "runif", n = c(10, 20, 30))
pg <- expand_tibble(proc = c("min", "max"))
eval_tibbles(
data_grid = dg, proc_grid = pg, replications = 1000,
summary_fun = list(mean = mean)
)
eval_tibbles(
data_grid = dg, proc_grid = pg, replications = 1000,
summary_fun = list(mean = mean, sd = sd)
)
eval_tibbles(
expand_tibble(fun = "regData", n = 5L, SD = 1:2),
expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
replications = 2
)
eval_tibbles(
expand_tibble(fun = "regData", n = 5L, SD = 1:2),
expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
post_analyze = purrr::compose(function(mat) mat["(Intercept)", "Estimate"], coef, summary.lm),
replications = 2
)
presever_rownames <- function(mat) {
rn <- rownames(mat)
ret <- tibble::as_tibble(mat)
ret$term <- rn
ret
}
eval_tibbles(
expand_tibble(fun = "regData", n = 5L, SD = 1:2),
expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
post_analyze = purrr::compose(presever_rownames, coef, summary),
replications = 3
)
eval_tibbles(
expand_tibble(fun = "regData", n = 5L, SD = 1:2),
expand_tibble(proc = "lm", formula = c("y~x", "y~I(x^2)")),
post_analyze = purrr::compose(presever_rownames, coef, summary),
summary_fun = list(mean = mean, sd = sd),
group_for_summary = "term",
replications = 3
)
eval_tibbles(
data_grid = dg, proc_grid = pg, replications = 10,
ncpus = 2, summary_fun = list(mean = mean)
)
library(parallel)
cl <- makeCluster(rep("localhost", 2), type = "PSOCK")
eval_tibbles(
data_grid = dg, proc_grid = pg, replications = 10,
cluster = cl, summary_fun = list(mean = mean)
)
stopCluster(cl)
library(boot)
ratio <- function(d, w) sum(d$x * w) / sum(d$u * w)
city.boot <- boot(city, ratio,
R = 999, stype = "w",
sim = "ordinary"
)
boot.ci(city.boot,
conf = c(0.90, 0.95),
type = c("norm", "basic", "perc", "bca")
)
returnCity <- function() {
city
}
bootConfInt <- function(data) {
city.boot <- boot(data, ratio,
R = 999, stype = "w",
sim = "ordinary"
)
boot.ci(city.boot,
conf = c(0.90, 0.95),
type = c("norm", "basic", "perc", "bca")
)
}
dg <- expand_tibble(fun = "returnCity")
pg <- expand_tibble(proc = "bootConfInt")
eval_tibbles(dg, pg,
replications = 10, ncpus = 2,
cluster_libraries = c("boot"),
cluster_global_objects = c("ratio")
)
summary <- function(x) tibble(sd = sd(x))
g <- function(x) tibble(q0.1 = quantile(x, 0.1))
someFunc <- function() {
summary <- function(x) tibble(sd = sd(x), mean = mean(x))
dg <- expand_tibble(fun = "runif", n = 100)
pg <- expand_tibble(proc = c("summary", "g"))
print(eval_tibbles(dg, pg))
cat("--------------------------------------------------\n")
print(eval_tibbles(dg, pg, envir = environment()))
}
someFunc()
dg <- expand_tibble(fun = c("rnorm"), mean = c(1,1000), sd = c(1,10), n = c(10L, 100L))
pg <- expand_tibble(proc = "quantile", probs = 0.975)
post_ana <- function(q_est, .truth){
tibble::tibble(bias = q_est - stats::qnorm(0.975, mean = .truth$mean, sd = .truth$sd))
}
eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
ncpus = 2,
post_analyze = post_ana,
summary_fun = list(mean = mean))
dg <- dplyr::bind_rows(
expand_tibble(fun = c("rnorm"), mean = 0, n = c(10L, 100L), .truth = qnorm(0.975)),
expand_tibble(fun = c("rexp"), rate = 1, n = c(10L, 100L), .truth = qexp(0.975, rate = 1)),
expand_tibble(fun = c("runif"), max = 2, n = c(10L, 100L), .truth = qunif(0.975, max = 2))
)
pg <- expand_tibble(proc = "quantile", probs = 0.975)
post_ana <- function(q_est, .truth){
ret <- q_est - .truth
names(ret) <- "bias"
ret
}
eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
ncpus = 2,
post_analyze = post_ana,
summary_fun = list(mean = mean))
dg <- dplyr::bind_rows(
expand_tibble(fun = c("rnorm"), mean = 0, n = c(10L, 1000L),
.truth = list(function(prob) qnorm(prob, mean = 0))),
expand_tibble(fun = c("rexp"), rate = 1, n = c(10L, 1000L),
.truth = list(function(prob) qexp(prob, rate = 1))),
expand_tibble(fun = c("runif"), max = 2, n = c(10L, 1000L),
.truth = list(function(prob) qunif(prob, max = 2)))
)
bias_quantile <- function(x, prob, .truth) {
est <- quantile(x, probs = prob)
ret <- est - .truth[[1]](prob)
names(ret) <- "bias"
ret
}
pg <- expand_tibble(proc = "bias_quantile", prob = c(0.9, 0.975))
eval_tibbles(dg, pg, replications = 10^3, discard_generated_data = TRUE,
ncpus = 1,
summary_fun = list(mean = mean))
EVAL <- FALSE
if (Sys.getenv("NOT_CRAN") == "true") {
EVAL <- TRUE
}
|
get.trait.data.pft <- function(pft, modeltype, dbfiles, dbcon, trait.names,
forceupdate = FALSE) {
if (!file.exists(pft$outdir) && !dir.create(pft$outdir, recursive = TRUE)) {
PEcAn.logger::logger.error(paste0("Couldn't create PFT output directory: ", pft$outdir))
}
old.files <- list.files(path = pft$outdir, full.names = TRUE, include.dirs = FALSE)
file.remove(old.files)
pftres <- query_pfts(dbcon, pft[["name"]], modeltype)
pfttype <- pftres[["pft_type"]]
pftid <- pftres[["id"]]
if (nrow(pftres) > 1) {
PEcAn.logger::logger.severe(
"Multiple PFTs named", pft[["name"]], "found,",
"with ids", PEcAn.utils::vecpaste(pftres[["id"]]), ".",
"Specify modeltype to fix this.")
}
if (nrow(pftres) == 0) {
PEcAn.logger::logger.severe("Could not find pft", pft[["name"]])
return(NA)
}
if (pfttype == "plant") {
pft_member_filename = "species.csv"
pft_members <- PEcAn.DB::query.pft_species(pft$name, modeltype, dbcon)
} else if (pfttype == "cultivar") {
pft_member_filename = "cultivars.csv"
pft_members <- PEcAn.DB::query.pft_cultivars(pft$name, modeltype, dbcon)
} else {
PEcAn.logger::logger.severe("Unknown pft type! Expected 'plant' or 'cultivar', got", pfttype)
}
pft_members <- pft_members %>%
dplyr::mutate_if(is.character, ~dplyr::na_if(., ""))
prior.distns <- PEcAn.DB::query.priors(pft = pftid, trstr = PEcAn.utils::vecpaste(trait.names), con = dbcon)
prior.distns <- prior.distns[which(!rownames(prior.distns) %in% names(pft$constants)),]
traits <- rownames(prior.distns)
trait.data.check <- PEcAn.DB::query.traits(ids = pft_members$id, priors = traits, con = dbcon, update.check.only = TRUE, ids_are_cultivars = (pfttype=="cultivar"))
traits <- names(trait.data.check)
forceupdate <- isTRUE(as.logical(forceupdate))
if (!forceupdate) {
if (is.null(pft$posteriorid)) {
recent_posterior <- dplyr::tbl(dbcon, "posteriors") %>%
dplyr::filter(.data$pft_id == !!pftid) %>%
dplyr::collect()
if (length(recent_posterior) > 0) {
pft$posteriorid <- dplyr::tbl(dbcon, "posteriors") %>%
dplyr::filter(.data$pft_id == !!pftid) %>%
dplyr::arrange(dplyr::desc(.data$created_at)) %>%
utils::head(1) %>%
dplyr::pull(id)
} else {
PEcAn.logger::logger.info("No previous posterior found. Forcing update")
}
}
if (!is.null(pft$posteriorid)) {
files <- dbfile.check(type = "Posterior", container.id = pft$posteriorid, con = dbcon,
return.all = TRUE)
need_files <- c(
trait_data = "trait.data.Rdata",
priors = "prior.distns.Rdata",
pft_membership = pft_member_filename
)
ids <- match(need_files, files$file_name)
names(ids) <- names(need_files)
if (any(is.na(ids))) {
missing_files <- need_files[is.na(ids)]
PEcAn.logger::logger.info(paste0(
"Forcing meta-analysis update because ",
"the following files are missing from the posterior: ",
paste0(shQuote(missing_files), collapse = ", ")
))
PEcAn.logger::logger.debug(
"\n `dbfile.check` returned the following output:\n",
PEcAn.logger::print2string(files),
wrap = FALSE
)
} else {
PEcAn.logger::logger.debug(
"All posterior files are present. Performing additional checks ",
"to determine if meta-analysis needs to be updated."
)
need_paths <- file.path(files$file_path[ids], need_files)
names(need_paths) <- names(need_files)
files_exist <- file.exists(need_paths)
foundallfiles <- all(files_exist)
if (!foundallfiles) {
PEcAn.logger::logger.warn(
"The following files are in database but not found on disk: ",
paste(shQuote(need_files[!files_exist]), collapse = ", "), ". ",
"Re-running meta-analysis."
)
} else {
PEcAn.logger::logger.debug("Checking if PFT membership has changed.")
if (pfttype == "plant") {
colClass = c("double", "character", "character", "character")
} else if (pfttype == "cultivar") {
colClass = c("double", "double", "character", "character", "character", "character")
}
existing_membership <- utils::read.csv(
need_paths[["pft_membership"]],
colClasses = colClass,
stringsAsFactors = FALSE,
na.strings = c("", "NA")
)
diff_membership <- symmetric_setdiff(
existing_membership,
pft_members,
xname = "existing",
yname = "current"
)
if (nrow(diff_membership) > 0) {
PEcAn.logger::logger.error(
"\n PFT membership has changed. \n",
"Difference is:\n",
PEcAn.logger::print2string(diff_membership),
wrap = FALSE
)
foundallfiles <- FALSE
}
PEcAn.logger::logger.debug("Checking if priors have changed")
existing_prior <- PEcAn.utils::load_local(need_paths[["priors"]])[["prior.distns"]]
diff_prior <- symmetric_setdiff(
dplyr::as_tibble(prior.distns, rownames = "trait"),
dplyr::as_tibble(existing_prior, rownames = "trait")
)
if (nrow(diff_prior) > 0) {
PEcAn.logger::logger.error(
"\n Prior has changed. \n",
"Difference is:\n",
PEcAn.logger::print2string(diff_prior),
wrap = FALSE
)
foundallfiles <- FALSE
}
PEcAn.logger::logger.debug("Checking if trait data have changed")
existing_trait_data <- PEcAn.utils::load_local(
need_paths[["trait_data"]]
)[["trait.data"]]
if (length(trait.data.check) != length(existing_trait_data)) {
PEcAn.logger::logger.warn(
"Lengths of new and existing `trait.data` differ. ",
"Re-running meta-analysis."
)
foundallfiles <- FALSE
} else if (length(trait.data.check) == 0) {
PEcAn.logger::logger.warn("New and existing trait data are both empty. Skipping this check.")
} else {
current_traits <- dplyr::bind_rows(trait.data.check, .id = "trait") %>%
dplyr::select(-mean, -.data$stat)
existing_traits <- dplyr::bind_rows(existing_trait_data, .id = "trait") %>%
dplyr::select(-mean, -.data$stat)
diff_traits <- symmetric_setdiff(current_traits, existing_traits)
if (nrow(diff_traits) > 0) {
diff_summary <- diff_traits %>%
dplyr::count(source, .data$trait)
PEcAn.logger::logger.error(
"\n Prior has changed. \n",
"Here are the number of differing trait records by trait:\n",
PEcAn.logger::print2string(diff_summary),
wrap = FALSE
)
foundallfiles <- FALSE
}
}
}
if (foundallfiles) {
PEcAn.logger::logger.info(
"Reusing existing files from posterior", pft$posteriorid,
"for PFT", shQuote(pft$name)
)
for (id in seq_len(nrow(files))) {
file.copy(from = file.path(files[[id, "file_path"]], files[[id, "file_name"]]),
to = file.path(pft$outdir, files[[id, "file_name"]]))
}
done <- TRUE
if (length(list.files(pft$outdir, "post.distns.Rdata")) == 0) {
all.files <- list.files(pft$outdir)
post.distn.file <- all.files[grep("post\\.distns\\..*\\.Rdata", all.files)]
if (length(post.distn.file) > 1)
PEcAn.logger::logger.severe(
"get.trait.data.pft() doesn't know how to ",
"handle multiple `post.distns.*.Rdata` files.",
"Found the following files: ",
paste(shQuote(post.distn.file), collapse = ", ")
)
else if (length(post.distn.file) == 1) {
link_input <- file.path(pft[["outdir"]], post.distn.file)
link_target <- file.path(pft[["outdir"]], "post.distns.Rdata")
PEcAn.logger::logger.debug(
"Found exactly one posterior distribution file: ",
shQuote(link_input),
". Symlinking it to PFT output directory: ",
shQuote(link_target)
)
file.symlink(from = link_input, to = link_target)
} else {
PEcAn.logger::logger.error(
"No previous posterior distribution file found. ",
"Most likely, trait data were retrieved, but meta-analysis ",
"was not run. Meta-analysis will be run."
)
done <- FALSE
}
}
if (done) return(pft)
}
}
}
}
trait.data <- query.traits(pft_members$id, traits, con = dbcon,
update.check.only = FALSE,
ids_are_cultivars = (pfttype == "cultivar"))
traits <- names(trait.data)
if (length(trait.data) > 0) {
trait_counts <- trait.data %>%
dplyr::bind_rows(.id = "trait") %>%
dplyr::count(.data$trait)
PEcAn.logger::logger.info(
"\n Number of observations per trait for PFT ", shQuote(pft[["name"]]), ":\n",
PEcAn.logger::print2string(trait_counts, n = Inf, na.print = ""),
wrap = FALSE
)
} else {
PEcAn.logger::logger.warn(
"None of the requested traits were found for PFT ",
format(pft_members[["id"]], scientific = FALSE)
)
}
old.files <- list.files(path = pft$outdir)
insert_result <- db.query(
paste0("INSERT INTO posteriors (pft_id) VALUES (", pftid, ") RETURNING id"),
con = dbcon)
pft$posteriorid <- insert_result[["id"]]
pathname <- file.path(dbfiles, "posterior", pft$posteriorid)
dir.create(pathname, showWarnings = FALSE, recursive = TRUE)
utils::write.csv(pft_members, file.path(pft$outdir, pft_member_filename),
row.names = FALSE)
save(prior.distns, file = file.path(pft$outdir, "prior.distns.Rdata"))
utils::write.csv(prior.distns, file.path(pft$outdir, "prior.distns.csv"),
row.names = TRUE)
PEcAn.logger::logger.info(
"\n Summary of prior distributions for PFT ", shQuote(pft$name), ":\n",
PEcAn.logger::print2string(prior.distns),
wrap = FALSE
)
trait.data.file <- file.path(pft$outdir, "trait.data.Rdata")
save(trait.data, file = trait.data.file)
utils::write.csv(
dplyr::bind_rows(trait.data),
file.path(pft$outdir, "trait.data.csv"),
row.names = FALSE
)
store_files_all <- list.files(path = pft[["outdir"]])
store_files <- setdiff(store_files_all, old.files)
PEcAn.logger::logger.debug(
"The following posterior files found in PFT outdir ",
"(", shQuote(pft[["outdir"]]), ") will be registered in BETY ",
"under posterior ID ", format(pft[["posteriorid"]], scientific = FALSE), ": ",
paste(shQuote(store_files), collapse = ", "), ". ",
"The following files (if any) will not be registered because they already existed: ",
paste(shQuote(intersect(store_files, old.files)), collapse = ", "),
wrap = FALSE
)
for (file in store_files) {
filename <- file.path(pathname, file)
file.copy(file.path(pft$outdir, file), filename)
dbfile.insert(in.path = pathname, in.prefix = file,
type = "Posterior", id = pft[["posteriorid"]],
con = dbcon)
}
return(pft)
}
|
library(knitr)
knitr::opts_chunk$set(autodep = TRUE, cache = FALSE)
library(fMRIscrub)
dim(Dat1)
dim(Dat2)
ps.Dat1 = pscrub(Dat1, verbose=TRUE, comps_mean_dt=1, comps_var_dt=1)
ps.Dat2 = pscrub(Dat2, verbose=TRUE, comps_mean_dt=1, comps_var_dt=1)
p1 <- plot(ps.Dat1, title="Dat1", show.legend=FALSE)
p2 <- plot(ps.Dat2, title="Dat2", show.legend=FALSE)
cowplot::plot_grid(p1, p2, nrow=1)
p1 <- plot(DVARS(Dat1), title="Dat1", show.legend=FALSE)
p2 <- plot(DVARS(Dat2), title="Dat2", show.legend=FALSE)
cowplot::plot_grid(p1, p2, nrow=1)
library(oro.nifti)
library(neurobase)
fname = system.file("extdata", "Dat1_mask.nii.gz", package = "fMRIscrub")
Mask1 = readNIfTI(fname) > 0
Mask1 = array(Mask1, dim=c(dim(Mask1), 1))
Img1 = fMRIscrub::unmask_vol(t(Dat1), Mask1)
mfrow_original <- par("mfrow")
par(mfrow=c(1,2))
levs = ps.Dat1$measure
t_med = order(levs)[ceiling(length(levs)/2)]
t_max = which.max(levs)
image(Img1[,,,t_med], main=paste0('Median lev (T = ', t_med, ')'))
image(Img1[,,,t_max], main=paste0('Maximum lev (T = ', t_max, ')'))
par(mfrow=mfrow_original)
psx <- pscrub(Dat1, projection="ICA", get_dirs=TRUE, comps_mean_dt=1, comps_var_dt=1)
artImg1 = artifact_images(psx)
par(mfrow=c(1,2))
artImg1.mean = unmask_vol(t(artImg1$mean), Mask1)
artImg1.top = unmask_vol(t(artImg1$top), Mask1)
idx = which(which(psx$outlier_flag) == t_max)
image(artImg1.mean[,,1,idx], main=paste0('Lev image, mean (T=',t_max,')'))
image(artImg1.top[,,1,idx], main=paste0('Lev image, top (T=',t_max,')'))
ps.Dat1.3 <- fMRIscrub:::pscrub_multi(
Dat1, projection=c("ICA_kurt", "fusedPCA_kurt", "ICA"), verbose=TRUE, comps_mean_dt=1, comps_var_dt=1
)
fMRIscrub:::plot.scrub_projection_multi(ps.Dat1.3, legend.position="bottom")
|
Act_logistic <- function (x) {
1 / (1 + exp(-x))}
setup_my_predict <- function () {
bias <<- c()
sObj <<- nn$snnsObject
weightvalues <<- weightMatrix(nn)
layers <<- c()
layer2 <<- c()
layer3 <<- c()
layer4 <<- c()
output <<- c()
nunits <<- sObj$getNoOfUnits()
for (i in 1:nunits) {
units <- sObj$getUnitName(i)
bias <<- c(bias, sObj$getUnitBias(i))
node_descriptor <- strsplit(units, '_')
node_descriptor <- unlist(node_descriptor)
if (node_descriptor[1] == 'Hidden') {
if (node_descriptor[2] == '2') {
layer2 <<- c(layer2, i)
}
if (node_descriptor[2] == '3') {
layer3 <<- c(layer3, i)
}
if (node_descriptor[2] == '4') {
layer4 <<- c(layer3, i)
}
}
if (node_descriptor[1] == 'Output') {
output <<- c(output, i)
}
}
if (length(layer2) > 0) {
layers <<- c(layers, 'layer2')
}
if (length(layer3) > 0) {
layers <<- c(layers, 'layer3')
}
if (length(layer4) > 0) {
layers <<- c(layers, 'layer4')
}
layers <<- c(layers, 'output')
}
my_predict_single <- function (data) {
xinput <- as.matrix(data)
nrows = nrow(xinput)
nextra <- (nunits - Dim)*nrows
hdata <- matrix(c(xinput, rep(0.0, nextra)), ncol = nunits, nrow = 1)
nlayers <- length(layers)
activity <- rep(0.0,nunits)
for (i in 1:Dim) {activity[i] <- data[i]}
n <- 1
while (n <= nlayers) {
hdata <- hdata %*% weightvalues
nodes_in_layer <- get(layers[n])
for (i in nodes_in_layer) {
hdata[i] <- Act_logistic(hdata[i] + bias[i])
activity[i] <- hdata[i]
}
n <- n + 1
}
return (unlist(activity))
}
my_predict <- function (data,disabled_nodes=NULL) {
xinput <- as.matrix(data)
nrows = nrow(xinput)
nextra <- (nunits - Dim)
zeros <- matrix(rep(0.0,nextra),ncol=nextra,nrow=nrows)
hdata <- cbind(xinput,zeros)
nlayers <- length(layers)
activity <- c()
for (i in 1:Dim) {activity[i] <- data[i]}
n <- 1
while (n <= nlayers) {
hdata <- hdata %*% weightvalues
nodes_in_layer <- get(layers[n])
for (i in nodes_in_layer) {
if (i %in% disabled_nodes) {
hdata[,i] = 0.0
} else {
hdata[,i] <- Act_logistic(hdata[,i] + bias[i])
}
}
n <- n + 1
}
return (hdata[,(ncol(hdata)-nclasses+1):ncol(hdata)])
}
my_predict_activation <- function (data) {
xinput <- as.matrix(data)
nrows = nrow(xinput)
activity <- c()
nextra <- (nunits - Dim)
zeros <- matrix(rep(0.0,nextra),ncol=nextra,nrow=nrows)
hdata <- cbind(xinput,zeros)
activation <- hdata
nlayers <- length(layers)
for (i in 1:Dim) {activity[i] <- data[i]}
n <- 1
while (n <= nlayers) {
hdata <- hdata %*% weightvalues
nodes_in_layer <- get(layers[n])
for (i in nodes_in_layer) {
hdata[,i] <- Act_logistic(hdata[,i] + bias[i])
activation[,i] <- hdata[,i]
}
n <- n + 1
}
return (activation)
}
library(plot3D)
plot_activation <- function (y1,y2,activation,node) {
scatter2D(y1,y2,clim = c(0.0,1.0), colvar=activation[,node])
}
|
context("Function createArea")
sapply(studies, function(study) {
setup_study(study, sourcedir)
opts <- antaresRead::setSimulationPath(studyPath, 1)
test_that("Cannot initialize a new area if not in 'Input' mode", {
expect_error(createArea(name = "myarea"))
})
opts <- antaresRead::setSimulationPath(studyPath, 'input')
test_that("Backup study/input", {
expect_length(backupStudy(what = "study"), 1)
expect_length(backupStudy(what = "input"), 1)
})
test_that("Initialize a new area", {
n_before <- length(getOption("antares")$areaList)
createArea(name = "myarea")
n_after <- length(getOption("antares")$areaList)
expect_equal(n_before + 1, n_after)
expect_true("myarea" %in% getOption("antares")$areaList)
})
test_that("nodal optimization options are properly written", {
createArea(
name = "testarea",
nodalOptimization = nodalOptimizationOptions(
non_dispatchable_power = FALSE,
dispatchable_hydro_power = TRUE,
other_dispatchable_power = FALSE,
spread_unsupplied_energy_cost = 10,
spread_spilled_energy_cost = 3.14,
average_unsupplied_energy_cost = 239,
average_spilled_energy_cost = 1000
)
)
optim_testarea <- readIniFile(file.path(opts$inputPath, "areas", "testarea", "optimization.ini"))
expect_equal(optim_testarea$`nodal optimization`$`dispatchable-hydro-power`, TRUE)
expect_equal(optim_testarea$`nodal optimization`$`spread-unsupplied-energy-cost`, 10)
expect_equal(optim_testarea$`nodal optimization`$`non-dispatchable-power`, FALSE)
expect_equal(optim_testarea$`nodal optimization`$`other-dispatchable-power`, FALSE)
expect_equal(optim_testarea$`nodal optimization`$`spread-spilled-energy-cost`, 3.14)
thermal_areas <- readIniFile(file.path(opts$inputPath, "thermal", "areas.ini"))
expect_equal(thermal_areas$spilledenergycost$testarea, 1000)
expect_equal(thermal_areas$unserverdenergycost$testarea, 239)
})
test_that("Remove an area", {
area2remove <- "myareatoremove"
createArea(name = area2remove)
ra <- checkRemovedArea(area = area2remove)
expect_true(length(ra$areaResiduFiles) > 0)
expect_true(length(ra$areaResidus) > 0)
removeArea(name = area2remove)
ra <- checkRemovedArea(area = area2remove)
expect_length(ra$areaResiduFiles, 0)
expect_length(ra$areaResidus, 0)
})
unlink(x = file.path(pathstd, "test_case"), recursive = TRUE)
})
|
str(exp1)
res1 <-
expirest_wisle(data = exp1[exp1$Batch %in% c("b2", "b5", "b7"), ],
response_vbl = "Potency", time_vbl = "Month",
batch_vbl = "Batch", rl = 98, rl_sf = 3, sl = 95,
sl_sf = 3, srch_range = c(0, 500))
res2 <-
expirest_wisle(data = exp1[exp1$Batch %in% c("b3", "b4", "b5"), ],
response_vbl = "Potency", time_vbl = "Month",
batch_vbl = "Batch", rl = 98, rl_sf = 3, sl = 95,
sl_sf = 3, srch_range = c(0, 500))
res3 <-
expirest_wisle(data = exp1[exp1$Batch %in% c("b4", "b5", "b8"), ],
response_vbl = "Potency", time_vbl = "Month",
batch_vbl = "Batch", rl = 98, rl_sf = 3, sl = 95,
sl_sf = 3, srch_range = c(0, 500))
res1
res2
res3
|
HMMfits <- function(m)
{
stopifnot(any(unlist(lapply(m,is.momentuHMM))))
obj <- m
class(obj) <- append("HMMfits",class(obj))
return(obj)
}
is.HMMfits <- function(x)
inherits(x,"HMMfits")
|
get.mpt.fia <- function(data, model.filename, restrictions.filename = NULL, Sample = 200000, model.type = c("easy", "eqn", "eqn2"), round.digit = 6, multicore = FALSE, split = NULL, mConst = NULL){
if(is.vector(data)) {
data <- array(data, dim = c(1, length(data)))
multiFit <- FALSE
} else
if(is.matrix(data) | is.data.frame(data)) {
if (is.data.frame(data)) data <- as.matrix(data)
multiFit <- TRUE
} else stop("data is neither vector, nor matrix, nor data.frame!")
