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selected <- function(x, ...)UseMethod('selected')
selected.default <- function(
x,
...
){
args <- quos(...)
vars <- args[names(args) == ""]
y <- names(x)
y <- y[y != '']
d <- lapply(y, function(i)character())
names(d) <- y
d <- data.frame(d)
vars <- names(select(d,!!!vars))
if(length(vars) == 0) vars <- y
class(vars) <- union('selected', class(vars))
return(vars)
} |
summaryAovTwoPhase <- function(design.df, blk.str1, blk.str2, trt.str, var.comp = NA,
blk.contr = NA, trt.contr = NA, table.legend = FALSE, response = NA, latex = FALSE,
fixed.names = NA, decimal = FALSE, digits = 2, list.sep = TRUE) {
design.df <- data.frame(sapply(design.df,
function(x) gsub("[[:punct:]]", "", as.character(x))),
stringsAsFactors = TRUE )
newTerms = adjustMissingLevels(design.df, trt.str)
design.df = newTerms$design.df
trt.str = newTerms$str.for
newTerms = adjustMissingLevels(design.df, blk.str1)
design.df = newTerms$design.df
blk.str1 = newTerms$str.for
rT1 <- stats::terms(stats::as.formula(paste("~", blk.str1, sep = "")), keep.order = TRUE)
rT2 <- stats::terms(stats::as.formula(paste("~", blk.str2, sep = "")), keep.order = TRUE)
fT <- stats::terms(stats::as.formula(paste("~", trt.str, sep = "")), keep.order = TRUE)
blkTerm1 <- attr(rT1, "term.labels")
blkTerm2 <- attr(rT2, "term.labels")
if (any(grepl("\\:", blkTerm2))) {
check.blkTerm2 <- unique(unlist(strsplit(blkTerm2, "\\:")))
} else {
check.blkTerm2 <- blkTerm2
}
if (any(grepl("\\:", blkTerm1))) {
check.blkTerm1 <- unique(unlist(strsplit(blkTerm1, "\\:")))
} else {
check.blkTerm1 <- blkTerm1
}
for (i in 1:length(check.blkTerm2)) {
if (length(check.blkTerm1) == 1) {
if (all(as.numeric(as.factor(design.df[, check.blkTerm1])) == as.numeric(as.factor(design.df[,
check.blkTerm2[i]])))) {
cat("Note: Complete confounding between ", check.blkTerm1, " and ",
check.blkTerm2[i], "!\n", sep = "")
colnames(design.df)[which(colnames(design.df) == check.blkTerm1)] <- paste(colnames(design.df)[which(colnames(design.df) ==
check.blkTerm1)], "CCW", sep = "")
blk.str1 <- paste(blkTerm1[which(blkTerm1 == check.blkTerm1)], "CCW",
sep = "")
if (!is.na(blk.contr)) {
names(blk.contr)[which(names(blk.contr) == check.blkTerm1)] <- paste(names(blk.contr)[which(names(blk.contr) ==
check.blkTerm1)], "CCW", sep = "")
}
check.blkTerm1 <- paste(check.blkTerm1, "CCW", sep = "")
}
} else {
check.temp <- apply(design.df[, check.blkTerm1], 2, function(x) all(as.numeric(as.factor(x)) ==
as.numeric(as.factor(design.df[, check.blkTerm2[i]]))))
if (any(check.temp)) {
cat("Note: Complete confounding between ", check.blkTerm1[which(check.temp)],
" and ", check.blkTerm2[i], "!\n", sep = "")
colnames(design.df)[which(colnames(design.df) == check.blkTerm1[which(check.temp)])] <- paste(colnames(design.df)[which(colnames(design.df) ==
check.blkTerm1[which(check.temp)])], "CCW", sep = "")
blk.str1 <- gsub(check.blkTerm1[which(check.temp)], paste(check.blkTerm1[which(check.temp)],
"CCW", sep = ""), blk.str1)
if (!is.na(blk.contr)) {
names(blk.contr)[which(names(blk.contr) == check.blkTerm1[which(check.temp)])] <- paste(names(blk.contr)[which(names(blk.contr) ==
check.blkTerm1[which(check.temp)])], "CCW", sep = "")
}
check.blkTerm1[which(check.blkTerm1 == check.blkTerm1[which(check.temp)])] <- paste(check.blkTerm1[which(check.blkTerm1 ==
check.blkTerm1[which(check.temp)])], "CCW", sep = "")
}
}
}
rT1 <- stats::terms(stats::as.formula(paste("~", blk.str1, sep = "")), keep.order = TRUE)
blkTerm1 <- attr(rT1, "term.labels")
Z1 <- makeBlkDesMat(design.df, rev(blkTerm1))
Z2 <- makeBlkDesMat(design.df, rev(blkTerm2))
Pb <- makeOrthProjectors(Z2)
if(length(blkTerm2) > 1)
blkTerm2 = adjustEffectNames(effectsMatrix = attr(rT2, "factors"), effectNames = blkTerm2)
if (names(Pb)[1] == "e") {
names(Pb)[1] <- paste("Within", paste(unique(unlist(strsplit(names(Pb)[-1],
"[[:punct:]]+"))), collapse = "."))
names(Pb)[-1] <- names(Z2)[-1] <-rev(blkTerm2)
} else {
names(Pb) <- names(Z2) <- rev(blkTerm2)
}
effectsMatrix <- attr(rT1, "factor")
effectsMatrix[nrow(effectsMatrix), effectsMatrix[nrow(effectsMatrix),]==2] <- 1
if(length(blkTerm1) > 1)
blkTerm1 = adjustEffectNames(effectsMatrix = effectsMatrix, effectNames = blkTerm1)
if (names(Z1)[1] == "e") {
names(Z1)[-1] <- rev(blkTerm1)
} else {
names(Z1) <- rev(blkTerm1)
}
T <- makeContrMat(design.df, effectNames = blkTerm1, effectsMatrix = effectsMatrix,
contr.vec = blk.contr)
N <- makeOverDesMat(design.df, blkTerm1)
res <- paste("Within", paste(unique(unlist(strsplit(names(T), "[[:punct:]]+"))),
collapse = "."))
Pb1 <- lapply(Pb, function(z) infoDecompMat(z, T, N))
t.name <- names(T)
if (length(Pb) > 1) {
pb1.names <- lapply((Pb1[-1]), names)
} else {
pb1.names <- lapply(Pb1, names)
}
for (i in 1:length(pb1.names)) {
comp <- t.name %in% pb1.names[[i]]
if (any(comp)) {
break
} else if (i == length(pb1.names)) {
names(Pb1)[1] <- ""
}
}
for (i in 1:length(Pb1)) {
names(Pb1[[i]])[which(names(Pb1[[i]]) == "Residual")] <- res
}
trtTerm <- attr(fT, "term.labels")
effectsMatrix <- attr(fT, "factor")
effectsMatrix[nrow(effectsMatrix), effectsMatrix[nrow(effectsMatrix),]==2] <- 1
if(length(trtTerm) > 1)
trtTerm = adjustEffectNames(effectsMatrix, trtTerm)
T <- makeContrMat(design.df = design.df, effectNames = trtTerm, effectsMatrix = effectsMatrix,
contr.vec = trt.contr)
N <- makeOverDesMat(design.df = design.df, effectNames = trtTerm)
Replist <- getTrtRep(design.df, trtTerm)
Rep <- Replist$Rep
trt.Sca <- Replist$Sca
if (any(grepl("\\.", names(T)))) {
colnames(Rep) <- trtTerm
names(trt.Sca) <- trtTerm
Rep <- Rep[, sapply(strsplit(names(T), "\\."), function(x) x[1])]
trt.Sca <- trt.Sca[sapply(strsplit(names(T), "\\."), function(x) x[1])]
} else {
colnames(Rep) <- trtTerm
names(trt.Sca) <- trtTerm
}
effFactors <- lapply(Pb1, function(y) lapply(y, function(z) getEffFactor(z, T, N,
Rep, trt.Sca)))
effFactors <- effFactors[sort(1:length(effFactors), decreasing = TRUE)]
v.mat <- getVMat.twoPhase(Z.Phase1 = Z1, Z.Phase2 = Z2, design.df = design.df, var.comp = var.comp)
if (all(is.na(var.comp))) {
names(v.mat)[-1] <- c(rev(blkTerm1), rev(blkTerm2))
}
ANOVA <- getCoefVC.twoPhase(Pb = effFactors, design.df = design.df, v.mat = v.mat,
response, table.legend, decimal = decimal, digits = digits)
effFactors <- lapply(Pb1, function(y) lapply(y, function(z) getEffFactor(z, T, N,
Rep, trt.Sca)))
effFactors <- effFactors[sort(1:length(effFactors), decreasing = TRUE)]
if (latex) {
EF <- getFixedEF.twoPhase(effFactors = effFactors, trt.Sca = trt.Sca, T = T, Rep = Rep,
table.legend, decimal = decimal, digits = digits, list.sep = FALSE)
return(toLatexTable(ANOVA = ANOVA, EF = EF, fixed.names = fixed.names))
} else {
EF <- getFixedEF.twoPhase(effFactors = effFactors, trt.Sca = trt.Sca, T = T, Rep = Rep,
table.legend, decimal = decimal, digits = digits, list.sep = list.sep)
return(list(ANOVA = ANOVA, Fixed = EF))
}
} |
ft_normalizer <- function(x, input_col = NULL, output_col = NULL,
p = 2, uid = random_string("normalizer_"), ...) {
check_dots_used()
UseMethod("ft_normalizer")
}
ml_normalizer <- ft_normalizer
ft_normalizer.spark_connection <- function(x, input_col = NULL, output_col = NULL,
p = 2, uid = random_string("normalizer_"), ...) {
.args <- list(
input_col = input_col,
output_col = output_col,
p = p,
uid = uid
) %>%
c(rlang::dots_list(...)) %>%
validator_ml_normalizer()
jobj <- spark_pipeline_stage(
x, "org.apache.spark.ml.feature.Normalizer",
input_col = .args[["input_col"]], output_col = .args[["output_col"]], uid = .args[["uid"]]
) %>%
invoke("setP", .args[["p"]])
new_ml_normalizer(jobj)
}
ft_normalizer.ml_pipeline <- function(x, input_col = NULL, output_col = NULL,
p = 2, uid = random_string("normalizer_"), ...) {
stage <- ft_normalizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
p = p,
uid = uid,
...
)
ml_add_stage(x, stage)
}
ft_normalizer.tbl_spark <- function(x, input_col = NULL, output_col = NULL,
p = 2, uid = random_string("normalizer_"), ...) {
stage <- ft_normalizer.spark_connection(
x = spark_connection(x),
input_col = input_col,
output_col = output_col,
p = p,
uid = uid,
...
)
ml_transform(stage, x)
}
new_ml_normalizer <- function(jobj) {
new_ml_transformer(jobj, class = "ml_normalizer")
}
validator_ml_normalizer <- function(.args) {
.args <- validate_args_transformer(.args)
.args[["p"]] <- cast_scalar_double(.args[["p"]])
if (.args[["p"]] < 1) stop("`p` must be at least 1.")
.args
} |
knitr::opts_chunk$set(echo = TRUE)
library(ape)
library(PCMBase)
library(data.table)
library(xtable)
if(!requireNamespace("ggtree")) {
message("Building the vignette requires ggtree R-package. Trying to install.")
status.ggtree <- try({
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ggtree", version = "3.9")
}, silent = TRUE)
if(class(status.ggtree) == "try-error") {
stop(
"The ggtree installation did not succeed. The vignette cannot be built.")
}
}
options(digits = 2)
tableOutputType <- 'html'
library(ape);
library(PCMBase);
treeNewick <- "((5:0.8,4:1.8)7:1.5,(((3:0.8,2:1.6)6:0.7)8:0.6,1:2.6)9:0.9)0;"
tree <- PCMTree(read.tree(text = treeNewick))
PCMTreeSetPartRegimes(tree, part.regime = c(`6`=2), setPartition = TRUE, inplace = TRUE)
pOrder <- c(PCMTreeGetLabels(tree)[tree$edge[PCMTreePostorder(tree), 2]], "0")
X <- cbind(
c(0.3, NaN, 1.4),
c(0.1, NaN, NA),
c(0.2, NaN, 1.2),
c(NA, 0.2, 0.2),
c(NA, 1.2, 0.4))
colnames(X) <- as.character(1:5)
model.OU.BM <- MixedGaussian(
k = nrow(X),
modelTypes = c(
BM = "BM__Omitted_X0__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Sigma_x__Omitted_Sigmae_x",
OU = "OU__Omitted_X0__H__Theta__UpperTriangularWithDiagonal_WithNonNegativeDiagonal_Sigma_x__Omitted_Sigmae_x"),
mapping = c(2, 1),
Sigmae_x = structure(
0,
class = c("MatrixParameter", "_Omitted",
description = "upper triangular factor of the non-phylogenetic variance-covariance")))
model.OU.BM <- PCMApplyTransformation(model.OU.BM)
model.OU.BM$X0[] <- c(NA, NA, NA)
model.OU.BM$`1`$H[,,1] <- cbind(
c(.1, -.7, .6),
c(1.3, 2.2, -1.4),
c(0.8, 0.2, 0.9))
model.OU.BM$`1`$Theta[] <- c(1.3, -.5, .2)
model.OU.BM$`1`$Sigma_x[,,1] <- cbind(
c(1, 0, 0),
c(1.0, 0.5, 0),
c(0.3, -.8, 1))
model.OU.BM$`2`$Sigma_x[,,1] <- cbind(
c(0.8, 0, 0),
c(1, 0.3, 0),
c(0.4, 0.5, 0.3))
print(
PCMTable(model.OU.BM, removeUntransformed = FALSE),
xtable = TRUE, type=tableOutputType)
options(digits = 4)
PCMLik(X[, tree$tip.label], tree, model.OU.BM)
metaI.variant2 <- PCMInfo(X[, tree$tip.label], tree, model.OU.BM)
colnames(metaI.variant2$pc) <- PCMTreeGetLabels(tree)
metaI.variant2$pc[, "0"] <- c(TRUE, FALSE, TRUE)
metaI.variant2$pc
PCMLik(X[, tree$tip.label], tree, model.OU.BM, metaI = metaI.variant2)
X3 <- X
X3[is.nan(X3)] <- NA_real_
PCMLik(X3[, tree$tip.label], tree, model.OU.BM)
traceTable1 <- PCMLikTrace(X[, tree$tip.label], tree, model.OU.BM)
traceTable1[, node:=.I]
setkey(traceTable1, i)
traceTable2 <- PCMLikTrace(
X[, tree$tip.label], tree, model.OU.BM, metaI = metaI.variant2)
traceTable2[, node:=.I]
setkey(traceTable2, i)
traceTable3 <- PCMLikTrace(X3[, tree$tip.label], tree, model.OU.BM)
traceTable3[, node:=.I]
setkey(traceTable3, i)
H <- model.OU.BM$`1`$H[,,1]
theta <- model.OU.BM$`1`$Theta[,1]
Sigma <- model.OU.BM$`1`$Sigma_x[,,1] %*% t(model.OU.BM$`1`$Sigma_x[,,1])
lambda <- eigen(H)$values
P <- eigen(H)$vectors
P_1 <- solve(P)
pc <- PCMInfo(X[, tree$tip.label], tree, model.OU.BM)$pc
t1 <- PCMTreeDtNodes(tree)[endNodeLab == "1", endTime - startTime]
k1 <- pc[, match("1", PCMTreeGetLabels(tree))]
k9 <- pc[, match("9", PCMTreeGetLabels(tree))]
LambdaMat <- matrix(0, 3, 3)
for(i in 1:3)
for(j in 1:3)
LambdaMat[i,j] <- 1/(lambda[i]+lambda[j])*(1-exp(-(lambda[i]+lambda[j])*t1))
print(omega1 <- (diag(1, 3, 3)[k1, ] - expm::expm(-H*t1)[k1, ]) %*% theta[])
print(Phi1 <- expm::expm(-H*t1)[k1, k9])
print(V1 <- (P %*% (LambdaMat * (P_1 %*% Sigma %*% t(P_1))) %*% t(P))[k1, k1])
Sigma <- model.OU.BM$`2`$Sigma_x[,,1] %*% t(model.OU.BM$`2`$Sigma_x[,,1])
pc <- PCMInfo(X[, tree$tip.label], tree, model.OU.BM)$pc
t2 <- PCMTreeDtNodes(tree)[endNodeLab == "2", endTime - startTime]
k2 <- pc[, match("2", PCMTreeGetLabels(tree))]
k6 <- pc[, match("6", PCMTreeGetLabels(tree))]
print(omega2 <- as.matrix(rep(0, 3)[k2]))
print(Phi2 <- as.matrix(diag(1, 3, 3)[k2, k6]))
print(V2 <- as.matrix((t2*Sigma)[k2, k2]))
options(digits = 2)
cat(FormatTableAsLatex(
traceTable1[list(pOrder), list(j, i, t_i, k_i, omega_i, Phi_i, V_i, V_1_i)],
type = tableOutputType))
cat(FormatTableAsLatex(
traceTable2[list(pOrder), list(j, i, t_i, k_i, omega_i, Phi_i, V_i, V_1_i)],
type = tableOutputType))
options(digits = 2)
cat(FormatTableAsLatex(
traceTable3[list(pOrder), list(j, i, t_i, k_i, omega_i, Phi_i, V_i, V_1_i)],
type = tableOutputType))
print(V1)
V1 <- Re(V1)
V1_1 <- solve(V1)
print(A1 <- -0.5*V1_1)
print(E1 <- t(Phi1) %*% V1_1)
print(b1 <- V1_1 %*% omega1)
print(C1 <- -0.5 * E1 %*% Phi1)
print(d1 <- -E1 %*% omega1)
print(f1 <- -0.5 * (t(omega1) %*% V1_1 %*% omega1 + sum(k1)*log(2*pi) + log(det(V1))))
cat(FormatTableAsLatex(
traceTable1[list(pOrder), list(j, i, k_i, A_i, b_i, C_i, d_i, E_i, f_i)],
type = tableOutputType))
cat(FormatTableAsLatex(
traceTable2[list(pOrder), list(j, i, k_i, A_i, b_i, C_i, d_i, E_i, f_i)],
type = tableOutputType))
cat(FormatTableAsLatex(
traceTable3[list(pOrder), list(j, i, k_i, A_i, b_i, C_i, d_i, E_i, f_i)],
type = tableOutputType))
A2 <- matrix(-0.17)
b2 <- 0.0
C2 <- rbind(c(-0.17, 0),
c(0, 0))
d2 <- c(0.0, 0.0)
E2 <- matrix(c(0.35, 0), nrow = 2, ncol = 1)
f2 <- -1.45
k2 <- 1
print(L62 <- C2)
print(m62 <- d2 + E2 %*% X[k2, "2", drop = FALSE])
print(r62 <- t(X[k2, "2", drop = FALSE]) %*% A2 %*% X[k2, "2", drop = FALSE] +
t(X[k2, "2", drop = FALSE]) %*% b2 + f2)
L63 <- rbind(c(-0.38, 0.51),
c(0.51, -7.62))
m63 <- c(-1.07, 18.09)
r63 <- -11.41
print(L6 <- L62 + L63)
print(m6 <- m62 + m63)
print(r6 <- r62 + r63)
A6 <- rbind(c(-0.44, 0.58),
c(0.58, -8.71))
b6 <- c(0.0, 0.0)
C6 <- rbind(c(-0.44, 0.58),
c(0.58, -8.71))
d6 <- c(0.0, 0.0)
E6 <- rbind(c(0.87, -1.16),
c(-1.16, 17.42))
f6 <- -0.52
k6 <- c(1, 3)
print(L86 <- C6 - (1/4)*E6 %*% solve(A6 + L6) %*% t(E6))
print(m86 <- d6 - (1/2)*E6 %*% solve(A6 + L6) %*% (b6+m6))
print(r86 <- f6+r6+(length(k6)/2)*log(2*pi) -
(1/2)*log(det(-2*(A6+L6))) -
(1/4)*t(b6+m6) %*% solve(A6+L6) %*% (b6+m6))
L8 <- L86; m8 <- m86; r8 <- r86;
options(digits = 3)
cat(FormatTableAsLatex(
traceTable1[
list(pOrder),
list(
j, i, X_i, k_i,
L_i, m_i, r_i,
`L_{ji}`, `m_{ji}`, `r_{ji}`)],
type = tableOutputType))
cat(FormatTableAsLatex(
traceTable2[
list(pOrder),
list(
j, i, X_i, k_i,
L_i, m_i, r_i,
`L_{ji}`, `m_{ji}`, `r_{ji}`)],
type = tableOutputType))
cat(FormatTableAsLatex(
traceTable3[
list(pOrder),
list(
j, i, X_i, k_i,
L_i, m_i, r_i,
`L_{ji}`, `m_{ji}`, `r_{ji}`)],
type = tableOutputType))
L0 <- rbind(c(-0.192, 0.214, 0.178),
c(0.214, -0.313, -0.265),
c(0.178, -0.265, -0.230))
m0 <- c(0.96, 0.026, 0.255)
r0 <- -18.377
print(t(x0Hat <- -0.5*solve(L0) %*% m0))
print(ll0 <- t(x0Hat) %*% L0 %*% x0Hat + t(x0Hat) %*% m0 + r0)
L0 <- rbind(c(-0.192, 0.178),
c(0.178, -0.230))
m0 <- c(0.96, 0.255)
r0 <- -18.377
print(t(x0Hat <- -0.5*solve(L0) %*% m0))
print(ll0 <- t(x0Hat) %*% L0 %*% x0Hat + t(x0Hat) %*% m0 + r0)
traceTable3 <- PCMLikTrace(X3[, tree$tip.label], tree, model.OU.BM)
setkey(traceTable3, i)
options(digits = 4)
print(L0 <- traceTable3[list("0")][["L_i"]][[1]])
print(m0 <- traceTable3[list("0")][["m_i"]][[1]])
print(r0 <- traceTable3[list("0")][["r_i"]][[1]])
print(t(x0Hat <- -0.5*solve(L0) %*% m0))
print(ll0 <- t(x0Hat) %*% L0 %*% x0Hat + t(x0Hat) %*% m0 + r0) |
haptable.default <- function(object){
return(object)
} |
rga <- setRefClass("rga",
fields = list(
client.id = "character",
client.secret = "character",
where = "character",
token = "list"),
methods = list(
initialize = function(client.id, client.secret, where, token) {
.self$where <- where
.self$client.id <- client.id
.self$client.secret <- client.secret
.self$setToken(token, initiate = TRUE)
return(.self)
},
prepare = function() {
if (.self$isTokenExpired()) {
.self$refreshToken()
}
},
isToken = function() {
if (!is.null(.self$token)) {
return(TRUE)
} else {
return(FALSE)
}
},
isTokenExpired = function() {
if (.self$isToken()) {
if (.self$tokenExpiresIn() <= 0) {
return(TRUE)
} else {
return(FALSE)
}
} else {
stop("token is not created")
}
},
tokenExpiresIn = function() {
if (.self$isToken()) {
return(.self$token$expires_in - (as.numeric(Sys.time()) - .self$token$timestamp.refresh))
} else {
stop("token is not created")
}
},
setToken = function(token, initiate = FALSE) {
.self$token <- token
if (.self$isWhere() && !initiate) {
saveRDS(.self, file = .self$where)
}
},
getToken = function(refresh = TRUE) {
if (.self$isToken()) {
if (.self$isTokenExpired() && refresh) {
.self$refreshToken()
}
return(.self$token)
} else {
stop("token is not created")
}
},
refreshToken = function() {
raw.response <- httr::POST("https://accounts.google.com/o/oauth2/token",
body = list(
refresh_token = .self$token$refresh_token,
client_id = .self$client.id,
client_secret = .self$client.secret,
grant_type = "refresh_token"))
token.data <- jsonlite::fromJSON(content(raw.response, "text"))
now <- as.numeric(Sys.time())
.self$setToken(c(
token.data, refresh_token = .self$token$refresh_token,
timestamp = c(first = .self$token$timestamp.first,refresh = now)))
},
isWhere = function() {
if (.self$where != "") {
return(TRUE)
} else {
return(FALSE)
}
}
)
) |
cutTask.mc <- function(nr, nbcores=detectCores()) {
if (nr < nbcores) stop("More cores than tasks, cannot distribute")
if (nr == nbcores) stop("Same number of cores and tasks, cannot simulate")
k <- nr %/% nbcores
klast <- nr - (nbcores-1)*k
nc <- c(rep(k, nbcores-1), klast)
j = 1
while (nc[nbcores] > k+1) {
nc[j] <- nc[j] + 1
nc[nbcores] <- nc[nbcores] - 1
j <- j+1
}
if (sum(nc) != nr) {cat("warning")}
ni <- c(1,cumsum(nc)+1)
nseq <- matrix(NA, nrow=nbcores, ncol=2)
for (j in 1:nbcores) {
nseq[j,1] <- ni[j]
nseq[j,2] <- ni[j+1]-1
}
a = list(nc=nc, ni=ni, nseq=nseq)
return(a)
}
MCMCcopies.mc <- function(mcmc_algo, n=100, nmc=10, Ptheta0,
target, f_param, q_param, verb=TRUE,
nbcores=detectCores()){
d <- dim(Ptheta0)[2]
Ptheta <- array(NA,c(n,d,nmc))
Ptheta[1,,] <- Ptheta0
pba <- 0
tsk <- cutTask.mc(nmc,nbcores)
if (min(tsk$nc) < 2) {
cat("Number of parallel chaines", nmc, "too small, each core needs at least 2 chains\n")
stop("use the single core version MCMCcopies() instead.")
} else if (verb) cat("multicore version: splitting",nmc,"chains between",nbcores,"cores.\n")
pids <- rep(NA,nbcores)
for (j in 1:nbcores) {
Ptheta0j <- matrix(NA, nrow=tsk$nc[j], ncol=d)
Ptheta0j <- Ptheta0[tsk$nseq[j,1]:tsk$nseq[j,2],]
pp <- parallel::mcparallel(MCMCcopies(mcmc_algo, n, tsk$nc[j], Ptheta0j,
target, f_param, q_param, verb=FALSE),
mc.set.seed=TRUE)
pids[j] <- pp$pid
}
cr <- parallel::mccollect(pids)
pba <- rep(NA, nbcores)
for (j in 1:nbcores) {
Ptheta[,,tsk$nseq[j,1]:tsk$nseq[j,2]] <- cr[[j]]$Ptheta
pba[j] <- cr[[j]]$prob.accept
}
return(structure(list(Ptheta=Ptheta, prob.accept=mean(pba),
algo=mcmc_algo$name, target=target,
f_param=f_param, q_param=q_param),
class="plMCMC"))
}
MCMCcopies.cl <- function(mcmc_algo, n=100, nmc=10, Ptheta0,
target, f_param, q_param,
cltype="PAR_SOCK", nbnodes=4) {
d <- dim(Ptheta0)[2];
pba <- 0;
Ptheta <- array(0,c(n,d,nmc)); Ptheta[1,,] <- Ptheta0
switch(cltype,
PAR_SOCK = {
cat("starting parallel ")
cl <- parallel::makeCluster(nbnodes)
print(cl)
parallel::clusterExport(cl, c("target","f_param","q_param","mcmc_algo"),
envir=environment())
},
SNOW_SOCK = {
cl <- snow::makeCluster(nbnodes, type="SOCK")
cat("starting snow socket cluster with",nbnodes,"nodes\n")
snow::clusterExport(cl, c("target","f_param","q_param","mcmc_algo"),
envir=environment())
},
SNOW_RMPI = {
cl <- snow::makeMPIcluster(nbnodes)
snow::clusterExport(cl, c("target","f_param","q_param","mcmc_algo"),
envir=environment())
})
upd <- parRapply(cl, Ptheta0, mcmc_algo$chain,
n, target, f_param, q_param,
mcmc_algo$q_pdf, mcmc_algo$q_proposal)
for(k in 1:nmc){
Ptheta[,,k] <- upd[[k]]$theta
pba <- pba + upd[[k]]$paccept
}
pba <- pba/nmc
stopCluster(cl); cat("cluster stopped.\n")
return(structure(list(Ptheta=Ptheta, prob.accept=pba,
algo=mcmc_algo$name, target=target,
f_param=f_param, q_param=q_param),
class="plMCMC"))
}
Entropy.NN.mc <- function(mcc, target, f_param) {
n <- dim(mcc)[1]
d <- dim(mcc)[2]
nmc <- dim(mcc)[3]
Kb <- Entp <- rep(NA,n)
for (it in 1:n){
if (d==1) x <- mcc[it,,] else x <- t(mcc[it,,])
ep <- Entropy.NN(x, target, f_param)
Kb[it] <- ep$Kb; Entp[it] <- ep$Entp
}
return(list(Kb=Kb, Entp=Entp))
}
Entropy.kNN.mc <- function(mcc, target, f_param, k) {
n <- dim(mcc)[1]
d <- dim(mcc)[2]
nmc <- dim(mcc)[3]
Kb <- Entp <- matrix(NA,n,1)
for (it in 1:n){
if (d==1) x <- mcc[it,,] else x <- t(mcc[it,,])
ep <- Entropy.kNN(x, target, f_param, k)
Kb[it,1] <- ep$Kb; Entp[it,1] <- ep$Entp
}
return(list(Kb=Kb, Entp=Entp))
}
Entropy.kNN.vect.mc <- function(mcc, target, f_param, k) {
n <- dim(mcc)[1]
d <- dim(mcc)[2]
nmc <- dim(mcc)[3]
Kb <- Entp <- matrix(NA,n,k)
for (it in 1:n){
if (d==1) x <- mcc[it,,] else x <- t(mcc[it,,])
ep <- Entropy.kNN.vect(x, target, f_param, k)
for(j in 1:k) {Kb[it,j] <- ep$Kb[j]; Entp[it,j] <- ep$Entp[j]}
}
return(list(Kb=Kb, Entp=Entp))
}
Entropy.ANN.mc <- function(mcc, target, f_param, k, eps, uselogtarget, logtarget) {
n <- dim(mcc)[1]
d <- dim(mcc)[2]
nmc <- dim(mcc)[3]
Kb <- Entp <- matrix(NA,n,1)
for (it in 1:n){
if (d==1) x <- mcc[it,,] else x <- t(mcc[it,,])
ep <- Entropy.ANN(x, target, f_param, k, eps, uselogtarget, logtarget)
Kb[it,1] <- ep$Kb; Entp[it,1] <- ep$Entp
}
return(list(Kb=Kb, Entp=Entp))
}
Entropy.ANN.vect.mc <- function(mcc, target, f_param, k, eps) {
n <- dim(mcc)[1]
d <- dim(mcc)[2]
nmc <- dim(mcc)[3]
Kb <- Entp <- matrix(NA,n,k)
for (it in 1:n){
if (d==1) x <- mcc[it,,] else x <- t(mcc[it,,])
ep <- Entropy.ANN.vect(x, target, f_param, k, eps)
for(j in 1:k) {Kb[it,j] <- ep$Kb[j]; Entp[it,j] <- ep$Entp[j]}
}
return(list(Kb=Kb, Entp=Entp))
}
EntropyMCMC.mc <- function(plmc1,
method = "A.Nearest.Neighbor", k = 1, trim = 0.02, eps = 0,
all.f = TRUE, verb = FALSE, EntVect = FALSE, nbcores=detectCores(),
uselogtarget = FALSE, logtarget = NULL){
if (EntVect == TRUE && k==1) {stop("Error: multinearest neighbour version not available for k < 2\n")}
mc1 <- plmc1$Ptheta
n <- dim(mc1)[1]
d <- dim(mc1)[2]
nmc <- dim(mc1)[3]
Kb1 <- Entp <- rep(0,n)
if(EntVect == TRUE) {Kb1k <- Entpk <- matrix(0,n,k)} else {Kb1k <- Entpk <- matrix(0,n,1)}
if (n < 2*nbcores) {stop("Error: multicore version not available for n < 2*nbcores\n")}
if (method=="Nearest.Neighbor") {
tsk <- cutTask.mc(n,nbcores)
cat("multicore version: splitting",n,"iterations between",nbcores,"cores\n")
pids <- rep(NA,nbcores)
for (j in 1:nbcores) {
mcj <- NULL
mcj <- mc1[tsk$nseq[j,1]:tsk$nseq[j,2],,]
pp <- parallel::mcparallel(Entropy.NN.mc(mcj, plmc1$target, plmc1$f_param),
mc.set.seed=TRUE)
pids[j] <- pp$pid
}
cr <- parallel::mccollect(pids)
for (j in 1:nbcores) {
Kb1[tsk$nseq[j,1]:tsk$nseq[j,2]] <- cr[[j]]$Kb
Entp[tsk$nseq[j,1]:tsk$nseq[j,2]] <- cr[[j]]$Entp
}
}
if (method=="k.Nearest.Neighbor") {
tsk <- cutTask.mc(n,nbcores)
cat("multicore version: splitting",n,"iterations between",nbcores,"cores\n")
pids <- rep(NA,nbcores)
for (j in 1:nbcores) {
mcj <- NULL
mcj <- mc1[tsk$nseq[j,1]:tsk$nseq[j,2],,]
if(EntVect == TRUE) pp <- parallel::mcparallel(Entropy.kNN.vect.mc(mcj, plmc1$target, plmc1$f_param, k),
mc.set.seed=TRUE)
else pp <- parallel::mcparallel(Entropy.kNN.mc(mcj, plmc1$target, plmc1$f_param, k),
mc.set.seed=TRUE)
pids[j] <- pp$pid
}
cr <- parallel::mccollect(pids)
for (j in 1:nbcores) {
Kb1k[tsk$nseq[j,1]:tsk$nseq[j,2],] <- cr[[j]]$Kb
Entpk[tsk$nseq[j,1]:tsk$nseq[j,2],] <- cr[[j]]$Entp
}
}
if (method=="A.Nearest.Neighbor") {
tsk <- cutTask.mc(n,nbcores)
cat("multicore version: splitting",n,"iterations between",nbcores,"cores\n")
pids <- rep(NA,nbcores)
for (j in 1:nbcores) {
mcj <- NULL
mcj <- mc1[tsk$nseq[j,1]:tsk$nseq[j,2],,]
if (EntVect == TRUE) pp <- parallel::mcparallel(Entropy.ANN.vect.mc(mcj, plmc1$target, plmc1$f_param, k, eps),
mc.set.seed=TRUE) else {
pp <- parallel::mcparallel(Entropy.ANN.mc(mcj, plmc1$target, plmc1$f_param,
k, eps,
uselogtarget, logtarget),
mc.set.seed=TRUE) }
pids[j] <- pp$pid
}
cr <- parallel::mccollect(pids)
for (j in 1:nbcores) {
Kb1k[tsk$nseq[j,1]:tsk$nseq[j,2],] <- cr[[j]]$Kb
Entpk[tsk$nseq[j,1]:tsk$nseq[j,2],] <- cr[[j]]$Entp
}
}
if (method != "Nearest.Neighbor" && method != "k.Nearest.Neighbor" && method != "A.Nearest.Neighbor") {
cat("multicore version not imlemented yet for this method,\n")
cat("running the single core version.\n")
EntropyMCMC(plmc1, method, trim, all.f, verb)
}
if (method=="k.Nearest.Neighbor" || method=="A.Nearest.Neighbor") {
return(structure(list(Kullback=Kb1k, Entp=Entpk, nmc=nmc, dim=d,
algo=plmc1$algo, target = plmc1$target, method=method, k=k, eps=eps,
f_param=plmc1$f_param, q_param=plmc1$q_param),
class="KbMCMC"))
}
else{
return(structure(list(Kullback=Kb1, Entp=Entp, nmc=nmc, dim=d,
algo=plmc1$algo, target=plmc1$target, method=method, k=k,
f_param=plmc1$f_param, q_param=plmc1$q_param),
class="KbMCMC"))
}
}
EntropyParallel.cl <- function(mcmc_algo, n = 100, nmc = 10, Ptheta0,
target, f_param, q_param,
method = "A.Nearest.Neighbor", k = 1, eps = 0,
trim = 0.02, verb = TRUE,
EntVect = FALSE, cltype="PAR_SOCK", nbnodes = 4,
par.logf = FALSE,
uselogtarget = FALSE, logtarget = NULL) {
if (EntVect == TRUE && k==1) {stop("Error: multinearest neighbour version not available for k < 2\n")}
d <- dim(Ptheta0)[2]
logc1 <- (d/2)*log(pi) - lgamma(d/2 + 1)
if (method != "Nearest.Neighbor" && method != "k.Nearest.Neighbor" && method != "A.Nearest.Neighbor") {
cat("only method = Nearest.Neighbor, method = k.Nearest.Neighbor and method = A.Nearest.Neighbor are implemented,\n")
cat("running with that method.\n")
method <- "k.Nearest.Neighbor"
}
q=round(n/10); pc <- 0
if (q==0) q <- 1
if (n < 20 && verb) {
cat("verbose mode disabled for n<20 iterations.\n")
verb <- FALSE}
Stheta <- Ptheta0
Kb1 <- rep(0,n); Entp <- rep(0,n)
if(EntVect == TRUE) {Kb1k <- Entpk <- matrix(0,n,k)}
else {Kb1k <- Entpk <- matrix(0,n,1)}
pba <- nba <- 0
switch(cltype,
PAR_SOCK = {
cat("starting parallel ")
cl <- parallel::makeCluster(nbnodes)
print(cl)
parallel::clusterExport(cl, c("target","logtarget","f_param","q_param","mcmc_algo"),
envir=environment())
},
SNOW_SOCK = {
cat("starting snow socket cluster with",nbnodes,"nodes\n")
cl <- snow::makeCluster(nbnodes, type="SOCK")
snow::clusterExport(cl, c("target","logtarget","f_param","q_param","mcmc_algo"),
envir=environment())
},
SNOW_RMPI = {
cat("starting snow MPI cluster:\n")
cl <- snow::makeMPIcluster(nbnodes)
snow::clusterExport(cl, c("target","logtarget","f_param","q_param","mcmc_algo"),
envir=environment())
})
if ((par.logf) & (uselogtarget == FALSE))
logtarget <- function(x, param) {log(target(x,param))}
for (it in 1:n) {
upd <- parRapply(cl, Stheta, mcmc_algo$step,
target,
mcmc_algo$q_pdf, mcmc_algo$q_proposal,
f_param, q_param, nba)
for (i in 1:nmc) {
Stheta[i,] <- upd[[i]]$theta_new
nba <- nba + upd[[i]]$nba
}
if (method == "Nearest.Neighbor"){
z <- .C("entropyNNC", as.integer(nmc), as.integer(d),
x=as.double(Stheta), logc1=as.double(logc1), result=double(1))
Entp[it] <- z$result
if (!par.logf) REnt <- mean(log(target(Stheta, f_param))) else {
REcl <- parRapply(cl, Stheta, logtarget, f_param)
REnt <- mean(REcl)}
Kb1[it] <- Entp[it] - REnt
}
if (method == "k.Nearest.Neighbor"){
if(EntVect == TRUE) ep <- Entropy.kNN.vect(Stheta, target, f_param, k)
else ep <- Entropy.kNN(Stheta, target, f_param, k)
Kb1k[it,] <- ep$Kb; Entpk[it,] <- ep$Entp
}
if (method == "A.Nearest.Neighbor"){
if(EntVect == TRUE) ep <- Entropy.ANN.vect(Stheta, target, f_param, k, eps = eps)
else ep <- Entropy.ANN(Stheta, target, f_param, k, eps = eps,
uselogtarget = uselogtarget, logtarget = logtarget)
Kb1k[it,] <- ep$Kb; Entpk[it,] <- ep$Entp
}
b=round(it/q); r=it-b*q
if (r==0 & verb) {
pc <- pc+10;
if (EntVect == FALSE) cat(pc,"% done, Kullback at ", k, "th Nearest Neighbor = ", Kb1k[it,],"\n")
else cat(pc,"% done, Kullback at ", k, "th Nearest Neighbor = ", Kb1k[it,k],"\n")}
}
pba <- nba/(n*nmc)
stopCluster(cl); cat("cluster stopped.\n")
if (method == "k.Nearest.Neighbor" || method == "A.Nearest.Neighbor"){
return(structure(list(Kullback=Kb1k, Entp=Entpk, nmc=nmc, dim=d,
algo=mcmc_algo$name, target=target, method=method, k=k, eps=eps,
f_param=f_param, q_param=q_param, prob.accept=pba,
Ptheta=NULL),
class="KbMCMC"))
}
else{
return(structure(list(Kullback=Kb1, Entp=Entp, nmc=nmc, dim=d,
algo=mcmc_algo$name, target=target, method=method,
f_param=f_param, q_param=q_param, prob.accept=pba,
nbnodes=nbnodes, lastTheta=Stheta, Ptheta=NULL),
class="KbMCMC"))
}
} |
truncate_model <- function(object, ...) {
UseMethod("truncate_model")
}
truncate_model.rvine_structure <- function(object, trunc_lvl, ...) {
check_trunc_lvl(object, trunc_lvl)
if (trunc_lvl < dim(object)["trunc_lvl"]) {
object$struct_array <- object$struct_array[seq_len(trunc_lvl)]
object$trunc_lvl <- trunc_lvl
}
object
}
truncate_model.rvine_matrix <- function(object, trunc_lvl, ...) {
check_trunc_lvl(object, trunc_lvl)
d <- dim(object)["dim"]
trees_to_truncate <- setdiff(seq_len(d - 1), seq_len(trunc_lvl))
for (tree in trees_to_truncate) {
object[tree, seq_len(d - tree)] <- 0
}
attr(object, "trunc_lvl") <- trunc_lvl
object
}
truncate_model.vinecop_dist <- function(object, trunc_lvl, ...) {
check_trunc_lvl(object, trunc_lvl)
if (trunc_lvl < dim(object)["trunc_lvl"]) {
object <- adjust_fit_stats(object, trunc_lvl)
object$structure <- truncate_model(object$structure, trunc_lvl)
object$pair_copulas <- object$pair_copulas[seq_len(trunc_lvl)]
}
object
}
truncate_model.vine_dist <- function(object, trunc_lvl, ...) {
check_trunc_lvl(object, trunc_lvl)
if (trunc_lvl < dim(object)["trunc_lvl"]) {
object <- adjust_fit_stats(object, trunc_lvl)
object$copula <- truncate_model(object$copula, trunc_lvl)
}
object
}
truncate_column <- function(column, trunc_lvl) {
column[1:min(length(column), trunc_lvl)]
}
check_trunc_lvl <- function(object, trunc_lvl) {
msg <- paste0(
"trunc_lvl should be a number between 0 and the number of trees (",
dim(object)["dim"] - 1, ")."