class.model <- class(model.filename)
if ("connection" %in% class.model) {
tmp.model <- readLines(model.filename)
model.filename <- textConnection(tmp.model)
}
model <- .get.mpt.model(model.filename, model.type)
n.data <- dim(data)[1]
if(!is.null(restrictions.filename)) {
restrictions <- .check.restrictions(restrictions.filename, model)
}
if (sum(sapply(model, length)) != length(data[1,])) stop(paste("Size of data does not correspond to size of model (i.e., model needs ", sum(sapply(model, length)), " datapoints, data gives ", length(data[1,]), " datapoints).", sep = ""))
df.n <- apply(data, 1, .DF.N.get, tree = model)
n_items <- sapply(df.n, function (x) sum(x[[2]]))
if ("connection" %in% class.model) {
model.filename <- textConnection(tmp.model)
}
mpt.string <- make.mpt.cf(model.filename = model.filename, model.type = model.type)
is.category <- grepl("^[[:digit:]]+$", mpt.string)
s <- paste(ifelse(is.category == 0, "p", "C"), collapse = "")
params <- mpt.string[!is.category]
category <- mpt.string[is.category]
category <- as.numeric(category)
is.p.join <- grepl("^hank\\.join\\.", params)
c.join <- sum(is.p.join)
p.join <- params[is.p.join]
p.n.join <- params[!is.p.join]
hank.restrictions <- vector("list", c.join)
ns <- t(sapply(df.n, function (x) x[[2]]))
p.n.join.lev <- sort(unique(p.n.join))
f.p.n.join <- factor(p.n.join, levels = p.n.join.lev)
names(p.n.join.lev) <- 1:length(p.n.join.lev)
if (c.join > 0) {
for (indiv in 1:n.data) {
for (c.hank in 1:(c.join)) {
hank.restrictions[[c.hank]][indiv] <- round(sum(ns[indiv,1:c.hank]) / sum(ns[indiv,1:(c.hank+1)]), round.digit)
}
}
params.join.mat <- matrix(NA, nrow = n.data, ncol = (c.join))
for (indiv in 1:n.data) {
for (par.join in 1:length(p.join)) {
params.join.mat[indiv,par.join] <- -hank.restrictions[[length(p.join)-(par.join-1)]][indiv]
}
}
parameters <- vector("list", n.data)
for (indiv in 1:n.data) {
parameters[[indiv]] <- c(params.join.mat[indiv,], as.numeric(f.p.n.join))
}
n.fia <- 1
i.data <- vector('list', n.data)
p.fia <- vector("numeric", n.data)
m.fit <- vector("numeric", n.data)
i.data[[1]] <- params.join.mat[1,]
p.fia[1] <- 1
m.fit[1] <- 1
if (n.data > 1) {
for (c in 2:n.data) {
tmp <- list(params.join.mat[c,])
if (tmp %in% i.data) p.fia[c] <- which(i.data %in% tmp)
else {
n.fia <- n.fia + 1
i.data[n.fia] <- tmp
p.fia[c] <- n.fia
m.fit[n.fia] <- c
}
}
}
m.fit <- m.fit[1:n.fia]
} else {
parameters <- vector("list", n.data)
for (indiv in 1:n.data) {
parameters[[indiv]] <- as.numeric(f.p.n.join)
}
m.fit <- 1
p.fia <- rep(1,n.data)
}
if(!is.null(restrictions.filename)) {
ineq <- vector('list', length(restrictions))
n.ineq <- 1
for (restr in 1:length(restrictions)) {
if (restrictions[[restr]][3] == "=") {
if (grepl("^[[:digit:]]", restrictions[[restr]][2])) {
for (indiv in 1:n.data) {
parameters[[indiv]][params == restrictions[[restr]][1]] <- -as.numeric(restrictions[[restr]][2])
}
} else {
for (indiv in 1:n.data) {
parameters[[indiv]][params == restrictions[[restr]][1]] <- (parameters[[indiv]][params == restrictions[[restr]][2]])[1]
}
}
} else {
if (restrictions[[restr]][3] == "<") {
ineq[[n.ineq]] <- matrix(c((parameters[[indiv]][params == restrictions[[restr]][1]])[1], (parameters[[indiv]][params == restrictions[[restr]][4]])[1]), 1,2)
n.ineq <- n.ineq + 1
}
}
}
ineq <- do.call("rbind", ineq)
} else ineq <- NULL
n.fit <- length(m.fit)
fia.result <- vector('list', n.fit)
for (counter in 1:n.fit) {
fia.result[[counter]] <- bmpt.fia(s, parameters[[m.fit[counter]]], category, n_items[m.fit[counter]], ineq, Sample, multicore = multicore, split = split, mConst = mConst)
}
n.params <- length(unique(parameters[[1]][parameters[[1]] > 0]))
res <- vector('list', n.data)
for (c in 1:n.data) {
res[[c]] <- fia.result[[p.fia[c]]]
res[[c]][["CFIA"]] <- res[[c]][["lnInt"]] + res[[c]][["lnconst"]]+n.params/2*log(n_items[c]/2/pi)
res[[c]][["CI.l"]] <- NA
res[[c]][["CI.u"]] <- NA
}
as.data.frame(do.call('rbind', res))
}
|
library(survRM2)
D = rmst2.sample.data()
nrow(D)
head(D[,1:3])
plot(survfit(Surv(time, status)~arm, data=D), col=c("blue","red"), lwd=2, mark.time=F, xlab="Years",ylab="Probability")
legend("bottomleft", c("Placebo (arm=0)","D-penicillamine (arm=1)"), col=c("blue","red"), lwd=2)
fit=survfit(Surv(D$time[D$arm==1], D$status[D$arm==1])~1)
tau=10
tmp.xx=c(0, fit$time); tmp.yy=c(1, fit$surv) ;
idx=tmp.xx<=tau
y.tau = min(tmp.yy[idx])
xx=c(tmp.xx[idx], tau)
yy=c(tmp.yy[idx], y.tau)
x.step=sort(c(0, tmp.xx, tmp.xx))
y.step=rev(sort(c(1,1,tmp.yy, tmp.yy[-length(tmp.yy)])))
rmst=summary(fit, rmean=10)$table[5]
par(mfrow=c(1,2))
plot(fit, mark.time=F, xlab="Years",ylab="Probability",conf.int=F, lwd=2, main="Restricted mean survival time (RMST)", col="red", cex.main=0.8)
for (i in 1: (length(xx)-1)){
polygon(c(xx[i], xx[i+1], xx[i+1], xx[i]), c(0, 0, yy[i+1], yy[i]), col="pink", density=80, angle=80, lwd=2)
}
lines(x.step, y.step, col="red", lwd=3)
text(5,0.4, paste(round(rmst, digits=2),"years"), cex=0.9)
plot(fit, mark.time=F, xlab="Years",ylab="Probability", conf.int=F, lwd=2, main="Restricted mean time lost (RMTL)", col="red",cex.main=0.8)
for (i in 1: (length(xx)-1)){
polygon(c(xx[i], xx[i+1], xx[i+1], xx[i]), c(yy[i], yy[i+1], 1,1), col="orange", density=80, angle=80, lwd=2)
}
lines(x.step, y.step, col="red", lwd=3)
text(7,0.8, paste(round(tau-rmst, digits=2),"years"), cex=0.9)
time = D$time
status = D$status
arm = D$arm
obj = rmst2(time, status, arm, tau=10)
print(obj)
plot(obj, xlab="Years", ylab="Probability")
x=D[,c(4,6,7)]
x=D[,c(4,6,7)]
head(x)
rmst2(time, status, arm, tau=10, covariates=x)
|
extract_IrradiationTimes <- function(
object,
file.BINX,
recordType = c("irradiation (NA)", "IRSL (UVVIS)", "OSL (UVVIS)", "TL (UVVIS)"),
compatibility.mode = TRUE,
txtProgressBar = TRUE
){
if(is.list(object)){
if(!missing(file.BINX)){
warning("[extract_IrradiationTimes()] argument 'file.BINX' is not supported in the self call mode.",
call. = FALSE)
}
if(is(recordType, "list")){
recordType <-
rep(recordType, length = length(object))
}else{
recordType <-
rep(list(recordType), length = length(object))
}
results <- lapply(1:length(object), function(x) {
extract_IrradiationTimes(
object = object[[x]],
recordType = recordType[[x]],
txtProgressBar = txtProgressBar
)
})
if(length(results) == 0){
return(NULL)
}else{
return(results)
}
}
if(is(object)[1] != "character" & is(object)[1] != "RLum.Analysis"){
stop("[extract_IrradiationTimes()] Input object is neither of type 'character' nor of type 'RLum.Analysis'.", call. = FALSE)
}else if(is(object)[1] == "character"){
file.XSYG <- object
if(file.exists(file.XSYG) == FALSE){
stop("[extract_IrradiationTimes()] Wrong XSYG file name or file does not exsits!", call. = FALSE)
}
if(tail(unlist(strsplit(file.XSYG, split = "\\.")), 1) != "xsyg" &
tail(unlist(strsplit(file.XSYG, split = "\\.")), 1) != "XSYG" ){
stop("[extract_IrradiationTimes()] File is not of type 'XSYG'!", call. = FALSE)
}
if(!missing(file.BINX)){
if(file.exists(file.BINX) == FALSE){
stop("[extract_IrradiationTimes()] Wrong BINX file name or file does not exist!", call. = FALSE)
}
if(tail(unlist(strsplit(file.BINX, split = "\\.")), 1) != "binx" &
tail(unlist(strsplit(file.BINX, split = "\\.")), 1) != "BINX" ){
stop("[extract_IrradiationTimes()] File is not of type 'BINX'!", call. = FALSE)
}
}
temp.XSYG <- read_XSYG2R(file.XSYG, txtProgressBar = txtProgressBar)
if(!missing(file.BINX)){
temp.BINX <- read_BIN2R(file.BINX, txtProgressBar = txtProgressBar)
temp.BINX.dirname <- (dirname(file.XSYG))
}
temp.sequence.list <- list()
for(i in 1:length(temp.XSYG)){
temp.sequence.list[[i]] <- get_RLum(temp.XSYG[[i]]$Sequence.Object,
recordType = recordType,
drop = FALSE)
temp.sequence.position <- as.numeric(as.character(temp.XSYG[[i]]$Sequence.Header["position",]))
}
}else{
temp.sequence.list <- list(object)
}
if(length(temp.sequence.list)>1){
temp.sequence <- merge_RLum(temp.sequence.list)
}else{
temp.sequence <- temp.sequence.list[[1]]
}
STEP <- names_RLum(temp.sequence)
temp.START <- vapply(temp.sequence, function(x){
get_RLum(x, info.object = c("startDate"))
}, character(1))
DURATION.STEP <- vapply(temp.sequence, function(x){
max(get_RLum(x)[,1])
}, numeric(1))
START <- strptime(temp.START, format = "%Y%m%d%H%M%S", tz = "GMT")
END <- START + DURATION.STEP
if(exists("file.XSYG")){
POSITION <- rep(temp.sequence.position, each = length_RLum(temp.sequence))
}else if(!inherits(try(
get_RLum(
get_RLum(temp.sequence, record.id = 1), info.object = "position"),
silent = TRUE), "try-error")){
POSITION <- vapply(temp.sequence, function(x){
get_RLum(x, info.object = c("position"))
}, numeric(1))
}else{
POSITION <- NA
}
temp.results <- data.frame(POSITION,STEP,START,DURATION.STEP,END)
IRR_TIME <- numeric(length = nrow(temp.results))
temp_last <- 0
for(i in 1:nrow(temp.results)){
if(grepl("irradiation", temp.results[["STEP"]][i])) {
temp_last <- temp.results[["DURATION.STEP"]][i]
next()
}
IRR_TIME[i] <- temp_last
}
time.irr.end <- NA
TIMESINCEIRR <- unlist(sapply(1:nrow(temp.results), function(x){
if(grepl("irradiation", temp.results[x,"STEP"])){
time.irr.end<<-temp.results[x,"END"]
return(-1)
}else{
if(is.na(time.irr.end)){
return(-1)
}else{
return(difftime(temp.results[x,"START"],time.irr.end, units = "secs"))
}
}
}))
TIMESINCELAST.STEP <- unlist(sapply(1:nrow(temp.results), function(x){
if(x == 1){
return(0)
}else{
return(difftime(temp.results[x,"START"],temp.results[x-1, "END"], units = "secs"))
}
}))
results <- cbind(temp.results,IRR_TIME, TIMESINCEIRR,TIMESINCELAST.STEP)
if(!missing(file.BINX)){
results.BINX <- results[-which(results[,"STEP"] == "irradiation (NA)"),]
temp.BINX@METADATA[["IRR_TIME"]] <- results.BINX[["IRR_TIME"]]
temp.BINX@METADATA[["TIMESINCEIRR"]] <- results.BINX[["IRR_TIME"]] + results.BINX[["TIMESINCEIRR"]]
if(!missing(file.BINX)){
if(nrow(results.BINX) == nrow(temp.BINX@METADATA)){
try <- write_R2BIN(temp.BINX, version = "06",
file = paste0(file.BINX,"_extract_IrradiationTimes.BINX"),
compatibility.mode = compatibility.mode,
txtProgressBar = txtProgressBar)
if(!inherits(x = try, 'try-error')){
message("[extract_IrradiationTimes()] 'Time Since Irradiation' was redefined in the exported BINX-file to: 'Time Since Irradiation' plus the 'Irradiation Time' to be compatible with the Analyst.")
}
}
}else{
try(
stop("[extract_IrradiationTimes()] XSYG-file and BINX-file did not contain similar entries. BINX-file update skipped!",call. = FALSE))
}
}
return(set_RLum(class = "RLum.Results", data = list(irr.times = results)))
}
|
AutoLightGBMHurdleCARMA <- function(data,
NonNegativePred = FALSE,
Threshold = NULL,
RoundPreds = FALSE,
TrainOnFull = FALSE,
TargetColumnName = 'Target',
DateColumnName = 'DateTime',
HierarchGroups = NULL,
GroupVariables = NULL,
EncodingMethod = "credibility",
TimeWeights = 1,
FC_Periods = 30,
TimeUnit = 'week',
TimeGroups = c('weeks','months'),
NumOfParDepPlots = 10L,
TargetTransformation = FALSE,
Methods = c('BoxCox', 'Asinh', 'Log', 'LogPlus1', 'Sqrt', 'Asin', 'Logit'),
AnomalyDetection = NULL,
XREGS = NULL,
Lags = c(1L:5L),
MA_Periods = c(2L:5L),
SD_Periods = NULL,
Skew_Periods = NULL,
Kurt_Periods = NULL,
Quantile_Periods = NULL,
Quantiles_Selected = c('q5','q95'),
Difference = TRUE,
FourierTerms = 6L,
CalendarVariables = c('second', 'minute', 'hour', 'wday', 'mday', 'yday', 'week', 'wom', 'isoweek', 'month', 'quarter', 'year'),
HolidayVariable = c('USPublicHolidays','EasterGroup','ChristmasGroup','OtherEcclesticalFeasts'),
HolidayLookback = NULL,
HolidayLags = 1L,
HolidayMovingAverages = 1L:2L,
TimeTrendVariable = FALSE,
ZeroPadSeries = NULL,
DataTruncate = FALSE,
SplitRatios = c(0.7, 0.2, 0.1),
PartitionType = 'timeseries',
Timer = TRUE,
DebugMode = FALSE,
EvalMetric = 'RMSE',
GridTune = FALSE,
PassInGrid = NULL,
ModelCount = 100,
MaxRunsWithoutNewWinner = 50,
MaxRunMinutes = 24L*60L,
input_model = list('classifier' = NULL, 'regression' = NULL),
task = list('classifier' = 'train', 'regression' = 'train'),
device_type = list('classifier' = 'CPU', 'regression' = 'CPU'),
objective = list('classifier' = 'binary', 'regression' = 'regression'),
metric = list('classifier' = 'binary_logloss', 'regression' = 'rmse'),
boosting = list('classifier' = 'gbdt', 'regression' = 'gbdt'),
LinearTree = list('classifier' = FALSE, 'regression' = FALSE),
Trees = list('classifier' = 1000L, 'regression' = 1000L),
eta = list('classifier' = NULL, 'regression' = NULL),
num_leaves = list('classifier' = 31, 'regression' = 31),
deterministic = list('classifier' = TRUE, 'regression' = TRUE),
force_col_wise = list('classifier' = FALSE, 'regression' = FALSE),
force_row_wise = list('classifier' = FALSE, 'regression' = FALSE),
max_depth = list('classifier' = NULL, 'regression' = NULL),
min_data_in_leaf = list('classifier' = 20, 'regression' = 20),
min_sum_hessian_in_leaf = list('classifier' = 0.001, 'regression' = 0.001),
bagging_freq = list('classifier' = 0, 'regression' = 0),
bagging_fraction = list('classifier' = 1.0, 'regression' = 1.0),
feature_fraction = list('classifier' = 1.0, 'regression' = 1.0),
feature_fraction_bynode = list('classifier' = 1.0, 'regression' = 1.0),
extra_trees = list('classifier' = FALSE, 'regression' = FALSE),
early_stopping_round = list('classifier' = 10, 'regression' = 10),
first_metric_only = list('classifier' = TRUE, 'regression' = TRUE),
max_delta_step = list('classifier' = 0.0, 'regression' = 0.0),
lambda_l1 = list('classifier' = 0.0, 'regression' = 0.0),
lambda_l2 = list('classifier' = 0.0, 'regression' = 0.0),
linear_lambda = list('classifier' = 0.0, 'regression' = 0.0),
min_gain_to_split = list('classifier' = 0, 'regression' = 0),
drop_rate_dart = list('classifier' = 0.10, 'regression' = 0.10),
max_drop_dart = list('classifier' = 50, 'regression' = 50),
skip_drop_dart = list('classifier' = 0.50, 'regression' = 0.50),
uniform_drop_dart = list('classifier' = FALSE, 'regression' = FALSE),
top_rate_goss = list('classifier' = FALSE, 'regression' = FALSE),
other_rate_goss = list('classifier' = FALSE, 'regression' = FALSE),
monotone_constraints = list('classifier' = NULL, 'regression' = NULL),
monotone_constraints_method = list('classifier' = 'advanced', 'regression' = 'advanced'),
monotone_penalty = list('classifier' = 0.0, 'regression' = 0.0),
forcedsplits_filename = list('classifier' = NULL, 'regression' = NULL),
refit_decay_rate = list('classifier' = 0.90, 'regression' = 0.90),
path_smooth = list('classifier' = 0.0, 'regression' = 0.0),
max_bin = list('classifier' = 255, 'regression' = 255),
min_data_in_bin = list('classifier' = 3, 'regression' = 3),
data_random_seed = list('classifier' = 1, 'regression' = 1),
is_enable_sparse = list('classifier' = TRUE, 'regression' = TRUE),
enable_bundle = list('classifier' = TRUE, 'regression' = TRUE),
use_missing = list('classifier' = TRUE, 'regression' = TRUE),
zero_as_missing = list('classifier' = FALSE, 'regression' = FALSE),
two_round = list('classifier' = FALSE, 'regression' = FALSE),
convert_model = list('classifier' = NULL, 'regression' = NULL),
convert_model_language = list('classifier' = "cpp", 'regression' = "cpp"),
boost_from_average = list('classifier' = TRUE, 'regression' = TRUE),
is_unbalance = list('classifier' = FALSE, 'regression' = FALSE),
scale_pos_weight = list('classifier' = 1.0, 'regression' = 1.0),
is_provide_training_metric = list('classifier' = TRUE, 'regression' = TRUE),
eval_at = list('classifier' = c(1,2,3,4,5), 'regression' = c(1,2,3,4,5)),
num_machines = list('classifier' = 1, 'regression' = 1),
gpu_platform_id = list('classifier' = -1, 'regression' = -1),
gpu_device_id = list('classifier' = -1, 'regression' = -1),
gpu_use_dp = list('classifier' = TRUE, 'regression' = TRUE),
num_gpu = list('classifier' = 1, 'regression' = 1)) {
Args <- CARMA_Define_Args(TimeUnit=TimeUnit,TimeGroups=TimeGroups,HierarchGroups=HierarchGroups,GroupVariables=GroupVariables,FC_Periods=FC_Periods,PartitionType=PartitionType,TrainOnFull=TrainOnFull,SplitRatios=SplitRatios)
IndepentVariablesPass <- Args$IndepentVariablesPass
HoldOutPeriods <- Args$HoldOutPeriods
HierarchGroups <- Args$HierarchGroups
GroupVariables <- Args$GroupVariables
TimeGroups <- Args$TimeGroups
FC_Periods <- Args$FC_Periods
TimeGroup <- Args$TimeGroupPlaceHolder
TimeUnit <- Args$TimeUnit
if(!data.table::is.data.table(data)) data.table::setDT(data)
if(!is.null(XREGS) && !data.table::is.data.table(XREGS)) data.table::setDT(XREGS)
if(!TrainOnFull) HoldOutPeriods <- round(SplitRatios[2L] * length(unique(data[[eval(DateColumnName)]])), 0L)
if(DebugMode) print('Feature Engineering: Add Zero Padding for missing dates----')
if(data[, .N] != unique(data)[, .N]) stop('There is duplicates in your data')
if(!is.null(ZeroPadSeries)) {
data <- TimeSeriesFill(data, DateColumnName=eval(DateColumnName), GroupVariables=GroupVariables, TimeUnit=TimeUnit, FillType=ZeroPadSeries, MaxMissingPercent=0.0, SimpleImpute=FALSE)
data <- ModelDataPrep(data=data, Impute=TRUE, CharToFactor=FALSE, FactorToChar=FALSE, IntToNumeric=FALSE, LogicalToBinary=FALSE, DateToChar=FALSE, RemoveDates=FALSE, MissFactor='0', MissNum=0, IgnoreCols=NULL)
} else {
temp <- TimeSeriesFill(data, DateColumnName=eval(DateColumnName), GroupVariables=GroupVariables, TimeUnit=TimeUnit, FillType='maxmax', MaxMissingPercent=0.25, SimpleImpute=FALSE)
if(temp[,.N] != data[,.N]) stop('There are missing dates in your series. You can utilize the ZeroPadSeries argument to handle this or manage it before running the function')
}
if(DebugMode) print('
Output <- CarmaFCHorizon(data.=data, XREGS.=XREGS, TrainOnFull.=TrainOnFull, Difference.= Difference, FC_Periods.=FC_Periods, HoldOutPeriods.=HoldOutPeriods, DateColumnName.=DateColumnName)
FC_Periods <- Output$FC_Periods
HoldOutPeriods <- Output$HoldOutPeriods; rm(Output)
if(DebugMode) print('merging xregs to data')
if(!is.null(XREGS)) {
Output <- CarmaMergeXREGS(data.=data, XREGS.=XREGS, TargetColumnName.=TargetColumnName, GroupVariables.=GroupVariables, DateColumnName.=DateColumnName)
data <- Output$data; Output$data <- NULL
XREGS <- Output$XREGS; rm(Output)
}
if(DebugMode) print('
if(!is.null(GroupVariables)) {
data.table::setkeyv(x = data, cols = c(eval(GroupVariables), eval(DateColumnName)))
if(!is.null(XREGS)) data.table::setkeyv(x = XREGS, cols = c('GroupVar', eval(DateColumnName)))
} else {
data.table::setkeyv(x = data, cols = c(eval(DateColumnName)))
if(!is.null(XREGS)) data.table::setkeyv(x = XREGS, cols = c(eval(DateColumnName)))
}
if(DebugMode) print('Data Wrangling: Remove Unnecessary Columns ----')
data <- CarmaSubsetColumns(data.=data, XREGS.=XREGS, GroupVariables.=GroupVariables, DateColumnName.=DateColumnName, TargetColumnName.=TargetColumnName)
if(DebugMode) print('Feature Engineering: Concat Categorical Columns - easier to deal with this way ----')
if(!is.null(GroupVariables)) {
data[, GroupVar := do.call(paste, c(.SD, sep = ' ')), .SDcols = GroupVariables]
if(length(GroupVariables) > 1L) data[, eval(GroupVariables) := NULL] else if(GroupVariables != 'GroupVar') data[, eval(GroupVariables) := NULL]
}
if(DebugMode) print('Variables for Program: Store unique values of GroupVar in GroupVarVector ----')
if(!is.null(GroupVariables)) {
GroupVarVector <- data.table::as.data.table(x = unique(as.character(data[['GroupVar']])))
data.table::setnames(GroupVarVector, 'V1', 'GroupVar')
}
if(DebugMode) print('Data Wrangling: Standardize column ordering ----')
if(!is.null(GroupVariables)) data.table::setcolorder(data, c('GroupVar', eval(DateColumnName), eval(TargetColumnName))) else data.table::setcolorder(data, c(eval(DateColumnName), eval(TargetColumnName)))
if(DebugMode) print('Data Wrangling: Convert DateColumnName to Date or POSIXct ----')
Output <- CarmaDateStandardize(data.=data, XREGS.=NULL, DateColumnName.=DateColumnName, TimeUnit.=TimeUnit)
data <- Output$data; Output$data <- NULL
XREGS <- Output$XREGS; rm(Output)
if(DebugMode) print('Data Wrangling: Ensure TargetColumnName is Numeric ----')
if(!is.numeric(data[[eval(TargetColumnName)]])) data[, eval(TargetColumnName) := as.numeric(get(TargetColumnName))]
if(DebugMode) print('Variables for Program: Store number of data partitions in NumSets ----')
NumSets <- length(SplitRatios)
if(DebugMode) print('Variables for Program: Store Maximum Value of TargetColumnName in val ----')
if(!is.null(Lags)) {
if(is.list(Lags) && is.list(MA_Periods)) val <- max(unlist(Lags), unlist(MA_Periods)) else val <- max(Lags, MA_Periods)
}
if(DebugMode) print('Data Wrangling: Sort data by GroupVar then DateColumnName ----')
if(!is.null(GroupVariables)) data <- data[order(GroupVar, get(DateColumnName))] else data <- data[order(get(DateColumnName))]
if(DebugMode) print('Feature Engineering: Fourier Features ----')
Output <- CarmaFourier(data.=data, XREGS.=XREGS, FourierTerms.=FourierTerms, TimeUnit.=TimeUnit, TargetColumnName.=TargetColumnName, GroupVariables.=GroupVariables, DateColumnName.=DateColumnName, HierarchGroups.=HierarchGroups)
FourierTerms <- Output$FourierTerms; Output$FourierTerms <- NULL
FourierFC <- Output$FourierFC; Output$FourierFC <- NULL
data <- Output$data; rm(Output)
if(DebugMode) print('Feature Engineering: Add Create Calendar Variables ----')
if(!is.null(CalendarVariables)) data <- CreateCalendarVariables(data=data, DateCols=eval(DateColumnName), AsFactor=FALSE, TimeUnits=CalendarVariables)
if(DebugMode) print('Feature Engineering: Add Create Holiday Variables ----')
if(!is.null(HolidayVariable)) {
data <- CreateHolidayVariables(data, DateCols = eval(DateColumnName), LookbackDays = if(!is.null(HolidayLookback)) HolidayLookback else LB(TimeUnit), HolidayGroups = HolidayVariable, Holidays = NULL)
if(!(tolower(TimeUnit) %chin% c('1min','5min','10min','15min','30min','hour'))) {
data[, eval(DateColumnName) := lubridate::as_date(get(DateColumnName))]
} else {
data[, eval(DateColumnName) := as.POSIXct(get(DateColumnName))]
}
}
if(DebugMode) print('Anomaly detection by Group and Calendar Vars ----')
if(!is.null(AnomalyDetection)) {
data <- GenTSAnomVars(
data = data, ValueCol = eval(TargetColumnName),
GroupVars = if(!is.null(CalendarVariables) && !is.null(GroupVariables)) c('GroupVar', paste0(DateColumnName, '_', CalendarVariables[1])) else if(!is.null(GroupVariables)) 'GroupVar' else NULL,
DateVar = eval(DateColumnName), KeepAllCols = TRUE, IsDataScaled = FALSE,
HighThreshold = AnomalyDetection$tstat_high,
LowThreshold = AnomalyDetection$tstat_low)
data[, paste0(eval(TargetColumnName), '_zScaled') := NULL]
data[, ':=' (RowNumAsc = NULL, CumAnomHigh = NULL, CumAnomLow = NULL, AnomHighRate = NULL, AnomLowRate = NULL)]
}
if(DebugMode) print('Feature Engineering: Add Target Transformation ----')
if(TargetTransformation) {
TransformResults <- AutoTransformationCreate(data, ColumnNames=TargetColumnName, Methods=Methods, Path=NULL, TransID='Trans', SaveOutput=FALSE)
data <- TransformResults$Data; TransformResults$Data <- NULL
TransformObject <- TransformResults$FinalResults; rm(TransformResults)
} else {
TransformObject <- NULL
}
if(DebugMode) print('Copy data for non grouping + difference ----')
if(is.null(GroupVariables) && Difference) antidiff <- data.table::copy(data[, .SD, .SDcols = c(eval(TargetColumnName),eval(DateColumnName))])
if(DebugMode) print('Feature Engineering: Add Difference Data ----')
Output <- CarmaDifferencing(GroupVariables.=GroupVariables, Difference.=Difference, data.=data, TargetColumnName.=TargetColumnName, FC_Periods.=FC_Periods)
data <- Output$data; Output$data <- NULL
dataStart <- Output$dataStart; Output$dataStart <- NULL
FC_Periods <- Output$FC_Periods; Output$FC_Periods <- NULL
Train <- Output$Train; rm(Output)
if(DebugMode) print('Feature Engineering: Lags and Rolling Stats ----')
Output <- CarmaTimeSeriesFeatures(data.=data, TargetColumnName.=TargetColumnName, DateColumnName.=DateColumnName, GroupVariables.=GroupVariables, HierarchGroups.=HierarchGroups, Difference.=Difference, TimeGroups.=TimeGroups, TimeUnit.=TimeUnit, Lags.=Lags, MA_Periods.=MA_Periods, SD_Periods.=SD_Periods, Skew_Periods.=Skew_Periods, Kurt_Periods.=Kurt_Periods, Quantile_Periods.=Quantile_Periods, Quantiles_Selected.=Quantiles_Selected, HolidayVariable.=HolidayVariable, HolidayLags.=HolidayLags, HolidayMovingAverages.=HolidayMovingAverages, DebugMode.=DebugMode)
IndependentSupplyValue <- Output$IndependentSupplyValue; Output$IndependentSupplyValue <- NULL
HierarchSupplyValue <- Output$HierarchSupplyValue; Output$HierarchSupplyValue <- NULL
GroupVarVector <- Output$GroupVarVector; Output$GroupVarVector <- NULL
Categoricals <- Output$Categoricals; Output$Categoricals <- NULL
data <- Output$data; rm(Output)
if(!is.null(GroupVariables) && !'GroupVar' %chin% names(data)) data[, GroupVar := do.call(paste, c(.SD, sep = ' ')), .SDcols = c(GroupVariables)]
if(DebugMode) print('Data Wrangling: ModelDataPrep() to prepare data ----')
data <- ModelDataPrep(data=data, Impute=TRUE, IntToNumeric=TRUE, DateToChar=FALSE, FactorToChar=FALSE, CharToFactor=TRUE, LogicalToBinary=FALSE, RemoveDates=FALSE, MissFactor='0', MissNum=-1, IgnoreCols=NULL)
if(DebugMode) print('Data Wrangling: Remove dates with imputed data from the DT_GDL_Feature_Engineering() features ----')
if(DataTruncate && !is.null(Lags)) data <- CarmaTruncateData(data.=data, DateColumnName.=DateColumnName, TimeUnit.=TimeUnit)
if(DebugMode) print('Feature Engineering: Add TimeTrend Variable----')
if(TimeTrendVariable) {
if(!is.null(GroupVariables)) data[, TimeTrend := seq_len(.N), by = 'GroupVar'] else data[, TimeTrend := seq_len(.N)]
}
if(DebugMode) print('Create TimeWeights ----')
train <- CarmaTimeWeights(train.=data, TimeWeights.=TimeWeights, GroupVariables.=GroupVariables, DateColumnName.=DateColumnName)
FutureDateData <- unique(data[, get(DateColumnName)])
if(DebugMode) print('Data Wrangling: Partition data with AutoDataPartition()----')
Output <- CarmaPartition(data.=data, SplitRatios.=if(TrainOnFull) NULL else SplitRatios, TrainOnFull.=TrainOnFull, NumSets.=NumSets, PartitionType.=PartitionType, GroupVariables.=GroupVariables, DateColumnName.=DateColumnName)
train <- Output$train; Output$train <- NULL
valid <- Output$valid; Output$valid <- NULL
data <- Output$data; Output$data <- NULL
test <- Output$test; rm(Output)
if(DebugMode) print('Variables for CARMA function:IDcols----')
IDcols <- names(data)[which(names(data) %chin% DateColumnName)]
IDcols <- c(IDcols, names(data)[which(names(data) == TargetColumnName)])
if(DebugMode) print('Data Wrangling: copy data or train for later in function since AutoRegression will modify data and train ----')
if(!is.null(GroupVariables)) data.table::setorderv(x = data, cols = c('GroupVar',eval(DateColumnName)), order = c(1,1)) else data.table::setorderv(x = data, cols = c(eval(DateColumnName)), order = c(1))
Step1SCore <- data.table::copy(data)
if(DebugMode) print('Define ML args ----')
Output <- CarmaFeatures(data.=data, train.=train, XREGS.=XREGS, Difference.=Difference, TargetColumnName.=TargetColumnName, DateColumnName.=DateColumnName, GroupVariables.=GroupVariables)
ModelFeatures <- Output$ModelFeatures
TargetVariable <- Output$TargetVariable; rm(Output)
if(!is.null(SplitRatios) || !TrainOnFull) TOF <- FALSE else TOF <- TRUE
if(DebugMode) print('Run AutoXGBoostHurdleModel() and return list of ml objects ----')
TestModel <- AutoLightGBMHurdleModel(
ModelID = 'ModelTest',
SaveModelObjects = FALSE,
ReturnModelObjects = TRUE,
data = data.table::copy(train),
TrainOnFull = TrainOnFull,
ValidationData = data.table::copy(valid),
TestData = data.table::copy(test),
Buckets = 0L,
TargetColumnName = TargetVariable,
FeatureColNames = ModelFeatures,
PrimaryDateColumn = eval(DateColumnName),
WeightsColumnName = if('Weights' %chin% names(train)) 'Weights' else NULL,
IDcols = IDcols,
EncodingMethod = EncodingMethod,
DebugMode = DebugMode,
Paths = getwd(),
MetaDataPaths = NULL,
TransformNumericColumns = NULL,
Methods = NULL,
ClassWeights = c(1,1),
SplitRatios = c(0.70, 0.20, 0.10),
NumOfParDepPlots = NumOfParDepPlots,
PassInGrid = PassInGrid,
GridTune = GridTune,
BaselineComparison = 'default',
MaxModelsInGrid = 500L,
MaxRunsWithoutNewWinner = 100L,
MaxRunMinutes = 60*60,
input_model = input_model,
task = task,
device_type = device_type,
objective = objective,
metric = metric,
boosting = boosting,
LinearTree = LinearTree,
Trees = Trees,
eta = eta,
num_leaves = num_leaves,
deterministic = deterministic,
force_col_wise = force_col_wise,
force_row_wise = force_row_wise,
max_depth = max_depth,
min_data_in_leaf = min_data_in_leaf,
min_sum_hessian_in_leaf = min_sum_hessian_in_leaf,
bagging_freq = bagging_freq,
bagging_fraction = bagging_fraction,
feature_fraction = feature_fraction,
feature_fraction_bynode = feature_fraction_bynode,
extra_trees = extra_trees,
early_stopping_round = early_stopping_round,
first_metric_only = first_metric_only,
max_delta_step = max_delta_step,
lambda_l1 = lambda_l1,
lambda_l2 = lambda_l2,
linear_lambda = linear_lambda,
min_gain_to_split = min_gain_to_split,
drop_rate_dart = drop_rate_dart,
max_drop_dart = max_drop_dart,
skip_drop_dart = skip_drop_dart,
uniform_drop_dart = uniform_drop_dart,
top_rate_goss = top_rate_goss,
other_rate_goss = other_rate_goss,
monotone_constraints = monotone_constraints,
monotone_constraints_method = monotone_constraints_method,
monotone_penalty = monotone_penalty,
forcedsplits_filename = forcedsplits_filename,
refit_decay_rate = refit_decay_rate,
path_smooth = path_smooth,
max_bin = max_bin,
min_data_in_bin = min_data_in_bin,
data_random_seed = data_random_seed,
is_enable_sparse = is_enable_sparse,
enable_bundle = enable_bundle,
use_missing = use_missing,
zero_as_missing = zero_as_missing,
two_round = two_round,
convert_model = convert_model,
convert_model_language = convert_model_language,
boost_from_average = boost_from_average,
is_unbalance = is_unbalance,
scale_pos_weight = scale_pos_weight,
is_provide_training_metric = is_provide_training_metric,
eval_at = eval_at,
num_machines = num_machines,
gpu_platform_id = gpu_platform_id,
gpu_device_id = gpu_device_id,
gpu_use_dp = gpu_use_dp,
num_gpu = num_gpu)
if(!is.null(Threshold)) {
threshold <- TestModel$ClassifierModel$EvaluationMetrics
col <- names(threshold)[grep(pattern = Threshold, x = names(threshold))]
Threshold <- threshold[, .SD, .SDcols = c('Threshold', eval(col))][order(-get(col))][1,1][[1]]
}
if(!TrainOnFull) return(TestModel)
if(DebugMode) options(warn = 2)
if(DebugMode) print('Variable for interation counts: max number of rows in Step1SCore data.table across all group ----')
N <- CarmaRecordCount(GroupVariables.=GroupVariables,Difference.=Difference, Step1SCore.=Step1SCore)
if(DebugMode) print('ARMA PROCESS FORECASTING----')
for(i in seq_len(FC_Periods+1L)) {
if(DebugMode) print('Row counts----')
if(i != 1) N <- as.integer(N + 1L)
if(DebugMode) print('Machine Learning: Generate predictions----')
if(i == 1L) {
if(!is.null(GroupVariables)) {
print("here yo")
print(Step1SCore)
Preds <- AutoLightGBMHurdleModelScoring(
TestData = data.table::copy(Step1SCore),
Path = NULL,
ModelID = 'ModelTest',
ModelList = TestModel$ModelList,
ArgsList = TestModel$ArgsList,
Threshold = Threshold,
CARMA = TRUE)
data.table::set(Preds, j = c(names(Preds)[2L:5L]), value = NULL)
data.table::set(Preds, j = eval(DateColumnName), value = NULL)
data.table::setnames(Preds, 'UpdatedPrediction', 'Predictions')
data.table::setcolorder(Preds, c(2L,1L,3L:ncol(Preds)))
if(RoundPreds) Preds[, Predictions := round(Predictions)]
} else {
Preds <- AutoLightGBMHurdleModelScoring(
TestData = data.table::copy(Step1SCore),
Path = NULL,
ModelID = 'ModelTest',
ModelList = TestModel$ModelList,
ArgsList = TestModel$ArgsList,
Threshold = Threshold,
CARMA = TRUE)
data.table::set(Preds, j = c(names(Preds)[2L:5L]), value = NULL)
if(DateColumnName %chin% names(Preds)) data.table::set(Preds, j = eval(DateColumnName), value = NULL)
data.table::setnames(Preds, 'UpdatedPrediction', 'Predictions')
data.table::setcolorder(Preds, c(2L,1L,3L:ncol(Preds)))
if(RoundPreds) Preds[, Predictions := round(Predictions)]
}
if(Difference) {
if(eval(TargetColumnName) %chin% names(Step1SCore) && eval(TargetColumnName) %chin% names(Preds)) {
data.table::set(Preds, j = eval(TargetColumnName), value = NULL)
}
if(eval(DateColumnName) %chin% names(Step1SCore)) data.table::set(Step1SCore, j = eval(DateColumnName), value = NULL)
if(eval(DateColumnName) %chin% names(Preds)) data.table::set(Preds, j = eval(DateColumnName), value = NULL)
if(!is.null(GroupVariables)) {
UpdateData <- cbind(FutureDateData, Step1SCore[, .SD, .SDcols = eval(TargetColumnName)],Preds)
} else {
UpdateData <- cbind(FutureDateData[2L:(nrow(Step1SCore)+1L)], Step1SCore[, .SD, .SDcols = eval(TargetColumnName)],Preds)
}
data.table::setnames(UpdateData, 'FutureDateData', eval(DateColumnName))
} else {
if(NonNegativePred) Preds[, Predictions := data.table::fifelse(Predictions < 0.5, 0, Predictions)]
UpdateData <- cbind(FutureDateData[1L:N], Preds)
data.table::setnames(UpdateData, c('V1'), c(eval(DateColumnName)))
}
} else {
if(!is.null(GroupVariables)) {
if(Difference) IDcols = 'ModTarget' else IDcols <- eval(TargetColumnName)
if(!is.null(HierarchGroups)) {
temp <- data.table::copy(UpdateData[, ID := seq_len(.N), by = c(eval(GroupVariables))])
temp <- temp[ID == N][, ID := NULL]
} else {
temp <- data.table::copy(UpdateData[, ID := seq_len(.N), by = 'GroupVar'])
temp <- temp[ID == N][, ID := NULL]
}
if('Predictions' %chin% names(temp)) data.table::set(temp, j = 'Predictions', value = NULL)
Preds <- AutoLightGBMHurdleModelScoring(
TestData = temp,
Path = NULL,
ModelID = 'ModelTest',
ModelList = TestModel$ModelList,
ArgsList = TestModel$ArgsList,
Threshold = Threshold,
CARMA = TRUE)
Preds[, (setdiff(names(Preds),'UpdatedPrediction')) := NULL]
data.table::setnames(Preds, 'UpdatedPrediction', 'Predictions')
if(RoundPreds) Preds[, Predictions := round(Predictions)]
if(DebugMode) print('Update data group case----')
data.table::setnames(Preds, 'Predictions', 'Preds')
if(NonNegativePred & !Difference) Preds[, Preds := data.table::fifelse(Preds < 0.5, 0, Preds)]
Preds <- cbind(UpdateData[ID == N], Preds)
if(Difference) Preds[, ModTarget := Preds][, eval(TargetColumnName) := Preds] else Preds[, eval(TargetColumnName) := Preds]
Preds[, Predictions := Preds][, Preds := NULL]
UpdateData <- UpdateData[ID != N]
if(any(class(UpdateData$Date) %chin% c('POSIXct','POSIXt')) && any(class(Preds$Date) == 'Date')) UpdateData[, eval(DateColumnName) := as.Date(get(DateColumnName))]
UpdateData <- data.table::rbindlist(list(UpdateData, Preds))
if(Difference) UpdateData[ID %in% c(N-1,N), eval(TargetColumnName) := cumsum(get(TargetColumnName)), by = 'GroupVar']
UpdateData[, ID := NULL]
} else {
temp <- UpdateData[.N, .SD, .SDcols = c(setdiff(names(UpdateData), "Predictions"))]
Preds <- AutoLightGBMHurdleModelScoring(
TestData = temp,
Path = NULL,
ModelID = 'ModelTest',
ModelList = TestModel$ModelList,
ArgsList = TestModel$ArgsList,
Threshold = Threshold,
CARMA = TRUE)
Preds[, (setdiff(names(Preds),'UpdatedPrediction')) := NULL]
data.table::setnames(Preds, 'UpdatedPrediction', 'Predictions')
if(RoundPreds) Preds[, Predictions := round(Predictions)]
if(DebugMode) print('Update data non-group case----')
data.table::set(UpdateData, i = UpdateData[, .N], j = which(names(UpdateData) %chin% c(TargetColumnName, "Predictions")), value = Preds[[1L]])
}
}
if(i != FC_Periods+1L) {
if(DebugMode) print('Timer----')
if(Timer) if(i != 1) print(paste('Forecast future step: ', i-1))
if(Timer) starttime <- Sys.time()
if(DebugMode) print('Create single future record ----')
CalendarFeatures <- NextTimePeriod(UpdateData.=UpdateData, TimeUnit.=TimeUnit, DateColumnName.=DateColumnName)
if(DebugMode) print('Update feature engineering ----')
UpdateData <- UpdateFeatures(UpdateData.=UpdateData, GroupVariables.=GroupVariables, CalendarFeatures.=CalendarFeatures, CalendarVariables.=CalendarVariables, GroupVarVector.=GroupVarVector, DateColumnName.=DateColumnName, XREGS.=XREGS, FourierTerms.=FourierTerms, FourierFC.=FourierFC, TimeGroups.=TimeGroups, TimeTrendVariable.=TimeTrendVariable, N.=N, TargetColumnName.=TargetColumnName, HolidayVariable.=HolidayVariable, HolidayLookback.=HolidayLookback, TimeUnit.=TimeUnit, AnomalyDetection.=AnomalyDetection, i.=i)
if(DebugMode) print('Update Lags and MAs ----')
UpdateData <- CarmaRollingStatsUpdate(ModelType='catboost', DebugMode.=DebugMode, UpdateData.=UpdateData, GroupVariables.=GroupVariables, Difference.=Difference, CalendarVariables.=CalendarVariables, HolidayVariable.=HolidayVariable, IndepVarPassTRUE.=IndepentVariablesPass, data.=data, CalendarFeatures.=CalendarFeatures, XREGS.=XREGS, HierarchGroups.=HierarchGroups, GroupVarVector.=GroupVarVector, TargetColumnName.=TargetColumnName, DateColumnName.=DateColumnName, Preds.=Preds, HierarchSupplyValue.=HierarchSupplyValue, IndependentSupplyValue.=IndependentSupplyValue, TimeUnit.=TimeUnit, TimeGroups.=TimeGroups, Lags.=Lags, MA_Periods.=MA_Periods, SD_Periods.=SD_Periods, Skew_Periods.=Skew_Periods, Kurt_Periods.=Kurt_Periods, Quantile_Periods.=Quantile_Periods, Quantiles_Selected.=Quantiles_Selected, HolidayLags.=HolidayLags, HolidayMovingAverages.=HolidayMovingAverages)
if("Weights" %chin% names(UpdateData)) data.table::set(UpdateData, i = N+1L, j = "Weights", value = 1.0)
if(Timer) endtime <- Sys.time()
if(Timer && i != 1) print(endtime - starttime)
}
}
gc()
if(DebugMode) print('Return data prep ----')
Output <- CarmaReturnDataPrep(UpdateData.=UpdateData, FutureDateData.=FutureDateData, dataStart.=dataStart, DateColumnName.=DateColumnName, TargetColumnName.=TargetColumnName, GroupVariables.=GroupVariables, Difference.=Difference, TargetTransformation.=TargetTransformation, TransformObject.=TransformObject, NonNegativePred.=NonNegativePred)
UpdateData <- Output$UpdateData; Output$UpdateData <- NULL
TransformObject <- Output$TransformObject; rm(Output)
if(is.null(GroupVariables) && "Predictions0" %chin% names(UpdateData)) data.table::set(UpdateData, j = 'Predictions0', value = NULL)
return(list(
Forecast = UpdateData,
ModelInformation = TestModel,
TransformationDetail = if(exists('TransformObject') && !is.null(TransformObject)) TransformObject else NULL))
}
|
context("getTimezone()")
testthat::test_that("get functions return correct name", {
testthat::expect_match(getTimezone(2, 47), "Europe/Paris")
testthat::expect_match(getTimezone(-80, 40), "America/New_York")
testthat::expect_match(getTimezone(c(120,-7), c(-1.5,15)), "Asia/Makassar|Africa/Bamako")
})
testthat::test_that("subsetting with countryCodes works", {
testthat::expect_match(getTimezone(2, 47), "Europe/Paris")
testthat::expect_match(getTimezone(2, 47, countryCodes = c("FR")), "Europe/Paris")
testthat::expect_match(getTimezone(2, 47, countryCodes = "FR"), "Europe/Paris")
})
testthat::test_that("allData returns are correct dimension and type", {
testthat::expect_s3_class(getTimezone(2, 47, allData = TRUE), "data.frame")
testthat::expect_equal(dim(getTimezone(2, 47, allData = TRUE)), c(1,9))
testthat::expect_s3_class(getTimezone(c(120,-17), c(-1.5,15), allData = TRUE), "data.frame")
testthat::expect_equal(dim(getTimezone(c(120,-17), c(-1.5,15), allData = TRUE)), c(2,9))
})
testthat::test_that("getPolygonID handles errors correctly", {
testthat::expect_error(getPolygonID(iris))
testthat::expect_is(getPolygonID(SimpleTimezones), "character")
testthat::expect_is(getPolygonID(SimpleCountries), "character")
})
|
rmix.norm <-
function (n,alpha,mu,sigma=rep(1,length(alpha)))
{
m=length(alpha)
alpha=alpha/sum(alpha)
data=c()
nindex=rmultinom(1,n,alpha)
for( i in 1:m)
data=c(data,rnorm(nindex[i],mu[i],sigma[i]))
data
}
|
check_dates <- function(x, error_on_NA = FALSE, ...) {
if (is.null(x)) {
stop("dates is NULL", call. = FALSE)
}
if (is.character(x)) {
x <- as.Date(x, ...)
}
not_finite <- !is.finite(x)
if (sum(not_finite) > 0) {
x[not_finite] <- NA
}
if (any(is.na(x)) && error_on_NA) {
msg <- "NA detected in the dates"
stop(msg, call. = FALSE)
}
if (sum(!is.na(x)) < 1) {
stop("At least one (non-NA) date must be provided", call. = FALSE)
}
if (inherits(x, "Date")) {
check_timespan(x)
return(x)
}
if (inherits(x, "POSIXt")) {
check_timespan(x)
return(x)
}
if (is.integer(x)) {
return(x)
}
if (is.numeric(x)) {
x_ori <- x
x <- as.integer(floor(x))
if (!isTRUE(note <- all.equal(x, x_ori))) {
msg <- paste0(
"Flooring from non-integer date caused approximations:\n",
note)
warning(msg, call. = FALSE)
}
return(x)
}
formats <- c("Date", "POSIXct", "integer", "numeric", "character")
msg <- paste0(
"Input could not be converted to date. Accepted formats are:\n",
paste(formats, collapse = ", "))
stop(msg)
}
|
jt <- function(x, evidence = NULL, flow = "sum", propagate = "full") UseMethod("jt")
jt.charge <- function(x, evidence = NULL, flow = "sum", propagate = "full") {
if (!attr(x, "cpts_initialized")) {
stop("The CPTs are not yet initialized. Use either 'set_evidence' or 'initialize'.")
}
if (!is.null(evidence)) {
if (!valid_evidence(attr(x, "dim_names"), evidence)) {
stop("evidence is not on correct form", call. = FALSE)
}
attr(x, "evidence") <- c(attr(x, "evidence"), evidence)
}
j <- new_jt(x, evidence, flow)
attr(j, "propagated") <- "no"
attr(j, "type") <- ifelse(inherits(x, "bn"), "bn", "mrf")
if (length(j$charge$C) == 1L && attr(j, "flow") == "max") {
max_cell <- sparta::which_max_cell(j$charge$C$C1)
attr(j, "mpe")[names(max_cell)] <- max_cell
}
if (propagate == "no") {
return(j)
} else if (propagate == "collect") {
m <- send_messages(j)
while (attr(m, "direction") != "distribute") m <- send_messages(m)
attr(m, "propagated") <- "collect"
return(m)
} else {
m <- send_messages(j)
while (attr(m, "direction") != "full") m <- send_messages(m)
attr(m, "propagated") <- "full"
if (attr(m, "inconsistencies")) {
m$charge$C <- lapply(m$charge$C, sparta::normalize)
m$charge$S <- lapply(m$charge$S, function(s) {
if (is.null(s) || is_scalar(s)) return(s)
sparta::normalize(s)
})
}
return(m)
}
stop("propagate must be either 'no', 'collect' or full", call. = TRUE)
}
propagate <- function(x, prop = "full") UseMethod("propagate")
propagate.jt <- function(x, prop = "full") {
if (prop == "collect") {
if (attr(x, "propagated") == "collect") return(x)
if (attr(x, "propagated") == "full") {
stop("the junction tree is already propageted fully", call. = FALSE)
}
m <- send_messages(x)
while (attr(m, "direction") != "distribute") m <- send_messages(m)
attr(m, "propagated") <- "collect"
return(m)
} else if (prop == "full") {
if (attr(x, "propagated") == "full") return(x)
m <- send_messages(x)
while (attr(m, "direction") != "full") m <- send_messages(m)
attr(m, "propagated") <- "full"
if (attr(m, "inconsistencies")) {
m$charge$C <- lapply(m$charge$C, sparta::normalize)
m$charge$S <- lapply(m$charge$S, function(s) {
if (is.null(s) || is_scalar(s)) return(s)
sparta::normalize(s)
})
}
return(m)
} else {
stop("propagate must be either 'collect' or full", call. = TRUE)
}
}
mpe <- function(x) UseMethod("mpe")
mpe.jt <- function(x) {
if (attr(x, "flow") != "max") stop("The flow of the junction tree is not 'max'.")
attr(x, "mpe")
}
get_cliques <- function(x) UseMethod("get_cliques")
get_cliques.jt <- function(x) x$cliques
get_cliques.charge <- function(x) x$cliques
get_cliques.pot_list <- function(x) attr(x, "cliques")
get_clique_root_idx <- function(x) UseMethod("get_clique_root_idx")
get_clique_root_idx.jt <- function(x) as.integer(gsub("C","",attr(x, "clique_root")))
get_clique_root <- function(x) UseMethod("get_clique_root")
get_clique_root.jt <- function(x) x$cliques[[get_clique_root_idx(x)]]
query_evidence <- function(x) UseMethod("query_evidence")
query_evidence.jt <- function(x) {
if (has_inconsistencies(x)) {
stop(
"The probability of evidence is not meaningful ",
"when there are inconsistencies in the evidence.",
call. = FALSE
)
}
if(attr(x, "flow") != "sum") {
stop(
"The flow of the junction tree must be 'sum'.",
call. = FALSE
)
}
if (attr(x, "propagated") == "no") {
stop("In order to query the probabilty of evidence, ",
"the junction tree must at least be propagted to ",
"the root node (collect).",
call. = FALSE
)
}
return(attr(x, "probability_of_evidence"))
}
leaves <- function(jt) UseMethod("leaves")
leaves.jt <- function(jt) {
direction <- attr(jt, "direction")
if (direction == "full") {
message("The junction tree is already fully propagated. NULL is returned")
return(NULL)
}
x <- if (direction == "collect") jt$schedule$collect else jt$schedule$distribute
lvs <- attr(x$tree, "leaves")
true_clique_names <- names(x$cliques)[lvs]
true_lvs_indicies <- as.integer(gsub("C", "", true_clique_names))
return(true_lvs_indicies)
}
parents <- function(jt) UseMethod("parents")
parents.jt <- function(jt) {
direction <- attr(jt, "direction")
if (direction == "full") {
message("The junction tree is already fully propagated. NULL is returned")
return(NULL)
}
x <- if (direction == "collect") jt$schedule$collect else jt$schedule$distribute
par <- attr(x$tree, "parents")
true_clique_names <- lapply(par, function(p) names(x$cliques)[p])
true_par_indicies <- lapply(true_clique_names, function(tcn) {
as.integer(gsub("C", "", tcn))
})
return(true_par_indicies)
}
print.jt <- function(x, ...) {
cls <- paste0("<", paste0(class(x), collapse = ", "), ">")
direction <- attr(x, "direction")
flow <- attr(x, "flow")
nv <- ncol(x$clique_graph)
ne <- sum(x$clique_graph)/2
clique_sizes <- .map_int(x$cliques, length)
max_C <- max(clique_sizes)
min_C <- min(clique_sizes)
avg_C <- mean(clique_sizes)
cat(" Junction Tree",
"\n -------------------------",
"\n Propagated:", attr(x, "propagated"),
"\n Flow:", flow,
"\n Cliques:", length(x$cliques),
"\n - max:", max_C,
"\n - min:", min_C,
"\n - avg:", round(avg_C, 2)
)
inc <- attr(x, "inconsistencies")
e <- attr(x, "evidence")
if (!is.null(e)) {
if (inc) cat("\n Evidence: (inconsistencies)") else cat("\n Evidence:")
for (i in seq_along(e)) {
cat(
"\n -", paste0(names(e[i]), ":"), unname(e[i])
)
}
}
cat(paste0("\n ", cls),
"\n -------------------------\n"
)
}
plot.jt <- function(x, ...) {
direction <- attr(x, "direction")
y <- if (direction == "collect") {
list(
cliques = x$schedule$collect$cliques,
tree = x$schedule$collect$tree,
type = "directed"
)
} else if (direction == "distribute") {
list(
cliques = x$schedule$distribute$cliques,
tree = x$schedule$distribute$tree,
type = "directed"
)
} else {
list(
cliques = x$cliques,
tree = x$clique_graph,
type = "undirected"
)
}
.names <- unlist(lapply(y$cliques, function(z) paste(z, collapse = "\n")))
dimnames(y$tree) <- list(.names, .names)
g <- igraph::graph_from_adjacency_matrix(y$tree, y$type)
graphics::plot(g, ...)
}
plot.charge <- function(x, ...) {
.names <- unlist(lapply(x$cliques, function(z) paste(z, collapse = "\n")))
dimnames(x$schedule$clique_graph) <- list(.names, .names)
g <- igraph::graph_from_adjacency_matrix(x$schedule$clique_graph, "undirected")
graphics::plot(g, ...)
}
|
extract_outputs <- function(docx,
track_changes,
wrap,
verbose = FALSE) {
md_tmp <- tempfile(fileext = ".md")
pandoc_convert(normalizePath(docx),
from = "docx+styles+empty_paragraphs",
to = "markdown+fenced_code_blocks",
options = c(
paste0(
"--lua-filter=",
system.file("lua-filters", "extract-outputs.lua", package = "redoc")
),
"--standalone"
),
output = md_tmp,
verbose = verbose
)
yml <- yaml::read_yaml(md_tmp)
yml
}
|
library(tidymodels)
library(nycflights13)
library(doMC)
library(rlang)
library(xgboost)
library(vctrs)
num_resamples <- 5
num_grid <- 10
num_cores <- 3
preproc <- "light preprocessing"
par_method <- "resamples"
set.seed(123)
flight_data <-
flights %>%
mutate(
arr_delay = ifelse(arr_delay >= 30, "late", "on_time"),
arr_delay = factor(arr_delay),
date = as.Date(time_hour)
) %>%
inner_join(weather, by = c("origin", "time_hour")) %>%
select(dep_time, flight, origin, dest, air_time, distance,
carrier, date, arr_delay, time_hour) %>%
na.omit() %>%
mutate_if(is.character, as.factor) %>%
sample_n(4000)
flights_rec <-
recipe(arr_delay ~ ., data = flight_data) %>%
update_role(flight, time_hour, new_role = "ID") %>%
step_date(date, features = c("dow", "month")) %>%
step_holiday(date, holidays = timeDate::listHolidays("US")) %>%
step_rm(date) %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
preproc_data <-
flights_rec %>%
prep() %>%
juice(all_predictors(), all_outcomes())
xgboost_spec <-
boost_tree(trees = tune(), min_n = tune(), tree_depth = tune(), learn_rate = tune(),
loss_reduction = tune(), sample_size = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
if (preproc != "no preprocessing") {
xgboost_workflow <-
workflow() %>%
add_recipe(flights_rec) %>%
add_model(xgboost_spec)
set.seed(33)
bt <- bootstraps(flight_data, times = num_resamples)
} else {
xgboost_workflow <-
workflow() %>%
add_variables(arr_delay, predictors = c(everything())) %>%
add_model(xgboost_spec)
set.seed(33)
bt <- bootstraps(preproc_data, times = num_resamples)
}
set.seed(22)
xgboost_grid <-
xgboost_workflow %>%
parameters() %>%
update(trees = trees(c(100, 2000))) %>%
grid_max_entropy(size = num_grid)
if (num_cores > 1) {
registerDoMC(cores=num_cores)
}
roc_res <- metric_set(roc_auc)
ctrl <- control_grid(parallel_over = par_method)
grid_time <- system.time({
set.seed(99)
xgboost_workflow %>%
tune_grid(bt, grid = xgboost_grid, metrics = roc_res, control = ctrl)
})
times <- tibble::tibble(
elapsed = grid_time[3],
num_resamples = num_resamples,
num_grid = num_grid,
num_cores = num_cores,
preproc = preproc,
par_method = par_method
)
save(times, file = paste0("xgb_", num_cores, format(Sys.time(), "_%Y_%m_%d_%H_%M_%S.RData")))
sessioninfo::session_info()
if (!interactive()) {
q("no")
}
|
"dataFigure3"
|
varcovcubshe <-function(m,pai1,pai2,csi,shelter,n){
pr<-probcubshe1(m,pai1,pai2,csi,shelter)
dd<-rep(0,m);dd[shelter]<-1;
bb<-probbit(m,csi)
aaa<-bb-dd
bbb<-(1/m)-dd
c4<-pai1*bb*(m-(1:m)-csi*(m-1))/(csi*(1-csi))
atilde<-aaa/pr; btilde<-bbb/pr; ctilde<-c4/pr;
d11<-sum(aaa*atilde); d22<-sum(bbb*btilde); dxx<-sum(c4*ctilde);
d12<-sum(bbb*atilde); d1x<-sum(c4*atilde); d2x<-sum(c4*btilde);
matinf<-matrix(c(d11,d12,d1x,d12,d22,d2x,d1x,d2x,dxx),nrow=3,byrow=T)
if(any(is.na(matinf))==TRUE){
warning("ATTENTION: NAs produced")
varmat<-matrix(NA,nrow=3,ncol=3)
} else {
if(det(matinf)<=0){
warning("ATTENTION: Variance-covariance matrix NOT positive definite")
varmat<-matrix(NA,nrow=3,ncol=3)
} else {
varmat<-solve(matinf)/n
}
}
return(varmat)
}
|
context("Function netLoadRamp")
sapply(studyPathS, function(studyPath){
opts <- setSimulationPath(studyPath)
describe("netLoadRamp", {
mydata <- readAntares(areas = "all", districts = "all", links = "all", showProgress = FALSE, mcYears = "all")
it("returns an antaresDataTable with correct number of lines and columns", {
s <- netLoadRamp(mydata$areas, ignoreMustRun = TRUE)
expect_is(s, "antaresDataTable")
expect_equal(nrow(s) / length(simOptions()$mcYears) / (24 * 7 * nweeks),
nrow(unique(mydata$areas[, .(area)])))
})
it("accepts 'antaresDataList' objects", {
s <- netLoadRamp(mydata, ignoreMustRun = TRUE)
expect_is(s, "antaresDataList")
expect_false(is.null(s$areas$areaRamp))
expect_false(is.null(s$districts$areaRamp))
})
it("creates min and max columns only if timeStep is not hourly or synthesis is true", {
s <- netLoadRamp(mydata$areas, ignoreMustRun = TRUE)
expect_true(is.null(s$min_areaRamp))
s <- netLoadRamp(x = mydata$areas, ignoreMustRun = TRUE, synthesis = TRUE)
expect_false(is.null(s$min_areaRamp))
s <- suppressWarnings(netLoadRamp(mydata$areas, ignoreMustRun = TRUE, timeStep = "monthly"))
expect_false(is.null(s$min_areaRamp))
})
it("stops if input does not contain area or district data", {
expect_error(netLoadRamp(mydata$links, ignoreMustRun = TRUE), "area")
})
it("stops if some 'necesary'BALANCE' column is missing", {
mydata <- readAntares(areas="all", showProgress = FALSE, select = "LOAD", mcYears = "all")
expect_error(netLoadRamp(mydata, ignoreMustRun = TRUE), "missing")
})
})
})
|
gx.pearson <-
function(xx, log = FALSE, ifclr = FALSE, ifwarn = TRUE)
{
if(!is.matrix(xx)) stop(deparse(substitute(xx)), " is not a Matrix")
temp.x <- remove.na(xx)
x <- temp.x$x
if(ifclr) log <- FALSE
if(log) {
x <- log10(x)
cat("Data have been Log10 transformed\n")
}
else if(ifclr) {
x <- clr(x, ifwarn = ifwarn)
cat("Data have been Centred Log-Ratio transformed\n")
}
z <- scale(x)
r <- (t(z) %*% z)/(temp.x$n - 1)
df.t <- temp.x$n - 2
df.term <- sqrt(df.t)
for(i in 2:temp.x$m) {
for(j in 1:(i - 1))
r[i, j] <- pt((abs(r[i, j]) * df.term)/
sqrt(1 - r[i, j] * r[i, j]), df.t)
}
r <- round(r, 3)
for(i in 1:temp.x$m) r[i,i] <- NA
cat("Pearson Correlation Coefficients and their Statistical Significance,",
"\nupper and lower triangles, respectively,",
paste("for matrix ", deparse(substitute(xx)), ", N = ", temp.x$n, "\n\n", sep = ""))
print(r, na.print = " ")
cat("\n")
invisible()
}
|
"add.lmomco.axis" <-
function(side=1, twoside=FALSE, twoside.suppress.labels=FALSE,
side.type=c("NPP", "RI", "SNV"), otherside.type=c("NA", "RI", "SNV", "NPP"),
alt.lab=NA, alt.other.lab=NA, npp.as.aep=FALSE, case=c("upper", "lower"),
NPP.control=NULL, RI.control=NULL, SNV.control=NULL, ...) {
case <- match.arg(case)
other.side <- switch(as.character(side), "1"=3, "2"=4, "3"=1, "4"=1)
side.type <- match.arg(side.type)
otherside.type <- match.arg(otherside.type)
if(otherside.type == "NA") otherside.type <- NA
if(twoside & ! is.na(otherside.type)) twoside <- FALSE
lims <- par()$usr;
ifelse(side == 1 | side == 3, lims <- lims[1:2], lims <- lims[3:4])
lims <- pnorm(lims)
"my.nonexceeds" <- function(minors=FALSE) {
if(minors) {
FF <- c(0.55, 0.65, 0.75, 0.825, 0.850, 0.875,
0.91, 0.92, 0.93, 0.94, 0.96, 0.97,
0.9925, 0.996, 0.997, 0.9996, 0.9997, 0.99996, 0.99997)
FF <- c(sort(1-FF), FF)
FF <- FF[FF >= lims[1]]; FF <- FF[FF <= lims[2]]
FF <- unique(c(lims[1],FF,lims[2]))
return(FF)
} else {
FF <- c(0.6, 0.7, 0.8, 0.9, 0.95, 0.98, 0.99, 0.995, 0.998, 0.999,
0.9995, 0.9998, 0.9999, 0.99995, 0.99998, 0.99999)
FF <- c(sort(1-FF), 0.5, FF)
FF <- FF[FF >= lims[1]]; FF <- FF[FF <= lims[2]]
FF <- unique(c(lims[1],FF,lims[2]))
return(FF)
}
}
if(is.null(NPP.control)) {
txt.npp <- ifelse(case == "upper", "NONEXCEEDANCE PROBABILITY", "Nonexceedance probability")
txt.epp <- ifelse(case == "upper", "EXCEEDANCE PROBABILITY", "Exceedance probability")
the.label <- ifelse(is.na(alt.lab), txt.npp, alt.lab)
the.other.label <- ifelse(is.na(alt.other.lab), txt.epp, alt.other.lab)
NPP.control <- list(label=the.label,
other.label=the.other.label,
probs=my.nonexceeds(minors=TRUE),
probs.label=my.nonexceeds(minors=FALSE),
digits=3, line=3, as.exceed=FALSE)
}
if(is.null(RI.control)) {
txt.ri <- ifelse(case == "upper", "RECURRENCE INTERVAL, IN YEARS", "Recurrence interval, in years")
the.label <- ifelse(is.na(alt.lab), txt.ri, alt.lab)
RI.control <- list(label=the.label,
Tyear=c(2, 5, 10, 25, 50, 100, 200, 500), line=2)
}
if(is.null(SNV.control)) {
txt.snv <- ifelse(case == "upper", "STANDARD NORMAL VARIATE", "Standard normal variate")
the.label <- ifelse(is.na(alt.lab), txt.snv, alt.lab)
SNV.control <- list(label=the.label,
begin=-5, end=5, by=0.5, line=2)
}
NPPf <- function(side, other.side) {
dots <- list(...)