)
assert_that(
is.number(trunc_lvl),
trunc_lvl == round(trunc_lvl),
trunc_lvl >= 0,
trunc_lvl < dim(object)["dim"],
msg = msg
)
if (trunc_lvl > dim(object)["trunc_lvl"]) {
warning("truncation has no effect; vine is already ",
dim(object)["trunc_lvl"], "-truncated.",
call. = FALSE
)
}
invisible(TRUE)
}
get_truncated_pcs <- function(object, trunc_lvl) {
check_trunc_lvl(object, trunc_lvl)
if (!is.null(object$copula)) {
pcs <- object$copula$pair_copulas[-seq_len(trunc_lvl)]
} else {
pcs <- object$pair_copulas[-seq_len(trunc_lvl)]
}
unlist(pcs, recursive = FALSE)
}
adjust_fit_stats <- function(object, trunc_lvl) {
trunc_pcs <- get_truncated_pcs(object, trunc_lvl)
if (length(trunc_pcs) == 0) {
return(object)
}
trunc_npars <- sum(sapply(trunc_pcs, function(x) x[["npars"]]))
object$npars <- object$npars - trunc_npars
if (!is.na(object$loglik)) {
trunc_loglik <- sum(sapply(trunc_pcs, function(x) x[["loglik"]]))
object$loglik <- object$loglik - trunc_loglik
}
object
} |
se <- function(y, y_hat) {
stopifnot(length(y) == length(y_hat),
is.numeric(y),
is.numeric(y_hat))
(y - y_hat) ^ 2
}
mse <- function(y, y_hat) mean(se(y, y_hat), na.rm = TRUE)
rmse <- function(y, y_hat) sqrt(mse(y, y_hat))
ae <- function(y, y_hat) {
stopifnot(length(y) == length(y_hat),
is.numeric(y),
is.numeric(y_hat))
abs(y - y_hat)
} |
createResample <- function(y, times = 10, list = TRUE) {
if (class(y)[1] == "Surv")
y <- y[, "time"]
trainIndex <- matrix(0, ncol = times, nrow = length(y))
out <- apply(
trainIndex, 2,
function(data) {
index <- seq(along = data)
out <-
sort(sample(index, size = length(index), replace = TRUE))
out
}
)
if (list) {
out <- as.data.frame(out, stringsAsFactors = TRUE)
attributes(out) <- NULL
names(out) <- prettySeq(out)
} else {
colnames(out) <- prettySeq(1:ncol(out))
}
out
} |
expchart <- function(device = png, file, ppi = 72, hprop = 6.7, wprop = 6.7, ...)
{
dev.copy(device,
file,
width = wprop*ppi,
height = hprop*ppi,
res = ppi,
...)
dev.off()
} |
explo.compa<-function(dendat,seed=1)
{
n<-dim(dendat)[1]
d<-dim(dendat)[2]
cova<-cov(dendat)
mu<-mean(data.frame(dendat))
eig<-eigen(cov(dendat),symmetric=TRUE)
sigsqm<-eig$vectors%*%diag(eig$values^{1/2})
set.seed(seed)
symmedata<-matrix(rnorm(d*n),n,d)
dendat.simu<-t(sigsqm%*%t(symmedata))
return(dendat.simu)
} |
draw.iie_br = function(){
iiebr = BETSget("ST_iiebr")
iiebr = ts(iiebr[,2], start = c(2000,1), frequency = 12)
iiebr.ma = ma(iiebr,6)
m <- list(
t = 50,
pad = 1
)
a <- list(
x = as.Date(iiebr)[length(iiebr)],
y = iiebr[length(iiebr)],
text = paste0("<b>",iiebr[length(iiebr)],"</b>"),
xref = "x",
yref = "y",
showarrow = TRUE,
arrowhead = 6,
ay = 50,
ax = 0,
font = list(size = 22)
)
p = plot_ly(mode = "lines", type = "scatter", x = as.Date(iiebr), y = iiebr, name = "IIE-Br",
line = list(color = "
add_trace(y = iiebr.ma, x = as.Date(iiebr.ma), name = "MA 6 periods", line = list(color = "
layout(title = "<b>UNCERTAINTY INDEX</b><br>IIE-Br (FGV/IBRE)",
yaxis = list(tickfont = list(size = 22), titlefont = list(size = 22)),
xaxis = list(tickfont = list(size = 22)),
margin = m,
titlefont = list(size = 19),
annotations = a,
legend = list(orientation = 'h', x = 0.3))
return(p)
} |
library(tidyverse)
library(caret)
library(SSLR)
library(tidymodels)
data <- iris
set.seed(1)
cls <- which(colnames(iris) == "Species")
labeled.index <- createDataPartition(data$Species, p = .2, list = FALSE)
data[-labeled.index,cls] <- NA
m <- constrained_kmeans() %>% fit(Species ~ ., data)
labels <- m %>% cluster_labels()
print(labels)
centers <- m %>% get_centers()
print(centers) |
.hessianRichardson<-function(x, temperatures, integral, derivate, weight,
hatchling.metric, M0, fixed.parameters) {
return(numDeriv::hessian(info.nests, x, method="Richardson",
temperatures=temperatures,
integral=integral, weight=weight, derivate=derivate,
hatchling.metric=hatchling.metric, M0=M0, fixed.parameters=fixed.parameters))
} |
get.interactions <-
function(mf, ncol_intercepts = 0){
factor_matrix <- attr(attr(mf,'terms'),'factors')
factor_order <- attr(attr(mf,'terms'),'order')
col_index_inter <- which(factor_order >= 2)
col_chosen_inter <- array(dim = 0)
col_chosen_inter_numeric <- array(dim = 0)
results <- list()
results_num <- list()
if (length(col_index_inter) > 0){
for (i in 1:length(col_index_inter)){
index_inter <- which(factor_matrix[,col_index_inter[i]] == 1)
if (sum(attr(attr(mf,'terms'),'dataClasses')[index_inter] != 'factor') == 0){
results[[length(results) + 1]] <- index_inter + ncol_intercepts
col_chosen_inter <- c(col_chosen_inter, col_index_inter[i] + ncol_intercepts)
}else{
results_num[[length(results_num) + 1]] <- index_inter + ncol_intercepts
col_chosen_inter_numeric <- c(col_chosen_inter_numeric, col_index_inter[i] + ncol_intercepts)
}
}
}
sol <- list()
sol$results <- results
sol$results_num <- results_num
sol$index <- col_chosen_inter
sol$numeric_index <- col_chosen_inter_numeric
return(sol)
} |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
library(gitear)
library(dplyr)
library(jsonlite)
r <- readRDS(system.file("helper_data/response_example.RDS",
package = "gitear"))
content_an_organization <- fromJSON(system.file("helper_data/get_an_org.json",
package = "gitear"))
content_issues <- fromJSON(system.file("helper_data/get_issues.json",
package = "gitear"))
content_list_repos_org <- fromJSON(system.file("helper_data/get_repos_org.json",
package = "gitear"))
content_list_users <- fromJSON(system.file("helper_data/get_users.json",
package = "gitear"))
content_commits <- fromJSON(system.file("helper_data/get_commits.json",
package = "gitear"))
mockery::stub(where = get_issues,
what = "GET",
how = r)
mockery::stub(where = get_issues,
what = "fromJSON",
how = content_issues)
api_token <- "gfdsgfd8ba18a866bsdfgsdfgs3a2dc9303453b0c92dcfb19"
url_ixpantia <- "https://prueba.com"
issues <- get_issues(base_url = url_ixpantia,
api_key = api_token,
owner = "empresa",
repo = "repo_prueba")
glimpse(issues)
example_key <- Sys.getenv("example_key")
example_url <- Sys.getenv("example_url")
mockery::stub(where = get_an_organization,
what = "GET",
how = r)
mockery::stub(where = get_an_organization,
what = "fromJSON",
how = content_an_organization)
organizations <- get_an_organization(base_url = example_url,
api_key = example_key,
org = "empresa")
glimpse(organizations)
mockery::stub(where = get_list_repos_org,
what = "GET",
how = r)
mockery::stub(where = get_list_repos_org,
what = "fromJSON",
how = content_list_repos_org)
repos <- get_list_repos_org(base_url = example_url,
api_key = example_key,
org = "empresa")
glimpse(repos)
mockery::stub(where = get_list_users,
what = "GET",
how = r)
mockery::stub(where = get_list_users,
what = "fromJSON",
how = content_list_users)
users <- get_list_users(base_url = example_url,
api_key = example_key)
glimpse(users)
mockery::stub(where = get_commits,
what = "GET",
how = r)
mockery::stub(where = get_commits,
what = "fromJSON",
how = content_commits)
commits <- get_commits(base_url = example_url,
api_key = example_key,
owner = "empresa",
repo = "repo_prueba")
head(commits) |
library("magick")
library("showtext")
font_add_google("Zilla Slab", "pf", regular.wt = 500)
hexSticker::sticker(
subplot = ~ plot.new(), s_x = 1, s_y = 1, s_width = 0.1, s_height = 0.1,
package = "liftr", p_x = 1, p_y = 1, p_size = 12, h_size = 1.2, p_family = "pf",
p_color = "
dpi = 320, filename = "man/figures/logo.png"
)
image_read("man/figures/logo.png")
rstudioapi::restartSession() |
varbgs<- function(b,nf, thresh)
{
bgmodel<- base::array(0,dim=c(dim(b)[1],dim(b)[2],nf))
for(i in 1:nf)
{
bgmodel[,,i]=b[,,i]
}
b[,,1]= base::abs( base::floor(bgmodel[,,2]-bgmodel[,,1]))
for(i in 3:dim(b)[3])
{
if(i<=(nf+1))
{
b[,,i-1]= base::floor(base::abs(b[,,i]- base::apply(bgmodel[,,1:i-1],c(1,2),FUN="var",na.rm=FALSE)))
}
else
{
j= (i-1)%%nf
if(j!=0)
{
bgmodel[,,j]=b[,,i-1]
}
else
{
bgmodel[,,nf]=b[,,i-1]
}
b[,,i-1]= base::floor(base::abs(b[,,i]- base::apply(bgmodel,c(1,2),FUN="var",na.rm=FALSE)))
}
b[,,i-1]=imager::threshold(b[,,i-1],thr=thresh,approx=TRUE)
graphics::plot(imager::as.cimg(b[,,i-1]), main=i)
}
return(b)
} |
ecmave <- function (y, xeq, xtr, lags=1, includeIntercept = TRUE, k, method = 'boot', seed = 5, weights = NULL, ...) {
if (!is.null(xtr)){
if (sum(grepl("^delta|Lag[0-9]$", names(xtr))) > 0) {
warning("You have column name(s) in xtr that begin with 'delta' or end with 'Lag[0-9]'. It is strongly recommended that you change this, otherwise the function 'ecmpredict' may result in errors or incorrect predictions.")
}
if (!inherits(xtr, "data.frame")) {
stop("xtr does not inherit class 'data.frame'. See details on how to input them as data frames.")
}
}
if (!is.null(xeq)){
if (sum(grepl("^delta", names(xeq))) > 0) {
warning("You have column name(s) in xeq that begin with 'delta' or end with 'Lag[0-9]'. It is strongly recommended that you change this, otherwise the function 'ecmpredict' may result in errors or incorrect predictions.")
}
if (!inherits(xeq, "data.frame")) {
stop("xeq does not inherit class 'data.frame'. See details on how to input them as data frames.")
}
if (nrow(xeq) < (lags+1)) {
stop("Insufficient data for the lags specified.")
}
}
if (!is.null(xeq)){
xeqnames <- names(xeq)
xeqnames <- paste0(xeqnames, paste0("Lag", as.character(lags)))
xeq <- data.frame(sapply(xeq, lagpad, lags))
}
if (!is.null(xtr)){
xtrnames <- names(xtr)
xtrnames <- paste0("delta", xtrnames)
xtr <- data.frame(apply(xtr, 2, diff, lags))
}
if (class(y)=='data.frame'){
if (ncol(y) > 1){
warning("You have more than one column in y, only the first will be used")
}
y <- y[,1]
}
yLag <- y[1:(length(y) - lags)]
if (!is.null(xtr) & !is.null(xeq)){
x <- cbind(xtr, xeq[complete.cases(xeq), ])
xnames <- c(xtrnames, xeqnames)
} else if (!is.null(xtr) & is.null(xeq)){
x <- xtr
xnames <- xtrnames
} else if (is.null(xtr) & !is.null(xeq)){
x <- xeq[complete.cases(xeq), ]
xnames <- xeqnames
}
x <- cbind(x, yLag)
names(x) <- c(xnames, paste0("yLag", as.character(lags)))
x$dy <- diff(y, lags)
if (includeIntercept){
formula <- 'dy ~ .'
} else {
formula <- 'dy ~ . - 1'
}
ecm <- lmave(formula, data = x, k = k, method = method, seed = seed, weights = weights, ...)
return(ecm)
} |
dummy.c <- function(x, ref = NULL, names = "d", as.na = NULL, check = TRUE) {
if (isTRUE(missing(x))) {
stop("Please specify a numeric vector with integer values, character vector or factor for the argument 'x'.",
call. = FALSE)
}
if (isTRUE(is.null(x))) {
stop("Input specified for the argument 'x' is NULL.", call. = FALSE)
}
x <- unlist(x, use.names = FALSE)
if (isTRUE(!is.null(as.na))) {
x <- misty::as.na(x, na = as.na, check = check)
if (isTRUE(all(is.na(x)))) {
stop("After converting user-missing values into NA, 'x' is completely missing.", call. = FALSE)
}
if (isTRUE(length(na.omit(unique(x))) == 1)) {
stop("After converting user-missing values into NA, 'x' has only one unique value.", call. = FALSE)
}
}
x.unique <- unique(na.omit(x))
x.length <- length(x)
if (isTRUE(!is.logical(check))) {
stop("Please specify TRUE or FALSE for the argument 'check'.", call. = FALSE)
}
if (isTRUE(check)) {
if (isTRUE(is.numeric(x))) {
if (isTRUE(any(na.omit(x) %% 1L != 0L))) {
stop("Please specify a vector with integer values, a character vector or a factor for the argument 'x'.",
call. = FALSE)
}
}
if (isTRUE(length(na.omit(unique(x))) == 1)) {
stop("Variable specified in 'x' havs only one unique value.", call. = FALSE)
}
if (isTRUE(!is.null(ref))) {
if (isTRUE(!ref %in% x)) {
stop("The reference category specified in 'ref' was not found in 'x'.", call. = FALSE)
}
}
if (isTRUE(!is.character(names))) {
stop("Please specify a character string or character vector for the argument 'names'.", call. = FALSE)
}
if (isTRUE(length(names) > 1L)) {
if (isTRUE(length(names) != (length(x.unique) - 1L))) {
stop("The length of the vector specified in 'names' does not match with the number of unique values minus one.",
call. = FALSE)
}
}
}
object <- matrix(0L, nrow = x.length, ncol = (length(x.unique) - 1L))
if (isTRUE(is.null(ref))) {
if (isTRUE(is.numeric(x) || is.character(x))) {
ref <- sort(x.unique)[length(x.unique)]
}
if (isTRUE(is.factor(x))) {
ref <- levels(x)[length(levels(x))]
}
}
colnames(object) <- sort(x.unique[!x.unique %in% ref])
if (isTRUE(any(is.na(x)))) {
object[is.na(x), ] <- NA
}
for (i in colnames(object)) {
object[which(as.character(x) == i), i] <- 1L
}
if (isTRUE(length(names) == ncol(object))) {
colnames(object) <- names
} else {
colnames(object) <- paste0(names, colnames(object))
}
return(object)
} |
library(tibble)
all_methods <- unique(tidygeocoder::api_parameter_reference[['method']])
test_that("geocode default colnames", {
result <- tibble::tibble(addr = NA) %>%
geocode(addr, no_query = TRUE)
expect_identical(colnames(result), c('addr', 'lat', 'long'))
expect_equal(nrow(result), 1)
})
test_that("geocode custom colnames", {
result <- tibble::tibble(addr = '') %>%
geocode(addr, lat = 'latitude', long = 'longitude', no_query = TRUE)
expect_identical(colnames(result), c('addr', 'latitude', 'longitude'))
expect_equal(nrow(result), 1)
})
test_that("geocode null/empty addresses", {
NA_result <- get_na_value('lat', 'long')
NA_data <- tibble::tribble(~addr,
" ",
NA,
"")
expected_colnames <- c(colnames(NA_data), 'lat', 'long')
for (method in all_methods) {
method_label = paste0('method = "', method, '"', ' ')
expect_identical(
geo(" ", method = method, return_addresses = FALSE, no_query = TRUE),
NA_result, label = method_label
)
result <- NA_data %>% geocode(addr, no_query = TRUE, method = method)
expect_identical(colnames(result), expected_colnames, label = method_label)
expect_equal(nrow(result), nrow(NA_data), label = method_label)
}
})
test_that("Test geo() and reverse_geo() error handling", {
expect_error(geo(no_query = TRUE, address = 'abc', method = '123'))
expect_error(reverse_geo(no_query = TRUE, lat = 1, long = 2, method = '123'))
expect_error(geo(no_query = TRUE, address = 'abc', mode = '123'))
expect_error(reverse_geo(no_query = TRUE, lat = 1, long = 2, mode = '123'))
expect_error(geo(no_query = TRUE, address = 'abc', street = 'xyz', no_query = TRUE))
expect_error(geo(no_query = TRUE, address = 'abc', api_options = list(census_return_type = 'xyz')))
expect_error(geo(no_query = TRUE, address = 'abc', api_options = list(bad_argument = 'xyz')))
expect_error(geo(no_query = TRUE, address = 'abc', limit = 0))
expect_error(reverse_geo(no_query = TRUE, lat = 1, long = 2, limit = 0))
expect_error(geo(no_query = TRUE, city = c('x', 'y'), state = 'ab'))
expect_error(reverse_geo(no_query = TRUE, lat = c(1,5), long = 2))
expect_error(geo('yz', no_query = TRUE, mode = 'batch', method = 'osm'))
expect_error(reverse_geo(lat = 1, long = 2, no_query = TRUE, mode = 'batch', method = 'osm'))
expect_error(geo('yz', no_query = TRUE, country = 'abc', method = 'census'))
expect_error(geo(no_query = TRUE, address = 'abc', api_options = list(mapbox_permanent = "AA")))
expect_error(geo(no_query = TRUE, address = 'abc', api_options = list(invalid_parameter = "blah")))
expect_error(geo(no_query = TRUE, address = 'abc', method = 'here', api_options = list(here_request_id = 12345)))
expect_error(reverse_geo(no_query = TRUE, lat = 1, long = 2, method = 'here', api_options = list(here_request_id = 12345)))
expect_error(reverse_geo(no_query = TRUE, lat = c(0,1), long = c(0,0),
method = 'here', api_options = list(here_request_id = 'asdf'), return_coords = TRUE, mode = 'batch'))
expect_error(geo(no_query = TRUE, address = c('xyz', 'abc'),
method = 'here', api_options = list(here_request_id = 'asdf'), return_addresses = TRUE, mode = 'batch'))
expect_error(geo(address = as.character(seq(1, 10)),
method = 'census', batch_limit = 5, no_query = TRUE))
expect_warning(geo(address = as.character(seq(1, 10)),
method = 'census', batch_limit = 5, no_query = TRUE, batch_limit_error = FALSE))
expect_error(reverse_geo(lat = c(1,2,3), long = c(0,0,0),
method = 'geocodio', batch_limit = 2, no_query = TRUE))
})
test_that("Test geocode() error handling", {
addr_df <- tibble::tibble(addr = 'xyz')
named_list <- list(addr = 'xyz')
expect_error(geocode(addr_df, no_query = TRUE, address = wrong))
expect_error(geocode(addr_df, no_query = TRUE, address = "wrong"))
expect_error(geocode(named_list, no_query = TRUE, address = 'addr'))
})
test_that("reverse geocode null/empty addresses", {
reverse_methods <- all_methods[!all_methods %in% pkg.globals$no_reverse_methods]
NA_result <- tibble::tibble(address = as.character(NA))
NA_data <- tibble::tribble(~lat,~lon,
as.numeric(NA), as.numeric(NA),
5000, 5000)
expected_colnames <- c(colnames(NA_data), 'address')
for (method in reverse_methods) {
method_label = paste0('method = "', method, '"', ' ')
expect_identical(colnames(reverse_geocode(NA_data, lat = lat, long = lon,
method = method, no_query = TRUE)), expected_colnames, label = method_label)
expect_equal(nrow(reverse_geocode(NA_data, lat = lat, long = lon,
method = method, no_query = TRUE)), nrow(NA_data), label = method_label)
expect_identical(reverse_geo(lat = " ", long = " ", method = method,
return_coords = FALSE, no_query = TRUE), NA_result, label = method_label)
}
})
test_that("Test limit related error handling", {
addr_input <- tibble(addr = c('zyx', 'etd'))
coord_input <- tibble(lat = c(1, 2), long = c(4, 5))
expect_error(reverse_geo(no_query = TRUE, method = 'geocodio', lat = 1, long = 2, mode = 'batch', limit = 5))
expect_error(reverse_geo(no_query = TRUE, method = 'geocodio', lat = 1, long = 2, mode = 'batch', limit = NULL))
expect_true(is_tibble(reverse_geo(no_query = TRUE, method = 'geocodio', lat = 1, long = 2, mode = 'batch', limit = 1)))
expect_true(is_tibble(reverse_geo(no_query = TRUE, method = 'geocodio', lat = 1, long = 2, mode = 'batch', limit = 5, return_coords = FALSE)))
expect_error(geo(no_query = TRUE, method = 'geocodio', address = 'xyz', mode = 'batch', limit = 5))
expect_error(geo(no_query = TRUE, method = 'geocodio', address = 'xyz', mode = 'batch', limit = NULL))
expect_true(is_tibble(geo(no_query = TRUE, method = 'geocodio', address = 'xyz', mode = 'batch', limit = 1)))
expect_true(geo(no_query = TRUE, method = 'geocodio', address = 'xyz', mode = 'batch', limit = 5, return_addresses = FALSE) %>% is_tibble())
expect_error(geocode(addr_input, address = addr, no_query = TRUE, method = 'osm', limit = 5, return_input = TRUE))
expect_error(geocode(addr_input, address = addr, no_query = TRUE, method = 'osm', limit = NULL, return_input = TRUE))
expect_true(is_tibble(geocode(addr_input, address = addr, no_query = TRUE, method = 'osm', return_input = TRUE)))
expect_true(is_tibble(geocode(addr_input, address = addr, no_query = TRUE, method = 'osm', unique_only = TRUE)))
expect_error(reverse_geocode(coord_input, lat = lat, long = long, no_query = TRUE, method = 'osm', return_input = TRUE, limit = 5))
expect_error(reverse_geocode(coord_input, lat = lat, long = long, no_query = TRUE, method = 'osm', return_input = TRUE, limit = NULL))
expect_true(reverse_geocode(coord_input, lat = lat, long = long, no_query = TRUE, method = 'osm', return_input = FALSE) %>% is_tibble())
expect_true(reverse_geocode(coord_input, lat = lat, long = long, no_query = TRUE, method = 'osm', unique_only = TRUE) %>% is_tibble())
})
test_that("Test reverse_geocode() error handling", {
addr_df <- tibble::tibble(lat = 1, long = 2)
named_list <- list(lat = 1, long = 2)
expect_error(reverse_geocode(addr_df, no_query = TRUE, lat = lat, long = wrong))
expect_error(reverse_geocode(addr_df, no_query = TRUE, lat = wrong, long = long))
expect_error(reverse_geocode(addr_df, no_query = TRUE, lat = wrong1, long = wrong2))
expect_error(reverse_geocode(named_list, no_query = TRUE, address = 'addr'))
}) |
expected <- eval(parse(text="FALSE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(list(c0 = structure(integer(0), .Label = character(0), class = \"factor\")), .Names = \"c0\", row.names = character(0), class = \"data.frame\"))"));
.Internal(isNamespaceEnv(argv[[1]]));
}, o=expected); |
env.evaluate <- function(species, model, env, bg.source = "background", n.background = 10000, test.eval = FALSE, verbose = FALSE, ...){
if(test.eval == TRUE){
if(is.na(model$test.data)){
stop("Test.eval set to TRUE but no test data present in model object!")
}
species$presence.points <- model$test.data
}
species <- check.bg(species, env, verbose = verbose)
if(!inherits(species, "enmtools.species")){
stop("Argument species must supply an enmtools.species object!")
}
presence <- species$presence.points[,1:2]
background <- species$background.points[,1:2]
if(inherits(model, "enmtools.model")){
model <- model$model
}
if(bg.source == "background"){
allpoints <- rbind(presence, background)
values <- extract(env, allpoints)
maxes <- apply(values, 2, function(x) max(x, na.rm = TRUE))
mins <- apply(values, 2, function(x) min(x, na.rm = TRUE))
}
if(bg.source == "env") {
maxes <- maxValue(env)
mins <- minValue(env)
}
this.lhs <- randomLHS(n.background, length(names(env)))
bg.table <- t(t(this.lhs) * (maxes - mins) + mins)
colnames(bg.table) <- names(env)
p.table <- extract(env, presence)
if(inherits(model, "ranger")) {
pred.p <- as.numeric(predict(model, data = data.frame(p.table), type = "response")$predictions[ , 2, drop = TRUE])
pred.bg <- as.numeric(predict(model, data = data.frame(bg.table), type = "response")$predictions[ , 2, drop = TRUE])
} else {
pred.p <- as.numeric(predict(model, newdata = data.frame(p.table), x = data.frame(p.table), type = "response"))
pred.bg <- as.numeric(predict(model, newdata = data.frame(bg.table), x = data.frame(bg.table), type = "response"))
}
env.evaluation <-dismo::evaluate(pred.p, pred.bg)
return(env.evaluation)
} |
glm.cis <- function(preds, ses, alpha, df){
tval = qt((1-alpha)/2, df, lower.tail = F)
raw_conf = cbind(preds-(tval*ses), preds+(tval*ses))
trans_conf = t(apply(raw_conf, 1, exp))
out <- cbind(preds, raw_conf, exp(preds), trans_conf)
colnames(out) <- c('eta', 'low.eta', 'hi.eta', 'pred', 'low.pred', 'hi.pred')
return(out)
} |
.onLoad <- function(libname, pkgname) {
sparkR.onLoad(libname, pkgname)
} |
local_edition(3)
skip_on_cran()
mtcars_short <- mtcars[1:5, ]
test_that("a gt table contains the expected heading components", {
tbl_latex <-
gt(mtcars_short) %>%
tab_header(title = "test title")
grepl(
"\\caption*{\n{\\large test title}\n} \\\\ \n\\toprule",
tbl_latex %>% as_latex() %>% as.character(),
fixed = TRUE
) %>%
expect_true()
tbl_latex <-
gt(mtcars_short) %>%
tab_header(title = "test title", subtitle = "test subtitle")
grepl(
paste0(
".*.large test title",
".*.small test subtitle",
".*"
),
tbl_latex %>% as_latex() %>% as.character()
) %>%
expect_true()
mtcars_short %>%
gt() %>%
tab_header(title = "test title") %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
mtcars_short %>%
gt() %>%
tab_header(title = "test title", subtitle = "test subtitle") %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
mtcars_short %>%
gt() %>%
tab_header(title = "test title", subtitle = "") %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
mtcars_short %>%
gt() %>%
tab_header(title = "test title", subtitle = " ") %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
})
test_that("a gt table contains the expected stubhead label", {
tbl_latex <-
gt(mtcars_short, rownames_to_stub = TRUE) %>%
tab_stubhead(label = "the mtcars")
grepl(
paste0(
".*multicolumn\\{1\\}\\{l\\}\\{the mtcars\\} & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb",
".*"),
tbl_latex %>% as_latex() %>% as.character()
) %>%
expect_true()
})
test_that("a gt table contains the expected column spanner labels", {
tbl_latex <-
gt(rock) %>%
tab_spanner(
label = "perimeter",
columns = c("peri", "shape")
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(rock) %>%
tab_spanner(
label = "perimeter",
columns = c(peri, shape)
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
expect_error(
gt(rock) %>%
tab_spanner(
label = "perimeter",
columns = c(peris, shapes)
)
)
tbl_latex <-
dplyr::tribble(
~v_1, ~v_2, ~v_3, ~v_4, ~v_5,
20.4, 36.1, 34.2, 21.6, 23.1,
25.8, 14.3, 13.7, 12.9, 75.9,
16.3, 34.3, 15.1, 93.2, 12.1
) %>%
gt() %>%
tab_spanner(
label = "v_1_2",
columns = c("v_1", "v_2")
) %>%
tab_spanner(
label = "v_4_5",
columns = c("v_4", "v_5")
) %>%
cols_move_to_start(columns = "v_3")
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(dplyr::tibble(A_X = c(1), B_X = c(2), A_Y = c(3), B_Y = c(4))) %>%
tab_spanner(label = "A", id = "y", columns = starts_with("A"), gather = FALSE) %>%
tab_spanner(label = "A", id = "z", columns = starts_with("B"), gather = FALSE) %>%
tab_footnote(footnote = "note", locations = cells_column_spanners("y"))
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(dplyr::tibble(A_X = c(1), B_X = c(2), A_Y = c(3), B_Y = c(4))) %>%
tab_spanner(label = "A", id = "y", columns = starts_with("A"), gather = TRUE) %>%
tab_spanner(label = "A", id = "z", columns = starts_with("B")) %>%
tab_footnote(footnote = "note", locations = cells_column_spanners("y"))
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(iris[1, ]) %>%
tab_spanner_delim(
delim = ".",
columns = c("Sepal.Length", "Sepal.Width")
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
})
test_that("a gt table contains the expected source note", {
tbl_latex <-
gt(mtcars_short) %>%
tab_source_note(
source_note = md("*Henderson and Velleman* (1981).")
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(mtcars_short) %>%
tab_source_note(
source_note = md("*Henderson and Velleman* (1981).")
) %>%
tab_source_note(
source_note = "This was in Motor Trend magazine, hence the `mt`."
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
})
test_that("a gt table contains the correct placement of row groups", {
tbl_latex <-
mtcars %>%
gt(rownames_to_stub = TRUE) %>%
tab_row_group(
label = "Mazda",
rows = c("Mazda RX4", "Mazda RX4 Wag")
)
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
tbl_latex <-
gt(mtcars, rownames_to_stub = TRUE) %>%
tab_row_group(
label = "Mercs",
rows = contains("Merc")
) %>%
tab_row_group(
label = "Mazda",
rows = c("Mazda RX4", "Mazda RX4 Wag")
) %>%
row_group_order(groups = c(NA, "Mazda", "Mercs"))
tbl_latex %>%
as_latex() %>%
as.character() %>%
expect_snapshot()
}) |
set.seed(111)
zdata <- data.frame(x2 = runif(nn <- 1000))
zdata <- transform(zdata, pstr01 = logit(-0.5 + 1*x2, inverse = TRUE),
pstr02 = logit( 0.5 - 1*x2, inverse = TRUE),
Ps01 = logit(-0.5 , inverse = TRUE),
Ps02 = logit( 0.5 , inverse = TRUE),
lambda1 = loge(-0.5 + 2*x2, inverse = TRUE),
lambda2 = loge( 0.5 + 2*x2, inverse = TRUE))
zdata <- transform(zdata, y1 = rzipois(nn, lambda = lambda1, pstr0 = Ps01),
y2 = rzipois(nn, lambda = lambda2, pstr0 = Ps02))
with(zdata, table(y1))
with(zdata, table(y2))
with(zdata, stem(y2))
fit1 <- vglm(y1 ~ x2, zipoisson(zero = 1), data = zdata, crit = "coef")
fit2 <- vglm(y2 ~ x2, zipoisson(zero = 1), data = zdata, crit = "coef")
coef(fit1, matrix = TRUE)
coef(fit2, matrix = TRUE)
head(fit1@misc$pobs0)
coef(fit1)
coef(fit1, matrix=TRUE)
Coef(fit1) |
context("showMeta")
test_that("showMeta", {
meta <- data.frame(id=as.character(1:3), date=as.Date(c("1960-01-01","1987-06-25","2014-08-06")), title=c("Title 1", "Title 2", "Title 3"), page=c(24,60,1), stringsAsFactors=FALSE)
expect_equal(showMeta(meta=meta, id = as.character(c(1,3)), cols = colnames(meta)), meta[c(1,3),])
expect_equal(showMeta(meta=meta, cols = colnames(meta)), meta)
idmatrix <- matrix(as.character(c(1,3,2,2)),ncol=2)
expect_equal(showMeta(meta=meta, id = idmatrix, cols = colnames(meta)), list("1"=meta[c(1,3),], "2"=meta[2,]), file=tempdir())
colnames(idmatrix) <- c("ID1", "ID2")
expect_equal(showMeta(meta=meta, id = idmatrix, cols = colnames(meta)), list("ID1"=meta[c(1,3),], "ID2"=meta[2,]))
}) |
ga_list = function(aspect_ratio_multiplier = NULL,
background_color = NULL,
background_alpha = NULL,
border_color = NULL,
border_sides = NULL,
border_width = NULL,
font_face = NULL,
group_elements = NULL,
text_color = NULL,
text_align = NULL,
text_v_align = NULL,
text_just = NULL,
text_v_just = NULL,
text_cex = NULL,
text_font = NULL,
text_rot = NULL,
replace_na = NULL,
round_rect_radius = NULL,
column_widths_p = NULL,
padding_p = NULL,
maintain_aspect_ratio = NULL,
n_lines = NULL,
cell_font_face = NULL,
cell_group_elements = NULL,
cell_background_color = NULL,
cell_background_alpha = NULL,
cell_border_color = NULL,
cell_border_sides = NULL,
cell_border_width = NULL,
cell_text_color = NULL,
cell_text_align = NULL,
cell_text_v_align = NULL,
cell_text_just = NULL,
cell_text_v_just = NULL,
cell_text_cex = NULL,
cell_text_font = NULL,
cell_text_rot = NULL,
cell_replace_na = NULL,
cell_round_rect_radius = NULL,
cell_column_widths_p = NULL,
cell_padding_p = NULL,
colname_font_face = NULL,
colname_group_elements = NULL,
colname_background_color = NULL,
colname_background_alpha = NULL,
colname_border_color = NULL,
colname_border_sides = NULL,
colname_border_width = NULL,
colname_text_color = NULL,
colname_text_align = NULL,
colname_text_v_align = NULL,
colname_text_just = NULL,
colname_text_v_just = NULL,
colname_text_cex = NULL,
colname_text_font = NULL,
colname_text_rot = NULL,
colname_replace_na = NULL,
colname_round_rect_radius = NULL,
colname_column_widths_p = NULL,
colname_padding_p = NULL) {
grob_aes_list = list(
font_face = font_face,
group_elements = group_elements,
background_color = background_color,
background_alpha = background_alpha,
border_color = border_color,
border_width = border_width,
border_sides = border_sides,
text_color = text_color,
text_align = text_align,
text_v_align = text_v_align,
text_just = text_just,
text_v_just = text_v_just,
text_cex = text_cex,
text_font = text_font,
replace_na = replace_na,
round_rect_radius = round_rect_radius,
column_widths_p = column_widths_p,
padding_p = padding_p,
cell_font_face = cell_font_face,
cell_group_elements = cell_group_elements,
cell_background_color = cell_background_color,
cell_background_alpha = cell_background_alpha,
cell_border_color = cell_border_color,
cell_border_width = cell_border_width,
cell_border_sides = cell_border_sides,
cell_text_color = cell_text_color,
cell_text_align = cell_text_align,
cell_text_v_align = cell_text_v_align,
cell_text_just = cell_text_just,
cell_text_v_just = cell_text_v_just,
cell_text_cex = cell_text_cex,
cell_text_font = cell_text_font,
cell_text_rot = cell_text_rot,
cell_replace_na = cell_replace_na,
cell_round_rect_radius = cell_round_rect_radius,
cell_column_widths_p = cell_column_widths_p,
cell_padding_p = cell_padding_p,
colname_font_face = colname_font_face,
colname_group_elements = colname_group_elements,
colname_background_color = colname_background_color,
colname_background_alpha = colname_background_alpha,
colname_border_color = colname_border_color,
colname_border_width = colname_border_width,
colname_border_sides = colname_border_sides,
colname_text_color = colname_text_color,
colname_text_align = colname_text_align,
colname_text_v_align = colname_text_v_align,
colname_text_just = colname_text_just,
colname_text_v_just = colname_text_v_just,
colname_text_cex = colname_text_cex,
colname_text_font = colname_text_font,
colname_text_rot = colname_text_rot,
colname_replace_na = colname_replace_na,
colname_round_rect_radius = colname_round_rect_radius,
colname_column_widths_p = colname_column_widths_p,
colname_padding_p = colname_padding_p,
maintain_aspect_ratio = maintain_aspect_ratio,
aspect_ratio_multiplier = aspect_ratio_multiplier,
n_lines = n_lines
)
grob_aes_list = lapply(X = grob_aes_list, FUN = convert_to_matrix)
class(grob_aes_list) = 'grob_aes_list'
return(grob_aes_list)
}
convert_to_matrix = function(x) {
if (length(x) > 0 & is.null(nrow(x))) {
x = t(as.matrix(x))
}
if (is.data.frame(x)) {
x = as.matrix(x)
}
return(x)
} |
ASDC_RNA <- function(seqs,ORF=FALSE,reverseORF=TRUE,label=c()){
upto=TRUE
dict<-list("A"=1,"C"=2,"G"=3,"U"=4)
if(length(seqs)==1&&file.exists(seqs)){
seqs<-fa.read(seqs,alphabet="rna")
seqs_Lab<-alphabetCheck(seqs,alphabet = "rna",label)
seqs<-seqs_Lab[[1]]
label<-seqs_Lab[[2]]
}
else if(is.vector(seqs)){
seqs<-sapply(seqs,toupper)
seqs_Lab<-alphabetCheck(seqs,alphabet = "rna",label)
seqs<-seqs_Lab[[1]]
label<-seqs_Lab[[2]]
}
else {
stop("ERROR: Input sequence is not in the correct format. It should be a FASTA file or a string vector.")