tcl <- ifelse("tcl" %in% names(dots), dots$tcl, par()$tcl)
NPP <- NPP.control$probs; NPP.lab <- NPP.control$probs.lab
if(NPP.control$as.exceed) {
the.true.NPP.lab <- 1 - NPP.lab
} else {
the.true.NPP.lab <- NPP.lab
}
qNPP <- qnorm(NPP); qNPP.lab <- qnorm(NPP.lab)
if(npp.as.aep) {
NPP.lab <- format(1-the.true.NPP.lab, nsmall=NPP.control$digits)
} else {
NPP.lab <- format( the.true.NPP.lab, nsmall=NPP.control$digits)
}
Axis(qNPP, at=qNPP, labels=NA, side=side, ..., tcl=0.8*tcl)
Axis(qNPP.lab, at=qNPP.lab, labels=NPP.lab, side=side, ..., tcl=1.3*tcl)
if(npp.as.aep) {
mtext(NPP.control$other.label, line=NPP.control$line, side=side)
} else {
mtext(NPP.control$label, line=NPP.control$line, side=side)
}
if(twoside) {
Axis(qNPP, at=qNPP, labels=NA, side=other.side, ..., tcl=0.8*tcl)
if(twoside.suppress.labels) {
Axis(qNPP.lab, at=qNPP.lab, labels=NA, side=other.side, ..., tcl=1.3*tcl)
} else {
Axis(qNPP.lab, at=qNPP.lab, labels=NPP.lab, side=other.side, ..., tcl=1.3*tcl)
}
}
}
RIf <- function(side, other.side) {
F <- 1 - 1/RI.control$Tyear; qF <- qnorm(F); labF <- RI.control$Tyear
Axis(qF, at=qF, labels=labF, side=side, ...)
if(twoside) {
Axis(at=qF, labels=NA, side=other.side, ...)
}
mtext(RI.control$label, line=RI.control$line, side=side)
}
SNVf <- function(side, other.side) {
SNV <- NULL
try( SNV <- seq(SNV.control$begin, SNV.control$end, by=SNV.control$by) )
if(is.null(SNV)) {
warning("Poorly constructed SNV.control, trapping, and using alternative")
SNV <- seq(-5, 5, by=0.5)
}
Axis(SNV, at=SNV, side=side, ...)
mtext(SNV.control$label, line=SNV.control$line, side=side)
if(twoside) {
Axis(SNV, at=SNV, side=other.side, ...)
}
}
NULLf <- function() { return("no axis function made") }
primary.axis <- switch(side.type, NPP=NPPf, RI=RIf, SNV=SNVf, NULLf)
primary.axis(side, other.side)
if(! is.na(otherside.type)) {
secondary.axis <- switch(otherside.type, NPP=NPPf, RI=RIf, SNV=SNVf, NULLf)
secondary.axis(other.side, side)
}
}
|
select_edges <- function(graph,
conditions = NULL,
set_op = "union",
from = NULL,
to = NULL,
edges = NULL) {
time_function_start <- Sys.time()
fcn_name <- get_calling_fcn()
if (graph_object_valid(graph) == FALSE) {
emit_error(
fcn_name = fcn_name,
reasons = "The graph object is not valid")
}
if (graph_contains_nodes(graph) == FALSE) {
emit_error(
fcn_name = fcn_name,
reasons = "The graph contains no nodes")
}
if (graph_contains_edges(graph) == FALSE) {
emit_error(
fcn_name = fcn_name,
reasons = "The graph contains no edges")
}
if (!is.null(edges)) {
if (!any(edges %in% graph$edges_df$id)) {
emit_error(
fcn_name = fcn_name,
reasons = "The values provided in `edges` do not all correspond to edge ID values in the graph")
}
}
conditions <- rlang::enquo(conditions)
edges_df <- graph$edges_df
n_e_select_properties_in <-
node_edge_selection_properties(graph = graph)
if (!is.null(
rlang::enquo(conditions) %>%
rlang::get_expr())) {
edges_df <- dplyr::filter(.data = edges_df, !!conditions)
}
if (!is.null(from)) {
if (any(!(from %in% edges_df$from))) {
emit_error(
fcn_name = fcn_name,
reasons = "One of more of the nodes specified as `from` not part of an edge")
}
from_val <- from
edges_df <-
edges_df %>%
dplyr::filter(from %in% from_val)
}
if (!is.null(to)) {
if (any(!(to %in% edges_df$to))) {
emit_error(
fcn_name = fcn_name,
reasons = "One of more of the nodes specified as `to` are not part of an edge")
}
to_val <- to
edges_df <-
edges_df %>%
dplyr::filter(to %in% to_val)
}
edges_selected <-
edges_df %>%
dplyr::select(id, from, to) %>%
dplyr::rename(edge = id)
edges_selected <- edges_selected$edge
if (!is.null(edges)) {
edges_selected <- intersect(edges, edges_selected)
}
edges_prev_selection <- graph$edge_selection$edge
if (set_op == "union") {
edges_combined <-
union(edges_prev_selection, edges_selected)
} else if (set_op == "intersect") {
edges_combined <-
intersect(edges_prev_selection, edges_selected)
} else if (set_op == "difference") {
edges_combined <-
base::setdiff(edges_prev_selection, edges_selected)
}
edges_combined <-
graph$edges_df %>%
dplyr::filter(id %in% edges_combined) %>%
dplyr::select(id, from, to) %>%
dplyr::rename(edge = id)
graph$edge_selection <- edges_combined
graph$node_selection <- create_empty_nsdf()
n_e_select_properties_out <-
node_edge_selection_properties(graph = graph)
graph$graph_log <-
add_action_to_log(
graph_log = graph$graph_log,
version_id = nrow(graph$graph_log) + 1,
function_used = fcn_name,
time_modified = time_function_start,
duration = graph_function_duration(time_function_start),
nodes = nrow(graph$nodes_df),
edges = nrow(graph$edges_df))
if (graph$graph_info$write_backups) {
save_graph_as_rds(graph = graph)
}
if (!is.null(graph$graph_info$display_msgs) &&
graph$graph_info$display_msgs) {
if (!n_e_select_properties_in[["node_selection_available"]] &
!n_e_select_properties_in[["edge_selection_available"]]) {
msg_body <-
glue::glue(
"created a new selection of \\
{n_e_select_properties_out[['selection_count_str']]}")
} else if (n_e_select_properties_in[["node_selection_available"]] |
n_e_select_properties_in[["edge_selection_available"]]) {
if (n_e_select_properties_in[["edge_selection_available"]]) {
msg_body <-
glue::glue(
"modified an existing selection of \\
{n_e_select_properties_in[['selection_count_str']]}:
* {n_e_select_properties_out[['selection_count_str']]} \\
are now in the active selection
* used the `{set_op}` set operation")
}
if (n_e_select_properties_in[["node_selection_available"]]) {
msg_body <-
glue::glue(
"created a new selection of \\
{n_e_select_properties_out[['selection_count_str']]}:
* this replaces \\
{n_e_select_properties_in[['selection_count_str']]} \\
in the prior selection")
}
}
emit_message(
fcn_name = fcn_name,
message_body = msg_body)
}
graph
}
|
get_ust = function(x,y, nonzero)
{
beta = as.numeric(t(x)%*%y)
return = rep(0, length(beta))
select = order(abs(beta), decreasing=TRUE)[1:nonzero]
return[select] = beta[select]
names(return) <- colnames(x)
return
}
|
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
library(knitr)
library(interlineaR)
library(kableExtra)
corpuspath <- system.file("exampleData", "tuwariInterlinear.xml", package="interlineaR")
corpus <- read.emeld(corpuspath, vernacular.languages="tww")
kable(head(corpus$morphemes))
kable(head(corpus$words))
kable(head(corpus$sentences), booktabs = T)
kable(head(corpus$texts))
morphemes_words <- merge(corpus$morphemes, corpus$words[,-c(1,2)], by="word_id", suffixes = c(".morpheme",".word"))
kable(head(morphemes_words))
corpuspath <- system.file("exampleData", "tuwariToolbox.txt", package="interlineaR")
corpus <- read.toolbox(corpuspath)
kable(head(corpus$morphemes))
path <- system.file("exampleData", "kakabe.txt", package="interlineaR")
corpus <- read.toolbox(path, morpheme.fields.suppl = c("gr", "gf"))
kable(head(corpus$morphemes))
kable(head(corpus$sentences))
dicpath <- system.file("exampleData", "tuwariDictionary.lift", package="interlineaR")
dictionary <- read.lift(dicpath, vernacular.languages="tww", simplify=TRUE)
kable(head(dictionary$entries))
kable(head(dictionary$senses))
kable(head(dictionary$examples))
|
R_EL2n_E <- function(R_EL) {
n_E <- R_EL %*% (base::t(c(0, 0, -1)) %>% as.vector())
n_E %>% as.vector()
}
|
palettes <- lapply(
list(
flat = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
'flat dark' = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
dust = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
light = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
earth = list(
background = '
text = list(inner = '
line = list(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = list(low='
),
fresh = list(
background = '
text = list(inner = '
line = list(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = list(low='
),
chalk = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
gradient = c(low = '
),
lilac = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
'
gradient = c(low = '
),
carrot = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
pale = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
'
gradient = c(low = '
),
copper = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
'
gradient = c(low = '
),
grape = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low='
),
greyscale = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
'
gradient = c(low = '
),
sky = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
gradient = c(low = '
),
solarized = list(
background = '
text = c(inner = '
line = c(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
gradient = c(low = '
),
grass = list(
background = '
text = c(inner = '
line = c(inner = NA, outer = '
gridline = '
swatch = structure(c(
'
'
'
'
gradient = c(low='
),
sea = list(
background = '
text = c(inner = '
line = c(inner = NA, outer = '
gridline = '
swatch = structure(c(
'
'
'
'
'
'
gradient = c(low='
),
camouflage = list(
background = '
text = list(inner = '
line = list(inner = '
gridline = '
swatch = structure(c(
'
'
'
'
gradient = list(low='
)
), function(x) {
class(x) <- 'ggthemr_palette'
x
})
|
test_that("checks on ticker and simfin_id are not too strict", {
skip("Very slow")
entities <- sfa_get_entities()
checkmate::expect_data_table(
sfa_get_info(simfin_id = entities[["simfin_id"]])
)
checkmate::expect_data_table(
sfa_get_info(ticker = entities[["ticker"]])
)
})
|
statvc4levelt2 <-
function(y,cluster,m1,n1,m2,n2,m3,n3,p,weight){
mat=matrix(0,p,p)
w=0
for(i in 1:m1)
{
clusteri=cluster[[i]]
m1i=m2[i]
m2i=m3[[i]]
n2i=n3[[i]]
mati=statvc3levelt1(y,clusteri,m1i,m2i,n2i,p,weight)
mat=mat+mati[[1]]
w=w+mati[[2]]
}
list(mat,w)
}
|
ipums_website <- function(
x, var, project = NULL, launch = TRUE, verbose = TRUE, var_label = NULL, homepage_if_missing = TRUE
) {
UseMethod("ipums_website")
}
ipums_website.ipums_ddi <- function(
x, var, project = NULL, launch = TRUE, verbose = TRUE, var_label = NULL, homepage_if_missing = TRUE
) {
if (is.null(project)) project <- x$ipums_project
var <- fix_for_detailed_var(x, var, var_label)
url <- get_ipums_url(var, project, verbose, homepage_if_missing)
if (launch) {
shell.exec(url)
invisible(url)
} else {
url
}
}
ipums_website.default <- function(
x, var, project = NULL, launch = TRUE, verbose = TRUE, var_label = NULL, homepage_if_missing = TRUE
) {
if (is.null(project)) project <- attributes(x)[["ipums_project"]]
if (missing(x)) x <- NULL
var <- fix_for_detailed_var(x, var, var_label)
url <- get_ipums_url(var, project, verbose, homepage_if_missing)
if (launch) {
shell.exec(url)
invisible(url)
} else {
url
}
}
get_ipums_url <- function(var, project, verbose = TRUE, homepage_if_missing = FALSE) {
if (is.null(project)) {
stop(paste(
custom_format_text(
"Project not found. Please specify the project name using ",
"'project' argument. Options include: ", indent = 2, exdent = 2
),
custom_format_text(
paste(all_proj_names(), collapse = ", "), indent = 4, exdent = 4
),
sep = "\n"
))
}
config <- get_proj_config(project)
if (is.null(config)) {
stop(paste(
custom_format_text(
"Unexpected project '", project, "'. ",
"Options include: ", indent = 2, exdent = 2
),
custom_format_text(
paste(all_proj_names(), collapse = ", "), indent = 4, exdent = 4
),
sep = "\n"
))
}
if (verbose && !config$var_url) {
message("Cannot give a variable-specific URL for this project.")
}
if (!homepage_if_missing && !config$var_url) {
return(NULL)
}
config$url_function(var)
}
fix_for_detailed_var <- function(object, var, var_label) {
if (is.null(var_label) & !is.null(object)) var_label <- ipums_var_label(object, one_of(var))
if (is.null(var_label)) return(var)
is_det <- grepl("detailed version", tolower(var_label), fixed = TRUE)
if (is_det && fostr_sub(var, -1) == "D") {
var <- fostr_sub(var, 1, -2)
}
var
}
|
"glosses_df"
|
docker_available <- function(..., verbose = FALSE) {
res <- tryCatch({
cl <- http_client(docker_config(...))
cl$ping()
}, error = identity)
ok <- !inherits(res, "error")
if (verbose && !ok) {
what <- if (!exists("cl", inherits = FALSE))
"create docker client" else "connect to docker daemon"
message(sprintf("Failed to %s with error message:\n %s",
what, res$message))
}
ok
}
|
setMethod("writeStart", signature(x="RasterLayer", filename="character"),
function(x, filename, options=NULL, format, prj=FALSE, ...) {
if (trim(filename) == "") {
filename <- rasterTmpFile()
}
filename <- .fullFilename(filename, expand=TRUE)
if (!file.exists(dirname(filename))) {
stop("Attempting to write a file to a path that does not exist:\n ", dirname(filename))
}
filetype <- .filetype(format=format, filename=filename)
filename <- .getExtension(filename, filetype)
if (filetype=="ascii") {
x <- .startAsciiWriting(x, filename, ...)
} else if ( filetype %in% .nativeDrivers() ) {
x <- .startRasterWriting(x, filename, format=filetype, ...)
} else if ( filetype == "CDF" ) {
x <- .startWriteCDF(x, filename, ...)
} else {
x <- .startGDALwriting(x, filename, options=options, format=filetype, ...)
}
if (prj) {
crs <-.getCRS(x)
if (crs != "") {
writeLines(wkt(x), extension(filename, "prj") )
}
}
return(x)
})
setMethod("writeStart", signature(x="RasterBrick", filename="character"),
function(x, filename, options=NULL, format, prj=FALSE, ...) {
if (trim(filename) == "") {
filename <- rasterTmpFile()
}
filename <- .fullFilename(filename, expand=TRUE)
filetype <- .filetype(format=format, filename=filename)
filename <- .getExtension(filename, filetype)
if (filetype=="ascii") {
stop("ARC-ASCII files cannot contain multiple layers")
}
native <- filetype %in% c(.nativeDrivers(), "ascii")
if (native) {
x <- .startRasterWriting(x, filename, format=filetype, ...)
} else if ( filetype == "CDF" ) {
x <- .startWriteCDF(x, filename, ...)
} else {
x <- .startGDALwriting(x, filename, options=options, format=filetype, ...)
}
if (prj) {
crs <-.getCRS(x)
if (!is.na(crs)) {
writeLines(wkt(x), extension(filename, "prj") )
}
}
return(x)
})
setMethod("writeStop", signature(x="RasterLayer"),
function(x) {
driver <- x@file@driver
if ( driver %in% .nativeDrivers() ) {
return( .stopRasterWriting(x) )
} else if ( driver == "ascii" ) {
return( .stopAsciiWriting(x) )
} else if ( driver == "netcdf" ) {
return( .stopWriteCDF(x) )
} else {
return( .stopGDALwriting(x) )
}
}
)
setMethod("writeStop", signature(x="RasterBrick"),
function(x) {
driver <- x@file@driver
if (driver %in% .nativeDrivers()) {
return( .stopRasterWriting(x) )
} else if ( driver == "netcdf" ) {
return( .stopWriteCDF(x) )
} else {
return( .stopGDALwriting(x) )
}
}
)
|
NULL
filter.dm <- function(.data, ...) {
check_zoomed(.data)
}
filter.zoomed_dm <- function(.data, ...) {
.data %>%
dm_filter_impl(..., set_filter = FALSE)
}
mutate.dm <- function(.data, ...) {
check_zoomed(.data)
}
mutate.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
mutated_tbl <- mutate(tbl, ...)
selected <- set_names(intersect(colnames(tbl), colnames(mutated_tbl)))
new_tracked_cols_zoom <- new_tracked_cols(.data, selected)
replace_zoomed_tbl(.data, mutated_tbl, new_tracked_cols_zoom)
}
transmute.dm <- function(.data, ...) {
check_zoomed(.data)
}
transmute.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
groups <- set_names(map_chr(groups(tbl), as_string))
transmuted_tbl <- transmute(tbl, ...)
selected <- set_names(intersect(colnames(tbl), colnames(transmuted_tbl)))
new_tracked_cols_zoom <- new_tracked_cols(.data, selected)
replace_zoomed_tbl(.data, transmuted_tbl, new_tracked_cols_zoom)
}
select.dm <- function(.data, ...) {
check_zoomed(.data)
}
select.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
selected <- eval_select_both(quo(c(...)), colnames(tbl))
selected_tbl <- select(tbl, !!!selected$indices)
new_tracked_cols_zoom <- new_tracked_cols(.data, selected$names)
replace_zoomed_tbl(.data, selected_tbl, new_tracked_cols_zoom)
}
relocate.dm <- function(.data, ...) {
check_zoomed(.data)
}
relocate.zoomed_dm <- function(.data, ..., .before = NULL, .after = NULL) {
tbl <- tbl_zoomed(.data)
relocated_tbl <- relocate(tbl, ..., .before = {{ .before }}, .after = {{ .after }})
replace_zoomed_tbl(.data, relocated_tbl)
}
rename.dm <- function(.data, ...) {
check_zoomed(.data)
}
rename.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
renamed <- eval_rename_both(quo(c(...)), colnames(tbl))
renamed_tbl <- rename(tbl, !!!renamed$indices)
new_tracked_cols_zoom <- new_tracked_cols(.data, renamed$all_names)
replace_zoomed_tbl(.data, renamed_tbl, new_tracked_cols_zoom)
}
distinct.dm <- function(.data, ...) {
check_zoomed(.data)
}
distinct.zoomed_dm <- function(.data, ..., .keep_all = FALSE) {
tbl <- tbl_zoomed(.data)
distinct_tbl <- distinct(tbl, ..., .keep_all = .keep_all)
selected <- set_names(intersect(colnames(tbl), colnames(distinct_tbl)))
new_tracked_cols_zoom <- new_tracked_cols(.data, selected)
replace_zoomed_tbl(.data, distinct_tbl, new_tracked_cols_zoom)
}
arrange.dm <- function(.data, ...) {
check_zoomed(.data)
}
arrange.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
arranged_tbl <- arrange(tbl, ...)
replace_zoomed_tbl(.data, arranged_tbl)
}
slice.dm <- function(.data, ...) {
check_zoomed(.data)
}
slice.zoomed_dm <- function(.data, ..., .keep_pk = NULL) {
sliced_tbl <- slice(tbl_zoomed(.data), ...)
orig_pk <- dm_get_pk_impl(.data, orig_name_zoomed(.data))
tracked_cols <- col_tracker_zoomed(.data)
if (is_null(.keep_pk)) {
if (has_length(orig_pk) && any(unlist(orig_pk) %in% tracked_cols)) {
message(
paste(
"Keeping PK column, but `slice.zoomed_dm()` can potentially damage the uniqueness of PK columns (duplicated indices).",
"Set argument `.keep_pk` to `TRUE` or `FALSE` to ensure the behavior you intended."
)
)
}
} else if (!.keep_pk) {
tracked_cols <- discard(tracked_cols, tracked_cols == orig_pk)
}
replace_zoomed_tbl(.data, sliced_tbl, tracked_cols)
}
group_by.dm <- function(.data, ...) {
check_zoomed(.data)
}
group_by.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
grouped_tbl <- group_by(tbl, ...)
replace_zoomed_tbl(.data, grouped_tbl)
}
group_data.dm <- function(.data) {
check_zoomed(.data)
}
group_data.zoomed_dm <- function(.data) {
tbl <- tbl_zoomed(.data)
group_data(tbl)
}
group_keys.dm <- function(.tbl, ...) {
check_zoomed(.tbl)
}
group_keys.zoomed_dm <- function(.tbl, ...) {
.data <- .tbl
tbl <- tbl_zoomed(.data)
group_keys(tbl, ...)
}
group_indices.dm <- function(.data, ...) {
check_zoomed(.data)
}
group_indices.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
group_indices(tbl, ...)
}
group_vars.dm <- function(x) {
check_zoomed(x)
}
group_vars.zoomed_dm <- function(x) {
.data <- x
tbl <- tbl_zoomed(.data)
group_vars(tbl)
}
groups.dm <- function(x) {
check_zoomed(x)
}
groups.zoomed_dm <- function(x) {
.data <- x
tbl <- tbl_zoomed(.data)
groups(tbl)
}
ungroup.dm <- function(x, ...) {
check_zoomed(x)
}
ungroup.zoomed_dm <- function(x, ...) {
tbl <- tbl_zoomed(x)
ungrouped_tbl <- ungroup(tbl, ...)
replace_zoomed_tbl(x, ungrouped_tbl)
}
summarise.dm <- function(.data, ...) {
check_zoomed(.data)
}
summarise.zoomed_dm <- function(.data, ...) {
tbl <- tbl_zoomed(.data)
groups <- set_names(map_chr(groups(tbl), as_string))
summarized_tbl <- summarize(tbl, ...)
new_tracked_cols_zoom <- new_tracked_cols(.data, groups)
replace_zoomed_tbl(.data, summarized_tbl, new_tracked_cols_zoom)
}
count.dm <- function(x, ...) {
check_zoomed(x)
}
count.zoomed_dm <- function(x, ..., wt = NULL, sort = FALSE, name = NULL,
.drop = group_by_drop_default(x)) {
tbl <- tbl_zoomed(x)
if (!missing(...)) {
out <- group_by(tbl, ..., .add = TRUE, .drop = .drop)
} else {
out <- tbl
}
groups <- set_names(map_chr(groups(out), as_string))
out <- tally(out, wt = !!enquo(wt), sort = sort, name = name)
if (is.data.frame(tbl)) {
out <- dplyr_reconstruct(out, tbl)
}
new_tracked_cols_zoom <- new_tracked_cols(x, groups)
replace_zoomed_tbl(x, out, new_tracked_cols_zoom)
}
tally.dm <- function(x, ...) {
check_zoomed(x)
}
tally.zoomed_dm <- function(x, ...) {
tbl <- tbl_zoomed(x)
groups <- set_names(map_chr(groups(tbl), as_string))
out <- tally(tbl, ...)
if (is.data.frame(tbl)) {
out <- dplyr_reconstruct(out, tbl)
}
new_tracked_cols_zoom <- new_tracked_cols(x, groups)
replace_zoomed_tbl(x, out, new_tracked_cols_zoom)
}
pull.dm <- function(.data, var = -1, name = NULL) {
check_zoomed(.data)
}
pull.zoomed_dm <- function(.data, var = -1, ...) {
tbl <- tbl_zoomed(.data)
pull(tbl, var = {{ var }}, ...)
}
compute.zoomed_dm <- function(x, ...) {
zoomed_df <-
tbl_zoomed(x) %>%
compute(...)
replace_zoomed_tbl(x, zoomed_df)
}
NULL
left_join.dm <- function(x, ...) {
check_zoomed(x)
}
left_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy)
joined_tbl <- left_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
inner_join.dm <- function(x, ...) {
check_zoomed(x)
}
inner_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy)
joined_tbl <- inner_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
full_join.dm <- function(x, ...) {
check_zoomed(x)
}
full_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy)
joined_tbl <- full_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
right_join.dm <- function(x, ...) {
check_zoomed(x)
}
right_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy)
joined_tbl <- right_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
semi_join.dm <- function(x, ...) {
check_zoomed(x)
}
semi_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy, disambiguate = FALSE)
joined_tbl <- semi_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
anti_join.dm <- function(x, ...) {
check_zoomed(x)
}
anti_join.zoomed_dm <- function(x, y, by = NULL, copy = NULL, suffix = NULL, select = NULL, ...) {
y_name <- as_string(enexpr(y))
join_data <- prepare_join(x, {{ y }}, by, {{ select }}, suffix, copy, disambiguate = FALSE)
joined_tbl <- anti_join(join_data$x_tbl, join_data$y_tbl, join_data$by, copy = FALSE, ...)
replace_zoomed_tbl(x, joined_tbl, join_data$new_col_names)
}
prepare_join <- function(x, y, by, selected, suffix, copy, disambiguate = TRUE) {
y_name <- dm_tbl_name(x, {{ y }})
select_quo <- enquo(selected)
if (!is_null(suffix)) message("Column names are disambiguated if necessary, `suffix` ignored.")
if (!is_null(copy)) message("Tables in a `dm` are necessarily on the same `src`, setting `copy = FALSE`.")
zoomed <- dm_get_zoom(x, c("table", "zoom", "col_tracker_zoom"))
x_tbl <- zoomed$zoom[[1]]
x_orig_name <- zoomed$table
y_tbl <- dm_get_tables_impl(x)[[y_name]]
all_cols_y <- colnames(y_tbl)
if (quo_is_null(select_quo)) {
select_quo <- quo(everything())
}
selected <- eval_select_both(select_quo, colnames(y_tbl))$names
new_col_names <- zoomed$col_tracker_zoom[[1]]
if (is_null(by)) {
by <- get_by(x, x_orig_name, y_name)
if (!all(names(by) %in% new_col_names)) abort_fk_not_tracked(x_orig_name, y_name)
}
by <- repair_by(by)
selected_wo_by <- selected[selected %in% setdiff(selected, by)]
if (disambiguate) {
x_disambig_name <- x_orig_name
y_disambig_name <- y_name
if (x_disambig_name == y_disambig_name) {
x_disambig_name <- paste0(x_disambig_name, ".x")
y_disambig_name <- paste0(y_disambig_name, ".y")
}
table_colnames <-
vec_rbind(
tibble(table = x_disambig_name, column = colnames(x_tbl)),
tibble(table = y_disambig_name, column = names(selected_wo_by))
)
recipe <- compute_disambiguate_cols_recipe(table_colnames, sep = ".")
explain_col_rename(recipe)
x_renames <-
recipe %>%
filter(table == x_disambig_name) %>%
pull(renames)
y_renames <-
recipe %>%
filter(table == y_disambig_name) %>%
pull(renames)
if (has_length(x_renames)) {
x_tbl <- x_tbl %>% rename(!!!x_renames[[1]])
names(by) <- recode(names2(by), !!!prep_recode(x_renames[[1]]))
names(new_col_names) <- recode(names(new_col_names), !!!prep_recode(x_renames[[1]]))
}
if (has_length(y_renames)) {
names(selected_wo_by) <- recode(names(selected_wo_by), !!!prep_recode(y_renames[[1]]))
}
}
if (!all(by %in% selected)) {
new_cols <- glue_collapse(tick_if_needed(setdiff(by, selected)), ", ")
message(glue("Using `select = c({as_label(select_quo)}, {new_cols})`."))
}
prefix <- unique_prefix(names(selected_wo_by))
by_rhs_rename <- by
names(by_rhs_rename) <- paste0(prefix, seq_along(by_rhs_rename))
stopifnot(!any(names(selected_wo_by) %in% names(by_rhs_rename)))
selected_repaired <- c(selected_wo_by, by_rhs_rename)
y_tbl <- select(y_tbl, !!!selected_repaired)
repaired_by <- set_names(recode(by, !!!prep_recode(by_rhs_rename)), names(by))
list(x_tbl = x_tbl, y_tbl = y_tbl, by = repaired_by, new_col_names = new_col_names)
}
unique_prefix <- function(x) {
if (is_empty(x)) {
return("...")
}
dots <- max(max(nchar(x, "bytes")), 3)
paste(rep(".", dots), collapse = "")
}
safe_count <- function(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = group_by_drop_default(x)) {
quos <- enquos(...)
if (has_length(quos)) {
named <- names2(quos) != ""
if (any(named)) {
quos <- as.list(quos)
named_quos <- quos[named]
x <- mutate(x, !!!named_quos)
quos[named] <- syms(names2(quos)[named])
names(quos) <- NULL
}
out <- group_by(x, !!!quos, .add = FALSE, .drop = .drop)
} else {
out <- ungroup(x)
}
if (is.null(name)) {
out <- tally(out, wt = !!enquo(wt), sort = sort)
} else {
out <- tally(out, wt = !!enquo(wt), sort = sort, name = name)
}
ungroup(out)
}
new_tracked_cols <- function(dm, selected) {
tracked_cols <- col_tracker_zoomed(dm)
old_tracked_names <- names(tracked_cols)
selected_match <- selected[selected %in% old_tracked_names]
set_names(
tracked_cols[selected_match],
names(selected_match)
)
}
|
as.root_criterion <- function(...) {
as_root_criterion(...)
}
is.root_criterion <- function(...) {
is_root_criterion(...)
}
|
test_asymp <- function(Y, X, Z = NULL, space_y = FALSE, number_y = length(unique(Y))){
Y <- as.numeric(Y)
if (space_y){
y <- seq(ifelse(length(which(Y==0))==0,min(Y),min(Y[-which(Y==0)])),max(Y[-which.max(Y)]),length.out=number_y)
}
else{
y <- sort(unique(Y))
}
if (is.null(Z)){
colnames(X) <- sapply(1:ncol(X), function(i){paste0('X',i)})
modelmat <- model.matrix(~.,data=X)
}
else{
colnames(X) <- sapply(1:ncol(X), function(i){paste0('X',i)})
colnames(Z) <- sapply(1:ncol(Z), function(i){paste0('Z',i)})
modelmat <- model.matrix(~.,data=cbind(X,Z))
}
ind_X <- which(substring(colnames(modelmat),1,1)=="X")
beta <- matrix(NA,(length(y)-1),length(ind_X))
indi_pi <- matrix(NA,length(Y),(length(y)-1))
Phi <- (1/length(Y))*(t(as.matrix(modelmat))%*%as.matrix(modelmat))
H <- (solve(Phi)%*%t(as.matrix(modelmat)))
H <- H[ind_X,]
for (i in 1:(length(y)-1)){
indi_Y <- 1*(Y<=y[i])
indi_pi[,i] <- indi_Y
reg <- lm(indi_Y ~ as.matrix(modelmat[,-1]))
beta[i,] <- reg$coefficients[ind_X]
}
beta <- as.vector(beta)
prop <- colMeans(indi_pi)
if (is.null(dim(H))){
H_square <- sum(H^2)
Sigma <- sapply(1:(length(y)-1), function(i){sapply(1:((length(y)-1)*length(ind_X)), function(j){
if (i<=j){
(prop[i]-(prop[j]*prop[i]))
}
else{
(prop[j]-(prop[j]*prop[i]))
}
})})
Sigma <- (1/length(Y))*(H_square*Sigma)
}
else{
temp_Sigma <- lapply(1:ncol(H), function(k){sapply(1:nrow(H), function(s){sapply(1:nrow(H), function(r){H[s,k]*H[r,k]})})})
sum_temp_Sigma <- temp_Sigma[[1]]
for (i in 2:ncol(H)){
sum_temp_Sigma <- sum_temp_Sigma + temp_Sigma[[i]]
}
ind_sig <- rep(1:(length(y)-1),length(ind_X))
Sigma <- matrix(NA,((length(y)-1)*length(ind_X)),((length(y)-1)*length(ind_X)))
for (i in 1:((length(y)-1)*length(ind_X))){
for (j in 1:((length(y)-1)*length(ind_X))){
if (i<=j){
Sigma[i,j] <- sum_temp_Sigma[floor(i/(length(y))+1),floor(j/(length(y))+1)]*(prop[ind_sig[i]]-(prop[ind_sig[j]]*prop[ind_sig[i]]))
}
else{
Sigma[i,j] <- sum_temp_Sigma[floor(i/(length(y))+1),floor(j/(length(y))+1)]*(prop[ind_sig[j]]-(prop[ind_sig[j]]*prop[ind_sig[i]]))
}
}
}
Sigma <- (1/length(Y))*Sigma
}
decomp <- eigen(Sigma)
A <- matrix(0,(length(ind_X)*(length(y)-1)),(length(ind_X)*(length(y)-1)))
diag(A) <- decomp$values
z <- (sqrt(length(Y)))*beta
STAT <- sum(t(z)*z)
pval <- survey::pchisqsum(STAT, lower.tail = FALSE, df = rep(1,length(diag(A))), a = diag(A), method = "saddlepoint")
return(data.frame(raw_pval=pval,Stat=STAT))
}
|
plot_cluster <- function(y,H,sort=FALSE,sample_rate = 0.05,
y.axis.label = NULL,
smoother = TRUE,
fade = 0.2, cluster_order = NULL, plot_render = TRUE)
{
N <- nrow(y)
T <- ncol(y)
M <- length(unique(H))
cluster <- vector("list", M)
for(m in 1:M)
{
if(is.null(cluster_order))
{
cluster[[m]] <- which(H == m)
}else{
cluster[[m]] <- which(H == cluster_order[m])
}
names(cluster[[m]]) <- NULL
}
c.sizes <- sapply(cluster,length)
if(!sort)
{
clusterstoplot <- 1:M
}else{
clusterstoplot <- sort(c.sizes,decreasing = TRUE,index.return=TRUE)$ix[1:length(cluster)]
}
map <- vector(mode="list",length = length(clusterstoplot))
for(i in 1:length(clusterstoplot))
{
cluster.i <- cluster[[clusterstoplot[i]]]
map[[i]] <- as.data.frame(cbind(cluster.i,i),stringsAsFactors = FALSE)
names(map[[i]]) <- c("establishment","cluster")
}
map <- do.call("rbind",map)
map$establishment <- as.numeric(map$establishment)
y.hat <- matrix(y,(N*T),1,byrow=FALSE)
establishment <- rep(1:N,times=T)
month <- rep(1:T,each=N)
dat.b <- data.frame(y.hat,month,establishment)
names(dat.b) <- c("value","time","establishment")
map <- map[order(map$establishment),]
datb.clust <- merge(dat.b,map,all.x=TRUE,by="establishment",sort = FALSE)
rate <- sample_rate
tmp <- split(datb.clust,list(datb.clust$cluster))
tmp <- unlist(sapply(tmp,function(x){
tot_recs <- length(unique(x$establishment))
u_recs <- sort(unique(x$establishment))
inc_recs <- sample(u_recs,round(rate*tot_recs),replace = FALSE)
}))
datb_plot <- subset(datb.clust, establishment %in% tmp)
p <- ggplot(data=datb_plot,aes(x = time, y = value))
l <- geom_line(aes(group = establishment), alpha = fade)
if(is.null(y.axis.label))
{
axis <- labs( x = "time",
y = substitute(expression(paste(Delta," (Simulated) Employment Counts"))) )
}else{
axis <- labs( x = "time", y = eval(y.axis.label) )
}
if(smoother == TRUE)
{
l.2 <- geom_smooth(aes(group=1),method = "loess", alpha = 0.5,
linetype = 2, se = FALSE, colour = "brown")
f <- facet_wrap(~cluster, scales = "fixed")
p.basis <- p + l + l.2 + f + theme_bw() + axis +
theme(axis.text.x=element_text(angle=90, hjust=0))
}else{
f <- facet_grid(cluster~., scales = "fixed")
p.basis <- p + l + f + theme_bw() + axis +
theme(axis.text.x=element_text(angle=90, hjust=0))
}
if(plot_render)
{
suppressWarnings(print(p.basis))
}
value <- time <- NULL
return(list(map = map, p.basis = p.basis))
}
|
"fun.fmkl.L40"<-function(k,L3){
j<-0:k
result<-integrate(function(x,k,L3) ((x^L3-1)/L3-log(1-x))^k,0,1,
abs.tol=1e-100,k=k,L3=L3,stop.on.error=FALSE)
if(result$message!="OK"){return(NA)}
else return(result$value)}
|
context("Testing table_exists()")
tables <- c("users",
"groups",
"file_type",
"issue",
"author",
"object",
"object_component",
"code_run",
"storage_root",
"storage_location",
"external_object",
"quality_controlled",
"keyword",
"licence",
"namespace",
"data_product",
"code_repo_release",
"key_value")
endpoint <- Sys.getenv("FDP_endpoint")
test_that("check table returns true with correct tables", {
tmp <- lapply(seq_along(tables), function(x) {
check_table_exists(tables[x])
}) %>% unlist()
expect_true(all(tmp))
})
test_that("unknown table returns false", {
expect_false(check_table_exists("unknown"))
})
test_that("invalid table name returns error", {
expect_error(check_table_exists(NULL))
expect_error(check_table_exists(NA))
expect_error(check_table_exists(NaN))
expect_error(check_table_exists(list()))
expect_error(check_table_exists(TRUE))
expect_error(check_table_exists())
})
|
library(Sim.DiffProc)
library(knitr)
knitr::opts_chunk$set(comment="",prompt=TRUE, fig.show='hold', warning=FALSE, message=FALSE)
options(prompt="R> ",scipen=16,digits=5,warning=FALSE, message=FALSE,
width = 70)
set.seed(12345, kind = "L'Ecuyer-CMRG")
f <- expression( (1+2*x) ) ; g <- expression( 0.5*x^0.3 )
sim <- snssde1d(drift=f,diffusion=g,x0=2,N=10^4,Dt=10^-4)
mydata <- sim$X
fx <- expression( theta[1]+theta[2]*x )
gx <- expression( theta[3]*x^theta[4] )
fitmod <- fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1=1, theta2=1,
theta3=1,theta4=1),pmle="euler")
fitmod
coef(fitmod)
summary(fitmod)
vcov(fitmod)
logLik(fitmod)
AIC(fitmod)
BIC(fitmod)
confint(fitmod, level=0.95)
set.seed(1234, kind = "L'Ecuyer-CMRG")
f <- expression( 3*(2-x) ) ; g <- expression( 0.5 )
sim <- snssde1d(drift=f,diffusion=g,x0=5,Dt=0.01)
HWV <- sim$X
fx <- expression( theta[1]*(theta[2]- x) )
gx <- expression( theta[3] )
fitmod <- fitsde(data=HWV,drift=fx,diffusion=gx,start = list(theta1=1,theta2=1,
theta3=1),pmle="ozaki")
summary(fitmod)
confint(fitmod,parm=c("theta1","theta2"),level=0.95)
set.seed(1234, kind = "L'Ecuyer-CMRG")
f <- expression(-2*x*t) ; g <- expression(0.2*x)
sim <- snssde1d(drift=f,diffusion=g,N=1000,Dt=0.001,x0=10)
mydata <- sim$X
fx <- expression( theta[1]*x*t )
gx <- expression( theta[2]*x )
fitmod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
theta2=1),pmle="shoji",lower=c(-3,0),upper=c(-1,1))
summary(fitmod)
set.seed(1234, kind = "L'Ecuyer-CMRG")
f <- expression(3*t*(sqrt(t)-x)) ; g <- expression(0.3*t)
sim <- snssde1d(drift=f,diffusion=g,M=1,N=1000,x0=2,Dt=0.001)
mydata <- sim$X
fx <- expression( theta[1]*t* ( theta[2]*sqrt(t) - x ) )
gx <- expression( theta[3]*t )
fitmod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
theta2=1,theta3=1),pmle="kessler")
summary(fitmod)
set.seed(1234, kind = "L'Ecuyer-CMRG")
f <- expression( 2*x )
g <- expression( 0.3*x^0.5 )
sim <- snssde1d(drift=f,diffusion=g,M=1,N=10000,x0=2,Dt=0.0001)
mydata <- sim$X
fx <- expression( theta[1]*x )
gx <- expression( theta[2]*x^theta[3] )
truemod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
theta2=1,theta3=1),pmle="euler")
fx1 <- expression( theta[1]+theta[2]*x )
gx1 <- expression( theta[3]*x^theta[4] )
mod1 <- fitsde(data=mydata,drift=fx1,diffusion=gx1,start = list(theta1=1,
theta2=1,theta3=1,theta4=1),pmle="euler")
fx2 <- expression( theta[1]+theta[2]*x )
gx2 <- expression( theta[3]*sqrt(x) )
mod2 <- fitsde(data=mydata,drift=fx2,diffusion=gx2,start = list(theta1=1,
theta2=1,theta3=1),pmle="euler")
fx3 <- expression( theta[1] )
gx3 <- expression( theta[2]*x^theta[3] )
mod3 <- fitsde(data=mydata,drift=fx3,diffusion=gx3,start = list(theta1=1,
theta2=1,theta3=1),pmle="euler")
AIC <- c(AIC(truemod),AIC(mod1),AIC(mod2),AIC(mod3))
Test <- data.frame(AIC,row.names = c("True mod","Comp mod1","Comp mod2","Comp mod3"))
Bestmod <- rownames(Test)[which.min(Test[,1])]
Bestmod
Theta1 <- c(coef(truemod)[[1]],coef(mod1)[[1]],coef(mod2)[[1]],coef(mod3)[[1]])
Theta2 <- c(coef(truemod)[[2]],coef(mod1)[[2]],coef(mod2)[[2]],coef(mod3)[[2]])
Theta3 <- c(coef(truemod)[[3]],coef(mod1)[[3]],coef(mod2)[[3]],coef(mod3)[[3]])
Theta4 <- c("",round(coef(mod1)[[4]],5),"","")
Parms <- data.frame(Theta1,Theta2,Theta3,Theta4,row.names = c("True mod",
"Comp mod1","Comp mod2","Comp mod3"))
Parms
data(Irates)
rates <- Irates[, "r1"]
X <- window(rates, start = 1964.471, end = 1989.333)
plot(X)
fx <- expression( theta[1]+theta[2]*x )
gx <- expression( theta[3]*x^theta[4] )
pmle <- eval(formals(fitsde.default)$pmle)
fitres <- lapply(1:4, function(i) fitsde(X,drift=fx,diffusion=gx,pmle=pmle[i],
start = list(theta1=1,theta2=1,theta3=1,theta4=1)))
Coef <- data.frame(do.call("cbind",lapply(1:4,function(i) coef(fitres[[i]]))))
Info <- data.frame(do.call("rbind",lapply(1:4,function(i) logLik(fitres[[i]]))),
do.call("rbind",lapply(1:4,function(i) AIC(fitres[[i]]))),
do.call("rbind",lapply(1:4,function(i) BIC(fitres[[i]]))),
row.names=pmle)
names(Coef) <- c(pmle)
names(Info) <- c("logLik","AIC","BIC")
Coef
Info
set.seed(1234, kind = "L'Ecuyer-CMRG")
f <- expression( (2.076-0.263*x) )
g <- expression( 0.130*x^1.451 )
mod <- snssde1d(drift=f,diffusion=g,x0=X[1],M=500, N=length(X),t0=1964.471, T=1989.333)
mod
plot(mod,type="n",ylim=c(0,30))
lines(X,col=4,lwd=2)
lines(time(mod),apply(mod$X,1,mean),col=2,lwd=2)
lines(time(mod),apply(mod$X,1,bconfint,level=0.95)[1,],col=5,lwd=2)
lines(time(mod),apply(mod$X,1,bconfint,level=0.95)[2,],col=5,lwd=2)
legend("topleft",c("real data","mean path",paste("bound of", 95,"% confidence")),inset = .01,col=c(4,2,5),lwd=2,cex=0.8)
|
sim.es <- function(model="ANN", obs=10, nsim=1,
frequency=1, persistence=NULL, phi=1,
initial=NULL, initialSeason=NULL,
bounds=c("usual","admissible","restricted"),
randomizer=c("rnorm","rlnorm","rt","rlaplace","rs"),
probability=1, ...){
randomizer <- randomizer[1];
ellipsis <- list(...);
bounds <- bounds[1];
if(is.numeric(bounds)){
ellipsis$b <- bounds;
bounds <- "u";
}
if(all(bounds!=c("u","a","r","usual","admissible","restricted"))){
warning(paste0("Strange type of bounds provided: ",bounds,". Switching to 'usual'."),
call.=FALSE);
bounds <- "u";
}
bounds <- substring(bounds[1],1,1);
if(nchar(model)==4){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,4,4);
if(substring(model,3,3)!="d"){
warning(paste0("You have defined a strange model: ",model),call.=FALSE);
model <- paste0(Etype,Ttype,"d",Stype);
}
if(Ttype!="N" & phi==1){
model <- paste0(Etype,Ttype,Stype);
warning(paste0("Damping parameter is set to 1. Changing model to: ",model),call.=FALSE);
}
}
else if(nchar(model)==3){
Etype <- substring(model,1,1);
Ttype <- substring(model,2,2);
Stype <- substring(model,3,3);
if(phi!=1 & Ttype!="N"){
model <- paste0(Etype,Ttype,"d",Stype);
warning(paste0("Damping parameter is set to ",phi,". Changing model to: ",model),call.=FALSE);
}
}
else{
stop(paste0("You have defined a strange model: ",model,". Cannot proceed"),call.=FALSE);
}
nsim <- abs(round(nsim,0));
obs <- abs(round(obs,0));
frequency <- abs(round(frequency,0));
if(!is.null(persistence) & length(persistence)>3){
stop("The length of persistence vector is wrong! It should not be greater than 3.",call.=FALSE);
}
if(phi<0 | phi>2){
warning(paste0("Damping parameter should lie in (0, 2) region! You have chosen phi=",phi,
". Be careful!"),call.=FALSE);
}
if(Etype!="A" & Etype!="M"){
stop("Wrong error type! Should be 'A' or 'M'.",call.=FALSE);
}
else{
persistenceLength <- 1;
componentsNumber <- 1;
lagsModel <- 1;
componentsNames <- "level";
matw <- 1;
matF <- matrix(1,1,1);
}
if(Ttype!="N" & Ttype!="A" & Ttype!="M"){
stop("Wrong trend type! Should be 'N', 'A' or 'M'.",call.=FALSE);
}
else if(Ttype!="N"){
if(is.na(phi) | is.null(phi)){
phi <- 1;
}
persistenceLength <- persistenceLength + 1;
componentsNumber <- componentsNumber + 1;
lagsModel <- c(lagsModel,1);
componentsNames <- c(componentsNames,"trend");
matw <- c(matw,phi);
matF <- matrix(c(1,0,phi,phi),2,2);
componentTrend=TRUE;
if(phi!=1){
model <- paste0(Etype,Ttype,"d",Stype);
}
}
else{
componentTrend=FALSE;
}
if(Stype!="N" & Stype!="A" & Stype!="M"){
stop("Wrong seasonality type! Should be 'N', 'A' or 'M'.",call.=FALSE);
}
if(Stype!="N" & frequency==1){
stop("Cannot create the seasonal model with the data frequency 1!",call.=FALSE);
}
if(Stype!="N"){
persistenceLength <- persistenceLength + 1;
lagsModel <- c(lagsModel,frequency);
componentsNames <- c(componentsNames,"seasonality");
matw <- c(matw,1);
componentSeasonal <- TRUE;
if(!componentTrend){
matF <- matrix(c(1,0,0,1),2,2);
}
else{
matF <- matrix(c(1,0,0,phi,phi,0,0,0,1),3,3);
}
}
else{
componentSeasonal <- FALSE;
}
lagsModel <- matrix(lagsModel,persistenceLength,1);
lagsModelMax <- max(lagsModel);
matw <- matrix(matw,1,persistenceLength);
arrF <- array(matF,c(dim(matF),nsim));
if(!is.null(persistence)){
if(persistenceLength != length(persistence)){
if(length(persistence)!=1){
warning(paste0("The length of persistence vector does not correspond to the chosen model!\n",
"Falling back to random number generator."),call.=FALSE);
persistence <- NULL;
}
else{
persistence <- rep(persistence,persistenceLength);
}
}
}
if(!is.null(initial)){
if(length(initial)>2){
stop("The length of the initial value is wrong! It should not be greater than 2.",call.=FALSE);
}
if(componentsNumber!=length(initial)){
warning(paste0("The length of initial state vector does not correspond to the chosen model!\n",
"Falling back to random number generator."),call.=FALSE);
initial <- NULL;
}
else{
if(Ttype=="M" & initial[2]<=0){
warning(paste0("Wrong initial value for multiplicative trend! It should be greater than zero!\n",
"Falling back to random number generator."),call.=FALSE);
initial <- NULL;
}
}
}
if(!is.null(initialSeason)){
if(lagsModelMax!=length(initialSeason)){
warning(paste0("The length of seasonal initial states does not correspond to the chosen frequency!\n",
"Falling back to random number generator."),call.=FALSE);
initialSeason <- NULL;
}
}
if(all(randomizer!=c("rnorm","rt","rlaplace","rs","rlnorm")) & (length(ellipsis)==0)){
warning(paste0("The chosen randomizer - ",randomizer," - needs some arbitrary parameters! Changing to 'rnorm' now."),call.=FALSE);
randomizer = "rnorm";
}
if(is.vector(probability)){
if(any(probability!=probability[1])){
if(length(probability)!=obs){
warning("Length of probability does not correspond to number of observations.",call.=FALSE);
if(length(probability)>obs){
warning("We will cut off the excessive ones.",call.=FALSE);
probability <- probability[1:obs];
}
else{
warning("We will duplicate the last one.",call.=FALSE);
probability <- c(probability,rep(probability[length(probability)],obs-length(probability)));
}
}
}
else{
probability <- probability[1];
}
}
if(length(probability)==1){
intermittent <- "fixed";
}
else{
intermittent <- "tsb";
}
if(all(probability==1)){
intermittent <- "none";
}
matg <- matrix(NA,persistenceLength,nsim);
arrvt <- array(NA,c(obs+lagsModelMax,persistenceLength,nsim),dimnames=list(NULL,componentsNames,NULL));
materrors <- matrix(NA,obs,nsim);
matyt <- matrix(NA,obs,nsim);
matot <- matrix(NA,obs,nsim);
if(is.null(persistence)){
if(bounds=="u"){
matg[1,] <- runif(nsim,0,1);
}
else if(bounds=="r"){
matg[1,] <- runif(nsim,0,0.3);
}
if(bounds!="a"){
if(Ttype!="N"){
matg[2,] <- runif(nsim,0,matg[1,]);
}
if(Stype!="N"){
matg[persistenceLength,] <- runif(nsim,0,max(0,1-matg[1]));
}
}
else{
matg[,] <- runif(persistenceLength*nsim,1-1/phi,1+1/phi);
if(Ttype!="N"){
matg[2,] <- runif(nsim,matg[1,]*(phi-1),(2-matg[1,])*(1+phi));
if(Stype!="N"){
Theta.func <- function(Theta){
result <- (phi*matg[1,i]+phi+1)/(matg[3,i]) +
((phi-1)*(1+cos(Theta)-cos(lagsModelMax*Theta)) +
cos((lagsModelMax-1)*Theta)-phi*cos((lagsModelMax+1)*Theta))/(2*(1+cos(Theta))*(1-cos(lagsModelMax*Theta)));
return(abs(result));
}
for(i in 1:nsim){
matg[3,i] <- runif(1,max(1-1/phi-matg[1,i],0),1+1/phi-matg[1,i]);
B <- phi*(4-3*matg[3,i])+matg[3,i]*(1-phi)/lagsModelMax;
C <- sqrt(B^2-8*(phi^2*(1-matg[3,i])^2+2*(phi-1)*(1-matg[3,i])-1)+8*matg[3,i]^2*(1-phi)/lagsModelMax);
matg[1,i] <- runif(1,1-1/phi-matg[3,i]*(1-lagsModelMax+phi*(1+lagsModelMax))/(2*phi*lagsModelMax),(B+C)/(4*phi));
Theta <- 0.1;
Theta <- optim(Theta,Theta.func,method="Brent",lower=0,upper=1)$par;
D <- (phi*(1-matg[1,i])+1)*(1-cos(Theta)) - matg[3,i]*((1+phi)*(1-cos(Theta) - cos(lagsModelMax*Theta)) +
cos((lagsModelMax-1)*Theta)+phi*cos((lagsModelMax+1)*Theta))/
(2*(1+cos(Theta))*(1-cos(lagsModelMax*Theta)));
matg[2,i] <- runif(1,-(1-phi)*(matg[3,i]/lagsModelMax+matg[1,i]),D+(phi-1)*matg[1,i]);
}
}
}
else{
if(Stype!="N"){
matg[1,] <- runif(nsim,-2/(lagsModelMax-1),2);
for(i in 1:nsim){
matg[2,i] <- runif(1,max(-lagsModelMax*matg[1,i],0),2-matg[1,i]);
}
matg[1,] <- runif(nsim,-2/(lagsModelMax-1),2-matg[2,]);
}
}
}
}
else{
matg[,] <- rep(persistence,nsim);
}
if(is.null(initial)){
if(Ttype=="N"){
arrvt[1:lagsModelMax,1,] <- runif(nsim,0,1000);
}
else if(Ttype=="A"){
arrvt[1:lagsModelMax,1,] <- runif(nsim,0,5000);
arrvt[1:lagsModelMax,2,] <- runif(nsim,-100,100);
}
else{
arrvt[1:lagsModelMax,1,] <- runif(nsim,500,5000);
arrvt[1:lagsModelMax,2,] <- 1;
}
initial <- matrix(arrvt[1,1:componentsNumber,],ncol=nsim);
}
else{
arrvt[,1:componentsNumber,] <- rep(rep(initial,each=(obs+lagsModelMax)),nsim);
initial <- matrix(arrvt[1,1:componentsNumber,],ncol=nsim);
}
if(componentSeasonal & is.null(initialSeason)){
if(Stype == "A"){
arrvt[1:lagsModelMax,componentsNumber+1,] <- runif(nsim*lagsModelMax,-500,500);
for(i in 1:nsim){
arrvt[1:lagsModelMax,componentsNumber+1,i] <- arrvt[1:lagsModelMax,componentsNumber+1,i] - mean(arrvt[1:lagsModelMax,componentsNumber+1,i]);
}
}
else{
arrvt[1:lagsModelMax,componentsNumber+1,] <- runif(nsim*lagsModelMax,0.3,1.7);
for(i in 1:nsim){
arrvt[1:lagsModelMax,componentsNumber+1,i] <- arrvt[1:lagsModelMax,componentsNumber+1,i] / exp(mean(log(arrvt[1:lagsModelMax,componentsNumber+1,i])));
}
}
initialSeason <- matrix(arrvt[1:lagsModelMax,componentsNumber+1,],ncol=nsim);
}
else if(componentSeasonal & !is.null(initialSeason)){
arrvt[1:lagsModelMax,componentsNumber+1,] <- rep(initialSeason,nsim);
initialSeason <- matrix(arrvt[1:lagsModelMax,componentsNumber+1,],ncol=nsim);
}
if(length(ellipsis)==0){
ellipsis$n <- nsim*obs;
if(any(randomizer==c("rnorm","rlaplace","rs"))){
materrors[,] <- do.call(randomizer,ellipsis);
}
else if(randomizer=="rt"){
materrors[,] <- rt(nsim*obs,obs-(persistenceLength + lagsModelMax));
}
else if(randomizer=="rlnorm"){
materrors[,] <- rlnorm(n=nsim*obs,0,0.01+(1-probability));
materrors <- materrors - 1;
}
if(randomizer!="rlnorm"){
if(Etype=="M"){
if(any(probability!=1)){
materrors <- materrors * 0.5;
}
else{
materrors <- materrors * 0.1;
}
materrors <- exp(materrors) - 1;
}
else if(Etype=="A"){
if(all(arrvt[1,1,]!=0)){
materrors <- materrors * sqrt(abs(arrvt[1,1,]));
}
if(randomizer=="rs"){
materrors <- materrors / 4;
}
}
}
}
else{
ellipsis$n <- nsim*obs;
materrors[,] <- do.call(randomizer,ellipsis);
if(randomizer=="rbeta"){
materrors <- materrors - 0.5;
materrors <- materrors / rep(sqrt(colMeans(materrors^2)) * sqrt(abs(arrvt[1,1,])),each=obs);
}
else if(randomizer=="rt"){
materrors <- materrors * rep(sqrt(abs(arrvt[1,1,])),each=obs);
}
if(Etype=="M"){
materrors <- materrors - 1;
}
}
if(all(probability == 1)){
matot[,] <- 1;
}
else{
matot[,] <- rbinom(obs*nsim,1,probability);
}
simulateddata <- simulatorwrap(arrvt,materrors,matot,arrF,matw,matg,Etype,Ttype,Stype,lagsModel);
matyt <- simulateddata$matyt;
arrvt <- simulateddata$arrvt;
dimnames(arrvt) <- list(NULL,componentsNames,NULL);
if(any(randomizer==c("rnorm","rt"))){
veclikelihood <- -obs/2 *(log(2*pi*exp(1)) + log(colMeans(materrors^2)));
}
else if(randomizer=="rlaplace"){
veclikelihood <- -obs*(log(2*exp(1)) + log(colMeans(abs(materrors))));
}
else if(randomizer=="rs"){
veclikelihood <- -2*obs*(log(2*exp(1)) + log(0.5*colMeans(sqrt(abs(materrors)))));
}
else if(randomizer=="rlnorm"){
veclikelihood <- -obs/2 *(log(2*pi*exp(1)) + log(colMeans(materrors^2))) - colSums(log(matyt));
}
else if(randomizer=="rinvgauss"){
veclikelihood <- -0.5*(obs*(log(colMeans(materrors^2/(1+materrors))/(2*pi))-1) +
sum(log(matyt/(1+materrors))) - 3*sum(log(matyt)));
}
else{
veclikelihood <- NA;
}
if(nsim==1){
matyt <- ts(matyt[,1],frequency=frequency);
materrors <- ts(materrors[,1],frequency=frequency);
arrvt <- ts(arrvt[,,1],frequency=frequency,start=c(0,frequency-lagsModelMax+1));
matot <- ts(matot[,1],frequency=frequency);
}
else{
matyt <- ts(matyt,frequency=frequency);
materrors <- ts(materrors,frequency=frequency);
matot <- ts(matot,frequency=frequency);
}
if(Ttype!="N"){
rownames(matg) <- c("alpha","beta","gamma")[1:persistenceLength];
}
else{
rownames(matg) <- c("alpha","gamma")[1:persistenceLength];
}
model <- paste0("ETS(",model,")");
if(any(probability!=1)){
model <- paste0("i",model);
}
if(any(is.nan(matyt))){
warning("NaN values were produced by the simulator.",call.=FALSE);
}
model <- list(model=model, data=matyt, states=arrvt, persistence=matg, phi=phi,
initial=initial, initialSeason=initialSeason, probability=probability, intermittent=intermittent,
residuals=materrors, occurrence=matot, logLik=veclikelihood, other=ellipsis);
return(structure(model,class="smooth.sim"));
}
|
regmixEM = function (y, x, lambda = NULL, beta = NULL, sigma = NULL, k = 2,
addintercept = TRUE, arbmean = TRUE, arbvar = TRUE, epsilon = 1e-08, maxit = 10000,
verb = FALSE)
{
if(arbmean == FALSE && arbvar == FALSE){
stop(paste("Must change constraints on beta and/or sigma!","\n"))
}
s = sigma
if (addintercept) {
x = cbind(1, x)
}
n <- length(y)
p <- ncol(x)
tmp <- regmix.init(y = y, x = x, lambda = lambda, beta = beta,
s = s, k = k, addintercept = addintercept, arbmean = arbmean, arbvar = arbvar)
lambda <- tmp$lambda
beta <- tmp$beta
s <- tmp$s
k <- tmp$k
diff <- 1
iter <- 0
xbeta <- x %*% beta
res <- (y - xbeta)^2
if(arbmean == FALSE){
res <- sapply(1:k,function(i) res)
}
comp <- t((lambda/sqrt(2 * pi * s^2)) * t(exp(-t(t(res)/(2 *
s^2)))))
obsloglik <- sum(log(apply(comp, 1, sum)))
ll <- obsloglik
z = matrix(nrow = n, ncol = k)
restarts <- 0
while (diff > epsilon && iter < maxit) {
for (i in 1:n) {
for (j in 1:k) {
z.denom = c()
for (h in 1:k) {
z.denom = c(z.denom, (lambda[h]/lambda[j]) *
(s[j * arbvar + (1 - arbvar)]/s[h * arbvar +
(1 - arbvar)]) * exp(-0.5 * ((1/s[h * arbvar +
(1 - arbvar)]^2) * res[i, h] - (1/s[j * arbvar +
(1 - arbvar)]^2) * res[i, j])))
}
z[i, j] = 1/sum(z.denom)
}
}
z = z/apply(z,1,sum)
lambda.new <- apply(z, 2, mean)
if (sum(lambda.new < 1e-08)>0 || is.na(sum(lambda.new))) {
sing <- 1
}
else {
if (arbmean == FALSE) {
if (addintercept) {
beta.new <- lm(y~x[,-1],weights=apply(t(t(z)/(s^2)),1,sum))$coef
}
else beta.new <- lm(y~x-1,weights=apply(t(t(z)/(s^2)),1,sum))$coef
} else {
if (addintercept) {
lm.out <- lapply(1:k, function(i) lm(y ~ x[,
-1], weights = z[, i]))
}
else lm.out <- lapply(1:k, function(i) lm(y ~ x -
1, weights = z[, i]))
beta.new <- sapply(lm.out, coef)
}
xbeta.new <- x %*% beta.new
res <- (y - xbeta.new)^2
if(arbmean == FALSE){
res <- sapply(1:k,function(i) res)
}
if (arbvar) {
s.new <- sqrt(sapply(1:k, function(i) sum(z[,
i] * (res[, i]))/sum(z[, i])))
}
else s.new <- sqrt(sum(z * res)/n)
lambda <- lambda.new
beta <- beta.new
xbeta <- x%*%beta
s <- s.new
sing <- sum(s < 1e-08)
comp <- lapply(1:k, function(i) lambda[i] * dnorm(y, xbeta[,i * arbmean + (1 - arbmean)],
s[i * arbvar + (1 - arbvar)]))
comp <- sapply(comp, cbind)
compsum <- apply(comp, 1, sum)
newobsloglik <- sum(log(compsum))
}
if (sing > 0 || is.na(newobsloglik) || newobsloglik < obsloglik || abs(newobsloglik) ==
Inf){
cat("Need new starting values due to singularity...",
"\n")
restarts <- restarts + 1
if(restarts>15) stop("Too many tries!")