}
flag=0
if(ORF==TRUE){
if(length(label)==length(seqs)){
names(label)=names(seqs)
flag=1
}
seqs=maxORF_RNA(seqs,reverse=reverseORF)
if(flag==1)
label=label[names(seqs)]
}
numSeqs=length(seqs)
rng<-sapply(seqs, nchar)
rng<-rng-1
featureMatrix <- matrix(0 , ncol = 16,nrow = numSeqs)
dipep<-nameKmer(k=2,type = "rna")
featName<-vector()
colnames(featureMatrix)<-dipep
tempname<-dipep
for(n in 1:numSeqs){
seq<-seqs[n]
seqChars<-unlist(strsplit(seq,split = ""))
lenSeq<-length(seqChars)
len<-rng[n]
for(i in 1:len){
nums<-i-1
temp1<-seqChars[1:(lenSeq-nums-1)]
temp2<-seqChars[((nums+1)+1):(lenSeq)]
kmers<-paste(temp1,temp2,sep = "")
tbkmers<-table(kmers)
nmtbkmers<-names(tbkmers)
tempvect<-vector(mode = "numeric",length = 16)
names(tempvect)<-tempname
tempvect[nmtbkmers]<-tbkmers
featureMatrix[n,]<-featureMatrix[n,]+tempvect
}
featureMatrix[n,]<-featureMatrix[n,]/sum(featureMatrix[n,])
}
if(length(label)==numSeqs){
featureMatrix<-as.data.frame(featureMatrix)
featureMatrix<-cbind(featureMatrix,label)
}
row.names(featureMatrix)<-names(seqs)
return(featureMatrix)
} |
if (require(testthat)) {
context("Tests on output")
test_that("test for rf_forecast for a yearly series", {
library(Mcomp)
data(M1)
y1 <- subset(M1, "yearly")
z <- rf_forecast(predictions="rw", tslist=y1[[1]]$x, database="other", function_name=cal_MASE, h=6, accuracy=TRUE)
expect_equal(length(z), 4L)
expect_equal(as.vector(z$mean), c(322681, 322681, 322681, 322681, 322681, 322681), tolerance=1e-4)
expect_equal(as.vector(z$lower), c(269057.7, 246846.2, 229802.7, 215434.4, 202775.6, 191331.2), tolerance=1e-4)
expect_equal(as.vector(z$upper), c(376304.3, 398515.8, 415559.3, 429927.6, 442586.4, 454030.8), tolerance=1e-4)
expect_equal(z$accuracy, 5.093695, tolerance=1e-4)
})
}
if (require(testthat)) {
context("Tests on output")
test_that("test for rf_forecast for a monthly series", {
library(Mcomp)
data(M1)
m1 <- subset(M1, "monthly")
z2 <- rf_forecast(predictions="rw", tslist=m1[[1]]$x, database="other", function_name=cal_MASE, h=8, accuracy=TRUE)
expect_equal(length(z2), 4L)
expect_equal(as.vector(z2$mean), c(1330520, 1330520, 1330520, 1330520, 1330520, 1330520, 1330520, 1330520), tolerance=1e-4)
expect_equal(as.vector(z2$lower), c(442499.4, 74669.21, -207576.8, -445521.2, -655154.4, -844677.4, -1018962, -1181182), tolerance=1e-4)
expect_equal(as.vector(z2$upper), c(2218541, 2586371, 2868617, 3106561, 3316194, 3505717, 3680002, 3842222), tolerance=1e-4)
expect_equal(z2$accuracy, 1.212279, tolerance=1e-4)
})
} |
sq_1 <- sq(c("QWERTYUIOP", "ASDF-GHJKL", "ZXCV-BNM"), alphabet = "unt")
sq_2 <- sq(c("", "CAGTGT", "CGGCTATXT"), alphabet = LETTERS)
sq_3 <- sq(c("AreYouOK", "WhoAreYou", "YesWeCan"),
alphabet = c("Are", "You", "Who", "Yes", "We", "Can", "O", "K"))
sq_4 <- sq(character(), "dna_bsc")
test_that("find_invalid_letters() returns a list of correct length", {
expect_vector(find_invalid_letters(sq_1, "dna_ext"),
ptype = list(),
size = vec_size(sq_1))
})
test_that("each element of find_invalid_letters() is a character vector", {
for (inv_letters in find_invalid_letters(sq_1, "dna_ext")) {
expect_vector(inv_letters,
ptype = character())
}
})
test_that("find_invalid_letters() throws an error whenever passed object of class other that sq", {
expect_error(find_invalid_letters(1:7, "dna_bsc"))
expect_error(find_invalid_letters(LETTERS, "ami_ext"))
expect_error(find_invalid_letters(list(mean, sum, sd), "rna_ext"))
})
test_that("find_invalid_letters() correctly computes value", {
expect_equal(
find_invalid_letters(sq_1, "dna_ext"),
list(c("E", "I", "O", "P", "Q", "U"), c("F", "J", "L"), c("X", "Z"))
)
})
test_that("find_invalid_letters() consider NA part of alphabet", {
expect_equal(
find_invalid_letters(sq_2, "dna_bsc"),
list(character(), character(), "X")
)
})
test_that("find_invalid_letters() works for alphabet with multiple-character letters", {
expect_equal(
find_invalid_letters(sq_3, "ami_ext"),
list(c("Are", "You"), c("Are", "You", "Who"), c("Yes", "We", "Can"))
)
})
test_that("find_invalid_letters() returns value without additional attributes", {
expect_equal(
find_invalid_letters(sq_1, "dna_ext"),
list(c("E", "I", "O", "P", "Q", "U"), c("F", "J", "L"), c("X", "Z"))
)
expect_equal(
find_invalid_letters(sq_2, "dna_bsc"),
list(character(), character(), "X")
)
expect_equal(
find_invalid_letters(sq_3, "ami_ext"),
list(c("Are", "You"), c("Are", "You", "Who"), c("Yes", "We", "Can"))
)
})
test_that("passing empty sq to find_invalid_letters() returns empty list()", {
expect_equal(
find_invalid_letters(sq_4, "rna_bsc"),
list()
)
}) |
mapBatch <-
function (data, zoom = T, margin = 0.1, axes = T, shape=NULL,
export = "pdf", raster = NULL, RGB = NULL, points.col = "black",
points.border = "gray50", points.cex = 1, shape.col = "white",
shape.border = "black", raster.col = rev(gray.colors(65, start = 0, end = 1)),
raster.legend=F, hillshade = F, width = 8, height = 8, image.resolution = 100,
figure.number = T, title = T, box = T, add.minimap = F, minimap.shape=NULL,
minimap.shape.col = "white", minimap.shape.border = "gray50",
minimap.pos = "topleft", minimap.add.points = T, minimap.points.col = "black",
minimap.points.border = "gray50", minimap.points.cex = 1, minimap.extent = NULL,
minimap.rect.fill = NA, minimap.rect.border = NULL, maxpixels=100000, ...)
{
if (class(data) != "data.frame") {
stop("data must be a data.frame")
}
if (ncol(data) != 3) {
stop("data must have 3 columns, see help(\"mapBatch\")")
}
if (is.numeric(data[,2]) == F) {
stop("longitude must be numeric, see help(\"mapBatch\")")
}
if (is.numeric(data[,3]) == F) {
stop("latitude must be numeric, see help(\"mapBatch\")")
}
wrld_simpl = NULL
if (is.null(shape)) {
data(wrld_simpl, envir = environment())
wrld_simpl -> shape
}
if (add.minimap) {
try(dev.off(), silent=T)
zoom = T
}
if (is.null(raster) == F && hillshade == T) {
proj4string(raster) <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
slope <- terrain(raster, opt='slope')
aspect <- terrain(raster, opt='aspect')
hill <- hillShade(raster, aspect, 40, 270)
rev(gray.colors(100, start = 0, end = 1, alpha = 0.3)) -> hill.col
}
message("Assuming the columns are ordered as: species, longitude and latitude")
colnames(data) <- c("sp", "x", "y")
geo <- data
coordinates(geo) <- ~x + y
min.margin = 5
max.margin = 10
mmm <- (min.margin++(margin/mean(c(extent(geo)[2]-extent(geo)[1], extent(geo)[4]-extent(geo)[3]))))
if (mmm > max.margin) {
mmm <- max.margin
}
ext.all <- extent(geo)++mmm
spp <- as.character(unique(data[, 1]))
spp <- sort(spp)
if (export == "pdf") {
pdf("mapBatch.pdf", width=width, height=height)
}
for (i in 1:length(spp)) {
sp <- spp[i]
if (export == "tiff") {
if (figure.number) {
paste("Figure ", i, " - ", sp, ".tif", sep = "") -> lab0
} else {
paste(sp, ".tif", sep="") -> lab0
}
tiff(lab0, width = width, height = height, units = "in",
res = image.resolution, compression = "lzw")
}
if (export == "jpeg") {
if (figure.number) {
paste("Figure ", i, " - ", sp, ".jpg", sep = "") -> lab0
} else {
paste(sp, ".jpg", sep="") -> lab0
}
jpeg(lab0,width = width, height = height, units = "in",
quality = 90, res = image.resolution)
}
spRows <- which(data$sp == sp)
spData <- data[spRows, ]
xy <- spData
coordinates(xy) <- ~x + y
if (zoom == T) {
ext <- extent(xy)++mmm
xlim <- c(ext[1], ext[2])
ylim <- c(ext[3], ext[4])
}
else {
ext <- ext.all
xlim <- c(ext[1], ext[2])
ylim <- c(ext[3], ext[4])
}
if (class(shape) == "list") {
plot(shape[[1]], xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = F,
asp = 1, ...)
if (is.null(RGB) == F) {
plotRGB(RGB, add=T, maxpixels=maxpixels)
}
plot(shape[[1]], xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = T,
asp = 1, ...)
if (is.null(raster) == F) {
if (hillshade == T) {
plot(hill, col=hill.col, legend=F, axes=F, box=F, add=T, maxpixels=maxpixels)
plot(raster, col = raster.col, add = T, legend=raster.legend, maxpixels=maxpixels)
} else {
plot(raster, col = raster.col, add = T, legend=raster.legend, maxpixels=maxpixels)
}
plot(shape[[1]], xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = T,
asp = 1, ...)
}
for (k in 2:length(shape)) {
plot(shape[[k]], xlim = xlim, ylim = ylim,
axes = axes, col = shape.col, border = shape.border,
add = T, asp = 1)
}
} else {
plot(shape, xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = F,
asp = 1)
if (is.null(RGB) == F) {
plotRGB(RGB, add=T, maxpixels=maxpixels)
}
plot(shape, xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = F,
asp = 1)
if (is.null(raster) == F) {
if (hillshade == T) {
plot(hill, col=hill.col, legend=F, axes=F, box=F, add=T, maxpixels=maxpixels)
plot(raster, col = raster.col, add = T, legend=raster.legend, maxpixels=maxpixels)
} else {
plot(raster, col = raster.col, add = T, legend=raster.legend, maxpixels=maxpixels)
}
plot(shape, xlim = xlim, ylim = ylim, axes = axes,
col = shape.col, border = shape.border, add = T,
asp = 1)
}
}
plot(xy, pch = 21, col = points.border, bg = points.col, cex = points.cex, add = T)
if (box) { box() }
if (title) { title(sp) }
if (add.minimap) {
if (is.null(minimap.shape)) {
if (class(shape) == "list") {
shape[[length(shape)]] -> minimap.shape
} else {
shape -> minimap.shape
}
}
par()$usr -> lims
mw = min(width,height)*0.2
if (is.null(minimap.extent)) {
extent(geo)++1 -> ext.m
} else { minimap.extent -> ext.m }
xlim.m <- c(min(ext.m[1], lims[1]), max(ext.m[2], lims[2]))
ylim.m <- c(min(ext.m[3], lims[3]), max(ext.m[4], lims[4]))
png("temp.png", bg='transparent', width=mw, height=mw, units="in", res=400)
par(mar=c(0,0,0,0))
plot(minimap.shape, xlim=xlim.m, ylim=ylim.m, col=minimap.shape.col, border=minimap.shape.border)
if (minimap.add.points) {
plot(xy, pch = 21, col = minimap.points.border, bg = minimap.points.col, cex = minimap.points.cex, add = T)
}
rect(lims[1], lims[3], lims[2], lims[4], lwd=2, lty="dotted", col = minimap.rect.fill, border = minimap.rect.border)
dev.off()
jpg0 = readPNG("temp.png", native=T)
min(c(lims[2]-lims[1], lims[4]-lims[3])) -> min.plot
if (minimap.pos == "topleft") {
lims[1]++(min.plot*0.02) -> x1
lims[1]++(min.plot*0.27) -> x2
lims[4]-(min.plot*0.02) -> y2
lims[4]-(min.plot*0.27) -> y1
}
if (minimap.pos == "topright") {
lims[2]-(min.plot*0.27) -> x1
lims[2]-(min.plot*0.02) -> x2
lims[4]-(min.plot*0.02) -> y2
lims[4]-(min.plot*0.27) -> y1
}
if (minimap.pos == "bottomleft") {
lims[1]++(min.plot*0.02) -> x1
lims[1]++(min.plot*0.27) -> x2
lims[3]++(min.plot*0.27) -> y2
lims[3]++(min.plot*0.02) -> y1
}
if (minimap.pos == "bottomright") {
lims[2]-(min.plot*0.27) -> x1
lims[2]-(min.plot*0.02) -> x2
lims[3]++(min.plot*0.27) -> y2
lims[3]++(min.plot*0.02) -> y1
}
rasterImage(jpg0, x1, y1, x2, y2, angle=0, interpolate=T)
unlink("temp.png")
}
if (export == "tiff" || export == "jpeg") { dev.off() }
}
if (export == "pdf") { dev.off() }
cat("Maps were saved in:")
cat("\n", getwd())
} |
ref_lvl <- function(x, ..., lvl = NULL) {
.dat <- get_dot_data(x, dplyr::quos(...))
if (is.data.frame(x)) {
for (i in colnames(.dat)) {
x[[i]] <- ref_lvl_helper(.dat[[i]], value = lvl)
}
} else {
x <- ref_lvl_helper(.dat, value = lvl)
}
x
}
ref_lvl_helper <- function(x, value) {
if (is.null(x)) {
warning("`x` is NULL.", call. = FALSE)
return(x)
}
if (!is.factor(x)) {
warning("`x` needs to be a factor.", call. = FALSE)
return(x)
}
if (!is_num_fac(x)) {
return(stats::relevel(x, ref = value))
}
if (is.numeric(value)) {
vals <- as.numeric(levels(x))
} else {
lab.values <- sjlabelled::get_labels(
x,
attr.only = TRUE,
values = "n",
drop.na = TRUE
)
vals <- as.numeric(names(lab.values))
value <- as.numeric(names(lab.values[lab.values == value]))
}
if (!value %in% vals) {
warning("`x` has no factor level indicated by the reference level `value`.", call. = FALSE)
return(x)
}
val.labs <- sjlabelled::get_labels(x)
var.lab <- sjlabelled::get_label(x)
refpos <- which(vals == value)
neword <- c(vals[refpos], vals[-refpos])
rec.pattern <- paste0(sprintf("%i=%i;", neword, vals), collapse = "")
x <- rec(x, rec = rec.pattern, as.num = FALSE, append = FALSE)
if (!is.null(var.lab) && !sjmisc::is_empty(var.lab)) {
sjlabelled::set_label(x) <- var.lab
}
if (!is.null(val.labs)) {
x <- sjlabelled::set_labels(x, labels = val.labs[order(order(neword))])
}
x
} |
context("test-method_cutpoints")
test_that("cutpoints works with one-dimensional continuous data", {
x <- matrix(runif(40), nrow = 40, ncol = 1)
cuts <- seq(0, 1, length.out = 4)
xd <- as.numeric(cut(x, cuts))
xd <- t(t(xd))
theta <- matrix(c(0, 2, -2), ncol = 1, nrow = 3)
log_odd <- sapply(
seq_along(xd[, 1]),
function(row_id) {
sapply(
seq_along(xd[row_id, ]),
function(element) theta[xd[row_id, element], element]
)
}
)
y <- rbinom(40, 1, 1 / (1 + exp(-log_odd)))
sem_disc <- glmdisc(x,
y,
iter = 15,
m_start = 4,
test = FALSE,
validation = FALSE,
criterion = "aic",
interact = FALSE
)
les_cuts <- cutpoints(sem_disc)
expect_type(les_cuts, "list")
expect_length(les_cuts, 1)
expect_type(les_cuts[[1]], "double")
expect_equal(names(les_cuts), "V1")
x <- data.frame(x)
sem_disc <- glmdisc(x,
y,
iter = 15,
m_start = 4,
test = FALSE,
validation = FALSE,
criterion = "aic",
interact = FALSE
)
les_cuts <- cutpoints(sem_disc)
expect_equal(names(les_cuts), "x")
})
test_that("cutpoints works with multi-dimensional continuous data", {
x <- matrix(runif(120), nrow = 40, ncol = 3)
cuts <- seq(0, 1, length.out = 4)
xd <- apply(x, 2, function(col) as.numeric(cut(col, cuts)))
theta <- t(matrix(c(0, 0, 0, 2, 2, 2, -2, -2, -2), ncol = 3, nrow = 3))
log_odd <- rowSums(t(sapply(seq_along(xd[, 1]), function(row_id) sapply(seq_along(xd[row_id, ]), function(element) theta[xd[row_id, element], element]))))
y <- rbinom(40, 1, 1 / (1 + exp(-log_odd)))
sem_disc <- glmdisc(x, y, iter = 15, m_start = 4, test = FALSE, validation = FALSE, criterion = "aic")
les_cuts <- cutpoints(sem_disc)
expect_type(les_cuts, "list")
expect_length(les_cuts, 3)
expect_type(les_cuts[[1]], "double")
expect_equal(names(les_cuts), c("V1", "V2", "V3"))
})
test_that("cutpoints works with multi-dimensional categorical data", {
x <- matrix(runif(120), nrow = 40, ncol = 3)
cuts <- seq(0, 1, length.out = 4)
xd <- apply(x, 2, function(col) as.numeric(cut(col, cuts)))
theta <- t(matrix(c(0, 0, 0, 2, 2, 2, -2, -2, -2), ncol = 3, nrow = 3))
log_odd <- rowSums(t(sapply(seq_along(xd[, 1]), function(row_id) sapply(seq_along(xd[row_id, ]), function(element) theta[xd[row_id, element], element]))))
y <- rbinom(40, 1, 1 / (1 + exp(-log_odd)))
sem_disc <- glmdisc(data.frame(apply(xd, 2, factor), stringsAsFactors = TRUE), y, iter = 15, m_start = 4, test = FALSE, validation = FALSE, criterion = "aic")
les_cuts <- cutpoints(sem_disc)
expect_type(les_cuts, "list")
expect_length(les_cuts, 3)
expect_type(les_cuts[[1]], "integer")
expect_equal(names(les_cuts), c("X1", "X2", "X3"))
}) |
suppressPackageStartupMessages(library("argparse"))
parser = ArgumentParser()
parser$add_argument("--infercnv_obj", help="infercnv_obj file", required=TRUE, nargs=1)
args = parser$parse_args()
library(infercnv)
infercnv_obj_file = args$infercnv_obj
infercnv_obj = readRDS(infercnv_obj_file)
plot_cnv(infercnv_obj,
output_filename=basename(infercnv_obj_file)) |
.multiCA.test <- function(x, scores, outcomes){
K <- nrow(x)
full <- length(outcomes) == K
nidot <- apply(x, 2, sum)
n <- sum(nidot)
cbar <- sum(nidot * scores)/n
s2 <- sum(nidot * (scores - cbar)^2)
pdot <- prop.table(rowSums(x))[outcomes]
nonz <- (pdot > 0)
if (!any(nonz)) return(1)
X <- x[outcomes, ,drop=FALSE] %*% (scores - cbar)
Tt <- X[nonz] / sqrt(pdot[nonz] * (1-pdot[nonz])* s2)
CAT <- Tt^2
CAT.p.value <- pchisq(CAT, df=1, lower.tail=FALSE)
if (full || sum(pdot) >= 1){
W <- ( sum(X[nonz]^2 / pdot[nonz])) / s2
} else {
W <- (sum(X)^2 / (1-sum(pdot)) + sum(X[nonz]^2 / pdot[nonz])) / s2
}
df <- length(outcomes) - full
p.value <- pchisq(W, df=df, lower.tail=FALSE)
sqrt.or.pdot <- sqrt(pdot[nonz]/(1-pdot[nonz]))
Sigma0 <- -outer(sqrt.or.pdot, sqrt.or.pdot)
diag(Sigma0) <- 1
if (full){
coefs <- sqrt(pdot[nonz] * (1-pdot[nonz]))
C <- rbind(coefs[-1], diag(K-1))
} else {
C <- diag(length(nonz))
}
res <- list(statistic = W, parameter = df, p.value = p.value,
indiv.statistics = Tt, indiv.p.value = CAT.p.value,
sigma0 = Sigma0, contrast = C)
return(res)
}
multiCA.test.default <- function(x, scores=1:ncol(x), outcomes=1:nrow(x),
p.adjust.method=c("none","closed.set","Holm-Shaffer", "single-step", "Westfall"),...){
if (!is.matrix(x)) {
cat(str(x))
stop("x should be a two-dimensional matrix")
}
if (length(scores) != ncol(x)) stop("The length of the score vector should equal the number of columns of x")
testres <- .multiCA.test(x=x, scores=scores, outcomes=outcomes)
W <- c(W = testres$statistic)
df <- c(df = testres$parameter)
p.value <- testres$p.value
null.value <- 0
names(null.value) <- sprintf("slope for outcomes %s", deparse(substitute(outcomes)))
res <- list(statistic = W, parameter = df, p.value = p.value,
method="Multinomial Cochran-Armitage trend test",
alternative="two.sided",
null.value=null.value,
data.name = deparse(substitute(x)))
class(res) <- "htest"
if (missing(p.adjust.method)){
if (length(outcomes)<=3) p.adjust.method <- "closed.set"
else p.adjust.method <- "Holm-Shaffer"
} else {
p.adjust.method <- match.arg(p.adjust.method)
}
full.set <- (length(outcomes) == nrow(x))
if (p.adjust.method=="none") {
indiv.res <- testres$indiv.p.value
} else if (p.adjust.method=="closed.set") {
mytest <- function(hypotheses){
.multiCA.test(x, scores, hypotheses)$p.value
}
indiv.res <- .p.adjust.closed(mytest, outcomes, remove=full.set)
} else if (p.adjust.method=="Holm-Shaffer") {
s <- seq_along(testres$indiv.p.value)
if (full.set) s[2] <- 3
o <- order(testres$indiv.p.value)
ro <- order(o)
indiv.res <- pmin(1, cummax((length(outcomes) - s + 1L) * testres$indiv.p.value[o]))[ro]
} else if (p.adjust.method %in% c("single-step", "Westfall")) {
if (full.set) {
testparm <- parm(testres$indiv.statistics[-1], testres$sigma0[-1,-1])
} else {
testparm <- parm(testres$indiv.statistics, testres$sigma0)
}
g1 <- glht(model = testparm, linfct = testres$contrast)
indiv.res <- summary(g1, test=adjusted(type=p.adjust.method,...))$test$pvalues
}
attr(indiv.res, "method") <- p.adjust.method
return(list(overall = res, individual = indiv.res))
}
multiCA.test.formula <- function(formula, data, subset, na.action, weights, ...){
if (missing(formula) || (length(formula) != 3L) || (length(attr(terms(formula[-2L]),
"term.labels")) != 1L))
stop("'formula' missing or incorrect")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m[[1L]] <- quote(stats::model.frame)
m$... <- NULL
mf <- eval(m, parent.frame())
responsevar <- attr(attr(mf, "terms"), "response")
response <- mf[[responsevar]]
weightvar <- which(names(mf)=="(weights)")
w <- if(length(weightvar) > 0) mf[[weightvar]] else rep(1L, nrow(mf))
g <- factor(mf[,-c(responsevar, weightvar)])
tab <- xtabs(w ~ response + g)
multiCA.test(tab, ...)
}
.bit2boolean <- function (x, N)
{
base <- 2^(1:N - 1)
bitAnd(x, base) != 0
}
.p.adjust.closed <- function (test, hypotheses, remove=FALSE, ...)
{
N <- length(hypotheses)
Nmax <- log2(.Machine$integer.max + 1)
if (N > Nmax)
stop("no more than ", Nmax, " hypotheses supported in full closed testing.\n Use a shortcut-based test.")
closure <- 1:(2^N - 1)
base <- 2^(1:N - 1)
offspring <- function(x) {
res <- bitAnd(x, closure)
res[res != 0]
}
lengths <- rowSums(sapply(base, function(bs) bitAnd(closure, bs) != 0))
idx <- sort.list(lengths, decreasing = TRUE)
closure <- closure[idx]
lengths <- lengths[idx]
if (remove) closure <- closure[lengths != (N-1)]
adjusted <- numeric(2^N - 1)
for (i in closure) {
if (adjusted[i] < 1) {
localtest <- test(hypotheses[.bit2boolean(i,N)], ...)
if (localtest > adjusted[i]) {
offs <- offspring(i)
adjusted[offs] <- pmax(adjusted[offs], localtest)
}
}
}
out <- adjusted[base]
names(out) <- hypotheses
return(out)
}
cnonct <- function(x, p, df){
if (pchisq(x, df=df) < p) return(NA)
f <- function(ncp){pchisq(x, df=df, ncp=pmax(0,ncp)) - p}
res <- uniroot(f, interval=c(0, 100), extendInt="downX", tol=.Machine$double.eps^0.5)
res$root
}
power.multiCA.test <- function(N=NULL, power=NULL, pmatrix=NULL, p.ave=NULL, p.start=NULL,
p.end=NULL, slopes=NULL, scores=1:G, n.prop=rep(1, G),
G=length(p.ave), sig.level=0.05){
if (sum(sapply(list(N, power), is.null)) != 1)
stop("exactly one of 'N', and 'power' must be NULL")
if (!is.numeric(sig.level) || any(0 > sig.level | sig.level > 1))
stop("'sig.level' must be numeric in [0, 1]")
if (!is.null(pmatrix)){
K <- nrow(pmatrix)
G <- ncol(pmatrix)
if (!isTRUE(all.equal(colSums(pmatrix), rep(1, G),
check.attributes=FALSE, use.names=FALSE)))
stop("pmatrix should have column sums of 1.")
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
slopes <- as.vector(pmatrix %*% (n.prop * (scores-cbar))) / s2
p.ave <- as.vector(pmatrix %*% n.prop)
}
else {
if (sum(sapply(list(p.ave, slopes, p.start, p.end), is.null)) != 2)
stop("Either pmatrix, or exactly two of 'p.ave', 'slopes', 'p.start', and 'p.end' must be specified (ie not NULL)")
if (!is.null(p.ave) & !is.null(slopes)){
if (length(p.ave) != length(slopes))
stop("p.ave and slopes should have the same length")
K <- length(p.ave)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
}
else if (!is.null(p.ave) & !is.null(p.start)){
if (length(p.ave) != length(p.start))
stop("p.ave and p.start should have the same length")
K <- length(p.ave)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
slopes <- (p.start - p.ave) / (scores[1] - cbar)
}
else if (!is.null(p.ave) & !is.null(p.end)){
if (length(p.ave) != length(p.end))
stop("p.ave and p.end should have the same length")
K <- length(p.ave)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
slopes <- (p.end - p.ave) / (scores[G] - cbar)
}
else if (!is.null(p.start) & !is.null(p.end)){
if (length(p.start) != length(p.end))
stop("p.start and p.end should have the same length")
K <- length(p.start)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
slopes <- (p.end - p.start) / (scores[G] - scores[1])
p.ave <- p.start - slopes * (scores[1] - cbar)
}
else if (!is.null(p.start) & !is.null(slopes)){
if (length(p.start) != length(slopes))
stop("p.start and slopes should have the same length")
K <- length(p.start)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
p.ave <- p.start - slopes * (scores[1] - cbar)
}
else if (!is.null(p.end) & !is.null(slopes)){
if (length(p.end) != length(slopes))
stop("p.end and slopes should have the same length")
K <- length(p.end)
if (missing(G)){
if (!missing(scores)) G <- length(scores)
else if (!missing(n.prop)) G <- length(n.prop)
else stop("The number of groups G needs to be specified explicitly or implicitly through the dimensions of pmatrix, the scores, or the n.prop vector.")
}
if (sum(n.prop) != 1) n.prop <- n.prop/sum(n.prop)
cbar <- weighted.mean(scores, w=n.prop)
s2 <- sum(n.prop * (scores-cbar)^2)
p.ave <- p.end - slopes * (scores[G] - cbar)
}
if (!isTRUE(all.equal(sum(slopes), 0, check.attributes=FALSE, use.names=FALSE)))
stop("Implied or specified values of slopes should sum to 0.")
if (!isTRUE(all.equal(sum(p.ave), 1, check.attributes=FALSE, use.names=FALSE)))
stop("Implied or specified values of p.ave should sum to 1.")