tmp <- regmix.init(y = y, x = x, k = k, addintercept = addintercept,
arbmean = arbmean, arbvar = arbvar)
lambda <- tmp$lambda
beta <- tmp$beta
s <- tmp$s
k <- tmp$k
diff <- 1
iter <- 0
xbeta <- x %*% beta
res <- (y - xbeta)^2
if(arbmean == FALSE){
res <- sapply(1:k,function(i) res)
}
comp <- t((lambda/sqrt(2 * pi * s^2)) * t(exp(-t(t(res)/(2 *
s^2)))))
obsloglik <- sum(log(apply(comp, 1, sum)))
ll <- obsloglik
}
else {
diff <- newobsloglik - obsloglik
obsloglik <- newobsloglik
ll <- c(ll, obsloglik)
iter <- iter + 1
if (verb) {
cat("iteration=", iter, "diff=", diff, "log-likelihood",
obsloglik, "\n")
}
}
}
scale.order = order(s)
sigma.min = min(s)
if (iter == maxit) {
cat("WARNING! NOT CONVERGENT!", "\n")
}
cat("number of iterations=", iter, "\n")
if(arbmean == FALSE){
z=z[,scale.order]
names(beta) <- c(paste("beta", ".", 0:(p-1), sep = ""))
colnames(z) <- c(paste("comp", ".", 1:k, sep = ""))
a=list(x=x, y=y, lambda = lambda[scale.order], beta = beta, sigma = sigma.min, scale = s[scale.order]/sigma.min, loglik = obsloglik,
posterior = z[,scale.order], all.loglik=ll, restarts = restarts, ft="regmixEM")
class(a) = "mixEM"
a
} else {
rownames(beta) <- c(paste("beta", ".", 0:(p-1), sep = ""))
colnames(beta) <- c(paste("comp", ".", 1:k, sep = ""))
colnames(z) <- c(paste("comp", ".", 1:k, sep = ""))
a=list(x=x, y=y, lambda = lambda, beta = beta, sigma = s, loglik = obsloglik,
posterior = z, all.loglik=ll, restarts = restarts, ft="regmixEM")
class(a) = "mixEM"
a
}
}
|
GetWriteR =
function(UseGitHub = TRUE) {
if (interactive()) {
if (.Platform$OS.type == "windows") {
if (requireNamespace("BrailleR")) {
if (UseGitHub) {
download.file(
"https://raw.github.com/ajrgodfrey/WriteR/master/Python/WriteR.zip",
"WriteR.zip")
unzip("WriteR.zip")
file.remove("WriteR.zip")
file.rename("WriteR.exe",
paste0(getOption("BrailleR.Folder"), "WriteR.exe"))
message(
"The WriteR application has been added to your MyBrailleR folder.")
} else {
browseURL("https://R-Resources.massey.ac.nz/WriteR/WriteR.zip")
message(
"The WriteR application has been downloaded but you need to unzip it.")
message("Move it to your MyBrailleR folder and unzip before use.")
}
message(
"It is assumed you wanted to download this file by issuing the last command.")
message(
"You can delete WriteR.exe at any time to remove WriteR from your system.")
}
} else {
.WindowsOnly
}
} else {
.InteractiveOnly()
}
return(invisible(NULL))
}
GetWriteR = function(UseGitHub = TRUE) {
.TempUnavailable()
}
|
lv3 <- new("odeModel",
main = function(time, init, parms, inputs) {
s.in <- approxTime1(inputs, time, rule = 2)["s.in"]
with(as.list(c(init, parms)),{
ds <- s.in - b*s*p + g*k
dp <- c*s*p - d*k*p
dk <- e*p*k - f*k
list(c(ds, dp, dk), s.in = s.in)
})
},
parms = c(b = 0.1, c = 0.1, d = 0.1, e = 0.1, f = 0.1, g = 0),
times = c(from = 0, to = 200, by = 1),
inputs = as.matrix(
data.frame(
time = c(0, 99, 100, 101, 200),
s.in = c(0.1, 0.1, 0.5, 0.1, 0.1)
)
),
init = c(s = 1, p = 1, k = 1),
solver = "lsoda"
)
|
residualsStudy <- function(model)
{
residuals <- model$model$residuals
if (model$type=="PolyMARS"){
Yfit <- model$model$fitted
} else Yfit <- model$model$fitted.values
op <- par(mfrow = c(1, 3),pty="s")
plot(residuals ,ylab = "residuals")
plot(Yfit,residuals,xlab = "fitted values",ylab = "residuals")
hist(residuals, freq=FALSE,xlab = "residuals", ylab="density",main= "",
ylim=c(0,max(density(residuals)$y, hist(residuals,plot=FALSE)$density)))
lines(density(residuals) ,col="red")
par(op)
title("Residuals study", cex.main=1.3)
}
|
MixedDistributionError <- function(Means, SDs, Weights, Data, rho = 0.5, breaks = NULL, Kernels = NULL, ErrorMethod = "chisquare"){
if(!is.vector(Data)) stop("Data is not a vector")
iqr = quantile(Data, 0.75) - quantile(Data, 0.25)
obw = 3.49 * (min(sd(Data), iqr/1.349) / nrow(Data)^(1/3))
NoBins = max((max(Data) - min(Data)) / obw, 10)
if(is.null(breaks)) breaks = seq(min(Data),max(Data), length.out=length(NoBins)+1)
if(is.null(Kernels)) Kernels = as.vector(seq(min(Data), max(Data), length.out = NoBins))
distvec = (stats::dist(as.matrix(Data)))
ParetoRad = quantile(distvec, 18/100, na.rm = TRUE)
ParetoRad = ParetoRad * 4 / length(Data)^0.2
inRad = sapply(Kernels, function(k) (Data >= (k - ParetoRad)) & (Data <= (k + ParetoRad)))
nrInRad = as.vector(colSums(inRad))
normv = pracma::trapz(Kernels, nrInRad)
paretoDensity = nrInRad / normv
if(ErrorMethod == "pdeerror"){
V = Pdf4Mixtures(Kernels, Means, SDs, Weights)
SimilarityError = sum(abs(V$PDFmixture - paretoDensity))
}
else if(ErrorMethod == "chisquare"){
estimatedBins = BinProb4Mixtures(Means, SDs, Weights, breaks)*length(Data)
norm = estimatedBins
norm[norm<1]=1
observedBins = hist(Data, breaks=breaks, plot = F)$counts
diffssq = (observedBins - estimatedBins)^2
diffssq[diffssq<4] = 0
SimilarityError = sum(diffssq/norm)
SimilarityError = SimilarityError / length(Data)
}
else{
stop(paste("ErrorMethod", ErrorMethod, "is not recognized. Please use either 'pdeeerror' or 'chisquare'"))
}
OError = OverlapErrorByDensity(Means, SDs, Weights, Data, Kernels)$OverlapError
DistributionError = rho*SimilarityError + (1-rho)*OError
return(list(SimilarityError = SimilarityError, OverlapError = OError, MixedDistributionError = DistributionError))
}
|
dvariogram <-
function(obs, vth=NULL,vlen = NULL)
{
z <- matrix(obs[,4],ncol=1)
G <- (dist(z))^2/2
tstamp <- matrix(obs[,3],ncol=1)
coords <- matrix(obs[,1:2],ncol=2)
dx <- diff(range(coords[,1]))
dy <- diff(range(coords[,2]))
dmax <- sqrt(dx^2 + dy^2)
tmax <- diff(range(tstamp))
if(is.null(vth)) vth <- c(dmax,tmax)*.75
if(is.null(vlen)) vlen <- c(15,floor(tmax))
dmat <- dist(coords)
tmat <- dist(tstamp)
dbins <- seq(-1e-5,vth[1]+0.01,len=vlen[1]+1)
tbins <- seq(-0.01,vth[2]+0.01,len=vlen[2]+1)
dii <- findInterval(dmat,dbins)
tii <- findInterval(tmat,tbins)
tmp <- aggregate(x=as.vector(G),by=list(dbins=(dii),tbins=(tii)),FUN=mean)
ddist <- dbins[-1]
tdist <- tbins[-1]
gamma.long <- data.frame(s=ddist[tmp[,1]],t=tdist[tmp[,2]],gamma=tmp[,3])
vmm <- matrix(NA,ncol=length(dbins),nrow=length(tbins))
colnames(vmm) <- paste('(',round(dbins,2),
',',c(round(dbins[-1],2),999),')',sep='')
row.names(vmm) <- paste('(',round(tbins,2),
',',c(round(tbins[-1],2),999),')',sep='')
vmm[as.matrix(tmp[,2:1])] <- tmp[,3]
nmm <- matrix(0,ncol=length(dbins),nrow=length(tbins))
tmp <- aggregate(x=as.vector(G),
by=list(dbins=as.vector(dii),tbins=as.vector(tii)),
FUN=length)
gamma.long$n <- tmp[,3]
nmm[as.matrix(tmp[,2:1])] <- tmp[,3]
colnames(nmm) <- paste('(',round(dbins,2),
',',c(round(dbins[-1],2),999),')',sep='')
row.names(nmm) <- paste('(',round(tbins,2),
',',c(round(tbins[-1],2),999),')',sep='')
vmm <- vmm[-nrow(vmm),-ncol(vmm)]
nmm <- nmm[-nrow(nmm),-ncol(nmm)]
ii <- with(gamma.long, which(is.na(s) | is.na(t)))
gamma.s0 <- data.frame(x=ddist,n=nmm[1,],gamma=vmm[1,])
gamma.0t <- data.frame(x=tdist,n=nmm[,1],gamma=vmm[,1])
list(gamma=vmm,gamma.s0=gamma.s0,gamma.0t=gamma.0t,gamma.long=gamma.long[-ii,],np = nmm, ddist=ddist,tdist=tdist)
}
|
combinedist <-
function(..., method=c("median", "mean"))
{
v <- list(...)
if(length(v) == 1 && is.list(v[[1]])) v <- v[[1]]
if(!all(sapply(v, function(a) inherits(a, "lineupdist"))))
stop("Input distance matrices must each be of class \"lineupdist\".")
cl <- lapply(v, class)
cl_len <- sapply(cl, length)
if(!all(cl_len == cl_len[1])) {
stop("Need all of the distance matrices to be the same type.")
}
else {
same <- sapply(cl, function(a) all(a==cl[[1]]))
if(!all(same)) {
stop("Need all of the distance matrices to be the same type.")
}
}
rn <- unique(unlist(lapply(v, rownames)))
cn <- unique(unlist(lapply(v, colnames)))
method <- match.arg(method)
d <- array(dim=c(length(rn), length(cn), length(v)))
dimnames(d) <- list(rn, cn, names(v))
for(i in seq(along=v))
d[rownames(v[[i]]),colnames(v[[i]]),i] <- v[[i]]
if(method=="mean" && all(sapply(v, function(a) !is.null(attr(a, "denom"))))) {
use.denom <- TRUE
denom <- array(dim=c(length(rn), length(cn), length(v)))
dimnames(denom) <- list(rn, cn, names(v))
for(i in seq(along=v))
denom[rownames(v[[i]]), colnames(v[[i]]), i] <- attr(v[[i]], "denom")
}
else use.denom <- FALSE
if(method=="median")
ds <- apply(d, 1:2, median, na.rm=TRUE)
else if(use.denom) {
denom.sum <- apply(denom, 1:2, sum, na.rm=TRUE)
ds <- apply(d*denom, 1:2, sum, na.rm=TRUE)/denom.sum
attr(ds, "denom") <- denom.sum
}
else
ds <- apply(d, 1:2, mean, na.rm=TRUE)
class(ds) <- class(v[[1]])
possible.attributes <- c("d.method", "compareWithin")
for(i in possible.attributes[possible.attributes %in% names(attributes(v[[1]]))])
attr(ds, i) <- attr(v[[1]], i)
ds
}
|
geom_parallel_slopes <- function(mapping = NULL, data = NULL,
position = "identity", ...,
se = TRUE, formula = y ~ x, n = 100,
fullrange = FALSE, level = 0.95,
na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE) {
dots <- list(...)
if ("method" %in% names(dots)) {
warning(
"`geom_parallel_slopes()` doesn't need a `method` argument ",
'("lm" is used).',
call. = FALSE
)
dots <- dots[setdiff(names(dots), "method")]
}
stat_params <- c(
na.rm = na.rm, se = se, formula = formula, n = n, fullrange = fullrange,
level = level
)
params <- c(stat_params, dots)
ggplot2::layer(
geom = ggplot2::GeomSmooth, stat = StatParallelSlopes, data = data,
mapping = mapping, position = position, params = params,
inherit.aes = inherit.aes, show.legend = show.legend
)
}
StatParallelSlopes <- ggplot2::ggproto(
"StatParallelSlopes", ggplot2::Stat,
required_aes = c("x", "y"),
compute_panel = function(data, scales, se = TRUE, formula = y ~ x, n = 100,
fullrange = FALSE, level = 0.95) {
if (nrow(data) == 0) {
return(data[integer(0), ])
}
model_info <- compute_model_info(data, formula)
formula <- model_info$formula
data <- model_info$data
model <- stats::lm(formula = formula, data = data)
groups <- split(data, data$group)
groups_new_data <- lapply(
X = groups, FUN = compute_group_new_data,
scales = scales, n = n, fullrange = fullrange
)
stats <- lapply(
X = groups_new_data, FUN = predict_df,
model = model, se = se, level = level
)
stats <- mapply(restore_unique_cols, stats, groups, SIMPLIFY = FALSE)
dplyr::bind_rows(stats)
}
)
compute_model_info <- function(data, formula) {
if (has_unique_value(data$group)) {
warning(
"`geom_parallel_slopes()` didn't recieve a grouping variable with more ",
"than one unique value. Make sure you supply one. Basic model is fitted.",
call. = FALSE
)
} else {
data$group <- as.factor(data$group)
formula <- as.formula(paste0(deparse(formula), " + group"))
}
list(formula = formula, data = data)
}
compute_group_new_data <- function(group_df, scales, n, fullrange) {
if (fullrange) {
support <- scales$x$dimension()
} else {
support <- range(group_df$x, na.rm = TRUE)
}
x_seq <- seq(support[1], support[2], length.out = n)
group_seq <- rep(group_df$group[1], n)
data.frame(x = x_seq, group = group_seq)
}
predict_df <- function(model, new_data, se, level) {
pred <- stats::predict(
model,
newdata = new_data, se.fit = se, level = level,
interval = if (se) "confidence" else "none"
)
if (isTRUE(se)) {
fit <- as.data.frame(pred$fit)
names(fit) <- c("y", "ymin", "ymax")
data.frame(x = new_data$x, fit, se = pred$se.fit)
} else {
data.frame(x = new_data$x, y = as.vector(pred))
}
}
restore_unique_cols <- function(new, old) {
is_unique <- sapply(old, has_unique_value)
unique_df <- old[1, is_unique, drop = FALSE]
rownames(unique_df) <- seq_len(nrow(unique_df))
missing <- !(names(unique_df) %in% names(new))
cbind(new, unique_df[rep(1, nrow(new)), missing, drop = FALSE])
}
has_unique_value <- function(x) {
length(unique(x)) <= 1
}
|
ravelry_get <- function(path, query = NULL) {
url <- httr::modify_url(url = "https://api.ravelry.com/", path = path, query = query)
url <- gsub('\\%2B', '+', url)
response <- httr::GET(url,
authenticate(
Sys.getenv('RAVELRY_USERNAME'),
Sys.getenv('RAVELRY_PASSWORD')
),
user_agent("http://github.com/walkerkq/ravelRy")
)
if (httr::http_type(response) != "application/json") {
stop("API did not return json", call. = FALSE)
} else if(response$status_code != 200) {
stop(paste0("API returned status ", response$headers$status), call. = FALSE)
}
response
}
|
"traffic2"
|
NN_EXECUTION <<- TRUE
nn.modelo <- function(variable.pr = NULL, threshold = 0.01, stepmax = 1000, cant.cap = 2, ...){
threshold <- ifelse(threshold == 0, 0.01, threshold)
stepmax <- ifelse(stepmax < 100, 100, stepmax)
capas <- as.string.c(as.numeric(list(...)[1:cant.cap]), .numeric = TRUE)
return(paste0("modelo.nn <<- train.neuralnet(",variable.pr,"~., data = datos.aprendizaje, hidden = ",capas,",\n\t\t\tlinear.output = FALSE,",
"threshold = ",threshold,", stepmax = ",stepmax,")\n"))
}
nn.prediccion <- function() {
return(paste0("prediccion.nn <<- predict(modelo.nn, datos.prueba, type = 'class')"))
}
nn.MC <- function(){
return(paste0("MC.nn <<- confusion.matrix(datos.prueba, prediccion.nn)","\n"))
}
nn.plot <- function(){
paste0("plot(modelo.nn,,arrow.length = 0.1, rep = 'best', intercept = T,x.entry = 0.1, x.out = 0.9,\n\t",
"information=F,intercept.factor = 0.8,col.entry.synapse='red',col.entry='red',col.out='green',col.out.synapse='green',\n\t",
"dimension=15, radius = 0.2, fontsize = 10)")
}
|
uptimerobot.account.details <- function(api.key, unlist = FALSE){
if(is.null(api.key) |
is.na(api.key) |
(is.character(api.key) & nchar(api.key)==0)
) stop("api.key cannot be empty or NULL")
data <- fromJSON(
getURL(
URLencode(paste0("https://api.uptimerobot.com/getAccountDetails?apiKey=",
api.key,
"&format=json&noJsonCallback=1"
)
)
),
unexpected.escape="keep"
)
if(data$stat=="ok") {
if(!unlist) return(lapply(data$account, function(x){ as.integer(x)}))
data.unlisted <- as.integer(unlist(data$account))
names(data.unlisted) <- names(unlist(data$account))
return(data.unlisted)
}
else {
stop(data$message)
}
}
|
makeLabels <- function(theMin,theMax,unit="") {
if (!is.na(as.numeric(theMin)) & !is.na(as.numeric(theMax))) {
step <- 10^floor(log10(theMax-theMin))
minorFactor <- 2
if ((theMax-theMin)/step < 6) {
step <- step/2
minorFactor <- 2
}
if ((theMax-theMin)/step < 6) {
step <- step/2
minorFactor <- 2.5
}
if ((theMax-theMin)/step < 6) {
step <- step/2.5
minorFactor <- 2
}
minTick <- step*ceiling(theMin/step)
if ((minTick-theMin)/step > 0.5) minTick <- minTick-step
maxTick <- step*floor(theMax/step)
if ((theMax-maxTick)/step > 0.5) maxTick <- maxTick+step
labels <- seq(from = minTick, to = maxTick, by = step)
if (log10(step) < 0) {
prefix <- switch(abs(ceiling(log10(step)/3))+1,"","m","\u00B5","n","p","f","a")
divisor <- switch(abs(ceiling(log10(step)/3))+1,1,0.001,0.000001,0.000000001,0.000000000001,0.000000000000001,0.000000000000000001)
} else {
prefix <- switch(floor(log10(step)/3)+1,"","k","M","G","T","P","E")
divisor <- switch(floor(log10(step)/3)+1,1,1000,1000000,1000000000,1000000000000,1000000000000000,1000000000000000000)
}
names <- paste0(labels/divisor,prefix,unit)
names <- gsub(paste0("^",0,prefix,unit),"0",names)
labels <- data.frame(ticks=labels,labels=names)
attr(labels,"minorTickFactor") <- as.character(minorFactor)
return(labels)
} else {
stop("There is a problem with the supplied minimum and maximum!")