check <- outer(1:K, 1:G, function(j,i)p.ave[j] + slopes[j]*(scores[i]-cbar))
if (!all(check >= 0) || !(all(check <=1)))
stop("The parameters do not define a valid probability matrix")
}
df <- K - 1
crit <- qchisq(sig.level, df=df, lower.tail=FALSE)
ncp0 <- sum(slopes^2 / p.ave) * s2
if (missing(power)){
ncp <- ncp0 * N
power <- pchisq(crit, df=df, ncp=ncp, lower.tail=FALSE)
}
else {
ncp <- cnonct(crit, p=1-power, df=df)
N <- ncp / ncp0
}
res <- structure(list(n = N, n.prop = n.prop, p.ave=p.ave, slopes = slopes, G = G,
sig.level = sig.level, power = power,
method = "Multinomial Cochran-Armitage trend test"),
class = "power.htest")
res
} |
document_color_reply <- function(id, uri, workspace, document) {
result <- NULL
parse_data <- workspace$get_parse_data(uri)
if (is.null(parse_data) ||
(!is.null(parse_data$version) && parse_data$version != document$version)) {
return(NULL)
}
xdoc <- parse_data$xml_doc
if (!is.null(xdoc)) {
str_tokens <- xml_find_all(xdoc, "//STR_CONST[@line1=@line2 and @col2>@col1+3 and @col2<@col1+22]")
str_line1 <- as.integer(xml_attr(str_tokens, "line1"))
str_col1 <- as.integer(xml_attr(str_tokens, "col1"))
str_col2 <- as.integer(xml_attr(str_tokens, "col2"))
str_expr <- substr(document$content[str_line1], str_col1, str_col2)
str_texts <- tryCatch(as.character(parse(text = str_expr, keep.source = FALSE)),
error = function(e) NULL)
if (length(str_texts)) {
is_color <- !grepl("^[rR]", str_expr) &
(grepl("^
str_texts %in% grDevices::colors())
color_texts <- str_texts[is_color]
color_line1 <- str_line1[is_color]
color_col1 <- str_col1[is_color]
color_col2 <- str_col2[is_color]
color_rgb <- grDevices::col2rgb(color_texts, alpha = TRUE) / 255
result <- .mapply(function(line, col1, col2, i) {
list(
range = range(
start = document$to_lsp_position(line - 1, col1),
end = document$to_lsp_position(line - 1, col2 - 1)
),
color = as.list(color_rgb[, i])
)
}, list(color_line1, color_col1, color_col2, seq_along(color_texts)), NULL)
}
}
Response$new(id, result = result)
}
color_presentation_reply <- function(id, uri, workspace, document, color) {
if (color$alpha == 1) {
hex_color <- grDevices::rgb(color$red, color$green, color$blue)
} else {
hex_color <- grDevices::rgb(color$red, color$green, color$blue, color$alpha)
}
result <- list(
list(label = tolower(hex_color)),
list(label = hex_color)
)
Response$new(id, result = result)
} |
chilling_hourtable <-
function (hourtemps,Start_JDay)
{
if((length(names(hourtemps))==2) & ("hourtemps" %in% names(hourtemps)) & ("QC" %in% names(hourtemps)))
{hourtemps<-hourtemps$hourtemps
QC<-hourtemps$QC}
cols<-colnames(hourtemps)
hourtemps<-hourtemps[which(!is.na(hourtemps[,"Temp"])),]
CH_range<-which(hourtemps$Temp<=7.2&hourtemps$Temp>=0)
hourtemps[,"CH_weights"]<-0
hourtemps[CH_range,"CH_weights"]<-1
hourtemps[,"CH"]<-0
Utah_range_0.5<-which(hourtemps$Temp<=2.4&hourtemps$Temp>1.4|
hourtemps$Temp<=12.4&hourtemps$Temp>9.1)
Utah_range_1.0<-which(hourtemps$Temp<=9.1&hourtemps$Temp>2.4)
Utah_range_min0.5<-which(hourtemps$Temp<=18.0&hourtemps$Temp>15.9)
Utah_range_min1.0<-which(hourtemps$Temp>18.0)
hourtemps[,"Utah_weights"]<-0
hourtemps[Utah_range_0.5,"Utah_weights"]<-0.5
hourtemps[Utah_range_1.0,"Utah_weights"]<-1
hourtemps[Utah_range_min0.5,"Utah_weights"]<-(-0.5)
hourtemps[Utah_range_min1.0,"Utah_weights"]<-(-1)
e0<-4153.5
e1<-12888.8
a0<-139500
a1<-2567000000000000000
slp<-1.6
tetmlt<-277
aa<-a0/a1
ee<-e1-e0
hourtemps[,"TK"]<-hourtemps$Temp+273
hourtemps[,"ftmprt"]<-slp*tetmlt*(hourtemps[,"TK"]-tetmlt)/hourtemps[,"TK"]
hourtemps[,"sr"]<-exp(hourtemps[,"ftmprt"])
hourtemps[,"xi"]<-hourtemps[,"sr"]/(1+hourtemps[,"sr"])
hourtemps[,"xs"]<-aa*exp(ee/hourtemps[,"TK"])
hourtemps[,"ak1"]<-a1*exp(-e1/hourtemps[,"TK"])
hourtemps[1,"interE"]<-0
memo<-new.env(hash=TRUE)
posi<-1
assign(x=paste(1),value=0,envir=memo)
E=0
xs<-hourtemps[,"xs"]
xi<-hourtemps[,"xi"]
ak1<-hourtemps[,"ak1"]
S<-ak1
S[1]<-0
E<-S
options(scipen=30)
for (l in 2:nrow(hourtemps)) {if(E[l-1]<1)
{S[l]<-E[l-1]
E[l]<-xs[l]-(xs[l]-S[l])*exp(-ak1[l])} else
{S[l]<-E[l-1]-E[l-1]*xi[l-1]
E[l]<-xs[l]-(xs[l]-S[l])*exp(-ak1[l])}
}
hourtemps[,"interE"]<-E
hourtemps[which(hourtemps$interE<1),"delt"]<-0
hourtemps[which(hourtemps$interE>=1),"delt"]<-hourtemps[which(hourtemps$interE>=1),"interE"]*hourtemps[which(hourtemps$interE>=1),"xi"]
Stress<-1
Tb<-4
Tu<-25
Tc<-36
hourtemps[,"GDH_weight"]<-0
hourtemps[which(hourtemps$Temp>=Tb&hourtemps$Temp<=Tu),"GDH_weight"]<-Stress*(Tu-Tb)/2*
(1+cos(pi+pi*(hourtemps[which(hourtemps$Temp>=Tb&hourtemps$Temp<=Tu),"Temp"]-Tb)/(Tu-Tb)))
hourtemps[which(hourtemps$Temp>Tu&hourtemps$Temp<=Tc),"GDH_weight"]<-Stress*(Tu-Tb)*
(1+cos(pi/2+pi/2*(hourtemps[which(hourtemps$Temp>Tu&hourtemps$Temp<=Tc),"Temp"]-Tu)/(Tc-Tu)))
add_up_weights<-function(hourtemps,outcol,weightcol,SDay)
{weights<-hourtemps[,weightcol]
SD<-hourtemps$JDay==SDay
temp<-weights
temp[1]<-0
nn<-nrow(hourtemps)
for (l in 2:nn)
if (SD[l]) {temp[l]<-0} else
{temp[l]<-temp[l-1]+weights[l]}
hourtemps[,outcol]<-temp
return(hourtemps)
}
hourtemps<-add_up_weights(hourtemps,"Chilling_Hours","CH_weights",Start_JDay)
hourtemps<-add_up_weights(hourtemps,"Chill_Portions","delt",Start_JDay)
hourtemps<-add_up_weights(hourtemps,"Chill_Units","Utah_weights",Start_JDay)
hourtemps<-add_up_weights(hourtemps,"GDH","GDH_weight",Start_JDay)
return(hourtemps[,c(cols,"Chilling_Hours","Chill_Portions","Chill_Units","GDH")])
} |
approxSSM <- function(model, theta, maxiter = 50, tol = 1e-08, expected = FALSE, H_tol = 1e15) {
if (maxiter < 1) {
stop("Argument maxiter must a positive integer. ")
}
if (tol < 0) {
stop("Argument tol must be non-negative. ")
}
is.SSModel(model, na.check = TRUE, return.logical = FALSE)
if (all(model$distribution == "gaussian")) {
stop("Model is completely Gaussian, nothing to approximate. ")
}
if (!is.logical(expected))
stop("Argument expected should be logical. ")
expected <- as.integer(expected)
p <- attr(model, "p")
m <- attr(model, "m")
k <- attr(model, "k")
n <- attr(model, "n")
tv <- attr(model, "tv")
ymiss <- is.na(model$y)
storage.mode(ymiss) <- "integer"
if (missing(theta) || is.null(theta)) {
theta <- initTheta(model$y, model$u, model$distribution)
} else theta <- array(theta, dim = c(n, p))
dist <- pmatch(x = model$distribution, duplicates.ok = TRUE,
table = c("gaussian", "poisson",
"binomial", "gamma", "negative binomial"))
out <-
.Fortran(fapprox, NAOK = TRUE, model$y, ymiss, tv, model$Z, model$T,
model$R, Htilde = array(0, c(p, p, n)), model$Q, model$a1,
model$P1, model$P1inf, p, n, m, k, theta = theta, model$u,
ytilde = array(0, dim = c(n, p)), dist,
maxiter = as.integer(maxiter), model$tol,
as.integer(sum(model$P1inf)), as.double(tol), diff = double(1),
double(1),info = integer(1), expected, H_tol = H_tol)
if (out$info != 0) {
warning(switch(as.character(out$info),
"-5" = paste0("Gaussian approximation converged to a potentially degenerate case with max(H) = ", out$H_tol, "."),
"-3" = "Couldn't compute LDL decomposition of P1.",
"-2" = "Couldn't compute LDL decomposition of Q.",
"1" = paste0("Gaussian approximation failed due to ",
"non-finite value in linear predictor."),
"2" = paste0("Gaussian approximation failed due to ",
"non-finite value of p(theta|y)."),
"3" = paste0("Maximum number of iterations reached, latest ",
"difference was ", signif(out$diff, 3))
))
}
model$distribution <- rep("gaussian", p)
model$y[] <- out$ytilde
model$y[as.logical(ymiss)] <- NA
model$H <- out$Htilde
model$thetahat <- out$theta
model$iterations <- out$maxiter
model$difference <- out$diff
class(model) <- c("approxSSM", "SSModel")
invisible(model)
} |
source("ESEUR_config.r")
pal_col=rainbow(2)
glibc=read.csv(paste0(ESEUR_dir, "ecosystems/glibc_evo.csv.xz"), as.is=TRUE)
glibc$date=as.POSIXct(glibc$date, format="%Y-%m-%d")
start_date=as.POSIXct("1990-01-01", format="%Y-%m-%d")
glibc$Number_days=as.integer(difftime(glibc$date,
start_date,
units="days"))
glibc_main=subset(glibc, branch_id == 1)
glibc_main=subset(glibc_main, Number_days > 0)
x_bounds=min(glibc_main$Number_days):max(glibc_main$Number_days)
glibc_main=glibc_main[order(glibc_main$Number_days), ]
glibc_main$ksloc=glibc_main$sloc/1e3
plot(glibc_main$Number_days, glibc_main$ksloc, col=pal_col[2],
xaxs="i", yaxs="i",
xlab="Days since 1 Jan 1990", ylab="KSLOC\n")
l_mod=nls(ksloc ~ SSfpl(Number_days, a, b, c, d), data=glibc_main)
pred=predict(l_mod, list(Number_days=x_bounds))
lines(x_bounds, pred, col=pal_col[1]) |
buildReferenceFromHarmonyObj <- function(harmony_obj,
metadata,
vargenes_means_sds,
pca_loadings,
verbose = TRUE,
do_umap = TRUE,
save_uwot_path = NULL,
umap_min_dist = 0.1,
seed = 111) {
set.seed(seed)
if (verbose) message('Save metadata, vargenes (S), and loadings (U)')
res = list(meta_data = metadata)
res$vargenes = vargenes_means_sds
res$loadings = pca_loadings
if(verbose) message('Save R, Z_orig, Z_corr, and betas from Harmony object')
res$R = harmony_obj$R
res$Z_orig = harmony_obj$Z_orig
res$Z_corr = harmony_obj$Z_corr
res$betas = harmony::moe_ridge_get_betas(harmony_obj)
if(verbose) message('Calculate final L2 normalized reference centroids (Y_cos)')
res$centroids = t(cosine_normalize_cpp(harmony_obj$R %*% t(harmony_obj$Z_corr), 1))
if(verbose) message('Calculate reference compression terms (Nr and C)')
res$cache = compute_ref_cache(res$R, res$Z_corr)
colnames(res$Z_orig) = row.names(metadata)
rownames(res$Z_orig) = paste0("PC_", seq_len(nrow(res$Z_corr)))
colnames(res$Z_corr) = row.names(metadata)
rownames(res$Z_corr) = paste0("harmony_", seq_len(nrow(res$Z_corr)))
cluster_sizes = res$cache[[1]] %>% as.matrix()
centroid_sums = t(res$Z_corr %*% t(res$R)) %>% as.data.frame()
centroids_pc = sweep(centroid_sums, 1, cluster_sizes, "/")
colnames(centroids_pc) = paste0("harmony_", seq_len(nrow(res$Z_corr)))
rownames(centroids_pc) = paste0("centroid_", seq_len(nrow(res$R)))
res$centroids_pc = centroids_pc
if (do_umap) {
if (verbose) message('UMAP')
umap = uwot::umap(
t(res$Z_corr), n_neighbors = 30, learning_rate = 0.5, init = "laplacian",
metric = 'cosine', fast_sgd = FALSE, n_sgd_threads = 1,
min_dist = umap_min_dist, n_threads = 4, ret_model = TRUE
)
res$umap$embedding = umap$embedding
colnames(res$umap$embedding) = c('UMAP1', 'UMAP2')
if (!is.null(save_uwot_path)) {
if (file.exists(save_uwot_path)) {
if (verbose) message(paste('File already exists at that path... overwriting...'))
file.remove(save_uwot_path)
}
model = uwot::save_uwot(umap, file = save_uwot_path, unload = FALSE, verbose = FALSE)
res$save_uwot_path = save_uwot_path
if (verbose) message(paste('Saved uwot model'))
}
}
if (verbose) message('Finished nicely.')
return(res)
} |
CONGL<-function(Argt,cong,design="MAS",type="selec",parameter="total",yk=NULL,zk=NULL,dk=NULL,Ek=NULL,Nc=0.95){
if(!(parameter=="total" | parameter=="mean" | parameter=="prop" | parameter=="ratio")){stop("Error, argument in parameter is not valid \n")}
if(!(type=="selec" | type=="estm" | type=="estm.Ud")){stop("Error, argument in type is not valid \n")}
if(missing(design)){stop("Error, the vector design is absent \n")}
if(!(design=="MAS" | design=="MCR" | design=="BER" | design=="PPT" | design=="PiPT")){stop("Error, design is not valid \n")}
if(missing(Argt)){stop("Error, the data.frame Argt is absent \n")}
if(type=="estm" | type=="estm.Ud"){
if(missing(yk)){stop("Error, the vector yk is absent \n")}
if(parameter=="prop"){if(!is.factor(yk)){stop("Error, yk should be a factor \n")}}
if(missing(cong)){stop("Error, the vector cong is absent \n")}
if(length(yk)!=length(cong)){stop("Error, vectors yk and cong are not equal in length \n")}
if(parameter=="ratio"){if(missing(zk)){stop("Error, the vector zk is absent \n")}
if(length(yk)!=length(zk)){stop("Error, vectors yk and zk are not equal in length \n")}}
if(!(Nc>0 & Nc<1)){stop("Error, the confidence level is not valid \n")}
}
if(type=="estm.Ud"){
if(missing(dk)){stop("Error, the vector dk is absent \n")}
if(!is.factor(dk)){stop("Error, dk should be a factor \n")}
if(length(yk)!=length(dk)){stop("Error, vectors yk and dk are not equal in length \n")}
if(parameter=="ratio"){if(length(zk)!=length(dk)){stop("Error, vectors yk and dk are not equal in length \n")}}
}
if(type=="selec"){
if(missing(Ek)){Ek<-numeric()}
if(design=="MAS"){if(length(Ek)==0){Ek<-runif(Argt$NI)};Result<-MAS(N=Argt$NI,n=Argt$nI,Ek=Ek)}
if(design=="MCR"){if(length(Ek)==0){Ek<-runif(Argt$mI)};Result<-MCR(N=Argt$NI,m=Argt$mI,Ek=Ek)}
if(design=="BER"){if(length(Ek)==0){Ek<-runif(Argt$NI)};Result<-BER(N=Argt$NI,Pi=Argt$PiI,Ek=Ek)}
if(design=="PPT"){if(length(Ek)==0){Ek<-runif(Argt$mI)};Result<-PPT(xk=Argt$txkI,m=Argt$mI,Ek=Ek)}
if(design=="PiPT"){if(length(Ek)==0){Ek<-runif(length(Argt$txkI))};Result<-PiPT(xk=Argt$txkI,n=Argt$nI,Ek=Ek)}
return(Result)
}
F.TV<-function(yk,cong,design,Argt,opc=1){
tyi<-as.vector(tapply(yk,cong,sum))
R1<-data.frame(ty.e=numeric(),Vest=numeric())
if(design=="BER"){R1[1,]<-BER(yk=tyi,Pi=Argt$PiI,type="estm")$Estimation[,1:2]}
if(design=="MCR"){R1[1,]<-MCR(yk=tyi,N=Argt$NI,m=Argt$mI,type="estm")$Estimation[,1:2]}
if(design=="MAS"){R1[1,]<-MAS(yk=tyi,N=Argt$NI,n=Argt$nI,type="estm")$Estimation[,1:2]}
if(design=="PPT"){R1[1,]<-PPT(yk=tyi,pk=Argt$pkI,type="estm")$Estimation[,1:2]}
if(design=="PiPT"){R1[1,]<-PiPT(yk=tyi,pik=Argt$pikI,mpikl=Argt$mpiklI,type="estm")$Estimation[,1:2]}
if(opc==1){return(R1[,1])}else{return(R1[,2])}
}
if(type=="estm" | type=="estm.Ud"){
if(type=="estm"){dk<-as.matrix(rep(1,length(yk)))}
if(parameter!="ratio"){zk<-rep(1,length(yk))}
dk<-Dk(dk)
if(parameter=="prop"){yk<-as.factor(yk);ydk<-PRODM(dk,Dk(yk))}else{ydk<-yk*dk}
if(parameter=="prop"){zk<-matrix(rep(zk,ncol(Dk(yk))),ncol=ncol(Dk(yk)));zdk<-PRODM(dk,zk)}else{zdk<-zk*dk}
if(parameter=="total"){
Estd<-apply(ydk,2,F.TV,cong,design,Argt,1)
Vest<-apply(ydk,2,F.TV,cong,design,Argt,2)
NI<-length(tapply(yk,cong,length))
N.e<-ceiling(F.TV(yk=rep(1,length(yk)),cong=cong,design=design,Argt=Argt))
S2yw<-(1/(N.e-NI))*sum((tapply(yk,cong,length)-1)*tapply(yk,cong,var))
S2y<-var(yk)
Ivi<-1-(S2yw/S2y)
}else{
tdy.e<-apply(ydk,2,F.TV,cong,design,Argt,1)
tdz.e<-apply(zdk,2,F.TV,cong,design,Argt,1)
Estd<-tdy.e/tdz.e
udk.e<-matrix(0,ncol=length(Estd),nrow=length(yk));for(i in 1:length(Estd)){udk.e[,i]<-(ydk[,i]-Estd[i]*zdk[,i])/tdz.e[i]}
Vest<-apply(udk.e,2,F.TV,cong,design,Argt,2)
}
e.e<-sqrt(Vest)
Cve<-(sqrt(Vest)/Estd)*100
n<-length(yk)
if(parameter=="prop"){IC<-F.IC(n,tdy.e,tdz.e,Estd,e.e,Nc,parameter)}else{IC<-F.IC(n=n,Estd=Estd,e.e=e.e,Nc=Nc,parameter=parameter)}
IC.inf<-IC$IC1;IC.sup<-IC$IC2
if(parameter=="total"){
Resul<-data.frame(Estd,Vest,e.e,Cve,IC.inf,IC.sup,Ivi)
names(Resul)[1]<-parameter
}else{
if(parameter=="prop"){
if(type=="estm" ){Uz=levels(yk);Resul<-data.frame(Uz,Estd,Vest,e.e,Cve,IC.inf,IC.sup);names(Resul)[2]<-parameter
}else{
Uzd=expand.grid(Uz=levels(yk),Ud=colnames(dk));Resul<-data.frame(Ud=Uzd$Ud,Uz=Uzd$Uz,Estd,Vest,e.e,Cve,IC.inf,IC.sup);names(Resul)[3]<-parameter}
}else{
Resul<-data.frame(Estd,Vest,e.e,Cve,IC.inf,IC.sup)
names(Resul)[1]<-parameter}
}
return(list(Estimation=Resul))
}
} |
source("ESEUR_config.r")
library("cmprsk")
plot_layout(2, 1)
pal_col=rainbow(2)
rats=read.csv(paste0(ESEUR_dir, "survival/tool_rep/splint.csv.xz"), as.is=TRUE)
rats$failtime=rats$END-rats$START
rats$type=rats$WASREM+2*rats$DISAP
plot_cif=function(sys_str)
{
t=cuminc(rats$failtime, rats$type, cencode=0,
subset=(rats$SYSTEM == sys_str))
plot(t, col=pal_col, cex=1.25,
xaxs="i", yaxs="i",
curvlab=c("was removed", "disappeared"),
xlab="Snapshot", ylab="Proportion flagged issues 'dead'\n")
text(max(t[[1]]$time)/1.5, 0.9, sys_str, cex=1.5)
}
plot_cif("samba")
plot_cif("squid") |
revolution <- c(CRAN = "https://cloud.r-project.org")
pkgs <- c("foreach")
pdb <- cranJuly2014
if (interactive()) {
pdb <- pkgAvail(repos = revolution, type = "source")
pkgList <- pkgDep(pkgs, availPkgs = pdb, repos = revolution, type = "source", suggests = FALSE)
pkgList
dir.create(pth <- file.path(tempdir(), "miniCRAN"))
makeRepo(pkgList, path = pth, repos = revolution, type = "source", download = FALSE)
makeRepo(pkgList, path = pth, repos = revolution, type = "win.binary", download = TRUE)
oldVers <- data.frame(package = c("foreach", "codetools", "iterators"),
version = c("1.4.0", "0.2-7", "1.0.5"),
stringsAsFactors = FALSE)
addOldPackage(pkgList, path = pth, repos = revolution, vers = oldVers$version, type = "source")
oldPackages(path = pth, repos = revolution, type = "source")
oldPackages(path = pth, repos = revolution, type = "win.binary")
updatePackages(path = pth, repos = revolution, type = "source", ask = FALSE)
updatePackages(path = pth, repos = revolution, type = "win.binary")
unlink(pth, recursive = TRUE)
} |
adapt_fICA <- function(X, gs=gf, dgs=dgf, name=gnames, kj=0, inR=TRUE, eps=1e-06, maxiter=100)
{
n <- nrow(X)
p <- ncol(X)
eps <- p*eps
init_est <- "k-JADE"
if(!(kj %in% 1:p)){
W0 <- FOBI(X)$W
init_est <- "FOBI"
}else{
W0 <- k_JADE(X,k=kj,eps=eps,maxiter=maxiter)$W
init_est <- paste(kj,"-JADE",sep="")
}
Z <- tcrossprod(X,W0)
Z <- sweep(Z,2,colMeans(Z))
if(inR){
if(length(name)!=length(gs)){
name <- paste("g",1:length(gs))
}
res <- adapt_fICA.R(Z, gs=gs, dgs=dgs, name=name, kj=kj, eps=eps, maxiter=maxiter)
}else{
name <- gnames
res <- .Call("adfica",Z,eps,maxiter,PACKAGE="fICA")
}
cnam <- paste("comp",1:p)
V <- res$W
alphas <- res$alphas
ord <- res$ord+1
usedg <- res$usedg+1
used_gs <- NULL
for(i in 1:(p-1)){
used_gs[i] <- name[usedg[i]]
}
if(length(ord)==(p-1)){
ord[p] <- sum(1:p)-sum(ord)
}else ord <- 1:p
W <- crossprod(V,W0)
W <- crossprod(diag(sign(rowMeans(W))),W)
S <- tcrossprod(sweep(X,2,colMeans(X)),W)
alphas <- matrix(alphas[,ord],ncol=p)
dimnames(alphas) <- list(name, cnam)
res <- list(W=W, gs=name, used_gs=used_gs, alphas=alphas, init_est=init_est, S=S)
class(res) <- "bss"
res
}
adapt_fICA.R <- function(Z, gs, dgs, name, kj, eps, maxiter)
{
n <- nrow(Z)
p <- ncol(Z)
ng <- length(gs)
usedg <- NULL
alphas <- compute_alphas(Z,gs,dgs,name)
ca <- ifelse(alphas>0,alphas,Inf)
ord <- NULL
VN <- diag(p)
V <- matrix(0,p,p)
for(i in 1:(p-1)){
mina <- which.min(ca)
comp <- ceiling(mina/ng)
gc <- mina-(comp-1)*ng
vn <- VN[,comp]
iter <- 0
a <- 0
if(min(ca)==Inf){ stop("no convergence")
}else{
while(TRUE){
iter <- iter+1
v <- vn
Zv <- crossprod(t(Z),v)
vn <- colMeans(sweep(Z,1,gs[[gc]](Zv),"*"))-mean(dgs[[gc]](Zv))*v
vn <- vn/sqrt(sum(vn^2))
if(sqrt(sum((vn-v)^2))>1) vn <- -vn
if((a>0)&&(floor(iter/a)==iter/a)){
vn <- (1-1/5)*vn+v/5
}else vn <- (1-1/(iter+20))*vn+v/(iter+20)
vn <- vn-crossprod(tcrossprod(V,V),vn)
vn <- vn/sqrt(sum(vn^2))
if(sqrt(sum((v-vn)^2))<eps || sqrt(sum((v+vn)^2))<eps){
usedg[i] <- mina-(comp-1)*ng
ord[i] <- comp
ca[,comp] <- Inf
break
}
if(iter==maxiter){
a <- a+1
iter <- 0
if(a>10){
ca[mina] <- Inf
mina <- which.min(ca)[1]
comp <- ceiling(mina/ng)
vn <- VN[,comp]
if(min(ca)==Inf) stop("no convergence")
}
}
}
V[,i] <- t(vn)
}
}
vn <- VN[,1]
vn <- vn-crossprod(tcrossprod(V,V),vn)
vn <- vn/sqrt(sum(vn^2))
V[,p] <- t(vn)
res <- list(W=V, usedg=usedg-1, alphas=alphas, ord=ord-1)
res
} |
library(mlmRev)
options(digits=6, useFancyQuotes = FALSE)
fm <- glmer(immun ~ kid2p + mom25p + ord + ethn + momEd +
husEd + momWork + rural + pcInd81 + (1|mom) + (1|comm),
data = guImmun, family = binomial)
print(fm, symbolic.cor = TRUE)
fm.h <- update(fm, ~ . - husEd)
print(fm.h, corr = FALSE)
fm.ho <- update(fm.h, ~ . - ord)
print(fm.ho, corr = FALSE)
anova(fm, fm.h, fm.ho)
(fm.hoe <- update(fm.ho, ~ . - ethn))
(fm.hoem <- update(fm.hoe, ~ . - mom25p))
(AN <- anova(fm, fm.h, fm.ho, fm.hoe, fm.hoem))
AN[, "logLik"] + 1362
if (packageVersion("lme4") <= "1.1.21") {
stopifnot(AN[,"Df"] == c(9,10,12,15,18), TRUE)
} else {
stopifnot(AN[,"npar"] == c(9,10,12,15,18), TRUE)
}
cat('Time elapsed: ', proc.time(),'\n') |
MGGM.path <-
function(S_bar,
nn,
Lambda1.vec, Lambda2.vec,
graph,
tau = .01, MAX_iter=200, eps_mat = 1e-4){
p = dim(S_bar)[1]
L = dim(S_bar)[2] / p
grid.lambda1 = length(Lambda1.vec)
grid.lambda2 = length(Lambda2.vec)
covmat_inverse_path = matrix(rep(diag(p),L),p,p*L*grid.lambda1*grid.lambda2)
covmat_inverse_con_path = covmat_inverse_path
covmat_path = covmat_inverse_path
covmat_con_path = covmat_inverse_path
NumOfEdge = dim(graph)[2]
out <- .C("matrix_grouping_path", S_bar = as.double(S_bar),
covmat_inverse_path=as.double(covmat_inverse_path),
covmat_path = as.double(covmat_path),
covmat_inverse_con_path=as.double(covmat_inverse_con_path),
covmat_con_path = as.double(covmat_con_path),
Lambda1=as.double(Lambda1.vec),Lambda2 = as.double(Lambda2.vec),
Tau=as.double(tau), grid_lambda1 = as.integer(grid.lambda1),
grid_lambda2 = as.integer(grid.lambda2),
Graph=as.integer(graph),sample_size=as.double(nn),
pp=as.integer(p),LL=as.integer(L),
NumOfEdges=as.integer(NumOfEdge),eps_mat=as.double(eps_mat))
sol_path = list()
sol_path$sol_nonconvex = array(out$covmat_inverse_path, dim=c(p,p*L,grid.lambda2,grid.lambda1))
sol_path$sol_convex = array(out$covmat_inverse_con_path, dim=c(p,p*L,grid.lambda2,grid.lambda1))
return (sol_path)
} |
fmap <- function(x,
y = NULL,
what = c("fitted", "predicted"),
conf = TRUE,
obs = FALSE,
obs.shp = 17,
by.date = TRUE,
crs = NULL,
ext.rng = c(0.05, 0.05),
size = 0.25,
col = "black",
lines = FALSE,
landfill = grey(0.6),
pal = "Zissou1",
rev = FALSE)
{
what <- match.arg(what)
assert_that(inherits(x, "fG_ssm"), msg = "x must be a foieGras ssm fit object with class `fG_ssm`")
if(!inherits(y, "fG_mpm") & !is.null(y)) stop("y must either be NULL or a foieGras mpm fit object with class `fG_mpm`")
if(inherits(x, "fG_ssm")) {
if(length(unique(sapply(x$ssm, function(.) st_crs(.$predicted)$epsg))) == 1) {
if(!is.null(y)) {
conf <- FALSE
if(size[1] == 0.25) size[1] <- 1
sf_locs <- try(join(x, y, what.ssm = what), silent = TRUE)
if(inherits(sf_locs, "try-error")) stop("number of rows in ssm object do not match the number of rows in mpm object, try modifying the `what` argument")
} else {
sf_locs <- grab(x, what=what)
if(conf) locs <- grab(x, what = what, as_sf = FALSE)
}
} else if(length(unique(sapply(x$ssm, function(.) st_crs(.$predicted)$epsg))) > 1) {
stop("individual foieGras ssm fit objects with differing projections not currently supported")
}
}
if(conf) {
locs.lst <- split(locs, locs$id)
conf_poly <- lapply(locs.lst, function(x) {
conf <- lapply(1:nrow(x), function(i)
with(x, elps(x[i], y[i], x.se[i], y.se[i], 90))
)
lapply(conf, function(x) st_polygon(list(x))) %>%
st_multipolygon()
})
sf_conf <- st_as_sfc(conf_poly) %>%
st_as_sf(crs = st_crs(sf_locs)) %>%
mutate(id = unique(locs$id))
sf_conf <- st_union(sf_conf, by_feature = TRUE)
}
if (is.null(crs)) prj <- st_crs(sf_locs)
else {
prj <- crs
if(!is.character(prj)) stop("\ncrs must be a proj4string,
\neg. `+proj=stere +lat_0=-90 +lon_0=0 +ellps=WGS84 +units=km +no_defs`")
if(length(grep("+units=km", prj, fixed = TRUE)) == 0) {
cat("\nconverting units from m to km to match SSM output")
prj <- paste(prj, "+units=km")
}
sf_locs <- st_transform(sf_locs, crs = prj)
if(conf) sf_conf <- st_transform(sf_conf, crs = prj)
}
if (obs) {
sf_data <- grab(x, "data") %>%
st_transform(., crs = prj)
bounds <- st_bbox(sf_data)
} else {
bounds <- st_bbox(sf_locs)
}
bounds[c("xmin","xmax")] <- extendrange(bounds[c("xmin","xmax")], f = ext.rng[1])
bounds[c("ymin","ymax")] <- extendrange(bounds[c("ymin","ymax")], f = ext.rng[2])
if(lines) {
sf_lines <- sf_locs %>%
group_by(id) %>%
summarise(do_union = FALSE) %>%
st_cast("MULTILINESTRING")
}
coast <- sf::st_as_sf(rworldmap::countriesLow) %>%
st_transform(crs = prj)
p <- ggplot() +
geom_sf(data = coast,
fill = landfill,
lwd=0) +
xlim(bounds[c("xmin","xmax")]) +
ylim(bounds[c("ymin","ymax")])
if(obs) {
if(length(size) == 1) {
cat(paste0("geom size not specified for observations, using 'size = c(", size, ", 0.8)'"))
size <- c(size, 0.8)
}
p <- p + geom_sf(data = sf_data, colour = col, size = size[2], shape = obs.shp, alpha = 0.75)
}
if(nrow(x) > 1) {
if(conf) {
p <- p + geom_sf(data = sf_conf,
aes_string(fill = "id"),
colour = NA,
lwd = 0,
alpha = 0.4,
show.legend = ifelse(!lines & is.na(size), TRUE, FALSE)
)
}
if(is.null(y)) {
if(lines & is.na(size)[1]) {
p <- p + geom_sf(data = sf_lines,
aes_string(colour = "id"),
lwd = 0.25,
show.legend = "line"
)
} else if(lines & !is.na(size)[1]) {
p <- p + geom_sf(data = sf_lines,
aes_string(colour = "id"),
lwd = 0.25
) + geom_sf(data = sf_locs,
aes_string(colour = "id"),
size = ifelse(length(size) == 2, size[1], size),
show.legend = "point"
)
} else if(!lines & !is.na(size)[1]) {
p <- p + geom_sf(data = sf_locs,
aes_string(colour = "id"),
size = ifelse(length(size) == 2, size[1], size),
show.legend = "point"
)
}
p <- p + scale_colour_manual(values =
hcl.colors(n = nrow(x),
palette = pal,
rev = rev)
)
} else if(!is.null(y)) {
if(lines) {
p <- p + geom_sf(data = sf_lines,
aes_string(colour = "id"),
lwd = 0.25
)
}
p <- p + geom_sf(data = sf_locs,
aes_string(colour = "g"),
size = ifelse(length(size) == 2, size[1], size)
) +
scale_colour_gradientn(colours =
hcl.colors(n = 100,
palette = pal,
rev = rev),
name = expression(gamma[t]),
limits = c(0,1)
)
}
p <- p + theme_minimal() +
theme(legend.position = "bottom",
legend.text = element_text(size = 8, vjust = 0)
)
if(conf) {
p <- p + scale_fill_manual(values =
hcl.colors(n = nrow(x),
palette = pal,
rev = rev)
)
}
} else if(nrow(x) == 1) {
if(is.null(y)) {
if(by.date) {
lab_dates <- with(sf_locs, pretty(seq(min(date), max(date), l = 5))) %>% as.Date()
}
if(conf & !by.date) {
p <- p + geom_sf(data = sf_conf,
fill = "
colour = NA,
lwd = 0,
alpha = 0.5)
} else if(conf & by.date) {
p <- p + geom_sf(data = sf_conf,
fill = grey(0.5),
colour = NA,
lwd = 0,
alpha = 0.25)
}
if(by.date) {
if(lines) {
p <- p + geom_sf(data = sf_lines,
colour = grey(0.5),
alpha = 0.75,
lwd = 0.25
)
}
if(!is.na(size)[1]) {
p <- p + geom_sf(data = sf_locs,
aes(colour = as.numeric(as.Date(date))),
size = size[1]
) +
scale_colour_gradientn(breaks = as.numeric(lab_dates),
colours = hcl.colors(n = 100,
palette = pal,
rev = rev),
labels = lab_dates)
}
p <- p + labs(title = paste("id:", x$id)) +
theme_minimal() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(size = 8, vjust = 0),
legend.key.width = unit(0.12, "npc"),
legend.key.height = unit(0.025, "npc"),
panel.grid = element_line(size = 0.2)
)
} else if(!by.date) {
if(lines) {
p <- p + geom_sf(data = sf_lines,
colour = hcl.colors(n = 5,
palette = pal)[4],
alpha = 0.75,
lwd = 0.25
)
}
if(!is.na(size)[1]) {
p <- p + geom_sf(data = sf_locs,
colour = hcl.colors(n = 5,
palette = pal)[3],
size = size[1]
)
}
p <- p + labs(title = paste("id:", x$id)) +
theme_minimal() +
theme(legend.position = "none",
panel.grid = element_line(size = 0.2)
)
}
} else if(!is.null(y)) {
if(lines) {
p <- p + geom_sf(data = sf_lines,
colour = hcl.colors(n = 5,
palette = pal)[4],
alpha = 0.75,
lwd = 0.25
)
}
p <- p + geom_sf(data = sf_locs,
aes_string(colour = "g"),
size = ifelse(length(size) == 2, size[1], size)
) +
scale_colour_gradientn(colours =
hcl.colors(n = 100,
palette = pal,
rev = rev),
name = expression(gamma[t]),
limits = c(0,1)
)
p <- p + labs(title = paste("id:", x$id)) +
theme_minimal() +
theme(legend.position = "bottom",
legend.text = element_text(size = 8, vjust = 0)
)
}
}
return(p)
} |
library(shiny)
library(esquisse)
library(datamods)
ui <- navbarPage(
title = "My navbar app",
tabPanel(
title = "esquisse",
esquisse_ui(
id = "esquisse",
header = FALSE,
container = esquisseContainer(
fixed = c(55, 0, 0, 0)
)
)
)
)
server <- function(input, output, session) {
import_modal(
id = "import-data",
from = c("env", "file", "copypaste"),
title = "Import data"
)
data_imported_r <- datamods::import_server("import-data")
data_rv <- reactiveValues(data = data.frame())
observeEvent(data_imported_r$data(), {
data_rv$data <- data_imported_r$data()
data_rv$name <- data_imported_r$name()
})
esquisse_server(id = "esquisse", data_rv = data_rv)
}
if (interactive())
shinyApp(ui, server) |
context("Variable summaries")
with_mock_crunch({
ds <- cachedLoadDataset("test ds")
gen <- ds$gender
test_that("table 'method' dispatch", {
expect_identical(table(1:5), base::table(1:5))
expect_identical(
table(useNA = "ifany", 1:5),
base::table(useNA = "ifany", 1:5)
)
expect_identical(
table(useNA = "ifany", c(NA, 1:5)),
base::table(useNA = "ifany", c(NA, 1:5))
)
})
test_that("unsupported table methods", {
expect_error(
table(gen, 1:5),
"Cannot currently tabulate Crunch variables with non-Crunch vectors"
)
expect_error(
table(1:5, gen),
"Cannot currently tabulate Crunch variables with non-Crunch vectors"
)
expect_error(table(), "nothing to tabulate")
})
test_that("table makes a cube request", {
expect_GET(table(gen), "https://app.crunch.io/api/datasets/1/cube/")
})
test_that("unsupported aggregation methods", {
expect_error(
mean(ds$textVar),
paste(dQuote("mean"), "is not defined for TextVariable")
)
expect_error(
sd(ds$textVar),
paste(dQuote("sd"), "is not defined for TextVariable")
)
expect_error(
median(ds$textVar),
paste(dQuote("median"), "is not defined for TextVariable")
)
expect_error(
min(ds$textVar),
paste(dQuote("min"), "is not defined for TextVariable")
)
expect_error(
max(ds$textVar),
paste(dQuote("max"), "is not defined for TextVariable")
)
})
test_that("Summary method for numeric", {
expect_is(summary(ds$birthyr), "NumericVariableSummary")
})
test_that("max", {
expect_equal(max(ds$birthyr), 1.6662)
})
test_that("min", {
expect_equal(min(ds$birthyr), -1.4967)
})
})
with_test_authentication({
ds <- newDataset(df)
test_that("can fetch variable summaries", {
summ <- getSummary(ds$v1)
expect_true(is.list(summ))
expect_equivalent(summ$mean, mean(df$v1, na.rm = TRUE))
expect_equivalent(summ$stddev, sd(df$v1, na.rm = TRUE))
})
expect_stats_equal <- function(var, expected, stats = c("mean", "sd", "median", "min", "max")) {
for (stat in stats) {
fn <- get(stat)
expect_equal(fn(var), fn(expected), info = stat)
expect_equal(fn(var, na.rm = TRUE), fn(expected, na.rm = TRUE), info = stat)
}
}
test_that("Univariate statistics for numeric variable", {
expect_true(is.Numeric(ds$v1))
expect_stats_equal(ds$v1, df$v1)
})
test_that("Univariate statistics for datetime variable", {
expect_true(is.Datetime(ds$v5))
expect_stats_equal(ds$v5, df$v5, c("min", "max"))
})
test_that("table", {
expect_equivalent(table(ds$v4), table(df$v4))
expect_equivalent(table(ds$v4, ds$v3), table(df$v4, df$v3))
})
test_that("table works with CrunchExpr", {
expect_equivalent(table(ds$v4[ds$v3 < 10]), table(df$v4[df$v3 < 10]))
})
test_that("table throws error if not equally filtered", {
expect_error(
table(ds$v4, ds$v2[ds$v3 < 10]),
"Filter expressions in variables must be identical"
)
})
test_that("summary", {
expect_equivalent(
round(unclass(summary(ds$v1)), 2),
round(unclass(summary(df$v1)), 2)
)
expect_equivalent(as.numeric(summary(ds$v4)), summary(df$v4))
})
test_that("Filtering summary and univariate stats", {
expect_stats_equal(ds$v1[4:15], df$v1[4:15])
expect_stats_equal(ds$v5[4:15], df$v5[4:15], c("min", "max"))
expect_equivalent(
round(unclass(summary(ds$v1[4:15])), 2),
round(unclass(summary(df$v1[4:15])), 2)
)
expect_equivalent(as.numeric(summary(ds$v4[4:15])), summary(df$v4[4:15]))
})
}) |
reshapeW2L <- function(data, within, levels, varying, ignore, id="id"){
if (missing(ignore)) ignore <- NULL
all <- colnames(data)
use <- setdiff(all, ignore)
all.varying <- unlist(varying)
between <- setdiff(use, all.varying)
levs <- expand.grid(rev(levels))
m <- nrow(levs)
n <- nrow(data) * m
out <- data.frame(id=character(0), stringsAsFactors=FALSE)
names(out)[1] <- id
for (bet in between){
b <- data[[bet]]
out[[bet]] <- if (is.factor(b)) factor(NULL, levels=levels(b)) else vector(0, mode=mode(b))
}
for (win in within){
out[[win]] <- factor(NULL, levels[[win]])
}
for (var in names(varying)){
v <- data[[varying[[var]][1]]]
out[[var]] <- if (is.factor(v)) factor(NULL, levels=levels(v)) else vector(0, mode=mode(v))
}
out[1:n, ] <- NA
for (i in 1:nrow(data)){
j <- ((i - 1)*m + 1):(i*m)
out[j, id] <- as.character(i)
out[j, between] <- data[i, between]
out[j, rev(within)] <- levs
for (var in names(varying)){
out[j, var] <- unlist(data[i, varying[[var]]])
}
}
rownames(out) <- paste0(out[[id]], ".", 1:m)
out
} |
LIK.density.spattemp.single <- function(bands,pp,tt,tlim,xyin,xys,sedge,tedge,parallelise,verbose){
if(any(bands<=0)) return(NA)
if(verbose) cat("h =",bands[1],"\b; lambda =",bands[2],"\n")
h <- bands[1]
lam <- bands[2]
temp.dens.pts <- spattemp.LOO(pp,tt,h,lam,tlim,xyin,xys,sedge,tedge,parallelise=parallelise)
if(any(temp.dens.pts<=0)) return(log(min(temp.dens.pts)))
return(-mean(log(temp.dens.pts)))
} |
library(hamcrest)
test.negativeComplex <- function() {
z <- 1+1i
assertThat( -z, equalTo( complex(real = -1, imaginary = -1)))
}
test.operations <- function() {
assertThat(Re(eigen(matrix(c(3, 4, -2, -1),2))$vectors[1]), closeTo(0.4082483, 1e-6))
assertThat(Im(as.complex(1)), identicalTo(0))
assertThat(Re(as.complex(1)), identicalTo(1))
assertThat(Mod(1+1i), closeTo(1.4142136, 1e-6))
assertThat(Im(1+1i + 1+3i), identicalTo(4.0))
assertThat(Im((1+1i) - (1+3i)), identicalTo(-2.0))
assertThat(Im(1+1i * 1+3i), identicalTo(4.0))
assertThat(Re((1+1i) * (1+3i)), identicalTo(-2.0))
} |
x <- rnorm(20L)
theta <- 0.1
prob.loss <- ecdf(x)(theta)
exponent <- 2
(cm <- mean((x[x < theta] - theta)^exponent))
xx <- x - theta; xx[xx > 0] <- 0
(pm <- mean(xx^exponent))
stopifnot(all.equal(cm * prob.loss, pm)) |
require(xgboost)
context("Garbage Collection Safety Check")
test_that("train and prediction when gctorture is on", {
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
train <- agaricus.train
test <- agaricus.test
gctorture(TRUE)
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
pred <- predict(bst, test$data)
gctorture(FALSE)
expect_length(pred, length(test$label))
}) |
fpath = system.file("testdata",
"ploglik_cmodStd_r_psill_ratio_par3.rda",
package = "gear")
load(fpath)
scmod = cmod_std(model = "matern", psill = 1, r = 1, par3 = 1,
angle = geoR_ml$aniso.pars[1] * 180/pi,
ratio = 1/1.5,
invert = TRUE)
weights = rep(1, length(y))
gear_ml = optimx::optimx(par = c(1, 1, 1/1.5, 1),
fn = ploglik_cmodStd_r_psill_ratio_par3,
lower = c(0.01, 0.01, 0.001, 0.001),
upper = c(5, 5, 1, 2.5),
method = "L-BFGS-B",
x = x, y = y, d = d, nugget = geoR_ml$nugget,
weights = weights, scmod = scmod,
reml = FALSE,
control = list(kkt = FALSE, dowarn = FALSE))
gear_reml = optimx::optimx(par = c(1, 1, 1/1.5, 1),
fn = ploglik_cmodStd_r_psill_ratio_par3,
lower = c(0.001, 0.001, 0.001, 0.001),
upper = c(5, 5, 1, 2.5),
method = "L-BFGS-B",
x = x, y = y, d = d, nugget = geoR_reml$nugget,
weights = weights, scmod = scmod,
reml = TRUE, control = list(kkt = FALSE, dowarn = FALSE))
test_that("ploglik_cmodStd_r_psill_ratio_par3 accuracy (geoR)", {
expect_true(abs(geoR_ml$cov.pars[1] - gear_ml$p2) < 1e-1/2)
expect_true(abs(geoR_ml$cov.pars[2] - gear_ml$p1 * gear_ml$p3) < 1e-4)
expect_true(abs(geoR_ml$aniso.pars[2] - 1/gear_ml$p3) < 1e-2)
expect_true(abs(geoR_ml$kappa - gear_ml$p4) < 1e-2)
expect_equal(geoR_ml$loglik, gear_ml$value/-2)
expect_true(abs(geoR_reml$cov.pars[1] - gear_reml$p2) < 1e-2)
expect_true(abs(geoR_reml$cov.pars[2] - gear_reml$p1 * gear_reml$p3) < 1e-2)
expect_true(abs(geoR_reml$aniso.pars[2] - 1/gear_reml$p3) < 1e-2)
expect_equal(geoR_reml$loglik, gear_reml$value/-2)
})
cmod = scmod
cmod$evar = geoR_ml$nugget
data = data.frame(y = y, x1 = coords[,1], x2 = coords[,2])
object = geolm(y ~ 1, data = data, coordnames = c("x1", "x2"),
mod = cmod)
object_ml = estimate(object, method = "L-BFGS-B",
lower = list(r = 0.01, psill = 0.01, ratio = 0.001, par3 = 0.001),
upper = list(r = 5, psill = 5,
ratio = 1, par3 = 2.5),
est_nugget = FALSE, est_par3 = TRUE,
est_angle = FALSE, est_ratio = TRUE,
verbose = FALSE)
object_reml = estimate(object, method = "L-BFGS-B",
upper = list(r = 5, psill = 5,
ratio = 1, par3 = 2.5),
est_nugget = FALSE, est_par3 = TRUE,
est_angle = FALSE, est_ratio = TRUE,
verbose = FALSE, reml = TRUE)
test_that("estimate r_psill_ratio_par3 accuracy (geoR)", {
expect_true(abs(geoR_ml$cov.pars[1] - object_ml$mod$psill) < 1e-2)
expect_true(abs(geoR_ml$cov.pars[2] - object_ml$mod$r*object_ml$mod$ratio) < 1e-4)
expect_true(abs(geoR_ml$aniso.pars[2] - 1/object_ml$mod$ratio) < 1e-2)
expect_true(abs(geoR_ml$kappa - object_ml$mod$par3) < 1e-2)
expect_true(abs(geoR_ml$beta - object_ml$coeff) < 1e-2)
expect_equivalent(gear_ml[1:5], object_ml$optimx[1:5])
expect_equal(geoR_ml$loglik, object_ml$loglik)
expect_true(abs(geoR_reml$cov.pars[1] - object_reml$mod$psill) < 1e-2)
expect_true(abs(geoR_reml$cov.pars[2] - object_reml$mod$r*object_reml$mod$ratio) < 1e-4)
expect_true(abs(geoR_reml$aniso.pars[2] - 1/object_reml$mod$ratio) < 1e-2)
expect_true(abs(geoR_reml$kappa - object_reml$mod$par3) < 1e-2)
expect_true(abs(geoR_reml$beta - object_reml$coeff) < 1e-2)
expect_equivalent(gear_ml[1:5], object_ml$optimx[1:5])
expect_true(abs(geoR_reml_noaniso_loglik - object_reml$loglik) < 1e-4)
})
cmod_radians = cmod_std(model = "matern", psill = 1, r = 1, par3 = 1,
angle = geoR_ml$aniso.pars[1],
ratio = 1/1.5,
evar = geoR_ml$nugget,
invert = TRUE, radians = TRUE)
object_radians = geolm(y ~ 1, data = data, coordnames = c("x1", "x2"),
mod = cmod_radians)
object_ml_radians = estimate(object_radians, method = "L-BFGS-B",
lower = list(r = 0.01, psill = 0.01, ratio = 0.001, par3 = 0.001),
upper = list(r = 5, psill = 5, ratio = 1, par3 = 2.5),
est_nugget = FALSE, est_par3 = TRUE,
est_angle = FALSE, est_ratio = TRUE,
verbose = FALSE)
test_that("estimate w/ and w/o radians r_psill_ratio_par3 accuracy", {
expect_equal(object_ml$mod$angle, object_ml_radians$mod$angle * 180/pi)
expect_equal(object_ml$mod$ratio, object_ml_radians$mod$ratio)
expect_equal(object_ml$mod$psill, object_ml_radians$mod$psill)
expect_equal(object_ml$mod$r, object_ml_radians$mod$r)
expect_equal(object_ml$mod$par3, object_ml_radians$mod$par3)
expect_equal(object_ml$coeff, object_ml_radians$coeff)
expect_equal(object_ml$loglik, object_ml_radians$loglik)
object_ml_radians$optimx$xtime = object_ml$optimx$xtime
expect_equal(object_ml$optimx, object_ml_radians$optimx)
}) |
NULL
utils::globalVariables(
c("Counts", "Actual"
)
) |
status <- function(project = NULL, lib.loc = libDir(project), quiet = FALSE) {
project <- getProjectDir(project)
stopIfNoLockfile(project)
projectDefault <- identical(project, '.')