}
}
|
vbmfa <- function(Y, qmax = NULL, maxtries = 3, verbose = FALSE, varimax = FALSE){
k <- qmax
if(is.null(k)){
k = dim(Y)[2]
qmax <- k-1
} else {
k <- qmax + 1
}
stopifnot("verbose should be either TRUE or FALSE"={verbose %in% c(TRUE,FALSE)})
stopifnot("varimax should be either TRUE or FALSE"={varimax %in% c(TRUE,FALSE)})
stopifnot("Y should be an n by p numeric matrix"={is.numeric(Y)&is.matrix(Y)})
stopifnot("qmax should be numeric"={is.numeric(qmax)})
stopifnot("qmax should be an integer"={qmax%%1==0})
stopifnot("qmax must be greater than zero and less than or equal to p-1" = {(qmax>0) & (qmax <= k-1)} )
cat(sprintf("Variational Bayesian MFA fitting process begun...\n"))
start.time <- Sys.time()
pcaflag=0
Fflag=0
dsp=pi
Y <- t(Y)
dims <- dim(Y)
p <- dims[1]; n <- dims[2]
selection_method = 4;
getbeta = NULL;
net = list(
model = "variational MFA",
hparams = list(mcl = NULL,psii=NULL,pa=NULL,pb=NULL,alpha=NULL),
params = list(Lm = NULL, Lcov = NULL, u = NULL),
hidden = list(Xm = NULL, Xcov = NULL, Qns = NULL, a = NULL, b = NULL),
Fhist = NULL
)
pa = 1; pb = 1; alpha = 1; psii = matrix(rep(1,p), nrow = p)
Lm = vector(mode = "list")
Lcov = vector(mode = "list")
Lm[[1]] = matrix(rnorm(p*k), nrow = p)
Lcov[[1]] = array(rep(diag(k),p),dim = c(k,k,p))
u = 1;
mean_mcl = matrix(rowMeans(Y),ncol = 1);
nu_mcl = (1/matrix(sqrt(rowSums((Y - matrix(rep(mean_mcl,n),ncol=n))^2/(n-1))),ncol=1))^2;
Xcov = vector(mode = "list")
Xcov[[1]] = array(0,dim = c(k,k,n));
s = length(Lm);
pu = alpha*matrix(rep(1,s),nrow = 1)/s;
psimin = 1e-5;
maintol = 6;
finetol = 8;
tries = 0;
removal = 1;
F_ = -Inf;
Fhist = NULL;
it = 0; I = diag(k-1);
tol = exp(-maintol);
Qns = matrix(rep(1,n*s),nrow = n)/s;
b <- list()
Xm = vector(mode = "list")
trXm = vector(mode = "list")
tempOut <- inferQX(Y,Lm,Lcov,psii,Xcov,trXm,Xm)
Xcov = tempOut$Xcov
trXm = tempOut$trXm
Xm = tempOut$Xm
candorder = matrix()
cophd = NULL
tempOut <- inferQns(Y,Lm,Qns,psii,Lcov,Xm,Xcov,candorder,b,u,pu,removal,parent,pos,cophd, verbose)
Qns = tempOut$Qns; Ps = tempOut$Ps; Lm = tempOut$Lm; Lcov = tempOut$Lcov;
Xm = tempOut$Xm; Xcov = tempOut$Xcov; b = tempOut$b; u = tempOut$u;
pu = tempOut$pu; s = tempOut$s; dQns_sagit = tempOut$dQns_sagit; candorder = tempOut$candorder;
cophd = tempOut$cophd;
tempOut <- inferQnu(Lm,pa,pb,Lcov,b)
s = tempOut$s; b = tempOut$b; a = tempOut$a
num = vector(mode = "list")
tempOut <- learn(it, Y,Lm,Lcov,psii,Xcov,trXm,Xm,
mean_mcl,nu_mcl,a,b,Qns,candorder,u,pu,
removal, alpha, pa, pb, pcaflag, psimin, num, parent, pos, cophd, verbose)
it = tempOut$it; Xm=tempOut$Xm; trXm= tempOut$trXm; Xcov = tempOut$Xcov;
mean_Lambda = tempOut$mean_Lambda; num = tempOut$num; Lcov = tempOut$Lcov;
Lm = tempOut$Lm; Qns = tempOut$Qns; Ps = tempOut$Ps; b = tempOut$b;
u = tempOut$u; pu = tempOut$pu; s = tempOut$s; dQns_sagit = tempOut$dQns_sagit;
a = tempOut$a; psi = tempOut$psi; psii = tempOut$psii; zeta = tempOut$zeta;
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl
candorder = tempOut$candorder; cophd = tempOut$cophd;
workspace = list(psii = psii, Lm = Lm, Lcov = Lcov, Qns = Qns, a = a, b = b, u = u)
candorder = cbind(1:length(Lm),0); pos = 1; parent = candorder[pos];
epoch = 0;
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha,Lcov,Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
Ftarg = F_;
cumul_time = 0;
time.vec <- c(0)
birthFail = FALSE
while(parent != 0){
loop.start.time <- Sys.time()
epoch = epoch+1; cophd = 0;
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha,Lcov,Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
Ftarg = F_; dF = 0;
if(length(Lm) < parent){birthFail = TRUE; break}
tempOut = dobirth(Y,Lm,psii,parent,Qns,Lcov,b,u,pu,alpha, Xcov, trXm, Xm, mean_mcl,nu_mcl,a,num, verbose)
Lcov = tempOut$Lcov; Lm = tempOut$Lm; b=tempOut$b; u=tempOut$u; pu = tempOut$pu;
mean_Lambda = tempOut$mean_Lambda; num = tempOut$num;
Xcov = tempOut$Xcov; trXm = tempOut$trXm; Xm = tempOut$Xm
Qns = tempOut$Qns
tol = maintol;
dQns_sagit = tol;
counter = 0
while (length(which(dQns_sagit>=exp(-tol))) > 0 ) {
tempOut <- learn(it, Y,Lm,Lcov,psii,Xcov,trXm,Xm,
mean_mcl,nu_mcl,a,b,Qns,candorder,u,pu,
removal, alpha, pa, pb, pcaflag, psimin, num, parent, pos, cophd, verbose)
it = tempOut$it; Xm=tempOut$Xm; trXm= tempOut$trXm; Xcov = tempOut$Xcov;
mean_Lambda = tempOut$mean_Lambda; num = tempOut$num; Lcov = tempOut$Lcov;
Lm = tempOut$Lm; Qns = tempOut$Qns; Ps = tempOut$Ps; b = tempOut$b;
u = tempOut$u; pu = tempOut$pu; s = tempOut$s; dQns_sagit = tempOut$dQns_sagit;
a = tempOut$a; psi = tempOut$psi; psii = tempOut$psii; zeta = tempOut$zeta;
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl
candorder = tempOut$candorder; cophd = tempOut$cophd;
counter = counter + 1
if(Fflag == 1){
dF_old = dF;
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha, Lcov, Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
if(verbose){
print(paste('Iteration: ',it, ', F-value: ', F_, ', Change in F: ', dF, '.'))}
}
}
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha, Lcov, Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
if(F_ > Ftarg){
if(verbose){
print(paste('Accepting F= ', F_, ' (', Ftarg,')'))};
if(cophd == 1){
if(verbose){
print('Child of parent has died, no reordering.')};
pos = pos+1;
} else {
tempOut <- ordercands(Y,Lm,Fmatrix,Qns,getbeta,selection_method, verbose)
pos = tempOut$pos; candorder = tempOut$candorder
tries = 0;
}
workspace = list( psii=psii, Lm = Lm, Lcov = Lcov, Qns = Qns, a = a, b = b,u= u);
tempOut <- inferQX(Y,Lm,Lcov,psii,Xcov,trXm,Xm)
Xcov = tempOut$Xcov; trXm = tempOut$trXm; Xm = tempOut$Xm
tempOut <- infermcl(Lm,Lcov)
if(class(tempOut) != "character"){
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl}
tempOut <- learn(it, Y,Lm,Lcov,psii,Xcov,trXm,Xm,
mean_mcl,nu_mcl,a,b,Qns,candorder,u,pu,
removal, alpha, pa, pb, pcaflag, psimin, num, parent, pos, cophd, verbose)
it = tempOut$it; Xm=tempOut$Xm; trXm= tempOut$trXm; Xcov = tempOut$Xcov;
mean_Lambda = tempOut$mean_Lambda; num = tempOut$num; Lcov = tempOut$Lcov;
Lm = tempOut$Lm; Qns = tempOut$Qns; Ps = tempOut$Ps; b = tempOut$b;
u = tempOut$u; pu = tempOut$pu; s = tempOut$s; dQns_sagit = tempOut$dQns_sagit;
a = tempOut$a; psi = tempOut$psi; psii = tempOut$psii; zeta = tempOut$zeta;
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl
candorder = tempOut$candorder; cophd = tempOut$cophd;
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha, Lcov, Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
Ftarg = F_;
} else {
if(verbose){
print(paste('Rejecting F= ', F_, ' (', Ftarg , ')'))};
psii = workspace$psii
Lm = workspace$Lm
Lcov = workspace$Lcov
Qns = workspace$Qns
a = workspace$a
b = workspace$b
u = workspace$u
tempOut <- inferQX(Y,Lm,Lcov,psii,Xcov,trXm,Xm)
Xcov = tempOut$Xcov; trXm = tempOut$trXm; Xm = tempOut$Xm
tempOut <- infermcl(Lm,Lcov)
if(class(tempOut) != "character"){
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl}
tempOut <- learn(it, Y,Lm,Lcov,psii,Xcov,trXm,Xm,
mean_mcl,nu_mcl,a,b,Qns,candorder,u,pu,
removal, alpha, pa, pb, pcaflag, psimin, num, parent, pos, cophd, verbose)
it = tempOut$it; Xm=tempOut$Xm; trXm= tempOut$trXm; Xcov = tempOut$Xcov;
mean_Lambda = tempOut$mean_Lambda; num = tempOut$num; Lcov = tempOut$Lcov;
Lm = tempOut$Lm; Qns = tempOut$Qns; Ps = tempOut$Ps; b = tempOut$b;
u = tempOut$u; pu = tempOut$pu; s = tempOut$s; dQns_sagit = tempOut$dQns_sagit;
a = tempOut$a; psi = tempOut$psi; psii = tempOut$psii; zeta = tempOut$zeta;
mean_mcl = tempOut$mean_mcl; nu_mcl = tempOut$nu_mcl
candorder = tempOut$candorder; cophd = tempOut$cophd;
pos = pos +1;
}
if(verbose){
print(paste("Iteration: ", it, " Completion: ", ( tries/maxtries+(pos-1)/(dim(candorder)[2]-1)/maxtries )*100 , '%'))
} else {
cat(sprintf(paste('\rIteration: ',it, ', F-value: ', round(F_,6),'.', sep = "")))
}
if(pos > dim(candorder)[2]){
pos <- dim(candorder)[2];
}
parent = candorder[pos];
if(parent == 0){
tries = tries+1;
if(tries != maxtries){
if(verbose){ print('End of ordering reached, reordering and trying more splits')}
tempOut <- inferQX(Y,Lm,Lcov,psii,Xcov,trXm,Xm)
Xcov = tempOut$Xcov; trXm = tempOut$trXm; Xm = tempOut$Xm
tempOut <- inferQns(Y,Lm,Qns,psii,Lcov,Xm,Xcov,candorder,b,u,pu,removal,parent,pos,cophd, verbose)
Qns = tempOut$Qns; Ps = tempOut$Ps; Lm = tempOut$Lm; Lcov = tempOut$Lcov;
Xm = tempOut$Xm; Xcov = tempOut$Xcov;
b = tempOut$b; u = tempOut$u; pu = tempOut$pu; s = tempOut$s;
dQns_sagit = tempOut$dQns_sagit; candorder = tempOut$candorder; cophd = tempOut$cophd;
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha, Lcov, Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
tempOut = ordercands(Y,Lm,Fmatrix,Qns,getbeta,selection_method, verbose)
pos = tempOut$pos; candorder = tempOut$candorder
parent = candorder[pos];
}
}
loop.end.time <- Sys.time()
loop.time <- loop.end.time - loop.start.time
time.vec <- c(time.vec, loop.time)
cumul_time <- cumul_time + loop.time
if(verbose){
print(paste("Time since last update: ", loop.time, "s. Cumulative running time: ", cumul_time, 's.'))}
}
if(birthFail == TRUE){
cat(sprintf(paste('Problem encountered while fitting. Consider refitting model.')))
}
end.time <- Sys.time()
cat(sprintf(paste('\nOptimisation complete with ', length(Lm), 'components.\n')))
totaltime = difftime(end.time,start.time,units = "secs")
cat(sprintf(paste('The total time taken for completion was ',totaltime , 's.\n')))
tempOut = Fcalc(Lm,Y,Qns,F_,a,u,pu,Xm,Xcov,Ps,psii,pi,b,pa,pb,nu_mcl,mean_mcl,it,alpha, Lcov, Fhist);
Fmatrix = tempOut$Fmatrix; pu = tempOut$p; Ps = tempOut$Ps;
F_old = tempOut$F_old;Qnsmod = tempOut$Qnsmod; FmatrixKLpi = tempOut$FmatrixKLpi;
F_ = tempOut$F_; dF = tempOut$dF; Fhist = tempOut$Fhist;
g <- length(Lm)
Mu <- vector(mode = 'list'); Lambda <- vector(mode = 'list');
for(i in 1:g){
Mu[[i]] <- matrix(Lm[[i]][,1],ncol = 1)
Lambda[[i]] <- matrix(Lm[[i]][,2:dim(Lm[[i]])[2]], ncol = dim(Lm[[i]])[2] - 1)
}
final.Lambda <- do.call(cbind, Lambda);
final.Mu <- do.call(cbind, Mu)
final.Pi <- matrix((1/n)*colSums(Qns), nrow = 1)
final.Psi <- matrix(rep(1/psii, g), ncol = g)
numFactors <- matrix(ncol = g)
for(i in 1:g){
numFactors[1,i] = dim(Lambda[[i]])[2]
}
tempLL <- logL(t(Y),g,final.Pi,final.Lambda,final.Mu,final.Psi,numFactors)
ll <- tempLL$logL
q <- max(numFactors);
d <- (g - 1) + 2 * g * p + sum(p * as.vector(numFactors) -
as.vector(numFactors)* (as.vector(numFactors) - 1)/2)
bic <- log(n)*d -2*ll;
D <- array(dim = c(p,p,g));
D[,,] <-diag(1/as.vector(psii))
out <- vector(mode = 'list')
out$model$pivec = (1/n)*colSums(Qns)
out$model$mu = abind::abind(Mu,along = 3)
out$model$B = abind::abind(Lambda,along=3)
out$model$D = D
out$model$numFactors <- numFactors
out$diagnostics$bic = bic
out$diagnostics$logL = ll
out$diagnostics$Fhist = Fhist
out$diagnostics$times = time.vec
out$diagnostics$totalTime = totaltime
out$clustering$responsibilities = Qns;
out$clustering$allocations = Rfast::rowMaxs(Qns);
if(birthFail){
out$diagnostics$model_complete = FALSE
}else{
out$diagnostics$model_complete = TRUE
}
if(varimax){
p <- dim(out$model$B)[1]
no.comp <- dim(out$model$B)[3]
for(i in 1:no.comp){
out$model$B[,,i] <- matrix(out$model$B[,,i], nrow = p) %*% stats::varimax(matrix(out$model$B[,,i], nrow = p))$rotmat
}
}
return(out)
}
|
split_shape <- function(x, f, drop=TRUE, ...) {
if (!is.factor(f)) {
warning("f is not a factor", call. = FALSE)
f <- as.factor(f)
}
lev <- if (drop) {
intersect(levels(f), f)
} else levels(f)
xlist <- lapply(lev, function(l) {
ids <- which(f==l)
if (length(ids)==0L) NULL else x[ids,]
})
names(xlist) <- lev
xlist
}
|
spectrogram <- function(signal,
sRate,
maxFreq = 25,
n = 1024,
window = n*2,
overlap = 0,
cols = c(rep("
freq = 4,
plot = TRUE,
startTime = as.POSIXct("1970/01/01 00:00:00")){
resample_sRate <- maxFreq*2
x <- signal::resample(signal,resample_sRate,sRate)
spec <- signal::specgram(x = x,
Fs = resample_sRate,
n = n,
window = window,
overlap = overlap)
spec$S <- apply(spec$S,2,Re)
suppressWarnings(
spec$S <- apply(spec$S,2,function(x){
stats::aggregate(stats::ts(as.numeric(x), frequency=freq), 1, max)
}))
spec$f <- as.numeric(stats::aggregate(stats::ts(spec$f, frequency=freq), 1, max))
endTime <- startTime + round(length(x)/resample_sRate)
spec$t <- seq(startTime,endTime,(as.numeric(endTime)-as.numeric(startTime))/length(spec$t))
if(plot){
plot(spec, col = cols,ylab="Frequency (Hz)",xlab="")
} else {
return(spec)
}
}
bands_power <- function(bands, signal , sRate, broadband = c(0.5,40)){
s <- phonTools::pwelch(sound = signal,fs = sRate,points = 1000, show = FALSE)
s[,2] <- s[,2]+abs(min(s[,2]))
lapply(bands, function(band){
s_filtered <- s[s[,1] >= band[1] & s[,1] < band[2],]
s_broadband <- s[s[,1] >= broadband[1] & s[,1] < broadband[2],]
(sum(s_filtered[,2])/dim(s_filtered)[1])/sum(s_broadband[,2])
})
}
pwelch <- function(
x,
sRate,
points = 0,
overlap = 0,
padding = 0){
n = length(x)
if (points == 0)
points = ceiling(n/10)
x = c(x, rep(0, points))
spots = seq(1, n, points - overlap)
if ((points + padding)%%2 == 1)
padding = padding + 1
n = points + padding
psd = rep(0, n)
for (i in 1:length(spots)) {
tmp = x[spots[i]:(spots[i] + points - 1)] * signal::hamming(points)
tmp = c(tmp, rep(0, padding))
tmp = stats::fft(tmp)
tmp = tmp * Conj(tmp)
psd = psd + tmp
}
psd = psd/length(spots)
psd = psd[1:(n/2 + 1)]
psd = abs(psd)
psd = log(psd)
psd = psd - max(psd)
hz = seq(0, sRate/2, length.out = (n/2) + 1)
return(data.frame("hz" = hz, "psd" = psd))
}
psm <- function(x, sRate, length=0){
options(psd.ops=list(
tapmin = 1,
tapcap = 1000,
names = list(
fft = "working_fft",
fft.padded = "fft_even_demeaned_padded",
last.taper = "last_taper_sequence",
last.psdcore = "last_psdcore_psd",
last.psdcore.extrap = "last_psdcore_psd_extrap",
series.even = "ser_orig_even",
var.even = "ser_even_var",
n.even = "len_even",
n.even.half = "len_even_half",
series.orig = "ser_orig",
n.orig = "len_orig"
)
))
res <- psd::pspectrum(x,plot=FALSE,verbose=FALSE)
df <- data.frame("hz" = res$freq, "psd" = res$spec)
df$psd <- log(df$psd)
df$hz <- df$hz*sRate
if(length > 0){
psd <- signal::resample(x = df$psd,
p = length,
q = nrow(df))
hz <- signal::resample(x = df$hz,
p = length,
q = nrow(df))
df <- data.frame("psd" = psd,
"hz" = hz)
}
return(df)
}
|
runsum.print <- function(object,
modfile=paste("run",object@Runno,".mod",sep=""),
listfile=paste("run",object@Runno,".lst",sep=""),
print.cex=0.45,
print.columns=3,
...) {
cat("Type the name of the model file (0=cancel, return=",modfile,")\n",sep="")
ans <- readline()
cmdfile <- NULL
if(ans==0) {
return()
} else if (ans=="") {
if(is.readable.file(modfile)) {
cmdfile <- modfile
}
} else {
if(is.readable.file(ans)) {
cmdfile <- ans
}
}
if(is.null(cmdfile)) {
cat("The specified file couldn't be found in the current directory.\n")
return()
}
cat("Type the name of the output file (0=cancel, return=",listfile,")\n",sep="")
ans <- readline()
lstfile <- NULL
if(ans==0) {
return()
} else if (ans=="") {
if(is.readable.file(listfile)) {
lstfile <- listfile
}
} else {
if(is.readable.file(ans)) {
lstfile <- ans
}
}
if(is.null(lstfile)) {
cat("The specified file couldn't be found in the current directory.\n")
return()
}
cat("Do you want to optimize the summary output for printing n(y)?\n")
printit <- readline()
if(printit=="y") {
dev.new(height=11.7,width=8.25)
}
if(printit=="y") {
runsum(object,
modfile=cmdfile,
listfile = lstfile,
txt.columns=print.columns,
txt.cex=print.cex,
...)
} else {
runsum(object,
modfile=cmdfile,
listfile = lstfile,
...)
}
}
|
expected <- eval(parse(text="\"1967-09-13\""));
test(id=0, code={
argv <- eval(parse(text="list(structure(-841, class = \"Date\"))"));
do.call(`as.character`, argv);
}, o=expected);
|
CheckSVDOptions <- function(Ly1, Lt1, Ly2, Lt2, SVDoptns){
if( (SVDoptns[['dataType1']]=='Sparse' && is.null(SVDoptns[['userMu1']])) ||
(SVDoptns[['dataType2']]=='Sparse' && is.null(SVDoptns[['userMu2']])) ){
stop('User specified mean function required for sparse functional data for cross covariance estimation.')
}
if(is.numeric(SVDoptns$methodSelectK)){
if(SVDoptns$methodSelectK != round(SVDoptns$methodSelectK) ||
SVDoptns$methodSelectK <= 0){
stop("FSVD is aborted: 'methodSelectK' is invalid!\n")
}
}
if( !(SVDoptns$regulRS %in% c('sigma2','rho') ) ){
stop("FSVD is aborted: Unknown regularization option. The argument 'regulRS' should be 'rho' or 'sigma2'!")
}
}
|
fs_image_url <- function(id, debug = FALSE) {
a <- fs_details(id)
b <- GET(a$doi)
if(debug | b$status_code != 200)
b
else {
doc <- htmlParse(content(b, as = "text"))
path <- xpathSApply(doc, "//div[@class='filesdownload' and @id='download_all']/a/@href")[[1]]
path
}
}
fs_embed <- function(file) {
title <- file
description <- "embedded file automatically uploaded to figshare from R"
id <- fs_new_article(title, description, type = "figure", visibility = "public")
fs_image_url(id)
}
|
NULL
read.hills<-function(file="HILLS", per=c(FALSE, FALSE), pcv1=c(-pi,pi), pcv2=c(-pi,pi), ignoretime=FALSE) {
hillsf<-read.table(file, header=F, comment.char="
if(ncol(hillsf)==5 || ncol(hillsf)==6) {
cat("1D HILLS file read\n")
if(ignoretime) {
cat("Warning: The time will be updated automatically from zero\n")
cat("according to the first step!\n")
hillsf[,1]<-seq(from=hillsf[1,1], by=hillsf[1,1], length.out=nrow(hillsf))
}
hills<-list(hillsfile=hillsf, time=hillsf[,1], cv1=hillsf[,2], cv2=NULL,
size=dim(hillsf), filename=file, per=per, pcv1=pcv1)
class(hills) <- "hillsfile"
return(hills)
} else if(ncol(hillsf)==7 || ncol(hillsf)==8) {
cat("2D HILLS file read\n")
if(ignoretime) {
cat("Warning: The time will be updated automatically from zero\n")
cat("according to the first step!\n")
hillsf[,1]<-seq(from=hillsf[1,1], by=hillsf[1,1], length.out=nrow(hillsf))
}
hills<-list(hillsfile=hillsf, time=hillsf[,1], cv1=hillsf[,2], cv2=hillsf[,3],
size=dim(hillsf), filename=file, per=per, pcv1=pcv1, pcv2=pcv2)
class(hills) <- "hillsfile"
return(hills)
} else {
stop("Error: Number of columns in HILLS file must be 5 or 6 (1D) or 7 or 8 (2D)")
}
}
print.hillsfile<-function(x,...) {
hills <- x
if(hills$size[2]==5) {
cat("1D hills file ")
cat(hills$filename)
cat(" with ")
cat(hills$size[1])
cat(" lines\n")
}
if(hills$size[2]==7) {
cat("2D hills file ")
cat(hills$filename)
cat(" with ")
cat(hills$size[1])
cat(" lines\n")
}
}
summary.hillsfile<-function(object,...) {
hills <- object
if(hills$size[2]==5) {
cat("1D hills file ")
cat(hills$filename)
cat(" with ")
cat(hills$size[1])
cat(" lines\n")
cat("The CV1 ranges from ")
cat(min(hills$hillsfile[,2]))
cat(" to ")
cat(max(hills$hillsfile[,2]))
cat("\n")
}
if(hills$size[2]==7) {
cat("2D hills file ")
cat(hills$filename)
cat(" with ")
cat(hills$size[1])
cat(" lines\n")
cat("The CV1 ranges from ")
cat(min(hills$hillsfile[,2]))
cat(" to ")
cat(max(hills$hillsfile[,2]))
cat("\nThe CV2 ranges from ")
cat(min(hills$hillsfile[,3]))
cat(" to ")
cat(max(hills$hillsfile[,3]))
cat("\n")
}
}
head.hillsfile<-function(x, n=10,...) {
return(head(x$hillsfile, n=n))
}
tail.hillsfile<-function(x, n=10,...) {
return(tail(x$hillsfile, n=n))
}
`+.hillsfile`<-function(hills1, hills2) {
if(ncol(hills1$hillsfile)!=ncol(hills2$hillsfile)) {
stop("Error: You can sum only hills of same dimension")
}
if(hills1$per[1]!=hills2$per[1]) {
stop("Error: You can sum only hills of same periodicity")
}
if(ncol(hills1$hillsfile)==7 || ncol(hills1$hillsfile)==8) {
if(hills1$per[2]!=hills2$per[2]) {
stop("Error: You can sum only hills of same periodicity")
}
}
hills<-list(hillsfile=rbind(hills1$hillsfile, hills2$hillsfile), size=dim(rbind(hills1$hillsfile, hills2$hillsfile)),
filename=hills1$filename, per=hills1$per, pcv1=hills1$pcv1, pcv2=hills1$pcv2)
class(hills) <- "hillsfile"
return(hills)
}
plot.hillsfile<-function(x, ignoretime=FALSE,
xlab=NULL, ylab=NULL,
xlim=NULL, ylim=NULL,
main=NULL, sub=NULL,
pch=1, col="black", bg="red", cex=1,
asp=NULL, lwd=1, axes=TRUE,...) {
hills <-x
xlims<-NULL
ylims<-NULL
if(!is.null(xlim)) {xlims<-xlim}
if(!is.null(ylim)) {ylims<-ylim}
if(hills$size[2]==5) {
if((hills$per[1]==T)&is.null(ylim)) {ylims<-hills$pcv1}
if(is.null(xlab)) xlab="time"
if(is.null(ylab)) ylab="CV"
if(ignoretime) {
plot(seq(from=hills$hillsfile[1,1],by=hills$hillsfile[1,1],length.out=nrow(hills$hillsfile)),
hills$hillsfile[,2], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
xlim=xlims, ylim=ylims,
col=col, cex=cex, lwd=lwd,
asp=asp, axes=axes)
} else {
plot(hills$hillsfile[,1], hills$hillsfile[,2], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
xlim=xlims, ylim=ylims,
col=col, cex=cex, lwd=lwd,
asp=asp, axes=axes)
}
}
if(hills$size[2]==7) {
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
if((hills$per[2]==T)&is.null(ylim)) {ylims<-hills$pcv2}
if(is.null(xlab)) xlab="CV1"
if(is.null(ylab)) ylab="CV2"
plot(hills$hillsfile[,2], hills$hillsfile[,3], type="p",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
xlim=xlims, ylim=ylims,
pch=pch, col=col, bg=bg, cex=cex, lwd=lwd,
asp=asp, axes=axes)
}
}
points.hillsfile<-function(x, ignoretime=FALSE,
pch=1, col="black", bg="red", cex=1,
lwd=1, ...) {
hills <- x
if(hills$size[2]==5) {
if(ignoretime) {
points(seq(from=hills$hillsfile[1,1],by=hills$hillsfile[1,1],length.out=nrow(hills$hillsfile)),
hills$hillsfile[,2],
col=col, cex=cex, lwd=lwd)
} else {
points(hills$hillsfile[,1], hills$hillsfile[,2],
col=col, cex=cex, lwd=lwd)
}
}
if(hills$size[2]==7) {
points(hills$hillsfile[,2], hills$hillsfile[,3],
pch=pch, col=col, bg=bg, cex=cex, lwd=lwd)
}
}
lines.hillsfile<-function(x, ignoretime=FALSE,
lwd=1, col="black",...) {
hills <- x
if(hills$size[2]==5) {
if(ignoretime) {
lines(seq(from=hills$hillsfile[1,1],by=hills$hillsfile[1,1],length.out=nrow(hills$hillsfile)),
hills$hillsfile[,2],
col=col, lwd=lwd)
} else {
lines(hills$hillsfile[,1], hills$hillsfile[,2],
col=col, lwd=lwd)
}
}
if(hills$size[2]==7) {
lines(hills$hillsfile[,2], hills$hillsfile[,3],
col=col, lwd=lwd)
}
}
plotheights<-function(hills, ignoretime, xlab, ylab,
xlim, ylim, main, sub,
col, asp, lwd, axes) {
UseMethod("plotheights")
}
plotheights.hillsfile<-function(hills, ignoretime=FALSE,
xlab=NULL, ylab=NULL,
xlim=NULL, ylim=NULL,
main=NULL, sub=NULL,
col="black", asp=NULL, lwd=1, axes=TRUE) {
if(is.null(xlab)) xlab="time"
if(is.null(ylab)) ylab="hill height"
if(hills$size[2]==5) {
if(ignoretime) {
plot(seq(from=hills$hillsfile[1,1],by=hills$hillsfile[1,1],length.out=nrow(hills$hillsfile)),
hills$hillsfile[,4], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
col=col, lwd=lwd,
asp=asp, axes=axes)
} else {
plot(hills$hillsfile[,1], hills$hillsfile[,4], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
col=col, lwd=lwd,
asp=asp, axes=axes)
}
}
if(hills$size[2]==7) {
if(ignoretime) {
plot(seq(from=hills$hillsfile[1,1],by=hills$hillsfile[1,1],length.out=nrow(hills$hillsfile)),
hills$hillsfile[,6], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
col=col, lwd=lwd,
asp=asp, axes=axes)
} else {
plot(hills$hillsfile[,1], hills$hillsfile[,6], type="l",
xlab=xlab, ylab=ylab,
main=main, sub=sub,
col=col, lwd=lwd,
asp=asp, axes=axes)
}
}
}
fes<-function(hills, imin, imax, xlim, ylim, zlim, npoints) {
UseMethod("fes")
}
fes.hillsfile<-function(hills, imin=1, imax=NULL, xlim=NULL, ylim=NULL, zlim=NULL, npoints=256) {
if(!is.null(imax)) {
if(hills$size[1]<imax) {
cat("Warning: You requested more hills by imax than available, using all hills\n")
imax<-hills$size[1]
}
}
if(is.null(imax)) {
imax<-hills$size[1]
}
if(imin>imax) {
stop("Error: imax cannot be lower than imin")
}
if(hills$size[2]==7) {
if(max(hills$hillsfile[,4])/min(hills$hillsfile[,4])>1.00000000001) {
stop("Error: Bias Sum algorithm works only with hills of the same sizes")
}
if(max(hills$hillsfile[,5])/min(hills$hillsfile[,5])>1.00000000001) {
stop("Error: Bias Sum algorithm works only with hills of the same sizes")
}
minCV1 <- min(hills$hillsfile[,2])
maxCV1 <- max(hills$hillsfile[,2])
minCV2 <- min(hills$hillsfile[,3])
maxCV2 <- max(hills$hillsfile[,3])
xlims<-c(minCV1-0.05*(maxCV1-minCV1), maxCV1+0.05*(maxCV1-minCV1))
ylims<-c(minCV2-0.05*(maxCV2-minCV2), maxCV2+0.05*(maxCV2-minCV2))
if(!is.null(xlim)) {xlims<-xlim}
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
if(!is.null(ylim)) {ylims<-ylim}
if((hills$per[2]==T)&is.null(ylim)) {ylims<-hills$pcv2}
if(hills$per[1]==T) {
if(min(hills$hillsfile[,2])<xlims[1]) {
stop("Error: The first collective variable outside pcv1")
}
if(max(hills$hillsfile[,2])>xlims[2]) {
stop("Error: The first collective variable outside pcv1")
}
}
if(hills$per[2]==T) {
if(min(hills$hillsfile[,3])<ylims[1]) {
stop("Error: The second collective variable outside pcv2")
}
if(max(hills$hillsfile[,3])>ylims[2]) {
stop("Error: The second collective variable outside pcv2")
}
}
x<-0:(npoints-1)*(xlims[2]-xlims[1])/(npoints-1)+xlims[1]
y<-0:(npoints-1)*(ylims[2]-ylims[1])/(npoints-1)+ylims[1]
if((hills$per[1]==F)&(hills$per[2]==F)) {
fesm<-hills1(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==F)) {
fesm<-hills1p1(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==F)&(hills$per[2]==T)) {
fesm<-hills1p2(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==T)) {
fesm<-hills1p12(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=2, per=hills$per, x=x, y=y, pcv1=hills$pcv1, pcv2=hills$pcv2)
class(cfes) <- "fes"
}
if(hills$size[2]==5) {
if(max(hills$hillsfile[,3])/min(hills$hillsfile[,3])>1.00000000001) {
stop("Error: Bias Sum algorithm works only with hills of the same sizes")
}
minCV1 <- min(hills$hillsfile[,2])
maxCV1 <- max(hills$hillsfile[,2])
xlims<-c(minCV1-0.05*(maxCV1-minCV1), maxCV1+0.05*(maxCV1-minCV1))
if(!is.null(xlim)) {xlims<-xlim}
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
x<-0:(npoints-1)*(xlims[2]-xlims[1])/(npoints-1)+xlims[1]
if(hills$per[1]==F) {
fesm<-hills1d1(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,3])/(xlims[2]-xlims[1]),
hills$hillsfile[,4],npoints,imin-1,imax-1)
}
if(hills$per[1]==T) {
fesm<-hills1d1p(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
npoints*max(hills$hillsfile[,3])/(xlims[2]-xlims[1]),
hills$hillsfile[,4],npoints,imin-1,imax-1)
}
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per, x=x, pcv1=hills$pcv1, pcv2=hills$pcv2)
class(cfes) <- "fes"
}
return(cfes)
}
fes2<-function(hills, imin, imax, xlim, ylim, zlim, npoints) {
UseMethod("fes2")
}
fes2.hillsfile<-function(hills, imin=1, imax=NULL, xlim=NULL, ylim=NULL, zlim=NULL, npoints=256) {
if(!is.null(imax)) {
if(hills$size[1]<imax) {
cat("Warning: You requested more hills by imax than available, using all hills\n")
imax<-hills$size[1]
}
}
if(is.null(imax)) {
imax<-hills$size[1]
}
if(imin>imax) {
stop("Error: imax cannot be lower than imin")
}
if(hills$size[2]==7) {
minCV1 <- min(hills$hillsfile[,2])
maxCV1 <- max(hills$hillsfile[,2])
minCV2 <- min(hills$hillsfile[,3])
maxCV2 <- max(hills$hillsfile[,3])
xlims<-c(minCV1-0.05*(maxCV1-minCV1), maxCV1+0.05*(maxCV1-minCV1))
ylims<-c(minCV2-0.05*(maxCV2-minCV2), maxCV2+0.05*(maxCV2-minCV2))
if(!is.null(xlim)) {xlims<-xlim}
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
if(!is.null(ylim)) {ylims<-ylim}
if((hills$per[2]==T)&is.null(ylim)) {ylims<-hills$pcv2}
x<-0:(npoints-1)*(xlims[2]-xlims[1])/(npoints-1)+xlims[1]
y<-0:(npoints-1)*(ylims[2]-ylims[1])/(npoints-1)+ylims[1]
if((hills$per[1]==F)&(hills$per[2]==F)) {
fesm<-hills2((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
(npoints-1)*hills$hillsfile[,4]/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,5]/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==F)) {
fesm<-hills2p1((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
(npoints-1)*hills$hillsfile[,4]/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,5]/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==F)&(hills$per[2]==T)) {
fesm<-hills2p2((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
(npoints-1)*hills$hillsfile[,4]/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,5]/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==T)) {
fesm<-hills2p12((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]),
(npoints-1)*hills$hillsfile[,4]/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,5]/(ylims[2]-ylims[1]),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=2, per=hills$per, x=x, y=y, pcv1=hills$pcv1, pcv2=hills$pcv2)
class(cfes) <- "fes"
}
if(hills$size[2]==5) {
minCV1 <- min(hills$hillsfile[,2])
maxCV1 <- max(hills$hillsfile[,2])
xlims<-c(minCV1-0.05*(maxCV1-minCV1), maxCV1+0.05*(maxCV1-minCV1))
if(!is.null(xlim)) {xlims<-xlim}
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
x<-0:(npoints-1)*(xlims[2]-xlims[1])/(npoints-1)+xlims[1]
if(hills$per[1]==F) {
fesm<-hills1d2((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,3]/(xlims[2]-xlims[1]),
hills$hillsfile[,4],npoints,imin-1,imax-1)
}
if(hills$per[1]==T) {
fesm<-hills1d2p((npoints-1)*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]),
(npoints-1)*hills$hillsfile[,3]/(xlims[2]-xlims[1]),
hills$hillsfile[,4],npoints,imin-1,imax-1)
}
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per, x=x, pcv1=hills$pcv1, pcv2=hills$pcv2)
class(cfes) <- "fes"
}
return(cfes)
}
read.plumed<-function(file="fes.dat", dim=2, per=c(F,F,F)) {
hillsf<-read.table(file, header=F, comment.char="
bins<-round(nrow(hillsf)^(1/dim))
if(bins^dim!=nrow(hillsf)) {
stop("Error: the number of bins cannot be determined, it must be same for all dimension, or the number of dimensions is wrong.")