project <- normalizePath(project, winslash = '/', mustWork = TRUE)
packratPackages <- lockInfo(project, fatal = FALSE)
if (length(packratPackages) == 0) {
initArg <- if (projectDefault) '' else deparse(project)
cat('This directory does not appear to be using packrat.\n',
'Call packrat::init(', initArg, ') to initialize packrat.',
sep = '')
return(invisible())
}
packratNames <- getPackageElement(packratPackages, "name")
packratVersions <- getPackageElement(packratPackages, "version")
packratSources <- getPackageElement(packratPackages, "source")
installedPkgFolders <- list.files(lib.loc, full.names = TRUE)
installedPkgRecords <- lapply(installedPkgFolders, function(path) {
descPath <- file.path(path, "DESCRIPTION")
if (!file.exists(descPath)) {
warning("No DESCRIPTION file for installed package '", basename(path), "'")
return(NULL)
}
DESCRIPTION <- readDcf(descPath, all = TRUE)
list(
name = DESCRIPTION$Package,
source = DESCRIPTION$InstallSource,
version = DESCRIPTION$Version
)
})
installedPkgNames <- unlist(lapply(installedPkgRecords, `[[`, "name"))
names(installedPkgNames) <- installedPkgNames
installedPkgVersions <- unlist(lapply(installedPkgRecords, `[[`, "version"))
names(installedPkgVersions) <- installedPkgNames
availablePkgs <- if (hasCachedAvailablePackages())
availablePackages()
else
availablePackagesSkeleton()
inferredPkgNames <- appDependencies(
project,
available.packages = availablePkgs
)
suggestedPkgNames <- suppressWarnings(
appDependencies(project,
available.packages = availablePkgs,
fields = "Suggests")
)
allPkgNames <- sort_c(unique(c(
packratNames, installedPkgNames, inferredPkgNames
)))
.match <- function(what, from = allPkgNames) {
if (is.null(what)) NA
else what[from]
}
packrat.version <- .match(packratVersions)
packrat.source <- .match(packratSources)
library.version <- .match(installedPkgVersions)
currently.used <- allPkgNames %in% c(inferredPkgNames, suggestedPkgNames)
external.packages <- opts$external.packages()
statusTbl <- data.frame(stringsAsFactors = FALSE,
row.names = 1:length(allPkgNames),
package = allPkgNames,
packrat.version = packrat.version,
packrat.source = packrat.source,
library.version = library.version,
currently.used = currently.used,
external.package = allPkgNames %in% external.packages
)
statusTbl <- statusTbl[!statusTbl$external.package, ]
if (!quiet) {
onlyPackrat <- with(statusTbl,
!is.na(packrat.version) &
is.na(library.version) &
!currently.used
)
if (any(onlyPackrat)) {
prettyPrint(
searchPackages(packratPackages, statusTbl$package[onlyPackrat]),
header = c("The following packages are tracked by packrat, but are no longer ",
"available in the local library nor present in your code:"),
footer = c("You can call packrat::snapshot() to remove these packages from the lockfile, ",
"or if you intend to use these packages, use packrat::restore() to restore them ",
"to your private library.")
)
}
whichUntrackedPackages <- with(statusTbl,
currently.used &
is.na(packrat.version) &
is.na(library.version)
)
pkgNamesUntracked <- statusTbl$package[whichUntrackedPackages]
if (length(pkgNamesUntracked)) {
prettyPrintNames(
pkgNamesUntracked,
c("The following packages are referenced in your code, but are not present\n",
"in your library nor in packrat:\n"),
c("\nYou will need to install these packages manually, then use\n",
"packrat::snapshot() to record these packages in packrat.")
)
}
whichMissingFromPackrat <- with(statusTbl,
currently.used &
is.na(packrat.version) &
(!is.na(library.version))
)
missingFromPackrat <- statusTbl$package[whichMissingFromPackrat]
if (length(missingFromPackrat)) {
prettyPrintPair(
searchPackages(installedPkgRecords, missingFromPackrat),
searchPackages(packratPackages, missingFromPackrat),
"The following packages have been updated in your library, but have not been recorded in packrat:",
"Use packrat::snapshot() to record these packages in packrat.",
"library",
"packrat"
)
}
whichOutOfSync <- with(statusTbl,
currently.used &
!is.na(packrat.version) &
!is.na(library.version) &
packrat.version != library.version)
pkgNamesOutOfSync <- statusTbl$package[whichOutOfSync]
if (length(pkgNamesOutOfSync)) {
prettyPrintPair(
searchPackages(packratPackages, pkgNamesOutOfSync),
searchPackages(installedPkgRecords, pkgNamesOutOfSync),
"The following packages are out of sync between packrat and your current library:",
c("Use packrat::snapshot() to set packrat to use the current library, or use\n",
"packrat::restore() to reset the library to the last snapshot."),
"packrat",
"library"
)
}
whichDeletedButStillTracked <- with(statusTbl,
currently.used &
!is.na(packrat.version) &
is.na(library.version))
deletedButStillTracked <- statusTbl$package[whichDeletedButStillTracked]
if (length(deletedButStillTracked)) {
prettyPrintPair(
searchPackages(packratPackages, deletedButStillTracked),
searchPackages(installedPkgRecords, deletedButStillTracked),
"The following packages are used in your code, tracked by packrat, but no longer present in your library:",
c("Use packrat::restore() to restore these libraries.")
)
}
if (!(any(onlyPackrat) ||
length(missingFromPackrat) ||
length(pkgNamesUntracked) ||
length(pkgNamesOutOfSync) ||
length(deletedButStillTracked) ||
length(missingFromPackrat))) {
message("Up to date.")
}
}
invisible(statusTbl)
}
getPackageElement <- function(package, element) {
setNames(
unlist(lapply(package, "[[", element)),
unlist(lapply(package, "[[", "name"))
)
}
hasCachedAvailablePackages <- function() {
contrib.url <- contrib.url(getOption('repos'))
tempFiles <- list.files(tempdir())
repoNames <- paste("repos_", URLencode(contrib.url, TRUE), ".rds", sep = "")
all(repoNames %in% tempFiles)
} |
classifyPairs <-
function(classifier=NULL, data=NULL, pre=function(x, y=NULL) { list(features=x) }, combine=NULL, save.models=NA, use.weights = TRUE) {
if(!testClass(classifier, "Learner")) {
stop("Need classifier!")
}
assertClass(data, "llama.data")
hs = attr(data, "hasSplits")
if(is.null(hs) || hs != TRUE) {
stop("Need data with train/test split!")
}
totalBests = data.frame(target=factor(breakBestTies(data), levels=data$performance))
combns = combn(data$performance, 2)
predictions = rbind.fill(parallelMap(function(i) {
trf = pre(data$data[data$train[[i]],][data$features])
tsf = pre(data$data[data$test[[i]],][data$features], trf$meta)
ids = data$data[data$test[[i]],][data$ids]
trp = data$data[data$train[[i]],][data$performance]
trainpredictions = list()
pairpredictions = list()
for (j in 1:ncol(combns)) {
if(data$minimize) {
cmp = function(x, y) {
sapply(data$data[data$train[[i]],][x] < data$data[data$train[[i]],][y], function(z) { if(z) { x } else { y } })
}
} else {
cmp = function(x, y) {
sapply(data$data[data$train[[i]],][x] > data$data[data$train[[i]],][y], function(z) { if(z) { x } else { y } })
}
}
labels = data.frame(target=factor(cmp(combns[1,j], combns[2,j])))
levs = combns[,j]
if(hasLearnerProperties(classifier, "weights") && use.weights) {
trw = abs(data$data[data$train[[i]],combns[1,j]] - data$data[data$train[[i]],combns[2,j]])
task = makeClassifTask(id="classifyPairs", target="target", weights=trw, data=data.frame(labels, trf$features), fixup.data="quiet", check.data=FALSE)
} else {
task = makeClassifTask(id="classifyPairs", target="target", data=data.frame(labels, trf$features), fixup.data="quiet", check.data=FALSE)
}
if(length(unique(labels$target)) == 1) {
model = train(constantClassifier, task = task)
} else {
model = train(classifier, task = task)
}
if(!is.na(save.models)) {
saveRDS(list(model=model, train.data=task, test.data=tsf$features), file = paste(save.models, classifier$id, combns[1,j], combns[2,j], i, "rds", sep="."))
}
if(!is.null(combine)) {
preds = predict(model, newdata=trf$features)
trainpredictions[[j]] = if(preds$predict.type == "prob") {
getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
tmp = getPredictionResponse(preds)
rbind.fill(lapply(tmp, function(x) data.frame(t(setNames(as.numeric(x == levs), levs)))))
}
}
preds = predict(model, newdata=tsf$features)
pairpredictions[[j]] = if(preds$predict.type == "prob") {
getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
tmp = getPredictionResponse(preds)
rbind.fill(lapply(tmp, function(x) data.frame(t(setNames(as.numeric(x == levs), levs)))))
}
}
if(!is.null(combine)) {
trainBests = data.frame(target=factor(breakBestTies(data, i), levels=data$performance))
if(hasLearnerProperties(combine, "weights") && use.weights) {
trw = abs(apply(trp, 1, max) - apply(trp, 1, min))
task = makeClassifTask(id="classifyPairs", target="target", weights=trw, data=data.frame(trainBests, trf$features, trainpredictions), fixup.data="quiet", check.data=FALSE)
} else {
task = makeClassifTask(id="classifyPairs", target="target", data=data.frame(trainBests, trf$features, trainpredictions), fixup.data="quiet", check.data=FALSE)
}
if(length(unique(trainBests$target)) == 1) {
combinedmodel = train(constantClassifier, task = task)
} else {
combinedmodel = train(combine, task = task)
}
if(!is.na(save.models)) {
saveRDS(list(model=combinedmodel, train.data=task, test.data=data.frame(tsf$features, pairpredictions)), file = paste(save.models, combine$id, "combined", i, "rds", sep="."))
}
preds = predict(combinedmodel, newdata=data.frame(tsf$features, pairpredictions))
if(preds$predict.type == "prob") {
preds = getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
preds = getPredictionResponse(preds)
preds = rbind.fill(lapply(preds, function(x) data.frame(t(setNames(as.numeric(x == levels(preds)), levels(preds))))))
}
combinedpredictions = rbind.fill(lapply(1:nrow(preds), function(j) {
ss = preds[j,,drop=F]
ord = order(unlist(ss), decreasing = TRUE)
data.frame(ids[j,,drop=F], algorithm=factor(names(ss)[ord]), score=as.numeric(ss)[ord], iteration=i, row.names = NULL)
}))
} else {
tmp = cbind(tmp.names = unlist(lapply(pairpredictions, rownames)), rbind.fill(pairpredictions))
merged = ddply(tmp, "tmp.names", function(x) colSums(x[,-1], na.rm = TRUE))[-1]
combinedpredictions = rbind.fill(lapply(1:nrow(merged), function(j) {
ord = order(unlist(merged[j,]), decreasing = TRUE)
data.frame(ids[j,,drop=F], algorithm=factor(names(merged)[ord]), score=as.numeric(merged[j,])[ord], iteration=i, row.names = NULL)
}))
}
return(combinedpredictions)
}, 1:length(data$train), level = "llama.fold"))
fs = pre(data$data[data$features])
fp = data$data[data$performance]
fw = abs(apply(fp, 1, max) - apply(fp, 1, min))
models = lapply(1:ncol(combns), function(i) {
if(data$minimize) {
cmp = function(x, y) {
sapply(data$data[[x]] < data$data[[y]], function(z) { if(z) { x } else { y } })
}
} else {
cmp = function(x, y) {
sapply(data$data[[x]] > data$data[[y]], function(z) { if(z) { x } else { y } })
}
}
labels = data.frame(target=factor(cmp(combns[1,i], combns[2,i])))
if(hasLearnerProperties(classifier, "weights") && use.weights) {
task = makeClassifTask(id="classifyPairs", target="target", weights=abs(data$data[[combns[1,i]]] - data$data[[combns[2,i]]]), data=data.frame(labels, fs$features), fixup.data="quiet", check.data=FALSE)
} else {
task = makeClassifTask(id="classifyPairs", target="target", data=data.frame(labels, fs$features), fixup.data="quiet", check.data=FALSE)
}
if(length(unique(labels$target)) == 1) {
model = train(constantClassifier, task = task)
} else {
model = train(classifier, task = task)
}
return(model)
})
if(!is.null(combine)) {
trainpredictions = list()
for(i in 1:ncol(combns)) {
levs = combns[,i]
preds = predict(models[[i]], newdata=fs$features)
trainpredictions[[i]] = if(preds$predict.type == "prob") {
getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
tmp = getPredictionResponse(preds)
rbind.fill(lapply(tmp, function(x) data.frame(t(setNames(as.numeric(x == levs), levs)))))
}
}
if(hasLearnerProperties(combine, "weights") && use.weights) {
task = makeClassifTask(id="classifyPairs", target="target", weights=fw, data=data.frame(totalBests, fs$features, trainpredictions), fixup.data="quiet", check.data=FALSE)
} else {
task = makeClassifTask(id="classifyPairs", target="target", data=data.frame(totalBests, fs$features, trainpredictions), fixup.data="quiet", check.data=FALSE)
}
if(length(unique(totalBests$target)) == 1) {
combinedmodel = train(constantClassifier, task = task)
} else {
combinedmodel = train(combine, task = task)
}
}
predictor = function(x) {
tsf = pre(x[data$features], fs$meta)
if(length(intersect(colnames(x), data$ids)) > 0) {
ids = x[data$ids]
} else {
ids = data.frame(id = 1:nrow(x))
}
pairpredictions = list()
for(i in 1:ncol(combns)) {
levs = combns[,i]
preds = predict(models[[i]], newdata=tsf$features)
pairpredictions[[i]] = if(preds$predict.type == "prob") {
getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
tmp = getPredictionResponse(preds)
rbind.fill(lapply(tmp, function(x) data.frame(t(setNames(as.numeric(x == levs), levs)))))
}
}
if(!is.null(combine)) {
preds = predict(combinedmodel, newdata=data.frame(tsf$features, pairpredictions))
if(preds$predict.type == "prob") {
preds = getPredictionProbabilities(preds, preds$task.desc$class.levels)
} else {
preds = getPredictionResponse(preds)
preds = rbind.fill(lapply(preds, function(x) data.frame(t(setNames(as.numeric(x == levels(preds)), levels(preds))))))
}
combinedpredictions = rbind.fill(lapply(1:nrow(preds), function(j) {
ss = preds[j,,drop=F]
ord = order(unlist(ss), decreasing = TRUE)
data.frame(ids[j,,drop=F], algorithm=factor(names(ss)[ord]), score=as.numeric(ss)[ord], iteration=i, row.names = NULL)
}))
} else {
tmp = cbind(tmp.names = unlist(lapply(pairpredictions, rownames)), rbind.fill(pairpredictions))
merged = ddply(tmp, "tmp.names", function(x) colSums(x[,-1], na.rm = TRUE))[-1]
combinedpredictions = rbind.fill(lapply(1:nrow(merged), function(j) {
ord = order(unlist(merged[j,]), decreasing = TRUE)
data.frame(ids[j,,drop=F], algorithm=factor(names(merged)[ord]), score=as.numeric(merged[j,])[ord], iteration=i, row.names = NULL)
}))
}
return(combinedpredictions)
}
class(predictor) = "llama.model"
attr(predictor, "type") = "classifyPairs"
attr(predictor, "hasPredictions") = FALSE
attr(predictor, "addCosts") = TRUE
retval = list(predictions=predictions, models=models, predictor=predictor)
class(retval) = "llama.model"
attr(retval, "type") = "classifyPairs"
attr(retval, "hasPredictions") = TRUE
attr(retval, "addCosts") = TRUE
return(retval)
}
class(classifyPairs) = "llama.modelFunction" |
quos_list <- function(...) {
new_quosures(list(...))
} |
BaumWelch.mmglmlong1 <- function (object, control=bwcontrol(), PSOCKcluster=NULL,
tmpfile=NULL, ...){
tol <- control$tol
oldLL <- -Inf
m <- nrow(object$Pi)
if (is.null(object$longitude)) stop("No subjects specified.")
else {
tmp <- table(object$longitude)
if (min(tmp)!=max(tmp))
stop("All subjects must have the same number of obervations.")
N <- length(tmp)
subnms <- names(tmp)
}
n <- length(object$y)/N
Esteploop <- function(subnms, object, m, n){
subobject <- mmglm1(NULL, object$Pi, object$delta, object$glmfamily,
object$beta, NULL, sigma=object$sigma,
nonstat=object$nonstat, size=0, msg=FALSE)
sumcondu <- matrix(rep(0, m*n), nrow=n)
sumcondv <- matrix(0, nrow=m, ncol=m)
LL <- 0
condu <- NULL
for (subject in subnms){
tmp <- (object$longitude==subject)
subobject$y <- object$y[tmp]
if (object$glmfamily$family=="binomial")
subobject$size <- object$size[tmp]
subobject$Xdesign <- object$Xdesign[tmp,]
cond <- Estep.mmglm1(subobject, fortran=FALSE)
LL <- LL + cond$LL
sumcondu <- sumcondu + cond$u
sumcondv <- sumcondv + apply(cond$v, MARGIN=c(2,3), FUN=sum)
condu <- rbind(condu, cond$u)
}
return(list(LL=LL, sumcondu=sumcondu, sumcondv=sumcondv, condu=condu))
}
if (!is.null(PSOCKcluster)){
numnodes <- length(PSOCKcluster)
pernode <- trunc(N/numnodes)
tmp <- subnms
subnms <- list()
for (i in 1:(numnodes-1))
subnms[[i]] <- tmp[(1+(i-1)*pernode):(i*pernode)]
subnms[[numnodes]] <- tmp[(1+(numnodes-1)*pernode):N]
parallel::clusterExport(PSOCKcluster, c("mmglm1", "Estep.mmglm1", "dmmglm",
"forwardback.dthmm"))
}
for (iter in 1:control$maxiter) {
if (!is.null(PSOCKcluster)){
tmp <- parallel::clusterApply(PSOCKcluster, subnms, Esteploop,
object=object, m=m, n=n)
LL <- tmp[[1]]$LL
sumcondu <- tmp[[1]]$sumcondu
sumcondv <- tmp[[1]]$sumcondv
condu <- tmp[[1]]$condu
for (i in 2:length(PSOCKcluster)){
LL <- LL + tmp[[i]]$LL
sumcondu <- sumcondu + tmp[[i]]$sumcondu
sumcondv <- sumcondv + tmp[[i]]$sumcondv
condu <- rbind(condu, tmp[[i]]$condu)
}
} else {
tmp <- Esteploop(subnms, object, m, n)
LL <- tmp$LL
sumcondu <- tmp$sumcondu
sumcondv <- tmp$sumcondv
condu <- tmp$condu
}
diff <- LL - oldLL
if (control$prt) {
cat("iter =", iter, "\n")
cat("LL =", formatC(LL, digits=log10(1/tol)+2,
format="f"), "\n")
cat("diff =", diff, "\n\n")
}
if (diff < 0 & control$posdiff)
stop("Worse log-likelihood on last iteration")
if (eval(control$converge)) break
Pi <- diag(1/apply(sumcondv, MARGIN=1, FUN=sum)) %*% sumcondv
delta <- sumcondu[1, ]/N
tmp <- Mstep.mmglm1(object, condu)
oldLL <- LL
object$delta <- delta
object$Pi <- Pi
object$beta <- tmp$beta
object$sigma <- tmp$sigma
if (iter %% 10){
if (!is.null(tmpfile)) save(object, file=tmpfile)
}
}
rownames(object$beta) <- colnames(object$Xdesign)
colnames(object$beta) <- paste("State", 1:length(object$delta))
object$LL <- LL
object$iter <- iter
object$diff <- diff
return(object)
} |
setMethod("cmine", signature=c(object="PSTf"),
def=function(object, l, pmin, pmax, state, as.tree=FALSE, delete=TRUE) {
if (missing(l)) {
l <- 1:length(object)
}
res <- list()
for (i in l) {
if (!missing(pmin)) {
tmp <- lapply(object[[i]], node.mine, pmin=pmin, state=state)
} else if (!missing(pmax)) {
tmp <- lapply(object[[i]], node.mine, pmax=pmax, state=state)
}
tmp <- tmp[!unlist(lapply(tmp, is.null))]
res <- c(res, tmp)
}
if (length(res)> 0) {
p <- unlist(lapply(res, function(x) { rowSums([email protected][,state, drop=FALSE]) }))
if (!missing(pmin)) {
res <- res[order(p)]
} else if (!missing(pmax)) {
res <- res[order(p, decreasing=TRUE)]
}
}
if (as.tree) {
res <- prune(object, keep=names(res), delete=delete)
} else {
if (has.cdata(object)) {
cdata <- object@cdata
A <- alphabet(cdata)
cpal <- cpal(cdata)
stlab <- stlab(cdata)
res <- new("cprobd.list", res, alphabet=A, cpal=cpal, labels=stlab)
} else {
res <- new("cprobd.list", res, alphabet=object@alphabet, cpal=object@cpal,
labels=object@labels)
}
}
return(res)
}
) |
convert_selection <- function(envir = NULL) {
if (missing(envir)) {
envir <- ask_for_environment()
}
orig_code <- getSourceEditorContext()$selection[[1]]$text
new_code <- convert_assignments(parse(text = orig_code))
eval(parse(text = new_code) , envir = envir)
} |
lint <- function(...) .Deprecated("geojsonlint::geojson_hint") |
logLik.metaplus <- function(object, ...)
{
logLik(object$fittedmodel)
}
BIC.metaplus <-
function (object, ...)
{
if (!is.element("metaplus", class(object)))
stop("Argument 'object' must be an object of class \"metaplus\".")
BIC(object$fittedmodel)
}
AIC.metaplus <-
function(object,...) {
if (!inherits(object, "metaplus"))
stop("Use only with 'metaplus' objects.\n")
AIC(object$fittedmodel)
} |
subgroupset <- function(data, nsets = 5, nintersects = 40, sets = NULL, keep.order = F, set.metadata = NULL, intersections = NULL,
matrix.color = "gray23", main.bar.color = "gray23", mainbar.y.label = "Intersection Size", mainbar.y.max = NULL,
sets.bar.color = "gray23", sets.x.label = "Set Size", point.size = 2.2, line.size = 0.7,
mb.ratio = c(0.70,0.30), expression = NULL, att.pos = NULL, att.color = main.bar.color, order.by = c("freq", "degree"),
decreasing = c(T, F), show.numbers = "yes", number.angles = 0, group.by = "degree",cutoff = NULL,
queries = NULL, query.legend = "none", shade.color = "gray88", shade.alpha = 0.25, matrix.dot.alpha =0.5,
empty.intersections = NULL, color.pal = 1, boxplot.summary = NULL,
effects.summary = NULL, outcome.type = c("continuous", "binary", "survival"),
attribute.plots = NULL, scale.intersections = "identity",
scale.sets = "identity", text.scale = 1, set_size.angles = 0,
treatment.var = NULL, min.n = 20, icon = c("dots", "pm", "pm.circle", "value"),
fill.trt = TRUE, transpose = FALSE){
if (transpose){
subgroupset_transposed(data, nsets, nintersects, sets, keep.order, set.metadata, intersections,
matrix.color, main.bar.color, mainbar.y.label, mainbar.y.max,
sets.bar.color, sets.x.label, point.size, line.size,
mb.ratio, expression, att.pos, att.color, order.by,
decreasing, show.numbers, number.angles, group.by,cutoff,
queries, query.legend, shade.color, shade.alpha, matrix.dot.alpha,
empty.intersections, color.pal, boxplot.summary,
effects.summary, outcome.type,
attribute.plots, scale.intersections,
scale.sets, text.scale, set_size.angles,
treatment.var, min.n, icon, fill.trt)
} else {
if(is.null(treatment.var)) stop("Please provide a treatment variable in treatment.var")
outcome.type = match.arg(outcome.type)
icon = match.arg(icon)
startend <- FindStartEnd(data)
first.col <- startend[1]
last.col <- startend[2]
total_size = nrow(data)
if(color.pal == 1){
palette <- c("
"
} else {
palette <- c("
"
}
if(is.null(intersections) == F){
Set_names <- unique((unlist(intersections)))
Sets_to_remove <- Remove(data, first.col, last.col, Set_names)
New_data <- Wanted(data, Sets_to_remove)
Num_of_set <- Number_of_sets(Set_names)
if(keep.order == F){
Set_names <- order_sets(New_data, Set_names)
}
All_Freqs <- specific_intersections(data, first.col, last.col, intersections, order.by, group.by, decreasing,
cutoff, main.bar.color, Set_names)
} else if(is.null(intersections) == T){
Set_names <- sets
if(is.null(Set_names) == T || length(Set_names) == 0 ){
Set_names <- FindMostFreq(data, first.col, last.col, nsets)
}
Sets_to_remove <- Remove(data, first.col, last.col, Set_names)
New_data <- Wanted(data, Sets_to_remove)
Num_of_set <- Number_of_sets(Set_names)
if(keep.order == F){
Set_names <- order_sets(New_data, Set_names)
}
All_Freqs <- Counter_all(New_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,
order.by, group.by, cutoff, empty.intersections, decreasing)
Trt_data = New_data[which(New_data[[treatment.var]]==unique(New_data[[treatment.var]])[2]), ]
Trt_Freqs <- Counter_all(Trt_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,
order.by, group.by, cutoff, empty.intersections, decreasing)
Ctl_data = New_data[which(New_data[[treatment.var]]==unique(New_data[[treatment.var]])[1]), ]
Ctl_Freqs <- Counter_all(Ctl_data, Num_of_set, first.col, Set_names, nintersects, main.bar.color,
order.by, group.by, cutoff, empty.intersections, decreasing)
}
Matrix_setup <- Create_matrix(All_Freqs)
labels <- Make_labels(Matrix_setup)
att.x <- c(); att.y <- c();
if(is.null(attribute.plots) == F){
for(i in seq_along(attribute.plots$plots)){
if(length(attribute.plots$plots[[i]]$x) != 0){
att.x[i] <- attribute.plots$plots[[i]]$x
}
else if(length(attribute.plots$plots[[i]]$x) == 0){
att.x[i] <- NA
}
if(length(attribute.plots$plots[[i]]$y) != 0){
att.y[i] <- attribute.plots$plots[[i]]$y
}
else if(length(attribute.plots$plots[[i]]$y) == 0){
att.y[i] <- NA
}
}
}
BoxPlots <- NULL
if(is.null(boxplot.summary) == F){
warning("boxplot.summary is not available for subgroUpSet")
}
EffectPlots <- list()
if(is.null(effects.summary) == F){
EffectData <- IntersectionEffectPlot2(All_Freqs, New_data, first.col, Set_names, effects.summary, outcome.type)
EffectPlots <- list()
EffectPlots[[1]] <- EffectPlotsPlot(EffectData, effects.summary, att.color, outcome.type)
}
customAttDat <- NULL
customQBar <- NULL
Intersection <- NULL
Element <- NULL
legend <- NULL
EBar_data <- NULL
if(is.null(queries) == F){
warning("queries is not available for subgroUpSet")
Matrix_col <- NULL
} else{
Matrix_col <- NULL
}
Matrix_layout <- Create_layout(Matrix_setup, matrix.color, Matrix_col, matrix.dot.alpha)
Set_sizes <- FindSetFreqs(New_data, first.col, Num_of_set, Set_names, keep.order)
Bar_Q <- NULL
QInter_att_data <- NULL
QElem_att_data <- NULL
AllQueryData <- NULL
ShadingData <- NULL
set.metadata.plots <- NULL
if(is.null(set.metadata) == F){
warning("set.metadata is not available for subgroUpSet")
}
if(is.null(ShadingData) == TRUE){
ShadingData <- MakeShading(Matrix_layout, shade.color)
}
All_Freqs_Trt = rbind(data.frame(Trt_Freqs, trt = unique(New_data[[treatment.var]])[2]),
data.frame(Ctl_Freqs, trt = unique(New_data[[treatment.var]])[1]))
Main_bar <- suppressMessages(Make_main_bar(All_Freqs_Trt, Bar_Q, show.numbers, mb.ratio, customQBar, number.angles, EBar_data, mainbar.y.label,
mainbar.y.max, scale.intersections, text.scale, attribute.plots, treatment.var, fill.trt))
Matrix <- Make_matrix_plot2(Matrix_layout, Set_sizes, All_Freqs, point.size, line.size,
text.scale, labels, ShadingData, shade.alpha, icon)
Sizes <- Make_size_plot2(Set_sizes, sets.bar.color, mb.ratio, sets.x.label, scale.sets, text.scale, set_size.angles,
total_size)
Make_base_plot(Main_bar, Matrix, Sizes, labels, mb.ratio, att.x, att.y, New_data,
expression, att.pos, first.col, att.color, AllQueryData, attribute.plots,
legend, query.legend, EffectPlots, Set_names, set.metadata, set.metadata.plots)
}
} |
regmest <- function(x, y, alpha, nlambda = 50, lambda, lambda_min_ratio, scale, starting_points,
penalty_loadings, intercept = TRUE, cc = 4.7,
eps = 1e-6, explore_solutions = 10, explore_tol = 0.1,
max_solutions = 10, comparison_tol = sqrt(eps), sparse = FALSE,
ncores = 1, standardize = TRUE,
algorithm_opts = mm_algorithm_options(), add_zero_based = TRUE,
mscale_bdp = 0.25, mscale_opts = mscale_algorithm_options()) {
call <- match.call(expand.dots = TRUE)
args <- as.list(call[-1L])
args$standardize <- isTRUE(standardize)
args <- do.call(.regmest_args, args, envir = parent.frame())
fits <- mapply(
args$alpha, args$lambda,
SIMPLIFY = FALSE, USE.NAMES = FALSE,
FUN = function (alpha, lambda) {
fit <- .regmest_internal(args$std_data$x, args$std_data$y,
alpha = alpha,
lambda = lambda,
scale = args$scale,
penalty_loadings = args$penalty_loadings,
mest_opts = args$mest_opts,
optional_args = args$optional_args)
fit$estimates <- lapply(fit$estimates, function (ests) {
args$restore_coef_length(args$std_data$unstandardize_coefs(ests[[1L]]))
})
fit$estimates <- .metrics_attrib(fit$estimates, fit$metrics)
fit$lambda <- unlist(vapply(fit$estimates, FUN = `[[`, FUN.VALUE = numeric(1),
'lambda'), use.names = FALSE, recursive = FALSE)
fit$alpha <- alpha
fit
})
structure(list(
call = call,
scale = args$scale,
lambda = lapply(fits, `[[`, 'lambda'),
estimates = unlist(lapply(fits, `[[`, 'estimates'), recursive = FALSE, use.names = FALSE),
alpha = vapply(fits, FUN.VALUE = numeric(1L), FUN = `[[`, 'alpha', USE.NAMES = FALSE)),
class = c('mest', 'pense_fit'))
}
regmest_cv <- function(x, y, standardize = TRUE, lambda, cv_k, cv_repl = 1,
cv_metric = c('tau_size', 'mape', 'rmspe', 'auroc'),
fit_all = TRUE, cl = NULL, ...) {
call <- match.call(expand.dots = TRUE)
args <- do.call(.regmest_args, as.list(call[-1L]), envir = parent.frame())
fit_ses <- if (is.character(fit_all)) {
unique(vapply(fit_all, FUN = .parse_se_string, FUN.VALUE = numeric(1L), only_fact = TRUE,
USE.NAMES = FALSE))
} else if (isFALSE(fit_all)) {
.parse_se_string('min', only_fact = TRUE)
} else {
TRUE
}
if (length(fit_ses) < 1L) {
fit_ses <- TRUE
}
cv_k <- .as(cv_k[[1L]], 'integer')
cv_repl <- .as(cv_repl[[1L]], 'integer')
if (cv_k < 2L) {
abort("`cv_k` must be greater than 1.")
}
if (cv_repl < 1L) {
abort("`cv_repl` must be greater than 0.")
}
if (identical(cv_repl, 1L) && any(fit_ses > 0)) {
warn("To use `fit_all = \"se\"`, `cv_repl` must be 2 or greater.")
fit_ses <- 0
}
if (!is.null(cl)) {
if (args$mest_opts$num_threads > 1L) {
abort("`cl` can only be used if `ncores = 1`.")
}
if (!is(cl, 'cluster')) {
abort("`cl` must be a valid `parallel` cluster.")
}
}
cv_metric <- if (is.null(call$cv_metric) && args$binary_response) {
cv_measure_str <- 'auroc'
.cv_auroc
} else if (is.character(cv_metric)) {
cv_measure_str <- match.arg(cv_metric)
switch(cv_measure_str, mape = .cv_mape, rmspe = .cv_rmspe, tau_size = tau_size,
auroc = if (args$binary_response) {
.cv_auroc
} else {
abort("cv_metric=\"auroc\" is only valid for binary responses.")
})
} else {
cv_measure_str <- 'user_fun'
match.fun(cv_metric)
}
if (is.null(formals(cv_metric))) {
abort("Function `cv_metric` must accept at least 1 argument.")
}
fit_seed <- sample.int(.Machine$integer.max, 1L)
cv_curves <- mapply(
args$alpha, args$lambda,
SIMPLIFY = FALSE, USE.NAMES = FALSE,
FUN = function (alpha, lambda) {
cv_fun <- function (train_data, test_ind) {
cv_fit <- .regmest_internal(train_data$x, train_data$y,
alpha = alpha,
lambda = lambda,
scale = args$scale,
penalty_loadings = args$penalty_loadings,
mest_opts = args$mest_opts,
optional_args = args$optional_args)
lapply(cv_fit$estimates, `[[`, 1L)
}
set.seed(fit_seed)
cv_perf <- .run_replicated_cv(args$std_data,
cv_k = cv_k,
cv_repl = cv_repl,
metric = cv_metric,
cv_est_fun = cv_fun,
par_cluster = cl)
data.frame(lambda = lambda, alpha = alpha,
cvavg = rowMeans(cv_perf),
cvse = if (cv_repl > 1L) { apply(cv_perf, 1, sd) } else { 0 })
})
cv_curves <- do.call(rbind, cv_curves)
if (isTRUE(fit_ses)) {
fit_lambda <- args$lambda
} else {
fit_lambda <- lapply(args$alpha, function (alpha) {
rows <- which((cv_curves$alpha - alpha)^2 < .Machine$double.eps)
lambda_inds <- vapply(fit_ses, FUN.VALUE = numeric(1L), FUN = function (se_fact) {
which(.cv_se_selection(cv_curves$cvavg[rows], cv_curves$cvse[rows], se_fact) == 'se_fact')
})
unique(cv_curves$lambda[rows[lambda_inds]])
})
}
fits <- mapply(
args$alpha, fit_lambda,
SIMPLIFY = FALSE, USE.NAMES = FALSE,
FUN = function (alpha, lambda) {
fit <- .regmest_internal(args$std_data$x, args$std_data$y,
alpha = alpha,
lambda = lambda,
scale = args$scale,
penalty_loadings = args$penalty_loadings,
mest_opts = args$mest_opts,
optional_args = args$optional_args)
fit$estimates <- lapply(fit$estimates, function (ests) {
args$restore_coef_length(args$std_data$unstandardize_coefs(ests[[1L]]))
})
fit$estimates <- .metrics_attrib(fit$estimates, fit$metrics)
fit$lambda <- unlist(vapply(fit$estimates, FUN = `[[`, FUN.VALUE = numeric(1),
'lambda'), use.names = FALSE, recursive = FALSE)
fit$alpha <- alpha
fit
})
structure(list(
call = call,
scale = args$scale,
cvres = cv_curves,
cv_measure = cv_measure_str,
lambda = lapply(fits, `[[`, 'lambda'),
estimates = unlist(lapply(fits, `[[`, 'estimates'), recursive = FALSE, use.names = FALSE),
alpha = vapply(fits, FUN.VALUE = numeric(1L), FUN = `[[`, 'alpha', USE.NAMES = FALSE)),
class = c('mest', 'pense_cvfit'))
}
adamest_cv <- function (x, y, alpha, alpha_preliminary = 0, exponent = 1, ...) {
call <- match.call(expand.dots = TRUE)
if (!is.null(call$penalty_loadings)) {
abort(paste("Argument `penalty_loadings` not valid for `adamest_cv`",
"Penalty loadings are determined internally."))
}
exponent <- .as(exponent[[1L]], 'numeric')
prelim_call <- call
prelim_call[[1L]] <- quote(pense::regmest_cv)
prelim_call$alpha <- .as(alpha_preliminary[[1L]], 'numeric')
prelim_call$alpha_preliminary <- NULL
prelim_call$exponent <- NULL
prelim <- eval.parent(prelim_call)
prelim_coef <- coef(prelim, sparse = FALSE, concat = FALSE)
pen_loadings <- abs(prelim_coef$beta)^(-exponent)
adamstep <- regmest_cv(x, y, alpha = alpha, penalty_loadings = pen_loadings, ...)
adamstep$call <- call
adamstep$exponent <- exponent
adamstep$preliminary <- prelim
adamstep$penalty_loadings <- pen_loadings
return(adamstep)
}
.regmest_max_lambda <- function (x, y, alpha, scale, mest_options, penalty_loadings = NULL) {
optional_args <- list()
if (!is.null(penalty_loadings)) {
optional_args$pen_loadings <- penalty_loadings
}
.Call(C_mesten_max_lambda, x, y, scale, mest_options, optional_args) / max(0.01, alpha)
}
.regmest_lambda_grid <- function (x, y, alpha, scale, nlambda, lambda_min_ratio, mest_options,
penalty_loadings) {
alpha <- max(0.01, alpha)
x_dim <- dim(x)
if (is.null(lambda_min_ratio)) {
lambda_min_ratio <- alpha * if (x_dim[[1L]] > x_dim[[2L]]) { 1e-3 } else { 1e-2 }
}
max_lambda <- .regmest_max_lambda(x, y, alpha, scale, mest_options, penalty_loadings)
rev(exp(seq(log(lambda_min_ratio * max_lambda), log(max_lambda), length.out = nlambda)))
}
.regmest_internal <- function(x, y, alpha, scale, lambda, penalty_loadings = NULL, mest_opts,
optional_args) {
penalties <- lapply(lambda, function (l) { list(lambda = l, alpha = alpha) })
if (!is.null(penalty_loadings)) {
optional_args$pen_loadings <- penalty_loadings
}
.Call(C_mesten_regression, x, y, scale, penalties, mest_opts, optional_args)
}
.regmest_args <- function (x, y, alpha, nlambda = 50, lambda, lambda_min_ratio, scale,
starting_points, penalty_loadings, intercept = TRUE, cc = 4.7,
eps = 1e-6, explore_solutions = 10, explore_tol = 0.1,
max_solutions = 10, comparison_tol = sqrt(eps), sparse = FALSE,
ncores = 1, standardize = TRUE, algorithm_opts = mm_algorithm_options(),
add_zero_based = TRUE, mscale_bdp = 0.25,
mscale_opts = mscale_algorithm_options(), ...) {
args_call <- match.call(expand.dots = FALSE)
optional_args <- list()
response <- .validate_response(y)
y <- response$values
x_dim <- dim(x)
if (length(y) != x_dim[[1L]]) {
abort("Number of observations in `x` and `y` does not match.")
} else if (x_dim[[2L]] <= 1L) {
abort("`x` must be a matrix with at least 2 columns.")
}
alpha <- .as(alpha, 'numeric')
if (any(alpha < 0 | alpha > 1)) {
abort("`alpha` is outside 0 and 1.")
} else if (any(alpha < sqrt(.Machine$double.eps))) {
alpha[which(alpha < sqrt(.Machine$double.eps))] <- 0
if (any(alpha > 0)) {
abort("`alpha=0` cannot be mixed with other `alpha` values.")
}
}
scale <- .as(scale[[1L]], 'numeric')
if (!isTRUE(scale > .Machine$double.eps)) {
abort("`scale` must be positive.")
}
mscale_opts <- .full_mscale_algo_options(bdp = mscale_bdp, mscale_opts = mscale_opts)
mest_opts <- list(algo_opts = algorithm_opts,
cc = .as(cc[[1L]], 'numeric'),
strategy_0 = isTRUE(add_zero_based),
algorithm = .regmest_algorithm_id(algorithm_opts),
intercept = !isFALSE(intercept),
sparse = isTRUE(sparse),
eps = .as(eps[[1L]], 'numeric'),
max_it = mscale_opts$max_it,
comparison_tol = .as(comparison_tol[[1L]], 'numeric'),
explore_tol = .as(explore_tol[[1L]], 'numeric'),
nr_tracks = .as(explore_solutions[[1L]], 'integer'),
max_optima = .as(max_solutions[[1L]], 'integer'),
num_threads = max(1L, .as(ncores[[1L]], 'integer')))
if (mest_opts$cc < .Machine$double.eps) {
abort("`cc` must be positive.")
}
if (mest_opts$explore_tol < mest_opts$eps) {
abort("`explore_tol` must not be less than `eps`")
}
if (mest_opts$comparison_tol < mest_opts$eps) {
abort("`comparison_tol` must not be less than `eps`")
}
if (mest_opts$algorithm == 1L) {
mest_opts$algo_opts$en_options <- .select_en_algorithm(mest_opts$algo_opts$en_options,
alpha,
mest_opts$sparse, eps)
mest_opts$sparse <- mest_opts$algo_opts$en_options$sparse
}
if (mest_opts$num_threads > 1L && !isTRUE(.k_multithreading_support)) {
warn("Multithreading not supported. Using only 1 core.")
mest_opts$num_threads <- 1L
}
standardize <- if (is.character(standardize)) {
if (pmatch(standardize[[1L]], 'cv_only', nomatch = 0L) == 1L) {
standardize <- 'cv_only'
} else {
abort("`standardize` must be either TRUE/FALSE or \"cv_only\".")
}
} else {
isTRUE(standardize)
}
if (!missing(penalty_loadings) && !is.null(penalty_loadings)) {
checked_pls <- .prepare_penalty_loadings(penalty_loadings, x, alpha, sparse = mest_opts$sparse)
penalty_loadings <- checked_pls$loadings
restore_coef_length <- checked_pls$restore_fun
x <- checked_pls$trimmed_x
} else {
restore_coef_length <- function (coef) coef
penalty_loadings <- NULL
}
if (ncol(x) == 0L) {
warn("All values in `penalty_loadings` are infinite. Only computing the intercept.")
mest_opts$intercept <- TRUE
std_data <- .standardize_data(matrix(runif(x_dim[[1L]]), ncol = 1L), y, intercept = TRUE,
sparse = mest_opts$sparse,
standardize = standardize, robust = TRUE,
mscale_opts = mscale_opts,
bdp = mscale_opts$delta, scale_cc = mscale_opts$cc)
mest_opts$strategy_0 <- TRUE
lambda <- lapply(args$alpha, FUN = .regmest_lambda_grid,
x = std_data$x, y = std_data$y, scale = scale,
nlambda = 1, lambda_min_ratio = 1,
mest_options = mest_opts, penalty_loadings1 = NULL)
return(list(std_data = std_data,
alpha = args$alpha,
lambda = lambda,
scale = scale,
penalty_loadings = NULL,
mest_opts = mest_opts,
optional_args = optional_args,
restore_coef_length = restore_coef_length))
}
std_data <- .standardize_data(x, y, intercept = mest_opts$intercept,
standardize = standardize,
robust = TRUE,
sparse = mest_opts$sparse,
mscale_opts = mscale_opts,
bdp = mscale_opts$delta,
scale_cc = mscale_opts$cc)
penalty_loadings <- penalty_loadings / std_data$scale_x
if (length(penalty_loadings) == 0L) {
penalty_loadings <- NULL
}
lambda <- if (missing(lambda) || is.null(lambda)) {
if (missing(lambda_min_ratio)) {
lambda_min_ratio <- NULL
}
lapply(alpha, FUN = .regmest_lambda_grid,
x = std_data$x, y = std_data$y, scale = scale,
nlambda = nlambda,
lambda_min_ratio = lambda_min_ratio,
mest_options = mest_opts,
penalty_loadings = penalty_loadings)
} else if (!is.list(lambda)) {
rep.int(list(sort(.as(lambda, 'numeric'), decreasing = TRUE)), length(alpha))
} else if (identical(length(lambda), length(alpha))) {
lapply(lambda, function (l) {
if (any(l < .Machine$double.eps)) {
abort("All values in `lambda` must be positive.")
}
sort(.as(l, 'numeric'), decreasing = TRUE)
})
} else {
abort("`lambda` must either be a numeric vector or a list the same length as `alpha`.")
}
if (!missing(starting_points)) {
if (is(starting_points, 'pense_fit') || is(starting_points, 'pense_cvfit')) {
starting_points <- as_starting_point(starting_points)
} else if (is(starting_points, 'starting_point')) {
starting_points <- structure(list(starting_points), class = 'starting_points')
} else if (!is(starting_points, 'starting_points')) {
abort(paste("`starting_points` must be a list of starting points created by",
"`starting_point()`, `enpy_initial_estimates()`, or a combination thereof."))
}
optional_args$shared_starts <- lapply(.sparsify_other_starts(starting_points, mest_opts$sparse),
std_data$standardize_coefs)
}
return(list(std_data = std_data,
binary_response = response$binary,
alpha = alpha,
scale = scale,
lambda = lambda,
penalty_loadings = penalty_loadings,
mest_opts = mest_opts,
optional_args = optional_args,
restore_coef_length = restore_coef_length))
} |
pluck_body <- function(x) {
assert_request(x)
if (is_body_empty(x)) return(NULL)
if (!is.null(x$fields)) {
form_file_comp <- vapply(x$fields, inherits, logical(1), "form_file")
if (any(form_file_comp)) {
return(x$fields[form_file_comp])
} else {
return(x$fields)
}
} else if (!is.null(x$options$postfields) && is.raw(x$options$postfields)) {
return(rawToChar(x$options$postfields))
} else if (!is.null(x$options$postfieldsize_large)) {
return(paste0("upload, file size: ", x$options$postfieldsize_large))
} else {
stop("couldn't fetch request body; file an issue at \n",
" https://github.com/ropensci/webmockr/issues/",
call. = FALSE)
}
}
assert_request <- function(x) {
request_slots <- c("url", "method", "options", "headers")
if (!is.list(x) || !all(request_slots %in% names(x))) {
stop(deparse(substitute(x)), " is not a valid request ", call. = FALSE)
}
}
is_body_empty <- function(x) {
is.null(x$fields) &&
(is.null(x$options$postfieldsize) || x$options$postfieldsize == 0L)
} |
revdepcheck::revdep_check()
revdepcheck::revdep_check(num_workers = 4) |
bbl <- function(formula, data, weights, xlevels = NULL, verbose = 1,
method = 'pseudo', novarOk = FALSE, testNull = TRUE,
prior.count = 1, ...){
cl <- match.call()
if(missing(data))
stop('data argument required')
if(!missing(weights)){
mfrq <- which(names(cl) == 'weights')
if(length(mfrq) != 1) stop('Error in weights argument')
frq <- as.character(cl[mfrq])
if(frq %in% colnames(data)){
tmp <- data[, frq]
data <- data[,-which(colnames(data) == frq)]
weights <- tmp
}
if(length(weights)!=NROW(data))
stop('Length of weights does not match data')
zero <- weights==0
data <- data[!zero,]
weights <- weights[!zero]
} else{
weights <- NULL
}
term <- stats::terms(formula, data=data)
idy <- attributes(term)$response
vars <- as.character(attributes(term)$variables)
colnames(data) <- fixnames(colnames(data))
resp <- vars[[idy+1]]
vars <- vars[-1]
vars <- vars[!vars==resp]
if(is.null(xlevels))
xlevels <- getxlevels(vars, data=data)
formula <- formula(term)
if(!novarOk){
for(i in seq_along(xlevels)){
if(length(xlevels[[i]])==1)
stop(paste0('Predictor ',names(xlevels)[i],' has one level'))
}
}
m <- length(xlevels)
idy <- attributes(term)$response
resp <- as.character(attributes(term)$variables[[idy+1]])
y <- data[,resp]
x <- data[,names(xlevels)]
Ly <- length(levels(factor(y)))
if(Ly==1) warning('Only one response group in data')
label <- attr(term,'term.labels')
ilabel <- label[vapply(label,FUN=function(x){grepl(':',x)}, logical(1))]
ijlabel <- strsplit(ilabel,split=':')
qJ <- matrix(FALSE, nrow=m, ncol=m)
rownames(qJ) <- colnames(qJ) <- names(xlevels)
for(k in seq_along(ijlabel))
qJ[ijlabel[[k]][1],ijlabel[[k]][2]] <- TRUE
qJ <- qJ | t(qJ)
naive <- sum(qJ)==0
groups <- levels(factor(y))
bb <- list()
class(bb) <- 'bbl'
if(naive & method=='mf'){
b <- naivemf(xlevels=xlevels, y=y, weights=weights, data=data,
prior.count=prior.count)
lz <- rep(0, Ly)
for(iy in seq_len(Ly)){
for(i in seq_len(m))
lz[[iy]] <- lz[[iy]] + log(1+sum(exp(b$h[[iy]][[i]])))
lz[[iy]] <- lz[[iy]]/m
}
bb$coefficients <- list(h=b$h, h0=b$h0)
bb$lz <- lz
} else{
b <- bbl.fit(x=x, y=y, qJ=qJ, weights=weights, xlevels=xlevels,
verbose=verbose-1, method=method, prior.count=prior.count,
...)
if(testNull)
b0 <- bbl.fit(x=x, y=rep('pooled',length(y)), qJ=qJ, weights=weights,
xlevels=xlevels, verbose=verbose-1, method=method,
prior.count=prior.count, ...)
else b0 <- NULL
bb$coefficients <- list(h=b$h, J=b$J, h0=b0$h[[1]], J0=b0$J[[1]])
bb$lz <- b$lz
}
bb$xlevels <- xlevels
bb$terms <- term
bb$groups <- levels(factor(y))
bb$groupname <- resp
bb$qJ <- qJ
bb$model <- data[,c(resp, names(xlevels))]
bb$lkh <- b$lkh
bb$weights <- weights
bb$call <- cl
if(naive) bb$method <- 'mf'
else bb$method <- method
df <- 0
for(i in seq_len(m)){
ni <- length(xlevels[[i]])-1
df <- df + ni
if(naive) next()
if(i==m) next()
for(j in seq(i+1,m))
if(qJ[i,j]) df <- df + ni*(length(xlevels[[j]])-1)
}
bb$df <- df
return(bb)
}
bbl.fit <- function(x, y, qJ=NULL, weights=NULL, xlevels=NULL, verbose=1,
method='pseudo', prior.count=1, ...){
le <- levels(factor(y))
Ly <- length(le)
h <- J <- list()
m <- NCOL(x)
vars <- colnames(x)
if(is.null(xlevels))
xlevels <- getxlevels(colnames(x), data=x)
if(is.null(qJ)){
qJ <- matrix(TRUE, nrow=m, ncol=m)
diag(qJ) <- FALSE
rownames(qJ) <- colnames(qJ) <- vars
}
if(!all.equal(dim(qJ), c(m,m)))
stop('Incorrect dimension of qJ')
lkh <- 0
lz <- rep(0, Ly)
for(iy in seq_len(Ly)){
ny <- sum(y==le[iy])
if(ny==0)
stop(paste0('No instance of "',le[iy],'" in training data'))
da <- x[y==le[iy],]
if(!is.null(weights))
frq <- weights[y==le[iy]]
else frq <- rep(1, sum(y==le[iy]))
if(verbose > 1) cat(' Inference for y = ',le[iy],':\n',sep='')
xda <- matrix(0, nrow=NROW(da), ncol=NCOL(da))
L <- NULL
for(i in seq_len(NCOL(da))){
xda[,i] <- match(da[,i],xlevels[[i]])-1
L[i] <- length(xlevels[[i]])
}
b <- mlestimate(xi=xda, weights=frq, qJ=qJ, verbose=verbose-1,
method=method, L=L, prior.count=prior.count, ...)
names(b$h) <- names(b$J) <- names(xlevels)
for(i in seq_len(m)){
ni <- xlevels[[i]][-1][seq_along(b$h[[i]])]
names(b$h[[i]]) <- ni
names(b$J[[i]]) <- names(xlevels)
for(j in seq_len(m)){
if(is.null(b$J[[i]][[j]])) next()
if(NROW(b$J[[i]][[j]])>0)
rownames(b$J[[i]][[j]]) <- ni[seq_len(NROW(b$J[[i]][[j]]))]
if(NCOL(b$J[[i]][[j]])>0)
colnames(b$J[[i]][[j]]) <-
xlevels[[j]][-1][seq_len(NCOL(b$J[[i]][[j]]))]
}
}
h[[iy]] <- b$h
J[[iy]] <- b$J
lkh <- lkh + b$lkh*ny
lz[iy] <- b$lz
}
names(lz) <- names(h) <- names(J) <- le
return(list(h=h, J=J, lkh=lkh, lz=lz))
} |
library(canprot)
mkvig("osmotic_euk") |
write.mitmlSPSS <- function(x, filename, sep = "\t", dec = ".", na.value=-999, syntax = TRUE, locale = NULL){
if(!inherits(x, "mitml") && !inherits(x, "mitml.list")) stop("'x' must be of class 'mitml' or 'mitml.list'.")
if(!dec %in% c(",", ".")) stop("Only a dot '.' or a comma ',' may be specified as decimal separator.")
if(inherits(x, "mitml")){
x <- mitmlComplete(x, "all", force.list = TRUE)
}
for(ii in 1:length(x)){
x[[ii]] <- cbind(ii-1, x[[ii]])
colnames(x[[ii]])[1] <- "Imputation_"
}
out <- do.call(rbind, x)
num <- sapply(out, is.numeric)
chr <- sapply(out, is.character)
fac <- sapply(out, is.factor)
ord <- sapply(out, is.ordered)
conv <- as.list(which(fac))
for(ff in which(fac)){
out[,ff] <- as.factor(out[,ff])
conv[[colnames(out)[ff]]] <- matrix(c(levels(out[,ff]), 1:nlevels(out[,ff])), ncol = 2)
out[,ff] <- as.numeric(out[,ff])
}
ds <- paste(filename, ".dat", sep = "")
out[is.na(out)] <- na.value
write.table(out, file = ds, sep = sep, dec = dec, col.names = T, row.names = F, quote = F)
if(syntax){
sf <- paste(filename, ".sps", sep = "")
if(dec == ".") d <- "DOT" else d <- "COMMA"
cat(file = sf, "SET DECIMAL", d, ".\n")
if(!is.null(locale)) cat(file = sf, "SET LOCALE", locale, ".\n", append = T)
cat(file = sf, "\n", append = T)
cat(file = sf, append = T,
"GET DATA\n",
"/TYPE=TXT\n",
paste("/FILE=\"", ds, "\"\n", sep = ""),
"/DELCASE=LINE\n",
paste("/DELIMITERS=\"", sub("\t", "\\\\t", sep), "\"\n", sep = ""),
"/ARRANGEMENT=DELIMITED\n",
"/FIRSTCASE=2\n",
"/IMPORTCASE=ALL\n",
"/VARIABLES="
)
width <- sapply(as.matrix(out)[1,], nchar, type = "width")
width[chr] <- sapply(out[,chr, drop = FALSE], function(z) max(nchar(z, type = "width")))
fmt <- data.frame(v = colnames(out), f = character(ncol(out)), stringsAsFactors = F)
fmt[num|fac|ord, "f"] <- paste("F", width[num|fac|ord]+3, ".2", sep = "")
fmt[chr, "f"] <- paste("A", width[chr], sep = "")
fmt[num, "l"] <- "SCALE"
fmt[fac|chr, "l"] <- "NOMINAL"
fmt[ord, "l"] <- "ORDINAL"
fmt[1, "l"] <- "NOMINAL"
cat(file = sf, "\n ", append = T)
cat(file = sf, paste(fmt$v, fmt$f, collapse = "\n "), ".\n\n", append = T)
cat(file = sf, append = T, sep = "",
"CACHE .\n",
"EXECUTE .\n",
"DATASET NAME panImpute1 WINDOW=FRONT .\n\n"
)
cat(file = sf, "VALUE LABELS", append = T)
for(cc in 1:length(conv)){
cat(file = sf, "\n", paste("/", names(conv)[cc], sep = ""), append = T)
for(rr in 1:nrow(conv[[cc]])){
cat(file = sf, "\n", conv[[cc]][rr, 2], paste("\'", conv[[cc]][rr, 1], "\'", sep = ""), append = T)
}
}
cat(file = sf, " .\n\n", append = T)
cat(file = sf, append = T,
"MISSING VALUES\n",
paste(fmt$v[num|fac|ord], collapse = " "), paste("(", na.value, ")", sep = ""), "\n",
paste(fmt$v[chr], collapse = " "), paste("(\"", na.value, "\")", sep = ""),
".\n"
)
}
invisible()
} |
options( width=90,
SweaveHooks=list( fig=function()
par(mar=c(3,3,1,1),mgp=c(3,1,0)/1.6,las=1,bty="n") ) )
library(Epi)
print( sessionInfo(), l=F )
data( DMlate )
head( DMlate )
dmL <- Lexis( entry = list( per=dodm,
age=dodm-dobth,
tfD=0 ),
exit = list( per=dox ),
exit.status = factor( !is.na(dodth), labels=c("DM","Dead") ),
data = DMlate )
timeScales(dmL)
str( dmL )
head( dmL )[,1:10]
summary.Lexis( dmL, timeScales=TRUE )
plot( dmL )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6 )
plot( dmL, 1:2, lwd=1, col=c("blue","red")[dmL$sex],
grid=TRUE, lty.grid=1, col.grid=gray(0.7),
xlim=1960+c(0,60), xaxs="i",
ylim= 40+c(0,60), yaxs="i", las=1 )
points( dmL, 1:2, pch=c(NA,3)[dmL$lex.Xst],
col="lightgray", lwd=3, cex=0.3 )
points( dmL, 1:2, pch=c(NA,3)[dmL$lex.Xst],
col=c("blue","red")[dmL$sex], lwd=1, cex=0.3 )
box(bty='o')
dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) )
summary( dmL )
summary( dmS1 )
wh.id <- c(9,27,52,484)
subset( dmL , lex.id %in% wh.id )[,1:10]
subset( dmS1, lex.id %in% wh.id )[,1:10]
dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
subset( dmS2, lex.id %in% wh.id )[,1:10]
library( popEpi )
dmM <- splitMulti( dmL, age = seq(0,100,5),
tfD = c(0,1,2,5,10,20,30,40),
drop = FALSE )
summary( dmS2 )
summary( dmM )
identical( dmS2, dmM )
class( dmS2 )
class( dmM )
whc <- c(names(dmL)[1:7],"dodm","doins")
subset( dmL, lex.id %in% wh.id )[,whc]
dmC <- cutLexis( data = dmL,
cut = dmL$doins,
timescale = "per",
new.state = "Ins",
new.scale = "tfI",
precursor.states = "DM" )
whc <- c(names(dmL)[1:8],"doins")
subset( dmC, lex.id %in% wh.id )[,whc]
dmS2C <- cutLexis( data = dmS2,
cut = dmS2$doins,
timescale = "per",
new.state = "Ins",
new.scale = "tfI",
precursor.states = "DM" )
subset( dmS2C, lex.id %in% wh.id )[,whc]
summary( dmS2C, timeScales=TRUE )
boxes( dmC, boxpos=TRUE, scale.R=1000, show.BE=TRUE )
timeBand( dmS2C, "age", "middle" )[1:10]
data.frame( dmS2C[,c("per","age","tfD","lex.dur")],
mid.age=timeBand( dmS2C, "age", "middle" ),
mid.t=timeBand( dmS2C, "tfD", "middle" ),
left.t=timeBand( dmS2C, "tfD", "left" ),
right.t=timeBand( dmS2C, "tfD", "right" ),
fact.t=timeBand( dmS2C, "tfD", "factor" ) )[1:15,]
summary( (dmS2$age-dmS2$tfD) - (dmS2$dodm-dmS2$dobth) )
summary( timeBand( dmS2, "age", "middle" ) -
timeBand( dmS2, "tfD", "middle" ) - (dmS2$dodm-dmS2$dobth) )
dmCs <- splitMulti( dmC, age = seq(0,110,1/4) )
summary( dmCs, t=T )
( a.kn <- with( subset( dmCs, lex.Xst=="Dead" ),
quantile( age+lex.dur, (1:5-0.5)/5 ) ) )
( i.kn <- c( 0,
with( subset( dmCs, lex.Xst=="Dead" & lex.Cst=="Ins" ),
quantile( tfI+lex.dur, (1:4)/5 ) ) ) )
ma <- glm( (lex.Xst=="Dead") ~ Ns(age,knots=a.kn),
family = poisson,
offset = log(lex.dur),
data = dmCs )
summary( ma )
Ma <- glm( cbind(lex.Xst=="Dead",lex.dur) ~ Ns(age,knots=a.kn),
family = poisreg, data = dmCs )
summary( Ma )
Xa <- glm.Lexis( dmCs, from="DM", to="Dead",
formula = ~ Ns(age,knots=a.kn) )
attr( Xa, "Lexis" )
xa <- glm.Lexis( dmCs, formula = ~ Ns(age,knots=a.kn) )
c( deviance(ma), deviance(Ma), deviance(Xa), deviance(xa) )
nd <- data.frame( age=40:85, lex.dur=1000 )
pr.0 <- ci.pred( ma, newdata = nd )
pr.a <- ci.pred( Ma, newdata = nd )*1000
summary(pr.0/pr.a)
matshade( nd$age, pr.a, plot=TRUE,
type="l", lty=1,
log="y", xlab="Age (years)",
ylab="DM mortality per 1000 PY")
pm <- glm( cbind(lex.Xst=="Dead",lex.dur) ~ Ns(age,knots=a.kn)
+ lex.Cst + sex,
family=poisreg, data = dmCs )
round( ci.exp( pm ), 3 )
pm <- glm( cbind(lex.Xst=="Dead",lex.dur) ~ Ns(age,knots=a.kn)
+ Ns(tfI,knots=i.kn)
+ lex.Cst + sex,
family=poisreg, data = tsNA20(dmCs) )
Pm <- glm.Lexis( tsNA20(dmCs),
form = ~ Ns(age,knots=a.kn)
+ Ns(tfI,knots=i.kn)
+ lex.Cst + sex )
c( deviance(Pm), deviance(pm) )
identical( model.matrix(Pm), model.matrix(pm) )
round( ci.exp( Pm, subset="ex" ), 3 )
ndI <- data.frame( expand.grid( tfI=c(NA,seq(0,15,0.1)),
ai=seq(40,80,10) ),
sex="M",
lex.Cst="Ins" )
ndI <- transform( ndI, age=ai+tfI )
head( ndI )
ndA <- data.frame( age= seq(40,100,0.1), tfI=0, lex.Cst="DM", sex="M" )
pri <- ci.pred( Pm, ndI ) * 1000
pra <- ci.pred( Pm, ndA ) * 1000
matshade( ndI$age, pri, plot=TRUE, las=1,
xlab="Age (years)", ylab="DM mortality per 1000 PY",
log="y", lty=1, col="blue" )
matshade( ndA$age, pra )
library( survival )
cm <- coxph( Surv(age,age+lex.dur,lex.Xst=="Dead") ~
Ns(tfI,knots=i.kn) + lex.Cst + sex,
data = tsNA20(dmCs) )
Cm <- coxph.Lexis( tsNA20(dmCs),
form= age ~ Ns(tfI,knots=i.kn) + lex.Cst + sex )
cbind( ci.exp( cm ), ci.exp( Cm ) )
round( cbind( ci.exp( Pm ),
rbind( matrix(NA,5,3),
ci.exp( cm )[-6,] ) ), 3 )
nd <- data.frame( tfI=seq(0,15,,151), lex.Cst="Ins", sex="M" )
nr <- data.frame( tfI= 2 , lex.Cst="Ins", sex="M" )
ppr <- ci.exp( pm, list(nd,nr), xvars="age" )
cpr <- ci.exp( cm, list(nd,nr) )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( nd$tfI, cbind(ppr,cpr), plot=T,
lty=c(1,2), log="y",
xlab="Time since insulin (years)", ylab="Rate ratio")
abline( h=1, lty=3 )
nd <- data.frame( tfI=seq(0,15,,151), lex.Cst="Ins", sex="M" )
nr <- data.frame( tfI= 0 , lex.Cst="DM" , sex="M" )
ppr <- ci.exp( pm, list(nd,nr), xvars="age" )
cpr <- ci.exp( cm, list(nd,nr) )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( nd$tfI, cbind(ppr,cpr),
xlab="Time since insulin (years)",
ylab="Rate ratio relative to non-Insulin",
lty=c(1,2), log="y", plot=T )
imx <- glm.Lexis( tsNA20(dmCs),
formula = ~ Ns(age ,knots=a.kn)
+ Ns( tfI,knots=i.kn)
+ Ns(age-tfI,knots=a.kn)
+ lex.Cst + sex )
Im <- glm.Lexis( tsNA20(dmCs),
formula = ~ Ns(age ,knots=a.kn)
+ Ns( tfI,knots=i.kn)
+ Ns((age-tfI)*(lex.Cst=="Ins"),knots=a.kn)
+ lex.Cst + sex )
im <- glm.Lexis( tsNA20(dmCs),
formula = ~ Ns(age ,knots=a.kn)
+ Ns( tfI,knots=i.kn)
+ lex.Cst:Ns(age-tfI,knots=a.kn)
+ lex.Cst + sex )
anova( imx, Im, im, test='Chisq')
pxi <- ci.pred( imx, ndI )
pxa <- ci.pred( imx, ndA )
pIi <- ci.pred( Im , ndI )
pIa <- ci.pred( Im , ndA )
pii <- ci.pred( im , ndI )
pia <- ci.pred( im , ndA )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( ndI$age, cbind( pxi, pIi, pii)*1000, plot=T, log="y",
xlab="Age", ylab="Mortality per 1000 PY",
lty=1, lwd=2, col=c("blue","forestgreen","red"), alpha=0.1 )
matshade( ndA$age, cbind( pxa, pIa, pia)*1000,
lty=1, lwd=2, col=c("blue","forestgreen","red"), alpha=0.1 )
ndR <- transform( ndI, tfI=0, lex.Cst="DM" )
cbind( head(ndI), head(ndR) )
Rxi <- ci.exp( imx, list(ndI,ndR) )
Rii <- ci.exp( im , list(ndI,ndR) )
RIi <- ci.exp( Im , list(ndI,ndR) )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( ndI$age, cbind( Rxi, RIi, Rii), plot=T, log="y",
xlab="Age (years)", ylab="Rate ratio vs, non-Insulin",
lty=1, lwd=2, col=c("blue","forestgreen","red"), alpha=0.1 )
abline( h=1 )
abline( h=ci.exp(imx,subset="lex.Cst")[,1], lty="25", col="blue" )
gm <- glm.Lexis( tsNA20(dmCs),
formula = ~ Ns(age,knots=a.kn)
+ Ns(tfI,knots=i.kn)
+ lex.Cst:Ns(age,knots=a.kn):Ns(tfI,knots=i.kn)
+ lex.Cst + sex )
pgi <- ci.pred( gm, ndI )
pga <- ci.pred( gm, ndA )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( ndI$age, cbind( pgi, pii )*1000, plot=T,
lty=c("solid","21"), lend="butt", lwd=2, log="y",
xlab="Age (years)", ylab="Mortality rates per 1000 PY",
alpha=c(0.2,0.1), col=c("black","red") )
matshade( ndA$age, cbind( pga, pia )*1000,
lty=c("solid","21"), lend="butt", lwd=2,
alpha=c(0.2,0.1), col=c("black","red") )
ndR <- transform( ndI, lex.Cst="DM", tfI=0 )
iRR <- ci.exp( im, ctr.mat=list(ndI,ndR) )
gRR <- ci.exp( gm, ctr.mat=list(ndI,ndR) )
par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.6, las=1, bty="n" )
matshade( ndI$age, cbind(gRR,iRR), lty=1, log="y", plot=TRUE,
xlab="Age (years)", ylab="Rate ratio: Ins vs. non-Ins",
col=c("black","red") )
abline( h=1 )
dmd <- glm.Lexis( dmCs,
from="DM", to="Dead",
formula = ~ Ns(age,knots=a.kn)
+ sex )
ind <- glm.Lexis( dmCs,
from="Ins", to="Dead",
formula = ~ Ns(age,knots=a.kn)
+ Ns(tfI,knots=i.kn)
+ Ns(age-tfI,knots=a.kn)
+ sex )
ini <- ci.pred( ind, ndI )
dmi <- ci.pred( dmd, ndI )
dma <- ci.pred( dmd, ndA )
par(mar=c(3,3,1,1),mgp=c(3,1,0)/1.6,las=1,bty="n")
matshade( ndI$age, ini*1000, plot=TRUE, log="y",
xlab="Age (years)", ylab="Mortality rates per 1000 PY",
lwd=2, col="red" )
matshade( ndA$age, dma*1000,
lwd=2, col="black" )
par(mar=c(3,3,1,1),mgp=c(3,1,0)/1.6,las=1,bty="n")
matshade( ndI$age, ci.ratio(ini,dmi), plot=TRUE, log="y",
xlab="Age (years)", ylab="RR insulin vs. no insulin",
lwd=2, col="red" )
abline( h=1 )
dmCs <- cutLexis( data = dmS2,
cut = dmS2$doins,
timescale = "per",
new.state = "Ins",
new.scale = "tfI",
precursor.states = "DM",
split.states = TRUE )
summary( dmCs )
boxes( dmCs, boxpos=list(x=c(15,15,85,85),
y=c(85,15,85,15)),
scale.R=1000, show.BE=TRUE )
dmM <- mcutLexis( dmL,
timescale = "per",
wh = c("doins","dooad"),
new.states = c("Ins","OAD"),
new.scales = c("tfI","tfO"),
precursor.states = "DM",
ties.resolve = TRUE )
summary( dmM, t=T )
wh <- c(subset(dmM,lex.Cst=="Ins-OAD")$lex.id[1:2],
subset(dmM,lex.Cst=="OAD-Ins")$lex.id[1:2])
options( width=110 )
print( subset( dmM, lex.id %in% wh )[,c('lex.id',names(dmM[1:8]),c("doins","dooad"))],
digits=6, row.names=FALSE )
boxes( dmM, boxpos=list(x=c(15,80,40,40,85,85),
y=c(50,50,90,10,90,10)),
scale.R=1000, show.BE=TRUE )
summary( dmMr <- Relevel( dmM, list('OAD+Ins'=5:6), first=FALSE) )
boxes( dmMr, boxpos=list(x=c(15,50,15,85,85),
y=c(85,50,15,85,15)),
scale.R=1000, show.BE=TRUE ) |
intermediate.corr.PB <-
function(n.P, n.B, n.C, lambda.vec=NULL, prop.vec=NULL, coef.mat=NULL, corr.vec = NULL, corr.mat=NULL){
validation.bin(n.B, prop.vec)
if(is.null(corr.mat) && !is.null(corr.vec)) {
d=ceiling(uniroot(function(d) d^2-d-2*length(corr.vec), interval=c(0,1000))$root)
corr.mat=diag(1,d)
corr.mat[lower.tri(corr.mat)]=corr.vec
corr.mat=corr.mat+t(corr.mat)-diag(1,d)
}
if((n.P+n.B+n.C) !=ncol(corr.mat)) stop("Dimension of the correlation matrix is misspecied!")