}
if(dim==1) {
x <- hillsf[,1]
fesm <- hillsf[,2]
cfes <- list(fes=fesm, hills=NULL, rows=bins, dimension=1, per=per, x=x, pcv1=c(min(x), max(x)))
class(cfes) <- "fes"
} else if(dim==2) {
x <- hillsf[1:bins,1]
y <- hillsf[(0:(bins-1))*bins+1,2]
fesm <- matrix(hillsf[,3], nrow=bins)
cfes <- list(fes=fesm, hills=NULL, rows=bins, dimension=2, per=per, x=x, y=y,
pcv1=c(min(x), max(x)), pcv2=c(min(y), max(y)))
class(cfes) <- "fes"
} else {
stop("Error: for 3D fes use read.plumed3d from metadynminer3d, higher dimensions are not supported.")
}
return(cfes)
}
fes2d21d<-function(hills, remdim=2, temp=300, eunit="kJ/mol",
imin=1, imax=NULL, xlim=NULL, ylim=NULL, npoints=256) {
if(!is.null(imax)) {
if(hills$size[1]<imax) {
cat("Warning: You requested more hills by imax than available, using all hills\n")
imax<-hills$size[1]
}
}
if(is.null(imax)) {
imax<-hills$size[1]
}
if(imin>imax) {
stop("Error: imax cannot be lower than imin")
}
if(hills$size[2]==7) {
if(max(hills$hillsfile[,4])/min(hills$hillsfile[,4])>1.00000000001) {
stop("Error: Bias Sum algorithm works only with hills of the same sizes")
}
if(max(hills$hillsfile[,5])/min(hills$hillsfile[,5])>1.00000000001) {
stop("Error: Bias Sum algorithm works only with hills of the same sizes")
}
minCV1 <- min(hills$hillsfile[,2])
maxCV1 <- max(hills$hillsfile[,2])
minCV2 <- min(hills$hillsfile[,3])
maxCV2 <- max(hills$hillsfile[,3])
xlims<-c(minCV1-0.05*(maxCV1-minCV1), maxCV1+0.05*(maxCV1-minCV1))
ylims<-c(minCV2-0.05*(maxCV2-minCV2), maxCV2+0.05*(maxCV2-minCV2))
if(!is.null(xlim)) {xlims<-xlim}
if((hills$per[1]==T)&is.null(xlim)) {xlims<-hills$pcv1}
if(!is.null(ylim)) {ylims<-ylim}
if((hills$per[2]==T)&is.null(ylim)) {ylims<-hills$pcv2}
x<-0:(npoints-1)*(xlims[2]-xlims[1])/(npoints-1)+xlims[1]
y<-0:(npoints-1)*(ylims[2]-ylims[1])/(npoints-1)+ylims[1]
binx<-(xlims[2]-xlims[1])/(npoints-1)
biny<-(ylims[2]-ylims[1])/(npoints-1)
if((hills$per[1]==F)&(hills$per[2]==F)) {
fesm<-hills1(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]+binx),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]+biny),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]+binx),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]+biny),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==F)) {
fesm<-hills1p1(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]+binx),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]+biny),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]+binx),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]+biny),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==F)&(hills$per[2]==T)) {
fesm<-hills1p2(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]+binx),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]+biny),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]+binx),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]+biny),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if((hills$per[1]==T)&(hills$per[2]==T)) {
fesm<-hills1p12(npoints*(hills$hillsfile[,2]-xlims[1])/(xlims[2]-xlims[1]+binx),
npoints*(hills$hillsfile[,3]-ylims[1])/(ylims[2]-ylims[1]+biny),
npoints*max(hills$hillsfile[,4])/(xlims[2]-xlims[1]+binx),
npoints*max(hills$hillsfile[,5])/(ylims[2]-ylims[1]+biny),
hills$hillsfile[,6],npoints,imin-1,imax-1)
}
if(eunit=="kJ/mol") {
prob<- exp(-1000*fesm/8.314/temp)
if(remdim==1) {
fesm <- -8.314*temp*log(apply(prob, 2, sum))/1000
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per[2], x=y, pcv1=hills$pcv2)
}
if(remdim==2) {
fesm <- -8.314*temp*log(apply(prob, 1, sum))/1000
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per[1], x=x, pcv1=hills$pcv1)
}
}
if(eunit=="kcal/mol") {
prob<- exp(-1000*4.184*fesm/8.314/temp)
if(remdim==1) {
fesm <- -8.314*temp*log(apply(prob, 2, sum))/1000/4.184
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per[2], x=y, pcv1=hills$pcv2)
}
if(remdim==2) {
fesm <- -8.314*temp*log(apply(prob, 1, sum))/1000/4.184
cfes<-list(fes=fesm, hills=hills$hillsfile, rows=npoints, dimension=1, per=hills$per[1], x=x, pcv1=hills$pcv1)
}
}
class(cfes) <- "fes"
}
if(hills$size[2]==5) {
stop("Error: Your free energy surface is already 1D")
}
return(cfes)
}
`+.fes`<-function(fes1, fes2) {
if((class(fes1)=="fes")&(class(fes2)=="fes")) {
if(fes1$rows!=fes2$rows) {
stop("Error: Free energy surfaces have different numbers of points, exiting")
}
if(fes1$dimension!=fes2$dimension) {
stop("Error: Free energy surfaces have different dimension, exiting")
}
if(sum(fes1$x!=fes2$x)>0) {
stop("Error: Free energy surfaces have different CV1 axes, exiting")
}
if(fes1$dimension==2) {
if(sum(fes1$y!=fes2$y)>0) {
stop("Error: Free energy surfaces have different CV2 axes, exiting")
}
}
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes+fes2$fes, hills=rbind(fes1$hills, fes2$hills), rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes+fes2$fes, hills=rbind(fes1$hills, fes2$hills), rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(fes1)=="fes") {
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes+fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes+fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(fes2)=="fes") {
if(fes2$dimension==1) {
cfes<-list(fes=fes1+fes2$fes, hills=fes2$hills, rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
if(fes2$dimension==2) {
cfes<-list(fes=fes1+fes2$fes, hills=rbind(fes1$hills,fes2$hills), rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, y=fes2$y, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
}
class(cfes) <- "fes"
return(cfes)
}
`-.fes`<-function(fes1, fes2) {
if((class(fes1)=="fes")&(class(fes2)=="fes")) {
if(fes1$rows!=fes2$rows) {
stop("Error: Free energy surfaces have different numbers of points, exiting")
}
if(fes1$dimension!=fes2$dimension) {
stop("Error: Free energy surfaces have different dimension, exiting")
}
if(sum(fes1$x!=fes2$x)>0) {
stop("Error: Free energy surfaces have different CV1 axes, exiting")
}
if(fes1$dimension==2) {
if(sum(fes1$y!=fes2$y)>0) {
stop("Error: Free energy surfaces have different CV2 axes, exiting")
}
}
cat("Warning: FES obtained by subtraction of two FESes\n")
cat(" will inherit hills only from the first FES\n")
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes-fes2$fes, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes-fes2$fes, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(fes1)=="fes") {
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes-fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes-fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(fes2)=="fes") {
if(fes2$dimension==1) {
cfes<-list(fes=fes1-fes2$fes, hills=fes2$hills, rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
if(fes2$dimension==2) {
cfes<-list(fes=fes1-fes2$fes, hills=fes2$hills, rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, y=fes2$y, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
}
class(cfes) <- "fes"
return(cfes)
}
`*.fes`<-function(fes1, fes2) {
if((class(fes1)=="fes")&(class(fes2)=="fes")) {
stop("Error: You cannot multiply fes by fes")
} else if(class(fes1)=="fes") {
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes*fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes*fes2, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(fes2)=="fes") {
if(fes2$dimension==1) {
cfes<-list(fes=fes1*fes2$fes, hills=fes2$hills, rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
if(fes2$dimension==2) {
cfes<-list(fes=fes1*fes2$fes, hills=fes2$hills, rows=fes2$rows, dimension=fes2$dimension, per=fes2$per, x=fes2$x, y=fes2$y, pcv1=fes2$pcv1, pcv2=fes2$pcv2)
}
}
cat("Warning: multiplication of FES will multiply\n")
cat(" the FES but not hill heights\n")
class(cfes) <- "fes"
return(cfes)
}
`/.fes`<-function(fes1, coef) {
if((class(fes1)=="fes")&(class(coef)=="fes")) {
stop("Error: You cannot divide fes by fes")
} else if(class(fes1)=="fes") {
if(fes1$dimension==1) {
cfes<-list(fes=fes1$fes/coef, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
if(fes1$dimension==2) {
cfes<-list(fes=fes1$fes/coef, hills=fes1$hills, rows=fes1$rows, dimension=fes1$dimension, per=fes1$per, x=fes1$x, y=fes1$y, pcv1=fes1$pcv1, pcv2=fes1$pcv2)
}
} else if(class(coef)=="fes") {
stop("Error: You cannot divide something by fes")
}
cat("Warning: division of FES will divide\n")
cat(" the FES but not hill heights\n")
class(cfes) <- "fes"
return(cfes)
}
min.fes<-function(inputfes, na.rm=NULL,...) {
return(min(inputfes$fes, na.rm=na.rm))
}
max.fes<-function(inputfes, na.rm=NULL,...) {
return(max(inputfes$fes, na.rm=na.rm))
}
print.fes<-function(x,...) {
inputfes<-x
if(inputfes$dimension==1) {
cat("1D free energy surface with ")
cat(inputfes$rows)
cat(" points, maximum ")
cat(max(inputfes$fes))
cat(" and minimum ")
cat(min(inputfes$fes))
cat("\n")
}
if(inputfes$dimension==2) {
cat("2D free energy surface with ")
cat(inputfes$rows)
cat(" x ")
cat(inputfes$rows)
cat(" points, maximum ")
cat(max(inputfes$fes))
cat(" and minimum ")
cat(min(inputfes$fes))
cat("\n")
}
}
summary.fes<-function(object,...) {
inputfes <- object
if(inputfes$dimension==1) {
cat("1D free energy surface with ")
cat(inputfes$rows)
cat(" points, maximum ")
cat(max(inputfes$fes))
cat(" and minimum ")
cat(min(inputfes$fes))
cat("\n")
}
if(inputfes$dimension==2) {
cat("2D free energy surface with ")
cat(inputfes$rows)
cat(" x ")
cat(inputfes$rows)
cat(" points, maximum ")
cat(max(inputfes$fes))
cat(" and minimum ")
cat(min(inputfes$fes))
cat("\n")
}
}
prob<-function(inputfes, temp=300, eunit="kJ/mol") {
if(class(inputfes)=="fes") {
if(eunit=="kJ/mol") {
if(inputfes$dimension==1) {
probs <- exp(-1000*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, pcv1=inputfes$pcv1)
}
if(inputfes$dimension==2) {
probs <- exp(-1000*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, y=inputfes$y, pcv1=inputfes$pcv1, pcv2=inputfes$pcv2)
}
} else if (eunit=="kJ/mol") {
if(inputfes$dimension==1) {
probs <- exp(-1000*4.184*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, pcv1=inputfes$pcv1)
}
if(inputfes$dimension==2) {
probs <- exp(-1000*4.184*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, y=inputfes$y, pcv1=inputfes$pcv1, pcv2=inputfes$pcv2)
}
} else {
stop("Error: Wrong eunit")
}
class(cfes) <- "fes"
return(cfes)
} else if(class(inputfes)=="fes3d") {
if(eunit=="kJ/mol") {
probs <- exp(-1000*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, y=inputfes$y, z=inputfes$z, pcv1=inputfes$pcv1, pcv2=inputfes$pcv2, pcv3=inputfes$pcv3)
} else if (eunit=="kJ/mol") {
probs <- exp(-1000*4.184*inputfes$fes/8.314/temp)
cfes<-list(fes=probs/sum(probs), hills=inputfes$hills, rows=inputfes$rows, dimension=inputfes$dimension, per=inputfes$per, x=inputfes$x, y=inputfes$y, z=inputfes$z, pcv1=inputfes$pcv1, pcv2=inputfes$pcv2, pcv3=inputfes$pcv3)
} else {
stop("Error: Wrong eunit")
}
class(cfes) <- "fes3d"
return(cfes)
} else {
stop("Error: Input must be fes or fes3d object")
}
}
plot.fes<-function(x, plottype="both",
colscale=F, xlim=NULL, ylim=NULL, zlim=NULL,
main=NULL, sub=NULL,
xlab=NULL, ylab=NULL,
nlevels=10, levels=NULL,
col=rainbow(135)[100:1],
labels=NULL, labcex=0.6, drawlabels=TRUE,
colscalelab="free energy",
method="flattest",
contcol=par("fg"), lty=par("lty"),
lwd=1, asp=NULL, axes=T,...) {
close.screen(all.screens=TRUE)
inputfes<-x
fes<-inputfes$fes
rows<-inputfes$rows
if(inputfes$dimension==1) {
x<-inputfes$x
if(is.null(xlab)) xlab="CV"
if(is.null(ylab)) ylab="free energy"
if(is.null(xlim)) xlim<-c(min(x),max(x))
if(is.null(ylim)) {
ylim<-range(pretty(range(fes)))
}
plot(x, fes, type="l", lwd=lwd,
col=col, xlim=xlim, ylim=ylim,
xlab=xlab, ylab=ylab, axes=axes,
main=main, sub=sub, asp=asp)
} else {
x<-inputfes$x
y<-inputfes$y
if(is.null(xlab)) xlab="CV1"
if(is.null(ylab)) ylab="CV2"
if(is.null(zlim)) {
zlim<-range(pretty(range(fes)))
}
if(is.null(levels)) {
levels<-pretty(zlim, nlevels)
}
if(is.null(xlim)) xlim<-c(min(x),max(x))
if(is.null(ylim)) ylim<-c(min(y),max(y))
if(colscale) {
split.screen(matrix(c(0,0.75,0,1,0.75,1,0,1), byrow=T, ncol=4))
screen(2)
smat<-matrix(seq(from=zlim[1], to=zlim[2], length.out=100))
image(c(0), seq(from=zlim[1], to=zlim[2], length.out=100),
t(smat), zlim=zlim, col=col, xlab="", ylab=colscalelab, axes=F)
axis(2, lty=lty, lwd=lwd)
box(lwd=lwd)
screen(1)
}
if(plottype=="image" || plottype=="both") {
image(x, y, fes, zlim=zlim,
col=col, xlim=xlim, ylim=ylim,
xlab=xlab, ylab=ylab, axes=axes,
main=main, sub=sub, asp=asp)
}
if(plottype=="contour") {
contour(x, y, fes, zlim=zlim,
nlevels=nlevels, levels=levels,
labels=labels, labcex=labcex, drawlabels=drawlabels,
method=method, col=contcol, lty=lty, lwd=lwd,
main=main, sub=sub, asp=asp)
}
if(plottype=="both") {
contour(x, y, fes, zlim=zlim,
nlevels=nlevels, levels=levels,
labels=labels, labcex=labcex, drawlabels=drawlabels,
method=method, col=contcol, lty=lty, lwd=lwd, add=T)
}
}
}
points.fes<-function(x, pch=1, col="black", bg="red", cex=1, lwd=1,...) {
fes<-x$fes
if(x$dimension==1) {
x<-x$x
points(x, fes,
pch=pch, col=col, bg=bg, cex=cex, lwd=lwd)
} else {
stop("Error: points available only for 1D free energy surfaces\n")
}
}
lines.fes<-function(x, lwd=1, col="black",...) {
fes<-x$fes
if(x$dimension==1) {
x<-x$x
lines(x, fes, lwd=lwd, col=col)
} else {
stop("Error: points available only for 1D free energy surfaces\n")
}
}
|
test_that("qgis_function() works", {
skip_if_not(has_qgis())
expect_error(
qgis_function("native:buffer", NOT_AN_ARG = "value"),
"must be valid input names"
)
expect_error(
qgis_function("native:buffer", "no name"),
"must be named"
)
qgis_buffer <- qgis_function("native:buffer")
expect_is(qgis_buffer, "function")
expect_identical(parent.env(environment(qgis_buffer)), baseenv())
expect_true(rlang::is_call(body(qgis_buffer), "qgis_run_algorithm", ns = "qgisprocess"))
buffer_args <- qgis_arguments("native:buffer")
expect_identical(
names(formals(qgis_buffer)),
c(
buffer_args$name,
setdiff(names(formals(qgis_run_algorithm)), c("algorithm", "...", ".raw_json_input"))
)
)
result <- qgis_buffer(
system.file("longlake/longlake_depth.gpkg", package = "qgisprocess"),
DISTANCE = 100,
DISSOLVE = TRUE,
MITER_LIMIT = 2,
OUTPUT = qgis_tmp_vector(),
END_CAP_STYLE = 0,
JOIN_STYLE = 0
)
expect_is(result, "qgis_result")
})
test_that("qgis_pipe() works", {
skip_if_not(has_qgis())
result <- system.file("longlake/longlake_depth.gpkg", package = "qgisprocess") %>%
qgis_pipe(
"native:buffer",
DISTANCE = 100,
DISSOLVE = TRUE,
MITER_LIMIT = 2,
OUTPUT = qgis_tmp_vector(),
END_CAP_STYLE = 0,
JOIN_STYLE = 0
)
expect_is(result, "qgis_result")
})
|
test_that( "js events can be chained ", {
fns <- list(
a = htmlwidgets::JS("function(a) { a + 1 }"),
b = htmlwidgets::JS("function(b) { b + 3 }"),
c = htmlwidgets::JS("function(c) { c + 5 }")
)
js <- chain_js_events(
NULL,
fns$a,
NULL,
fns$b,
fns$c,
NULL,
NULL
)
expect_equal(
js,
htmlwidgets::JS("function() {
try {
(function(a) { a + 1 }).apply(this, arguments);
} catch(e) {
if (window.console && window.console.error) window.console.error(e);
}
try {
(function(b) { b + 3 }).apply(this, arguments);
} catch(e) {
if (window.console && window.console.error) window.console.error(e);
}
try {
(function(c) { c + 5 }).apply(this, arguments);
} catch(e) {
if (window.console && window.console.error) window.console.error(e);
}
}")
)
js <- chain_js_events(
NULL,
fns$a,
NULL
)
expect_equal(
js, fns$a
)
})
|
dmoz_cat <- function(domains = NULL, use_file = NULL) {
c_domains <- gsub("^ *| *$", "", domains)
c_domains_http <- gsub("^http://", "", c_domains)
c_domains <- gsub("^www.", "", c_domains)
dmoz <- NA
domain_cat <- data.frame(domain_name = c_domains, dmoz_category = NA)
if (is.character(use_file)) {
if (!file.exists(use_file)) {
stop("Please provide correct path to the file.
Or download it using get_dmoz_data().")
}
dmoz <- read.csv(use_file, header = FALSE, stringsAsFactors = FALSE)
} else {
if (!file.exists("dmoz_domain_category.csv")) {
stop("Please provide path to the dmoz file.
Or download it using get_dmoz_data().")
}
dmoz <- read.csv("dmoz_domain_category.csv",
header = FALSE, stringsAsFactors = FALSE)
}
names(dmoz) <- c("hostname", "category")
domain_cat$dmoz_category <-
dmoz$category[match(c_domains_http, dmoz$hostname)]
domain_cat$dmoz_category <-
ifelse(
is.na(domain_cat$dmoz_category),
dmoz$category[match(c_domains, dmoz$hostname)],
domain_cat$dmoz_category
)
domain_cat
}
|
library(usethis)
source("data-raw/01-countrypops.R")
source("data-raw/02-sza.R")
source("data-raw/03-gtcars.R")
source("data-raw/04-sp500.R")
source("data-raw/05-pizzaplace.R")
source("data-raw/06-exibble.R")
usethis::use_data(
countrypops, sza, gtcars, sp500, pizzaplace, exibble,
internal = FALSE, overwrite = TRUE
)
|
are_mrca_taxon_names_in_fasta <- function(
mrca_prior,
fasta_filename
) {
testit::assert(beautier::is_mrca_prior(mrca_prior))
testit::assert(
beautier::is_mrca_align_id_in_fasta(
mrca_prior = mrca_prior,
fasta_filename = fasta_filename
)
)
if (beautier::get_alignment_id(fasta_filename) == mrca_prior$alignment_id) {
for (name in mrca_prior$taxa_names) {
if (!name %in% beautier::get_taxa_names(fasta_filename)) {
return(FALSE)
}
}
}
TRUE
}
|
kernelAuto <- function(data.x,
data.y,
kType,
lType,
time,
distanceFunction,
nCores,
verbose, ...){
nCov <- ncol(data.x) - 2L
patientIDs <- sort(unique(data.y[,1L]))
nPatients <- length(patientIDs)
xIs <- list()
for( i in 1L:nrow(data.y) ) {
xIs[[i]] <- list()
xIs[[i]]$v <- which( data.x[,1L] == data.y[i,1L] )
xIs[[i]]$n <- as.integer(round(length(xIs[[i]]$v),0L))
}
yIs <- list()
for( i in 1L:nPatients ) {
yIs[[i]] <- list()
yIs[[i]]$v <- which( data.y[,1L] == patientIDs[i] )
yIs[[i]]$n <- as.integer(round(length(yIs[[i]]$v),0L))
}
range <- c(data.x[, 2L], data.y[,2L])
bw <- seq(from = 2*(stats::quantile(x = range, probs = 0.75) -
stats::quantile(x = range, probs = 0.25)) *
nPatients^(-0.7),
to = 2*(stats::quantile(x = range, probs = 0.75) -
stats::quantile(x = range, probs = 0.25)) *
nPatients^(-0.3),
length = 50)
lbd <- length(bw)
cvlabel <- sample(x = rep( c(1L:2L), length.out = nPatients ))
betaHat0 <- matrix(data = 0.0, nrow = lbd, ncol = nCov)
hatV <- matrix(data = 0.0, nrow = lbd, ncol = nCov)
guess0 <- NULL
guess1 <- NULL
guess2 <- NULL
tempFunc <- function(x,
data.y,
data.x,
bw,
kType,
lType,
nPatients,
xIs,
yIs,
tt,
guess0,
guess1,
guess2,
distanceFunction,
cvlabel){
betaHat0 <- betaHat(data.y = data.y,
data.x = data.x,
bandwidth = bw[x],
kType = kType,
lType = lType,
nPatients = nPatients,
xIs = xIs,
yIs = yIs,
tt = time,
guess = guess0,
distanceFunction = distanceFunction)
tst <- cvlabel == 1L
betaHat1 <- betaHat(data.y = data.y,
data.x = data.x,
bandwidth = bw[x],
kType = kType,
lType = lType,
nPatients = sum(tst),
xIs = xIs,
yIs = yIs[tst],
tt = time,
guess = guess1,
distanceFunction = distanceFunction)
tst <- cvlabel == 2L
betaHat2 <- betaHat(data.y = data.y,
data.x = data.x,
bandwidth = bw[x],
kType = kType,
lType = lType,
nPatients = sum(tst),
xIs = xIs,
yIs = yIs[tst],
tt = time,
guess = guess2,
distanceFunction = distanceFunction)
betaDiff <- betaHat2 - betaHat1
hatV <- nPatients * bw[x] * ( betaDiff * betaDiff ) * 0.25
return( list( "beta0" = betaHat0,
"beta1" = betaHat1,
"beta2" = betaHat2,
"hatV" = hatV) )
}
if( nCores > 1.1 ) {
cl <- makeCluster(nCores)
res <- parLapply(cl, 1L:lbd, tempFunc, data.y = data.y,
data.x = data.x,
bw = bw,
kType = kType,
lType = lType,
nPatients = nPatients,
xIs = xIs,
yIs = yIs,
tt = time,
guess0 = NULL,
guess1 = NULL,
guess2 = NULL,
distanceFunction = distanceFunction,
cvlabel = cvlabel)
stopCluster(cl)
for( bd in 1L:lbd ) {
betaHat0[bd,] <- res[[bd]]$beta0
hatV[bd,] <- res[[bd]]$hatV
}
} else {
for( bd in 1L:lbd ) {
res <- tempFunc(data.y = data.y,
data.x = data.x,
bw = bw[bd],
kType = kType,
lType = lType,
nPatients = nPatients,
xIs = xIs,
yIs = yIs,
tt = time,
guess0 = guess0,
guess1 = guess1,
guess2 = guess2,
distanceFunction = distanceFunction,
cvlabel = cvlabel)
betaHat0[bd,] <- res$beta0
guess0 <- res$beta0
guess1 <- res$beta1
guess2 <- res$beta2
hatV[bd,] <- res$hatV
}
}
hatC <- array(data = 0.0, dim = nCov)
for( p in 1L:nCov ) {
hatC[p] <- lm( betaHat0[,p] ~ bw^2 )$coef[2]
}
MSE <- bw^4 %o% ( hatC * hatC ) + hatV
mseFunc <- function(x) {
tst <- x > 0.0
if( sum(tst) == 0L ) {
stop("no positive MSE values", call. = FALSE)
}
x[!tst] <- NA
opt_h <- which.min(x)
if( length(opt_h) > 1L ) {
warning("Multiple minimums. Smallest bandwidth used.",
call. = FALSE)
opt_h <- opt_h[1]
}
return(opt_h)
}
opt_h <- apply(X = MSE, MARGIN = 2L, FUN = mseFunc)
if( any(opt_h == 1L) || any(opt_h == lbd) ) {
warning("At least 1 minimum is at bandwidth boundary.",
call. = FALSE)
}
tminMSE <- array(data = 0.0, dim = nCov)
for( i in 1L:nCov ) {
tminMSE[i] <- MSE[opt_h[i],i]
}
optH <- bw[opt_h]
minMSE <- tminMSE
bHat <- betaHat(data.y = data.y,
data.x = data.x,
bandwidth = bw[opt_h],
kType = kType,
lType = lType,
tt = time,
guess = guess0,
xIs = xIs,
yIs = yIs,
nPatients = nPatients,
distanceFunction = distanceFunction)
sdVec <- SD(data.y = data.y,
data.x = data.x,
bandwidth = bw[opt_h],
kType = kType,
lType = lType,
bHat = bHat,
tt = time,
xIs = xIs,
yIs = yIs,
nPatients = nPatients,
distanceFunction = distanceFunction)
results <- matrix(data = 0.0,
nrow = nCov,
ncol = 6L,
dimnames = list(paste("beta",0L:{nCov-1L},sep=""),
c("estimate","stdErr","z-value",
"p-value", "optBW", "minMSE")))
results[,1L] <- bHat
results[,2L] <- sdVec
results[,3L] <- bHat/sdVec
results[,4L] <- 2.0*pnorm(-abs(results[,3L]))
results[,5L] <- bw[opt_h]
results[,6L] <- tminMSE
if (verbose) {
print(results)
cat("\n")
}
return( list( "betaHat" = results[,1L],
"stdErr" = results[,2L],
"zValue" = results[,3L],
"pValue" = results[,4L],
"optBW" = results[,5L],
"minMSE" = results[,6L] ) )
}
|
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