correlation.bound.check(n.P, n.B, n.C, lambda.vec, prop.vec, coef.mat, corr.vec = NULL, corr.mat=corr.mat)
samples=1e+05
n1=rnorm(samples)
n2=n1
if(n.B>0) {
mydatabin=matrix(0,samples,n.B)
for(k in 1:n.B){
cv=qnorm(1-prop.vec[k])
for(i in 1:samples){
if(n1[i]>cv) mydatabin[i,k]=1
}
}
mydata=mydatabin
}
amat=matrix(rep(as.vector(cor(mydata,n2)),n.P),n.P,n.B, byrow=T)
corr.mat.PO=matrix(c(corr.mat[1:n.P,(n.P+1):(n.P+n.B)]),n.P,n.B)
corr.mat.PN2=matrix(0,n.P,n.B)
for(i in 1:n.P){
for(j in 1:n.B) {
corr.mat.PN2[i,j]=corr.mat.PO[i,j]/amat[i,j]
}
}
u=runif(samples)
zu=matrix(qnorm(u),samples,1)
pu=sapply(1:n.P, function(i) qpois(u,lambda.vec[i]))
chat=matrix(rep(cor(pu,zu),n.B),n.P,n.B)
int.corrmat=matrix(0,n.P,n.B)
for(i in 1:n.P){
for(j in 1:n.B) {
int.corrmat[i,j]=corr.mat.PN2[i,j]/chat[i,j]
}
}
return(int.corrmat)
} |
par2arglist = function(theta, fixPar, y, noObs, delta,
mov.mf, err.mfX, err.mfY, rho, activity,
n.errX, n.errY, n.mov, driftMod){
N <- nrow(y)
par <- fixPar
par[is.na(fixPar)] <- theta
out = vector("list",8)
names(out) = c("Hmat", "b", "sig2", "active", "b.drift", "sig2.drift", "a", "P")
if (!is.null(err.mfX)) {
theta.errX <- par[1:n.errX]
Hmat <- exp(2 * err.mfX %*% theta.errX)
} else Hmat <- rep(0.0, N)
if (!is.null(err.mfY)) {
theta.errY <- par[(n.errX + 1):(n.errX + n.errY)]
Hmat <- cbind(Hmat,exp(2 * err.mfY %*% theta.errY))
} else Hmat <- cbind(Hmat, Hmat)
if(!is.null(rho)){
Hmat = cbind(Hmat, exp(log(Hmat[,1])/2 + log(Hmat[,2])/2)*rho)
} else {Hmat = cbind(Hmat, rep(0,N))}
Hmat[noObs==1,] = 0
out$Hmat = Hmat
theta.mov <- par[(n.errX + n.errY + 1):(n.errX + n.errY + 2 * n.mov)]
out$sig2 <- exp(2 * (mov.mf %*% theta.mov[1:n.mov]))
out$b <- exp(mov.mf %*% theta.mov[(n.mov + 1):(2 * n.mov)])
if (!is.null(activity)) {
theta.stop <- par[(n.errX + n.errY + 2 * n.mov + 1)]
out$b <- out$b / ((activity) ^ exp(theta.stop))
out$sig2 = out$sig2 * ((activity) ^ exp(theta.stop))
out$active <- ifelse(out$b==Inf, 0, 1)
out$b <- ifelse(out$b==Inf, 0, out$b)
} else {
out$active=rep(1,N)
}
if (driftMod) {
theta.drift <- par[(n.errX + n.errY + 2 * n.mov + 1):
(n.errX + n.errY + 2 * n.mov + 2)]
out$b.drift <- exp(log(out$b) - log(1+exp(theta.drift[2])))
out$sig2.drift <- exp(log(out$sig2) + 2 * theta.drift[1])
out$a = c(y[1,1], 0, 0, y[1,2],0, 0)
out$P = diag(c(var(y[noObs==0,1], na.rm=T), out$sig2[1]*out$b[1], out$sig2.drift[1]*out$b.drift[1],
var(y[noObs==0,2],na.rm=T), out$sig2[1]*out$b[1], out$sig2.drift[1]*out$b.drift[1]))
} else {
out$b.drift = NULL
out$sig2.drift = NULL
out$a = c(y[1,1], 0, y[1,2],0)
out$P = diag(c(var(y[noObs==0,1], na.rm=T), out$sig2[1]*out$b[1], var(y[noObs==0,2],na.rm=T), out$sig2[1]*out$b[1]))
}
return(out)
} |
fore.sigplusnoise.wge=function(x,linear=TRUE,freq=0,max.p=5,n.ahead=10,lastn=FALSE,plot=TRUE,limits=TRUE)
{
n=length(x)
np1=n+1
npn.ahead=n+n.ahead
resid=rep(0,npn.ahead)
xd=rep(0,npn.ahead)
xhat=rep(0,npn.ahead)
pi=3.14159
const=1
xar=rep(0,npn.ahead)
tl=1:n
if(linear=='TRUE') {
ftl=lm(x~tl)
for(t in 1:n){
xar[t]=x[t]-ftl$coefficients[1]-t*ftl$coefficients[2]
xd[t]=x[t]-xar[t]
}
if(lastn=='FALSE') {for(t in np1:npn.ahead) {xd[t]=ftl$coefficients[1]+t*ftl$coefficients[2]}}
}
if(linear=='FALSE') {
x1=rep(0,n)
x2=rep(0,n)
for(t in 1:n) {
x1[t]=cos(2*pi*freq*t)
x2[t]=sin(2*pi*freq*t)}
xm=rbind(x1,x2)
xmt=t(xm)
ftc=lm(x~xmt)
for(t in 1:n){
xar[t]=x[t]-ftc$coefficients[1]-ftc$coefficients[2]*x1[t]-ftc$coefficients[3]*x2[t]
xd[t]=x[t]-xar[t]
}
if(lastn=='FALSE') {
x1=rep(0,npn.ahead)
x2=rep(0,npn.ahead)
for(t in 1:npn.ahead){
x1[t]=cos(2*pi*freq*t)
x2[t]=sin(2*pi*freq*t)
xd[t]=ftc$coefficients[1]+ftc$coefficients[2]*x1[t]+ftc$coefficients[3]*x2[t]
}
}
}
order=aic.wge(xar,p=0:max.p,q=0:0)
p=order$p
phi=0
if(p > 0) {w=est.ar.wge(xar,p=p,type='burg')
phi=w$phi
}
if (p > 0) {for(jp in 1:p) {const=const-phi[jp]}}
p1=p+1
xbar=mean(xar)
maconst=const*xbar
for (i in p1:n) {resid[i]=xar[i]
if ( p > 0) {for (jp in 1:p) {resid[i]=resid[i]-phi[jp]*xar[i-jp]}
resid[i]=resid[i]-maconst}}
npn.ahead=n+n.ahead
xhat=rep(0,npn.ahead)
mm=n
if(lastn==TRUE) {mm=n-n.ahead}
for (i in 1:mm) {xhat[i]=xar[i]}
for (h in 1:n.ahead) {
if (p > 0) {for (jp in 1:p) {xhat[mm+h]=xhat[mm+h]+phi[jp]*xhat[mm+h-jp]}
xhat[mm+h]=xhat[mm+h]+maconst}
}
xi=psi.weights.wge(phi,theta=0,lag.max=n.ahead)
nap1=n.ahead+1
fplot=rep(0,nap1)
maxh=mm+n.ahead
llplot=rep(0,nap1)
ulplot=rep(0,nap1)
f=rep(0,nap1)
ll=rep(0,nap1)
ul=rep(0,nap1)
wnv=0
xisq=rep(0,n.ahead)
se=rep(0,n.ahead)
se0=1
for (i in p1:n) {wnv=wnv+resid[i]**2}
wnv=wnv/(n-p)
xisq[1]=1
for (i in 2:n.ahead) {xisq[i]=xisq[i-1]+xi[i-1]^2}
for (i in 1:n.ahead) {se[i]=sqrt(wnv*xisq[i])}
fplot[1]=x[mm]
for (i in 1:n.ahead) {fplot[i+1]=xhat[mm+i]+xd[mm+i]}
ulplot[1]=x[mm]
for (i in 1:n.ahead) { ulplot[i+1]=fplot[i+1]+1.96*se[i]}
llplot[1]=x[mm]
for (i in 1:n.ahead) { llplot[i+1]=fplot[i+1]-1.96*se[i]}
if(limits==FALSE) {
if(lastn==TRUE) {maxp=max(x,xhat[1:n]+xd[1:n])
minp=min(x,xhat[1:n]+xd[1:n])
}
else {maxp=max(x,fplot)
minp=min(x,fplot)}}
if(limits==TRUE) {
maxp=max(x,ulplot)
minp=min(x,llplot)
}
numrows <- 1
numcols <- 1
timelab <- 'Time'
valuelab <- ''
fig.width <- 5
fig.height <- 2.5
cex.labs <- c(.8,.7,.8)
par(mfrow=c(numrows,numcols),mar=c(3.8,2.5,1,1))
t<-1:n;
np1=n+1
np.ahead=mm+n.ahead
tf<-mm:np.ahead
if (plot=='TRUE') {
fig.width <- 5
fig.height <- 2.5
cex.labs <- c(.8,.7,.8)
par(mfrow=c(numrows,numcols),mar=c(3.8,2.5,1,1))
plot(t,x,type='o',xaxt='n',yaxt='n',cex=.8,pch=16,cex.lab=.75,cex.axis=.75,lwd=.75,xlab='',ylab='',xlim=c(1,maxh),ylim=c(minp,maxp))
axis(side=1,cex.axis=.8,mgp=c(3,0.15,0),tcl=-.3);
axis(side=2,las=1,cex.axis=.8,mgp=c(3,.4,0),tcl=-.3)
mtext(side=c(1,2,1),cex=cex.labs,text=c(timelab,valuelab,""),line=c(.8,1.1,1.8))
points(tf,fplot,type='o',lty=3,cex=1,lwd=2,pch=18);
if(limits=='TRUE') {points(tf,ulplot,type='o',lty=3,cex=0.6,lwd=.75,pch=18)
points(tf,llplot,type='o',lty=3,cex=0.6,lwd=.75,pch=18) }
}
np1=n+1
nap1=n.ahead+1
f=fplot[2:nap1]
ll=llplot[2:nap1]
ul=ulplot[2:nap1]
if (linear==TRUE){
b0=ftl$coefficients[1]
b1=ftl$coefficients[1]
out1=list(b0=ftl$coefficients,sig=xd,z=xar,phi.z=phi,f=f,ll=ll,ul=ul,resid=resid,wnv=wnv,se=se,xi=xi)
return(out1)}
if (linear==FALSE){
b0=ftc$coefficients[1]
b1=ftc$coefficients[1]
b2=ftc$coefficients[3]
out1=list(b=ftc$coefficients,sig=xd,z=xar,phi.z=phi,f=f,ll=ll,ul=ul,resid=resid,wnv=wnv,se=se,xi=xi)
return(out1)}
} |
lower <- function(x, conf_level = 0.95, na_rm = FALSE) {
chk_numeric(x)
chk_number(conf_level)
chk_range(conf_level)
if(anyNA(x)) {
if(vld_false(na_rm)) return(NA_real_)
x <- as.vector(x)
x <- x[!is.na(x)]
}
if (!length(x)) {
return(NA_real_)
}
lower <- (1 - conf_level) / 2
x <- stats::quantile(x, lower)
x <- unname(x)
x
} |
size_n.three_way_cross.model_1_axbxc <- function(alpha, beta, delta, a, b, c, cases)
{
n <- 2
dfn <- (a-1)*(b-1)*(c-1)
dfd <- a*b*c*(n-1)
if (cases == "maximin")
{
lambda <- 0.5*n*delta*delta
}
else if (cases == "minimin")
{
lambda <- 0.25*a*b*c*n*delta^delta
}
beta.calculated <- Beta(alpha, dfn, dfd, lambda)
if (is.nan(beta.calculated) || beta.calculated < beta )
{
warning(paste("Given parameter will result in too high power.",
"To continue either increase the precision or ",
"decrease the level of factors."))
return(NA)
}
else
{
n <- 5
n.new <- 1000
while (abs(n -n.new)>1e-6)
{
n <- n.new
dfn <- (a-1)*(b-1)*(c-1)
dfd <- a*b*c*(n-1)
lambda <- ncp(dfn,dfd,alpha,beta)
if (cases == "maximin")
{
n.new <- 2*lambda/(delta*delta)
}
else if (cases == "minimin")
{
n.new <- 4*lambda/(a*b*c*delta*delta)
}
}
return(ceiling(n.new))
}
} |
test_that("simplifyMeasureNames", {
meas = list(mmce, acc, ber)
meas.aggr = vcapply(meas, measureAggrName)
meas.ids = extractSubList(meas, "id")
no.meas = c("abc", "def")
xs = c(meas.aggr, no.meas)
expected = c(meas.ids, no.meas)
expect_equal(expected, simplifyMeasureNames(xs))
xs = c("acc", "no measure")
expect_equal(xs, simplifyMeasureNames(xs))
xs = character(0L)
expect_equal(xs, simplifyMeasureNames(xs))
}) |
fitSplicedBayesLognormGPD<-function(cell,prior,burnin=10,niter=100,proposal_scale=evmix::flognormgpd(cell,method="Nelder-Mead")$se,
start=evmix::flognormgpd(cell,method="Nelder-Mead")$optim$par){
Sample_xi=numeric(niter+burnin)
Sample_tau=numeric(niter+burnin)
Sample_beta=numeric(niter+burnin)
Sample_mu=numeric(niter+burnin)
Sample_sigma=numeric(niter+burnin)
prior_xi<-function(x){
fun=truncnorm::dtruncnorm(x,0,Inf,prior$xi[1],prior$xi[2])
return(fun)
}
prior_tau<-function(x){
fun=truncnorm::dtruncnorm(x,0,Inf,prior$tau[1],prior$tau[2])
return(fun)
}
prior_beta<-function(x){
fun=truncnorm::dtruncnorm(x,0,Inf,prior$beta[1],prior$beta[2])
return(fun)
}
prior_mu<-function(x){
fun=dnorm(x,prior$mu[1],prior$mu[2])
return(fun)
}
prior_sigma<-function(x){
fun=truncnorm::dtruncnorm(x,0,Inf,prior$sigma[1],prior$sigma[2])
return(fun)
}
loglikelihood_lognormgpd<-function(x,vxi,vtau,vbeta,vmu,vsigma){
likeli=sum(evmix::dlognormgpd(x,vmu,vsigma,vtau,vbeta,vxi,log=TRUE))
return(likeli)}
acceptance_ratesymm<-function(vtheta_prop,vtheta_old,vlogposterior){
a=min(log(1),(vlogposterior(vtheta_prop)-vlogposterior(vtheta_old)))
return(a)
}
mhstep1<-function(vinitialval,cell){
xi=vinitialval[1]
tau=vinitialval[2]
beta=vinitialval[3]
mu=vinitialval[4]
sigma=vinitialval[5]
xi_old=xi
loglikelihoodfunction_xi<-function(x){
fun=loglikelihood_lognormgpd(cell,x,tau,beta,mu,sigma)
return(fun)
}
logposterior_xi<-function(x){
fun=log(prior_xi(x))+loglikelihoodfunction_xi(x)
return(fun)
}
m=xi_old
v=proposal_scale[5]
xi_prop=rnorm(1,m,v)
acc=acceptance_ratesymm(xi_prop,xi_old,logposterior_xi)
if(is.nan(acc)){acc=-Inf
}
u=log(runif(1))
if (u<acc){
xi_old=xi_prop
}
return(xi_old)
}
mhstep2<-function(vinitialval,cell){
xi=vinitialval[1]
tau=vinitialval[2]
beta=vinitialval[3]
mu=vinitialval[4]
sigma=vinitialval[5]
tau_old=tau
loglikelihoodfunction_tau<-function(x){
fun=loglikelihood_lognormgpd(cell,xi,x,beta,mu,sigma)
return(fun)
}
logposterior_tau<-function(x){
fun=log(prior_tau(x))+loglikelihoodfunction_tau(x)
return(fun)
}
m=tau_old
v=proposal_scale[3]
logproposal<-function(x,m){
fun=log(truncnorm::dtruncnorm(x,min(cell),max(cell),m,v))
return(fun)
}
tau_prop=truncnorm::rtruncnorm(1,min(cell),max(cell),m,v)
acc=min(log(1),(logposterior_tau(tau_prop)+logproposal(tau_old,tau_prop)-logposterior_tau(tau_old)-logproposal(tau_prop,tau_old)))
if(is.nan(acc)){acc=-Inf
}
u=log(runif(1))
if (u<acc){
tau_old=tau_prop
}
return(tau_old)
}
mhstep3<-function(vinitialval,cell){
xi=vinitialval[1]
tau=vinitialval[2]
beta=vinitialval[3]
mu=vinitialval[4]
sigma=vinitialval[5]
beta_old=beta
loglikelihoodfunction_beta<-function(x){
fun=loglikelihood_lognormgpd(cell,xi,tau,x,mu,sigma)
return(fun)
}
logposterior_beta<-function(x){
fun=log(prior_beta(x))+loglikelihoodfunction_beta(x)
return(fun)
}
m=log(beta_old)
v=proposal_scale[4]
logproposal<-function(x,m){
fun=dlnorm(x,m,v,log=TRUE)
return(fun)
}
beta_prop=rlnorm(1,m,v)
acc=min(log(1),(logposterior_beta(beta_prop)+logproposal(beta_old,log(beta_prop))-logposterior_beta(beta_old)-logproposal(beta_prop,log(beta_old))))
if(is.nan(acc)){acc=-Inf
}
u=log(runif(1))
if(u<acc){
beta_old=beta_prop
}
return(beta_old)
}
mhstep4<-function(vinitialval,cell){
xi=vinitialval[1]
tau=vinitialval[2]
beta=vinitialval[3]
mu=vinitialval[4]
sigma=vinitialval[5]
mu_old=mu
loglikelihoodfunction_mu<-function(x){
fun=loglikelihood_lognormgpd(cell,xi,tau,beta,x,sigma)
return(fun)
}
logposterior_mu<-function(x){
fun=log(prior_mu(x))+loglikelihoodfunction_mu(x)
return(fun)
}
m=mu_old
v=proposal_scale[1]
logproposal<-function(x){
fun=dnorm(x,m,v,log=TRUE)
return(fun)
}
mu_prop=rnorm(1,m,v)
acc=acceptance_ratesymm(mu_prop,mu_old,logposterior_mu)
if(is.nan(acc)){acc=-Inf}
u=log(runif(1))
if (u<acc){
mu_old=mu_prop
}
return(mu_old)
}
mhstep5<-function(vinitialval,cell){
xi=vinitialval[1]
tau=vinitialval[2]
beta=vinitialval[3]
mu=vinitialval[4]
sigma=vinitialval[5]
sigma_old=sigma
loglikelihoodfunction_sigma<-function(x){
fun=loglikelihood_lognormgpd(cell,xi,tau,beta,mu,x)
return(fun)
}
logposterior_sigma<-function(x){
fun=log(prior_sigma(x))+loglikelihoodfunction_sigma(x)
return(fun)
}
m=log(sigma_old)
v=proposal_scale[2]
logproposal<-function(x,m){
fun=dlnorm(x,m,v,log=TRUE)
return(fun)
}
sigma_prop=rlnorm(1,m,v)
acc=min(log(1),(logposterior_sigma(sigma_prop)+logproposal(sigma_old,log(sigma_prop))-logposterior_sigma(sigma_old)-logproposal(sigma_prop,log(sigma_old))))
if(is.nan(acc)){acc=-Inf}
u=log(runif(1))
if (u<acc){
sigma_old=sigma_prop
}
return(sigma_old)
}
initialval=c(start[5],start[3],start[4],start[1],start[2])
for(i in seq(1:(niter+burnin))){
initialval[1] = mhstep1(initialval, cell)
Sample_xi[i] = initialval[1]
initialval[2] = mhstep2(initialval, cell)
Sample_tau[i] = initialval[2]
initialval[3] = mhstep3(initialval, cell)
Sample_beta[i] = initialval[3]
initialval[4] = mhstep4(initialval, cell)
Sample_mu[i] = initialval[4]
initialval[5] = mhstep5(initialval, cell)
Sample_sigma[i] = initialval[5]
}
xi_estimator=mean(Sample_xi[(burnin+1):(niter+burnin)])
tau_estimator=mean(Sample_tau[(burnin+1):(niter+burnin)])
beta_estimator=mean(Sample_beta[(burnin+1):(niter+burnin)])
mu_estimator=mean(Sample_mu[(burnin+1):(niter+burnin)])
sigma_estimator=mean(Sample_sigma[(burnin+1):(niter+burnin)])
buildSplicedSevdist("lnorm",c(mu_estimator,sigma_estimator),"gpd",c(tau_estimator,beta_estimator,xi_estimator),tau_estimator,0.5)
} |
test_that("Check API Parameter Reference Dataset", {
expect_true(!any(is.na(tidygeocoder::api_parameter_reference$api_name)))
expect_true(!any(is.na(tidygeocoder::api_parameter_reference$method)))
unique_api_param_rows <- nrow(tidygeocoder::api_parameter_reference[c('method', 'generic_name')])
api_param_rows <- nrow(tidygeocoder::api_parameter_reference)
expect_equal(unique_api_param_rows, api_param_rows)
})
test_that('check batch function maps', {
for (method in names(tidygeocoder:::batch_func_map)) {
method_label = paste0('method = "', method, '"', ' ')
expect_true(method %in% tidygeocoder::batch_limit_reference[['method']], label = method_label)
expect_true(method %in% tidygeocoder::api_parameter_reference[['method']], label = method_label)
expect_true(is.function(tidygeocoder:::batch_func_map[[method]]), label = method_label)
}
for (method in names(tidygeocoder:::reverse_batch_func_map)) {
method_label = paste0('method = "', method, '"', ' ')
expect_true(method %in% tidygeocoder::batch_limit_reference[['method']], label = method_label)
expect_true(method %in% tidygeocoder::api_parameter_reference[['method']], label = method_label)
expect_true(is.function(tidygeocoder:::reverse_batch_func_map[[method]]), label = method_label)
}
})
test_that('Check Reference Datasets', {
expect_setequal(get_services_requiring_key(), tidygeocoder::api_key_reference[['method']])
expect_equal(
length(unique(tidygeocoder::api_key_reference[['method']])),
nrow(tidygeocoder::api_key_reference)
)
expect_equal(
length(unique(tidygeocoder::batch_limit_reference[['method']])),
nrow(tidygeocoder::batch_limit_reference)
)
expect_equal(
length(unique(tidygeocoder::min_time_reference[['method']])),
nrow(tidygeocoder::min_time_reference)
)
})
test_that("Test Duplicate and Blank/NA Address Handling", {
messy_addresses <- c('','', NA, tidygeocoder::sample_addresses$addr,
NA, '', NA, tidygeocoder::sample_addresses$addr)
addr_pack <- tidygeocoder:::package_addresses(address = messy_addresses)
results <- tidygeocoder::geo(messy_addresses, no_query = TRUE, unique_only = TRUE)[, c('lat', 'long')]
unpacked <- tidygeocoder:::unpackage_inputs(addr_pack, results, return_inputs = TRUE)
expect_true(is.list(addr_pack))
expect_length(addr_pack, 2)
expect_true(tibble::is_tibble(addr_pack$unique))
expect_true(tibble::is_tibble(addr_pack$crosswalk))
expect_equal(nrow(addr_pack$unique), length(unique(messy_addresses[!(messy_addresses %in% c(NA, ''))])))
expect_equal(nrow(addr_pack$crosswalk), length(messy_addresses))
expect_equal(nrow(unpacked), length(messy_addresses))
expect_equal(colnames(unpacked), c('address', colnames(results)))
})
test_that("Test Address Packaging Errors", {
expect_error(tidygeocoder:::package_addresses(address = c('xyz'), city = c('abc')))
expect_error(tidygeocoder:::package_addresses(city = c('xyz', 'abc'), state = c('abc')))
})
test_that("Test API Query Creation Functions", {
cust_arg_list <- list(cust1 = '123')
address_val <- '1500 Rushmore St'
census_addr_param <- create_api_parameter('census', 'address', 'xyz')
expect_true(is.list(census_addr_param))
expect_equal(length(census_addr_param), 1)
census_bad_addr_param <- create_api_parameter('census', 'country', 'xyz')
expect_true(is.list(census_bad_addr_param))
expect_equal(length(census_bad_addr_param), 0)
for (method in unique(tidygeocoder::api_parameter_reference[['method']])) {
method_label = paste0('method = "', method, '"', ' ')
expect_warning(tidygeocoder::get_api_query(method,
generic_parameters = list(address = 'abc'),
custom_parameters = tidygeocoder:::create_api_parameter(method, 'address', 'ghj')))
default_q <- tidygeocoder::get_api_query(method)
custom_q <- tidygeocoder::get_api_query(method, custom_parameters = cust_arg_list)
address_q <- tidygeocoder::get_api_query(method, generic_parameters = list(address = address_val))
expect_named(custom_q)
expect_named(address_q)
expect_true(is.list(default_q))
expect_true(is.list(custom_q))
expect_true(is.list(address_q))
expect_mapequal(custom_q, c(default_q, cust_arg_list))
if (!method %in% c('mapbox', 'tomtom')) {
expect_mapequal(address_q, c(default_q,
tidygeocoder:::create_api_parameter(method, 'address', address_val)))
}
if (length(default_q) > 0) expect_message(display_named_list(default_q), label = method_label)
expect_message(display_named_list(custom_q), label = method_label)
if (length(address_q) > 0) expect_message(display_named_list(address_q), label = method_label)
}
})
test_that("Test Miscellaneous Functions", {
num_rows <- 3
na_vals <- tidygeocoder:::get_na_value('lat', 'long', rows = num_rows)
expect_true(tibble::is_tibble(na_vals))
expect_true(nrow(na_vals) == num_rows)
}) |
distn.stats <- function(distn, a, b) {
mean <- sd <- NULL
if (distn == "beta") {
mean <- a / (a + b)
sd <- sqrt(a * b / ((a + b) ^ 2 * (a + b + 1)))
} else if (distn == "exp") {
mean <- 1 / a
sd <- 1 / a
} else if (distn == "f") {
mean <- b / (b - 2)
sd <- sqrt(2 * b * b * (a + b - 2) / (a * (b - 2) ^ 2 * (b - 4)))
} else if (distn == "gamma") {
mean <- a/b
sd <- sqrt(a / b ^ 2)
} else if (distn == "lnorm") {
mean <- exp(a + 0.5 * b ^ 2)
sd <- sqrt(exp(2 * a + b ^ 2) * (exp(b ^ 2) - 1))
} else if (distn == "norm") {
mean <- a
sd <- b
} else if (distn == "t") {
mean <- 0
sd <- sqrt(a / (a - 2))
} else if (distn == "unif") {
mean <- 0.5 * (a + b)
sd <- (b - a) / sqrt(12)
} else if (distn == "weibull") {
mean <- b * gamma(1 + 1 / a)
sd <- b ^ 2 * (gamma(1 + 2 / a) - (gamma(1 + 1 / a)) ^ 2)
}
return(c(mean, sd))
}
distn.table.stats <- function(distns) {
y <- as.data.frame(matrix(NA, nrow(distns), 2))
for (i in seq_len(nrow(distns))) {
x <- distns[i, ]
y[i, ] <- distn.stats(x[1], as.numeric(x[2]), as.numeric(x[3]))
}
rownames(y) <- rownames(distns)
colnames(y) <- c("mean", "sd")
return(y)
} |
structure(list(url = "/browse-edgar?action=getcompany&CIK=EA&owner=exclude&type=&dateb=&start=0&count=40&output=atom",
status_code = 200L, headers = structure(list(`content-encoding` = "gzip",
`content-type` = "application/atom+xml", server = "Apache",
`x-content-type-options` = "nosniff", `x-frame-options` = "SAMEORIGIN",
`x-xss-protection` = "1; mode=block", `content-length` = "4986",
`cache-control` = "no-cache", date = "Sun, 18 Apr 2021 16:54:08 GMT",
vary = "Accept-Encoding", `strict-transport-security` = "max-age=31536000 ; includeSubDomains ; preload",
location = character(0)), class = c("insensitive", "list"
)), all_headers = list(list(status = 200L, version = "HTTP/2",
headers = structure(list(`content-encoding` = "gzip",
`content-type` = "application/atom+xml", server = "Apache",
`x-content-type-options` = "nosniff", `x-frame-options` = "SAMEORIGIN",
`x-xss-protection` = "1; mode=block", `content-length` = "4986",
`cache-control` = "no-cache", date = "Sun, 18 Apr 2021 16:54:08 GMT",
vary = "Accept-Encoding", `strict-transport-security` = "max-age=31536000 ; includeSubDomains ; preload"), class = c("insensitive",
"list")))), cookies = structure(list(domain = logical(0),
flag = logical(0), path = logical(0), secure = logical(0),
expiration = structure(numeric(0), class = c("POSIXct",
"POSIXt")), name = logical(0), value = logical(0)), row.names = integer(0), class = "data.frame"),
content = as.raw(c(0x3c, 0x3f, 0x78, 0x6d, 0x6c, 0x20, 0x76,
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0x64, 0x3e, 0x0a)), date = structure(1618764848, class = c("POSIXct",
"POSIXt"), tzone = "GMT"), times = c(redirect = 0, namelookup = 2.4e-05,
connect = 2.5e-05, pretransfer = 7.8e-05, starttransfer = 0.097981,
total = 0.099531)), class = "response") |
opt.survPC <-
function (thetas) {
thetas <- relist(thetas, skeleton = list.thetas)
gammas <- thetas$gammas
alpha <- thetas$alpha
Dalpha <- thetas$Dalpha
xi <- exp(thetas$log.xi)
eta.tw <- if (!is.null(WW)) as.vector(WW %*% gammas) else 0
eta.t <- switch(parameterization,
"value" = eta.tw + c(WintF.vl %*% alpha) * Y,
"slope" = eta.tw + c(WintF.sl %*% Dalpha) * Y.deriv,
"both" = eta.tw + c(WintF.vl %*% alpha) * Y + c(WintF.sl %*% Dalpha) * Y.deriv)
eta.s <- switch(parameterization,
"value" = c(Ws.intF.vl %*% alpha) * Ys,
"slope" = c(Ws.intF.sl %*% Dalpha) * Ys.deriv,
"both" = c(Ws.intF.vl %*% alpha) * Ys + c(Ws.intF.sl %*% Dalpha) * Ys.deriv)
log.hazard <- log(xi[ind.D]) + eta.t
log.survival <- - exp(eta.tw) * rowsum(xi[ind.K] * wkP * exp(eta.s), id.GK, reorder = FALSE)
dimnames(log.survival) <- NULL
log.p.tb <- d * log.hazard + log.survival
p.bytn <- p.byt * log.p.tb
-sum(p.bytn %*% wGH, na.rm = TRUE)
} |
library(lifecontingencies)
context("Object Initialization")
test_that("Unequal lengths and population at risk is non-increasing", {
x <- 0:5
lx <- c(100, 75, 50, 51, 12)
expect_error(new("lifetable", x = x, lx = lx))
})
test_that("Increasing x", {
x <- c(0, 2, 1, 3)
lx <- c(100, 50, 75, 25)
tbl <- new("lifetable",x = x, lx = lx)
expect_equal(tbl@x, sort(x))
expect_equal(tbl@lx, sort(lx, decreasing = TRUE))
})
test_that("Integral, non-negative x and increasing by 1", {
x <- c(0, 1.5, 2, 3)
lx <- c(100, 75, 50, 25)
expect_error(tbl<-new("lifetable", x = x, lx = lx))
x <- c(-2, -1, 0, 1)
expect_error(tbl<-new("lifetable", x = x, lx = lx))
x <- c(0, 1, 3, 4)
expect_error(tbl<-new("lifetable", x = x, lx = lx))
})
test_that("Zeros and NAs in lx are removed", {
x <- 0:4
lx <- c(100, 75, 50, 25, 0)
tbl <- new("lifetable",x = x, lx = lx)
expect_equal(tbl@x, c(0, 1, 2, 3))
expect_equal(tbl@lx, c(100, 75, 50, 25))
x <- c(0, 1, 1, 2, 3)
lx <- c(100, NA, 50, 25, 12)
tbl <- new("lifetable",x = x, lx = lx)
expect_equal(tbl@x, c(0, 1, 2, 3))
expect_equal(tbl@lx, c(100, 50, 25, 12))
x <- c(0, 1, 1, 2, 3)
lx <- c(100, NA, 50, 25, 0)
tbl <- new("lifetable",x = x, lx = lx)
expect_equal(tbl@x, c(0, 1, 2))
expect_equal(tbl@lx, c(100, 50, 25))
x <- c(0, 1, 1, 2, 3)
lx <- c(100, NA, 50, NA, 0)
tbl <- new("lifetable",x = x, lx = lx)
expect_equal(tbl@x, c(0, 1))
expect_equal(tbl@lx, c(100, 50))
}) |
ISOKeywords <- R6Class("ISOKeywords",
inherit = ISOAbstractObject,
private = list(
xmlElement = "MD_Keywords",
xmlNamespacePrefix = "GMD"
),
public = list(
keyword = list(),
type = NULL,
thesaurusName = NULL,
initialize = function(xml = NULL){
super$initialize(xml = xml)
},
addKeyword = function(keyword, locales = NULL){
if(is.null(keyword)) return(FALSE);
if(is(keyword, "character")) if(is.na(keyword)) return(FALSE);
if(!is.null(locales)){
keyword <- self$createLocalisedProperty(keyword, locales)
}
return(self$addListElement("keyword", keyword))
},
delKeyword = function(keyword, locales = NULL){
if(is.null(keyword)) return(FALSE);
if(is(keyword, "character")) if(is.na(keyword)) return(FALSE);
if(!is.null(locales)){
keyword <- self$createLocalisedProperty(keyword, locales)
}
return(self$delListElement("keyword", keyword))
},
setKeywordType = function(keywordType){
if(!is(keywordType, "ISOKeywordType")){
keywordType <- ISOKeywordType$new(value = keywordType)
}
self$type <- keywordType
},
setThesaurusName = function(thesaurusName){
if(!is(thesaurusName, "ISOCitation")){
stop("The argument should be a 'ISOCitation' object")
}
self$thesaurusName = thesaurusName
}
)
) |
.removeSp <- function(x) {
rem <- which(colSums(x[, -(1:2), drop = FALSE]) == 0) + 2
if (length(rem) > 0) {
x <- x[, -rem, drop = FALSE]
}
if (ncol(x) == 2) {
stop("No species left after removing species without occurrences")
}
return(x)
} |
library("segregation")
context("test_mutual_total")
test_data <- data.frame(
u = c(rep("a", 4), rep("b", 4)),
g = rep(c(1, 2, 3, 4), 2),
supergroup = rep(c(12, 12, 34, 34), 2),
n = c(40, 20, 5, 1, 20, 40, 60, 80),
stringsAsFactors = FALSE
)
test_that("mutual M works both ways around", {
expect_equal(
mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est],
mutual_total(test_data, "g", "u", weight = "n")[stat == "M", est]
)
expanded <- test_data[rep(seq_len(nrow(test_data)), test_data$n), 1:3]
expect_equal(
mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est],
mutual_total(expanded, "u", "g")[stat == "M", est]
)
expect_equal(
mutual_total(expanded, "u", "g")[stat == "M", est],
mutual_total(expanded, "g", "u")[stat == "M", est]
)
expect_equal(
mutual_total(test_data, "u", "g", weight = "n", base = 2)[stat == "M", est],
mutual_total(test_data, "g", "u", weight = "n", base = 2)[stat == "M", est]
)
expect_equal(
mutual_total(test_data, "u", c("supergroup", "g"), weight = "n")[stat == "M", est],
mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est]
)
expect_equal(
mutual_total(test_data, "u", c("supergroup", "g"), weight = "n")[stat == "M", est],
mutual_total(test_data, c("supergroup", "g"), "u", weight = "n")[stat == "M", est]
)
})
test_that("between + within = total", {
expect_equal(
mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est],
mutual_total(test_data, "u", "supergroup", weight = "n")[stat == "M", est] +
mutual_total(test_data, "u", "g", within = "supergroup", weight = "n")[stat == "M", est]
)
expect_equal(
mutual_total(test_data, "u", "g", weight = "n")[stat == "H", est],
mutual_total(test_data, "u", "supergroup", weight = "n")[stat == "H", est] +
mutual_total(test_data, "u", "g", within = "supergroup", weight = "n")[stat == "H", est]
)
})
p_12 <- sum(test_data[test_data$supergroup == 12, "n"]) / sum(test_data$n)
p_34 <- sum(test_data[test_data$supergroup == 34, "n"]) / sum(test_data$n)
test_that("within estimations are correct", {
d_12 <- test_data[test_data$supergroup == 12, ]
d_34 <- test_data[test_data$supergroup == 34, ]
expect_equal(
p_12 * mutual_total(d_12, "u", "g", weight = "n")[stat == "M", est] +
p_34 * mutual_total(d_34, "u", "g", weight = "n")[stat == "M", est],
mutual_total(test_data, "u", "g", within = "supergroup", weight = "n")[stat == "M", est]
)
})
test_that("H is correct", {
ret <- mutual_total(test_data, "u", "g", weight = "n")
expect_equal(ret[stat == "H", est] >= 0 & ret[stat == "H", est] <= 1, TRUE)
})
test_that("bootstrapping works", {
ret <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10)
expect_equal(dim(ret), c(2, 5))
expect_equal(all(ret$se > 0), TRUE)
ret <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10,
within = "supergroup")
expect_equal(dim(ret), c(2, 5))
expect_equal(all(ret$se > 0), TRUE)
})
test_that("bootstrap attributes exists", {
ret <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10)
expect_equal(dim(attr(ret, "bootstrap")), c(2 * 10, 2))
})
test_that("bootstrapping fails when sample size is non-integer", {
test_data <- data.frame(
u = c(rep("a", 4), rep("b", 4)),
g = rep(c(1, 2, 3, 4), 2),
n = c(40, 20, 5, 1.8, 20, 40, 60, 80),
stringsAsFactors = FALSE
)
expect_error(mutual_total(test_data, "u", "g", weight = "n", se = TRUE, n_bootstrap = 10))
test_data$n2 <- test_data$n / sum(test_data$n) * round(sum(test_data$n))
ret <- mutual_total(test_data, "u", "g", weight = "n2", se = TRUE, n_bootstrap = 10)
expect_equal(dim(ret), c(2, 5))
expect_equal(all(ret$se > 0), TRUE)
})
test_data <- data.frame(
u = c(rep("a", 4), rep("b", 4)),
g = rep(c(1, 2, 3, 4), 2),
n = c(40, 0, 0, 0, 0, 0, 0, 40)
)
test_that("zero weights no problem", {
expect_equal(dim(mutual_total(test_data, "u", "g", weight = "n",
se = TRUE, n_bootstrap = 10)), c(2, 5))
expect_equal(dim(mutual_total(test_data, "u", "g", weight = "n")), c(2, 2))
expect_equal(mutual_total(test_data, "u", "g", weight = "n")[stat == "M", est], log(2))
expect_equal(mutual_total(test_data, "u", "g", weight = "n")[stat == "H", est], 1)
test_data2 <- copy(test_data)
test_data2$g <- as.factor(test_data2$g)
expect_equal(
mutual_total(test_data, "u", "g", weight = "n")[["est"]],
mutual_total(test_data2, "u", "g", weight = "n")[["est"]])
})
test_that("gives errors", {
expect_error(mutual_total("test_data", "u", "g", weight = "n"), "not a data.frame")
expect_error(mutual_total(test_data[test_data$u == "c", ], "u", "g", weight = "n"),
"data.frame is empty")
expect_error(mutual_total(test_data, "u2", "g", weight = "n"), "u2 not in data.frame")
expect_error(mutual_total(test_data, "u2", "g2", weight = "n"), "u2, g2 not in data.frame")
expect_error(mutual_total(test_data, "u2", "g2", weight = "n2"), "u2, g2, n2 not in data.frame")
test_data_constant1 <- data.frame(u = c(rep("a", 4), rep("b", 4)), g = 1)
expect_error(mutual_total(test_data_constant1, "g", "u"), "group variable is constant")
test_data_constant2 <- data.frame(g = c(rep("a", 4), rep("b", 4)), u = 1)
expect_error(mutual_total(test_data_constant2, "g", "u"), "unit variable is constant")
})
test_that("debiasing works correctly", {
nose <- mutual_total(test_data, "u", "g", weight = "n")
withse <- mutual_total(test_data, "u", "g", weight = "n", se = TRUE)
expect_equal(nose$est, withse$est + withse$bias)
}) |
context("Tests output ConsReg")
test_that("tests for dimension", {
data('fake_data')
fit1 = ConsReg(formula = y~x1+x2+x3+ I(x3^2) + x4, family = 'gaussian',
optimizer = 'mcmc',
data = fake_data)
expect_true(length(fit1$coefficients) == ncol(fit1$x))
expect_true(
nrow(fit1$x) == length(as.numeric(fit1$fitted))
)
testthat::expect_error(ConsReg(formula = y~x1+x2+x3+ I(x3^2) + x4, family = 'gaussian',
optimizer = 'mcmc', LOWER = 0,
data = fake_data))
testthat::expect_error(ConsReg(formula = y~x1+x2+x3+ I(x3^2) + x4, family = 'gaussian',
optimizer = 'solnp', UPPER = 0,
data = fake_data))
}) |
colBoxplot4 <- "
colBoxplot3 <- "
colBoxplot2 <- "
colBoxplot1 <- "
colDecrease <- "
colIncrease <- "
colLines <- "
colQualitative1 <- "
colQualitative2 <- "
colQualitative3 <- "
colQualitative4 <- "
colQualitative5 <- "
colQualitative6 <- "
colQualitative7 <- "
colQualitative8 <- "
colQualitative9 <- "
colQualitative10 <- "
colQualitative11 <- "
textcol <- "
arrowcol <- "
colRvbpPos <- "
colRvbpNeg <- "
ColorBG <- "
ColorApp <- "
ColorPanel <- "
ColorHighlight <- "
ColorElements <- "
ColorFont <- "
colChoice <- list(
'sequential orange' = list('col' = brewer.pal(9, 'Oranges'), 'gradient' = TRUE),
'sequential blue' = list('col' = brewer.pal(9, 'Blues'), 'gradient' = TRUE),
'sequential green' = list('col' = brewer.pal(9, 'Greens'), 'gradient' = TRUE),
'sequential grey' = list('col' = brewer.pal(9, 'Greys'), 'gradient' = TRUE),
'sequential purple' = list('col' = brewer.pal(9, 'Purples'), 'gradient' = TRUE),
'sequential red' = list('col' = brewer.pal(9, 'Reds'), 'gradient' = TRUE),
'sequential blue - green' = list('col' = brewer.pal(9, 'BuGn'), 'gradient' = TRUE),
'sequential blue - purple' = list('col' = brewer.pal(9, 'BuPu'), 'gradient' = TRUE),
'sequential green - blue' = list('col' = brewer.pal(9, 'GnBu'), 'gradient' = TRUE),
'sequential orange - red' = list('col' = brewer.pal(9, 'OrRd'), 'gradient' = TRUE),
'sequential purple - blue' = list('col' = brewer.pal(9, 'PuBu'), 'gradient' = TRUE),
'sequential purple - blue - green'= list('col' = brewer.pal(9, 'PuBuGn'),'gradient' = TRUE),
'sequential purple - red' = list('col' = brewer.pal(9, 'PuRd'), 'gradient' = TRUE),
'sequential red - purple' = list('col' = brewer.pal(9, 'RdPu'), 'gradient' = TRUE),
'sequential yellow - green' = list('col' = brewer.pal(9, 'YlGn'), 'gradient' = TRUE),
'sequential yellow - green - blue' = list('col' = brewer.pal(9, 'YlGnBu'),'gradient' = TRUE),
'sequential yellow - orange - brown'= list('col' = brewer.pal(9, 'YlOrBr'),'gradient' = TRUE),
'sequential yellow - orange - red' = list('col' = brewer.pal(9, 'YlOrRd'),'gradient' = TRUE)
)
boxPlotColorUI <- function(id) {
ns <- shiny::NS(id)
shiny::uiOutput(ns("controls"))
}
boxPlotColor <- function(input, output, session, dat, name, start_color, number) {
ns <- session$ns
output$controls <- shiny::renderUI({
if (start_color %in% c("Color1","Color5","Color9","Color13","Color17")) {
bg_col <- colBoxplot1
sel_col <- "Color1"
}
if (start_color %in% c("Color2","Color6","Color10","Color14","Color18")) {
bg_col <- colBoxplot2
sel_col <- "Color2"
}
if (start_color %in% c("Color3","Color7","Color11","Color15","Color19")) {
bg_col <- colBoxplot3
sel_col <- "Color3"
}
if (start_color %in% c("Color4","Color8","Color12","Color16","Color20")) {
bg_col <- colBoxplot4
sel_col <- "Color4"
}
tags$div(
tags$head(
tags$style(
shiny::HTML(
paste0("
.btn-info", number, "{color:
";}"
)
)
)
),
shinyWidgets::pickerInput(
inputId = ns("col"),
label = paste0(name),
choices = dat,
selected = sel_col,
multiple = FALSE,
options = list(style = paste0("btn-info", number)),
choicesOpt = list(
style = c(
"background-color:
"background-color:
"background-color:
"background-color:
)
)
), width = "100%")
})
return(
shiny::reactive({
shiny::validate(shiny::need(input$col, FALSE))
dat[,input$col]
})
)
}
elaborator_ui <- shinydashboard::dashboardPage(
title = "elaborator",
shinydashboard::dashboardHeader(
title = shiny::img(
src = 'www/BAY_eLaborator_Logo-lang_Negativ.svg',
height = 24,
align ="left"
),
titleWidth = 250
),
shinydashboard::dashboardSidebar(
shinydashboard::sidebarMenu(
id = 'sidebarmenu',
shinydashboard::menuItem(
text = 'Quantitative Trends',
icon = icon('chart-line'),
tabName = 'quant'
),
shinydashboard::menuItem(
text = 'Qualitative Trends',
icon = icon('buromobelexperte'),
tabName = 'qual'
),
shinydashboard::menuItem(
text = 'Reference-value Based Patterns',
icon = icon('cloudsmith'),
tabName = 'rvbp'
),
shinydashboard::menuItem(
text = 'Graphic Options',
tabName = 'options',
icon = icon('cogs'),
startExpanded = FALSE,
shinydashboard::menuItem(
text = 'Panel/Plot Size ',
tabName = 'panelsizeoptions',
icon = icon('arrows-alt'),
shiny::sliderInput(
inputId = 'zoompx',
label = 'Zoom / Pixel ratio (px)',
min = 10,
max = 820,
value = 100,
step = 10
),
shiny::sliderInput(
inputId = 'panelheight',
label = 'Change panel height',
min = 400,
max = 2400,
value = 500,
step = 100
)
),
shinydashboard::menuItem(
text = 'Arrange Lab Parameters ',
icon = icon('sort-alpha-down'),
tabName = 'ordersequoptions',
bsplus::use_bs_popover(),
bsplus::use_bs_tooltip(),
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Order of lab parameters", icon("question")))),
title = "You can choose between three options to arrange laboratory parameters. Details on the AI-sortng option are given in the 'Information'-tab.", placement = "top", expanded = TRUE
),
shinyWidgets::prettyRadioButtons(
inputId = "orderinglab",
label = "",
choices = c(
"As in input" = "asinp",
"AI sorted" = "auto",
"Alphabetically" = "alphabetically"
),
selected = "alphabetically",
status = "warning"
),
shiny::conditionalPanel(
condition = "input.orderinglab == 'auto'",
shinyWidgets::pickerInput(
inputId = 'select.ai.first',
label = 'Select first visit for change assessment',
choices = NULL,
selected = NULL
),
shinyWidgets::pickerInput(
inputId = 'select.ai.last',
label = 'Select second visit for change assessment',
choices = NULL,
selected = NULL
),
shinyWidgets::pickerInput(
inputId ='clusterMethod',
label = 'Seriation algorithm',
choices = sort(
c(
'BBURCG', 'BBWRCG', 'TSP', 'R2E', 'MDS_metric',
'GW_single', 'GW_complete', 'GW_average', 'GW_ward',
'OLO_single', 'OLO_complete', 'OLO_average', 'OLO_ward',
'VAT','SA', 'Spectral',
'SPIN_NH', 'SPIN_STS'
)
),
selected = 'OLO_average',
multiple = FALSE,
options = list(
`live-search` = TRUE,
`header` = 'Select item'
)
)
),
shiny::actionButton(
inputId = "go3",
label = "Update selection!",
icon = icon("redo"),
style = paste0(
"color: ",
ColorBG,"; background-color: ",
ColorHighlight,"; border-color: ",
ColorBG
)
)
),
shinydashboard::menuItem(
text = 'Boxplot Colors',
icon = icon('palette'),
selected = TRUE,
startExpanded = FALSE,
purrr::map(paste0("id", 1:20), ~ boxPlotColorUI(id = .x)),
shiny::actionButton(
inputId = "go",
label = "Update Colors!",
icon = icon("redo"),
style = paste0("color: ", ColorBG, "; background-color: ", ColorHighlight, "; border-color: ", ColorBG)
)
)
),
shinydashboard::menuItem(
text = 'Data Upload',
tabName = 'datimport',
icon = icon('file-upload'),
selected = TRUE,
startExpanded = TRUE,
shinyWidgets::prettyRadioButtons(
inputId = 'impswitch',
label = 'Select file format',
status ="warning",
shape = 'round',
animation = 'smooth',
choices = c('*.RData file', '*.CSV file')
),
htmlOutput("err_message"),
tags$head(
tags$style(
"
font-size: 12px;
margin-size: 20px;
}"
)
),
shiny::uiOutput('impdata'),
shiny::conditionalPanel(condition = "output.flag == true",
shiny::selectizeInput(
inputId = 'select.visit',
label = 'Visits (exclude and rearrange)',
choices = NULL,
selected = NULL,
multiple = TRUE,
options = list('plugins' = list('remove_button', 'drag_drop'))
),
shiny::selectizeInput(
inputId = 'select.treatments',
label = 'Treatment groups (exclude and rearrange)',
choices = NULL,
selected = NULL,
multiple = TRUE,
options = list(
'plugins' = list('remove_button', 'drag_drop')
)
),
shinyWidgets::pickerInput(
inputId = 'select.lab',
label = 'Lab parameters',
choices = NULL,
selected = NULL,
multiple = TRUE,
options = list(
`actions-box` = TRUE,
`selected-text-format` = 'count > 0',
`count-selected-text` = '{0} selected (of {1})',
`live-search` = TRUE,
`header` = 'Select multiple items',
`none-selected-text` = 'All dropped!'
)
),
shiny::sliderInput(
inputId = 'select.toleratedPercentage',
label = 'Select percentage of tolerated missing values',
min = 0.25,
max = 0.75,
value = 0.5,
step = 0.05
)
)
),
shinydashboard::menuItem(
text = "Data Manual",
icon = icon("file"),
tabName = "datamanual"
),
shinydashboard::menuItem(
text = "Information",
icon = icon("info"),
tabName = "helptext"
)
)
),
shinydashboard::dashboardBody(
shiny::tags$head(
shiny::tags$style(
HTML(".shiny-notification {
position:fixed;
top: calc(50%);
left: calc(40%);
width: 350px;
font-size: 30px;
background-color: white;
font-color: black;
color:
}"
)
)
),
tags$head(
tags$style(
".shiny-progress {
top: 50% !important;
left: 50% !important;
margin-top: -100px !important;
margin-left: -250px !important;
color: blue;
font-size: 20px;
font-style: italic;
}"
)
),
tags$head(
tags$script(
'$(document).on("shiny:connected", function(e) {
Shiny.onInputChange("innerWidth", window.innerWidth);
});
$(window).resize(function(e) {
Shiny.onInputChange("innerWidth", window.innerWidth);
});'
)
),
tags$head(
tags$style(
shiny::HTML(
paste0(
".content-wrapper, .right-side { background-color: ", ColorBG, ";}
.checkbox-inline, .radio-inline {text-align: center; margin-left: 0px;
margin-right: 0px;padding: 0px;width: 20%;}
.main-sidebar .sidebar .sidebar-menu .treeview-menu {background-color: ", ColorPanel, " !important;}
.main-sidebar .sidebar .sidebar-menu .treeview-menu li:hover a {background-color: ", ColorApp, " !important;}
.skin-blue .main-header .logo { background-color: ", ColorApp, ";}
.skin-blue .main-header .logo:hover {background-color: ", ColorApp, ";}
.progress-bar{background-color:", ColorHighlight, ";}
.radio-item-warning {color: ", ColorHighlight, "}
.btn-warning{ background-color:", ColorHighlight, ";}
.btn-warning:hover{ background-color:", ColorHighlight, ";}
.skin-blue .main-header .navbar {background-color: ", ColorApp, ";}
/* main sidebar */
.skin-blue .main-sidebar {background-color: ", ColorApp, ";}
/* active selected tab in the sidebarmenu */
.skin-blue .main-sidebar .sidebar .sidebar-menu .active a{background-color: ", ColorPanel, ";}
/* other links in the sidebarmenu */
.skin-blue .main-sidebar .sidebar .sidebar-menu a{background-color: ", ColorApp, ";color:
.skin-blue .sidebar-menu > li.active > a,
.skin-blue .sidebar-menu > li:hover > a {border-left-color: ", ColorHighlight, ";}
/* other links in the sidebarmenu when hovered */
.skin-blue .main-sidebar .sidebar .sidebar-menu a:hover{background-color: ", ColorPanel, ";}
/* toggle button when hovered */
.skin-blue .main-header .navbar .sidebar-toggle:hover{background-color: ", ColorBG, ";}
.skin-blue .main-sidebar .navbar { background-color: ", ColorApp, ";}
.skin-blue .main-header .navbar .sidebar-toggle:hover{background-color: ", ColorBG, ";}"
)
)
)
),
shinyWidgets::chooseSliderSkin(
skin = "Modern",
color = "
),
tags$style(
type = 'text/css',
paste0(".bg-black {background-color: ", ColorApp, "!important; }")
),
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shiny::HTML(
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alt = 'Graphic cannot be displayed'
width = '682'
height = '286'>"
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from different perspectives."
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tags$div(
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paste(
"<i class='fa fa-file-upload'></i> ",
tags$span(
style = "font-size:150%",
"Upload your",
tags$span(style = "color:
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Select the file format and click
the 'Browse...'-button.",
sep = ""
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tags$div(
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paste(
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"<h2>File Format and Structure </h2>
<h4>File Format</h4>
Currently, the following two file formats are supported:
<ul>
<li> A <b>c</b>omma <b>s</b>eparated <b>v</b>alues (CSV) file </li>
<li> An RData file <br>
The RData file has to include a data frame with the following variables and formats:<br>
<ul>
<samp>
'data.frame': x obs. of y variables: <br>
$ SUBJIDN : int <br>
$ AVISIT : Factor <br>
$ TRTP : Factor <br>
$ LBTESTCD: Factor <br>
$ LBORRES : num <br>
$ LBORNRLO: chr <br>
$ LBORNRHI: chr <br> </samp></ul> </li>
</ul><br>
<h4>File Structure</h4>
In order to use the e<b>lab</b>orator, your laboratory data file has to include the following columns:<br>
<ul>
<li> a subject identifier (called <kbd>SUBJIDN</kbd>) </li>
<li> the visit (called <kbd>AVISIT</kbd>) </li>
<li> the treatment group (called <kbd>TRTP</kbd>) </li>
<li> an (abbreviated) name of the laboratory parameter (called <kbd>LBTESTCD</kbd>) </li>
<li> the laboratory value measurement (called <kbd>LBORRES</kbd>) </li>
<li> the lower limit of normal (LLN) (called <kbd>LBORNRLO</kbd>) </li>
<li> the upper limit of normal (ULN) (called <kbd>LBORNRHI</kbd>) </li>
</ul>
<h5>Example</h5>
The first 6 lines of an <i> examplary dataset </i> are shown in the following.<br>
<ul>
<samp>
SUBJIDN   AVISIT           TRTP    LBTESTCD LBORRES LBORNRLO LBORNRHI<br>
100080021 Randomization    Placebo HGB       15.2    12.0     16.0<br>
100080021 Visit 5          Placebo HGB       15.3    12.0     16.0<br>
100080021 End of Treatment Placebo HGB       15.9    12.0     16.0<br>
100080021 Follow-up        Placebo HGB       16.2    12.0     16.0<br>
100080053 Randomization    1 mg    HGB       14.7    12.0     16.0<br>
100080053 Visit 5          1 mg    HGB       13.9    12.0     16.0<br>
</samp>
</ul>
<br>
<h4>Important points to consider</h4>
<ul>
<li> Missing laboratory values must be coded as NA . We recommend carefully reading the section
on <i>Handling Missing Data</i> in the <i class='fa fa-info'></i> "),
shiny::actionLink("link_to_tab_info", "Information"),
HTML(
"-tab for correct interpretation. The section describes in detail how the e<b>lab</b>orator deals with missing data. </li>
<li> If a laboratory parameter has no lower or upper limit of normal, please do not insert any character in the respective cell but leave the cell empty or use the NA coding. Please do not use blank/space. </li>
<li> Variable names must be spelled correctly as shown above (please use upper case letters). </li>
<li> Do not use special characters for variable names or laboratory parameter names. </li>
<li> All laboratory measurements have to be numeric. That means, do not use '+', '-', '>', '<', 'negative' etc. For example, '<1' is not a valid laboratory measurement. </li>
<li> <b> Please always check your data carefully before uploading it to the e<b>lab</b>orator. </b></li>
</ul>
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"<h2>The Concept of the e<b>lab</b>orator for Clinical Trial Laboratory Data</h2>
The e<b>lab</b>orator provides a <i>complete overview</i> of laboratory results for each laboratory parameter and treatment group in a matrix-like structure. All the results related to a specific laboratory parameter are shown in a specific column. Results for a treatment group are presented within a row.
By providing this overview, you will be able to
identify <i>differences between treatment groups, similarities in laboratory parameters</i> and <i>frequent patterns</i>.<br> <br>
By using various types of analyses you will be able to view your laboratory data from different perspectives. The following three different types of analyses are available:
<ul> <li> Quantitative trends analysis </li>
<li> Qualitative trends analysis </li>
<li> Reference-value based pattern analysis </li></ul>
You can find a concept description of each type of analysis in the following. Available graphic options as well as missing data handling are described below. <br>
<br>
<h4><i class='fa fa-chart-line'></i><b><i> Quantitative Trends</i></b></h4>
Aim: Examine changes in laboratory values across study visits and explore whether changes differ between treatment groups. <br><br>
This type of analysis depicts the distribution of laboratory parameters in each study visit. An example is shown in Figure 1. Figure 1 shows the distribution of platelets (in giga/l) in the 2 mg dose group at all four visits during a study ('Randomization', 'Treatment 1', 'End of Treatment' and 'Follow-up 3'). Distributions are shown using boxplots. The middle 50% of patient-specific values fall inside the box. The median value is represented by the horizontal line crossing through the box and might be used as an indicator for the central tendency. Changes over time can be easily detected by a shift in the boxplots along the y-axis. In this example, a decrease in platelets is observed until the End of Treatment-Visit followed by a subsequent increase between the End of Treatment-Visit and the Follow-Up 3-Visit. <br><br>
<img src='www/Fig1.png' alt='Graphic cannot be displayed' width='300' height='300'>
<p> <i><b>Figure 1</b>: Example plot for quantitative trends analysis. The distribution of platelets (in giga/l) is shown for the 2 mg dose group at four study visits 'Randomization', 'Treatment 1', 'End of Treatment' and 'Follow-up 3'. Normal range, i.e. upper limit of normal and lower limit of normal, are indicated by dotted horizontal lines. </i></p>
Click the 'Open/Close Zoom Panel'-button and use the mouse to hover over a specific plot. The respective plot is be shown in a larger window. Further options are described below.
<ul>
<li> <h6><b>Same scales within lab parameter</b></h6>
You can select whether the y-axis range is the same as a specific laboratory parameter (default) or it has to be on the data in the respective treatment group.
Using the same range, simplyfies the comparison between the treatment groups. Using this option, extreme outliers will not appear (due to the cut-off scale) but they are indicated by arrows.
The values next to the arrow indicate the values of the outliers. When 'same scale among treatments' is not ticked, outliers are still shown when present.</li>
<li> <h6><b>Patient-specific values</b></h6>
You can permit or plot patient-specific values. When permitted (default), patient-specific values will be added as circles to the boxplots.
Note that outliers are indicated through dots and 'belong' to the boxplot (i.e. you can not suppress showing outliers). Moreover, you can choose whether patient-specific values are sorted
from smallest to largest (default). When 'draw connection lines' is ticked, the patients measurements at
study visits are connected. A blue connection line indicates a decrease, and an orange
connection line indicates an increase in the values.</li>
<li> <h6><b>Test for explorative trend detection</b></h6>
You can determine for changes between study visits by applying hypothesis testing. Note that a comparison of test results between the treatment groups is only recommended for balanced treatment groups, i.e. if treatment groups are of the same size.
When treatment groups have different sizes, comparisons between treatment groups should not be made because of a difference in the statistical power. The figure background is colored if the p-value of the respective test falls below a specified local significance level (called 'p-value cutoff').
The background is green for decreases, and yellow for increases (see e.g. Figure 1). <br>
The user can choose between two types of tests: the sign test and the t-test. The sign test is recommended for general use as it does not rely on distributional assumptions. It performs a check for a specific laboratory parameter and treatment group, whether there are more patients with an increase than patients with a decrease
between the two visits, or vice versa. Patients with consistency values (i.e. without any change in the values) are eliminated when applying the sign test. The t-test is recommended for expert users only because test assumptions should apply. <br>
The user is required to select two visits for defining any changes. If more than two visits are selected the first visit selected is tested against each of the remaining visits (pairwise tests). <br>
No adjustments for multiple testing are performed(tests for several treatment groups, several laboratory parameters and eventually several visits). Be aware that the multiple testing problem might lead to many mistakenly detected changes.
<b>The use of this feature is specifically for exploration, where significant test results must be interpreted with caution.</b></li>
</ul><br>
<h4><i class='fab fa-buromobelexperte'></i><b><i> Qualitative Trends</i></b></h4>
Aim: Study frequent time courses and check if they differ between treatment groups. <br><br>
This type of analysis assesses frequent time courses that are described through increases/decreases between two subsequent study visits.
A patient might, for example, have the following measurements for a specific laboratory parameter:
Value 3.2 at Randomization Visit; 1.6 at Treatment 1-Visit; 2.9 at the End of Treatment-Visit; 2.9 at the Follow-Up 3-visit.
The time course for this patient will be characterized as decrease (from 3.2 to 1.6) - increase (from 1.6 to 2.9) - stable (from 2.9 to 2.9). This pattern is represented as '- + ='.<br>
In this way, the patterns / time courses for each patient can be derived and the frequency of each pattern / time course can be counted. The time courses and frequencies are transferred to a diagram-like structure. Each cell of this diagram
represents one specific pattern / time course. The time courses are arranged in a symmetric way within the diagram. For example, the time course '+ + +' is represented in the cell in the top, while
the 'opposite' time course '- - -' is in the cell at the bottom of the diagram. There are three entries within each of the cells: the first and second entries show the absolute and relative number of subjects in the treatment group which have the specific
time course, and the third entry shows the respective time course. You can use the font size slider to display the entries and increase the size of the numbers. By default, the font size is set at 0, that is, all entries are blocked.
When a time course does not occur at all (i.e. the frequency and percentage are 0), the entries of the cell are not shown by default.
<br>
The frequency of a time course is shown by the color of the cell.
Darker colors reflect more frequent and lighter colors less frequent time courses. The color key is provided on the right side of Figure 2. <br><br>
It can also be suppressed by clicking on the 'Open/close'-button above the color legend.
No more than approx. 5 visits are recommended because diagrams will get too complex with increasing number of cells.<br>
<img src='www/Fig2.png' alt='Graphic cannot be displayed' width='500' height='350'>
<p><i><b>Figure 2</b>: Example plot for qualitative trends analysis (left) and color key (right). Frequent patterns of increases/decreases in platelets between four subsequent study visits within the 2mg dose group are shown.
The background of the cell is colored depending on the frequency of the respective pattern (cf. color key).</i></p>
Use the 'Open/Close Zoom Panel'-button to inspect a specific plot and see details. Further options are described below.
<ul>
<li><h6><b>Font size</b></h6>
Use the slider to increase (larger value) or decrease (smaller value). If the font size slider is set at 0 (default), no numbers or patterns are printed inside the cells. The background colors are more visible when numbers are not printed.
</li>
<li><h6><b>Method for defining stability</b></h6>
Often laboratory parameters are measured on a continuous scale and measurements have several decimals. Then it might make sense not to consider very slight changes in laboratory values
from one visit to another as increases or decreases. Instead laboratory values might be considered equal/stable even though they differ slightly.
This 'tolerated difference' can be controlled by the user. By default, stability is defined only when two values are exactly equal, that is, the tolerated difference is set at 0.
There are three options available for determining the tolerated difference:
<ul>
<li> Select the option 'IQR' (for interquartile range derived based on patient data at first visit) to determine the tolerated difference
for each laboratory parameter as a (user-specified) percentage in the IQR. </li>
<li> Select the option 'range' (i.e., maximum value minus minimum value observed based on patients data at first visit) to determine the tolerated
difference for each laboratory parameter as a (user-specified) percentage of the range. Note that the range is sensitive to extreme values observed
in the data (outliers). </li>
<li> Select the option 'reference range' (i.e., upper limit of normal minus lower limit of normal) to determine the tolerated difference for each laboratory parameter as a (user-specified) percentage of the reference range.
Note that the tolerated difference cannot be calculated for laboratory parameters which do not have a reference range defined by both the upper and the lower limit of normal. </li>
</ul>
The tolerated differences derived based on the method you have chosen and the percentage will be printed next to the diagram for each laboratory parameter.
</li>
<li><h6><b>Percentage</b></h6>
Use the slider to specify the percentage of IQR, range or reference range to determine the tolerated difference (see also 'Method for defining stability'). If set at 0 (default) the tolerated difference is 0, that is, stability is defined only if two values are completely equal. <br>
The exact value ('tolerated difference') is printed for each laboratory parameter next to the diagram.
</li>
<li><h6><b>Color scale</b></h6>
Use the drop down menu to select your favorite color scale. This color scale is used to color the cell backgrounds. The darker the background color, the more frequent the pattern.
</li>
</ul>
<br>
<h4><i class='fab fa-cloudsmith'></i><b><i> Reference-value Based Patterns</i></b></h4>
Aim: Assess how many patients have laboratory values outside the normal range during the study and whether there is a difference between treatment groups. <br><br>
The tree diagram consists of a starting point (i.e. the root of the tree) and several layers. The first layer represents the first visit, the second layer
the second visit, and so on. An example for a specific laboratory parameter in the placebo group is shown in Figure 3. You are able to track patients during the trial, and identify at which visits abnormal laboratory values occur. From the starting
point the sample is split up into two groups: one group with patients who have laboratory values outside the normal range at the first visit (lower path / orange circle)
and the other group of patients with laboratory values inside the normal range at the first visit (upper path / green circle). Each of the groups is then split
up based on the laboratory values at the second visit, and so on. <br>
The size of the circles is proportional to the number of patients. This enables users to identify frequent patterns (e.g. normal - abnormal - abnormal - normal) among visits.
The total number of patients is depicted inside the circle at the starting point. <br><br>
No more than approx. 5 visits are recommended because tree structures will get too complex with an increasing number of layers.<br>
<img src='www/Fig3.png' alt='Graphic cannot be displayed' width='350' height='400'>
<p> <i><b>Figure 3</b>: Example plot for reference-value based pattern analysis. The number of patients with hematocrit (HCT) values within the reference range(green) or outside the reference range(orange) at four visits, 'Randomization', 'Treatment 1', 'End of Treatment' and 'Follow-up 3', for the placebo group are shown. </i></p>
Use the 'Open/Close Zoom Panel'-button to check a specific plot and see details. Further options are described below.
<ul>
<li><h6><b>Font size</b></h6>
Use the slider to increase (larger value) or decrease (smaller value) the fontsize of the numbers inside the circles. When the font size is set at 0 (default), the numbers inside the circles are not shown.
</li>
<li><h6><b>Definition of abnormal values</b></h6>
Choose the definition of abnormal values. The following three options are available:
<ul>
<li> Select the option 'above ULN or below LLN' if laboratory values are considered abnormal if they either exceed the upper limit of normal (ULN) or if they fall below the lower limit of normal (LLN). </li>
<li> Select the option 'above ULN' if laboratory values are considered abnormal only if they exceed the upper limit of normal (ULN). </li>
<li> Select the option 'below LLN' if laboratory values are considered abnormal only if they fall below the lower limit of normal (LLN). </li>
</ul>
</li>
<li><h6><b> Factor multiplied with ULN or LLN</b></h6>
Define abnormal values in terms of ULN or LLN multiplied with a positive value. For example, if entering the value 1.5 abnormal values will be defined as values above 1.5xULN and/or below 1.5xLLN depending on the selection within the option 'Definition of abnormal values'.
</ul><br>
<h4><i class='fa fa-file-upload'></i><b> Data Upload</b></h4>
The data structure and format required for upload is outlined in the <i class='fa fa-file'></i>"
),
shiny::actionLink("link_to_structure_info", "Data Manual"),
HTML(
"-tab. Options for omitting laboratory parameters, treatment groups or visits are described below. <br>
<ul>
<li>
<h6><b>Visits (exclude and rearrange)</b></h6>
Click the 'x' next to the visit to remove visits. Please note that visits will be removed for every laboratory parameter. There is no possibility to include the visit for some laboratory parameters but to exclude it for others (expect for manually setting the values to NA in your data file for the respective laboratory parameter).
Drag and drop visits to change the order of the visits in the three types of analyses.</li>
<li>
<h6><b>Treatment groups (exclude and rearrange)</b></h6>
Click the 'x' next to the treatment group to remove treatment groups.
Drag and drop visits to change the order of the visits in the three types of analyses.</li>
<li>
<h6><b>Lab parameters</b></h6>
Click the laboratory parameters in the drop-down menu to deselect laboratory parameters or re-select previously omitted laboratory parameters. You can also use the text field to search for specific laboratory parameters.<br>
If you want to change the order of the laboratory parameters, please see the section below on 'Graphic Options'.</li>
</ul><br>
<h4><i class='fa fa-cogs'></i> <b>Graphic Options</b></h4>
The following graphic options are available:
<ul><li> <h6> <i class='fa fa-arrows-alt'></i><b> Panel/Plot Size</b></h6>
Adjust the plot size and height by using the sliders and click the 'Create/Update Plots'-button to reload the plots. </li>
<li><h6><i class='fa fa-sort-alpha-down'> </i><b> Arrange Lab Parameters</b></h6>
Use one of three options to change the arrangement of laboratory parameters in the three types of analyses. The following options are available to arrange laboratory parameters:
<ul>
<li> Select the option 'as in input' (default) to arrange laboratory parameters according to your preference. You can implement your individual arrangement of laboratory parameters by modifying the
arrangement in your input data file, such that your input data file reflects your preferred arrangement. </li>
<li> Select the option 'AI' (for artificial intelligence) to use an intelligent data-driven ordering. This option searches for an arrangement which locates laboratory parameters with (either positively or negatively)
correlated changes over time close to each other. Use the drop-down menu to select the visits which will be used for deriving the change. Several sorting algorithms
can be selected by the user. The default method is hierarchical clustering combined with optimal leaf ordering. More information on the methodology can be found in a ")
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"For methods that are based on hierarchical clustering, a dendrogram is also shown as output above the results window.
When laboratory parameters are not included at a specific visit
and if you have chosen this visit for defining change, the laboratory parameters cannot be used in the sorting algorithm. Therefore, the respective laboratory parameters will simply be relocated
to the arrangement/list obtained by the algorithm, and thus will be relocated at the last position. </li>
<li> Select the option 'alphabetically' to arrange laboratory parameters alphabetically. </li>
</ul>
After your selection click 'Update Order!' and the 'Create/Update Plots'-button to reload the plots with the changed arrangement. <br></li>
<li><h6><i class='fa fa-palette'></i> <b> Colors</b></h6>
Use the drop-down menu to select colors for each visit in the quantitative analysis. You may e.g. choose one color for visits at which patients were on treatment and another
color for visits (e.g. randomization, follow-up) at which patients were off treatment.</li><br>
</ul>
<h4><b>Missing Value Handling</b></h4>
The analyses of a specific laboratory parameter require that the patients data must be complete (non-missing for all visits). The following mechanisms are implemented:
<ul>
<li> The e<b>lab</b>orator-app automatically omits study visits for a laboratory parameter if more than 50% of patients have missing values for that laboratory parameter. You can also change this percentage using the <i class='fa fa-file-upload'></i> <b> Data Upload</b>-tab. </li>
<li> Patients who have a missing laboratory value at any of the 'considered' visits (i.e., excluding visits with more than 50% missing values, see first item, and visits that are manually removed by the user) will be excluded from all analyses of the respective laboratory parameter.</li></li>
</ul>
There are many different 'patterns' of missing values that might lead to a substantially reduced sample size. The user can, however, decide to automatically exclude some visits in order to avoid a possible substantial reduction in the sample size.
For example, if a specific laboratory parameter is missing at a specific visit for 40% of the subjects, then the analyses can only use
the remaining 60% of subjects with non-missing values for that visit (assuming no missing values for the remaining subjects at any of the other visits). A single visit with many missing values can therefore reduce the number of evaluable patients drastically.
If you want to avoid the exclusion of too many subjects due to a large percentage of missing values at a specific visit (and accept the omission of visits instead), you can set the
percentage of 'tolerated' missing values (which is by default set to 50%) to a small value.<br> <br>
You can check the number of patients per treatment group used for all analyses of a specific laboratory parameters in the 'Reference-value Based Pattern' analysis: the number in the 'starting point', i.e. the root of the tree-like structures, shows the total patient number used for the analysis of a specific treatment group and laboratory parameter.
Note that the number of subjects analyzed might differ between the laboratory parameters because the laboratory parameters are analyzed independently of each other.<br><br>
The following example illustrates which subjects and visits will be used in the analysis if missing data exist. <br>
<h5> <b> Example </b></h5>
The data of a study consists of three subjects and two laboratory parameters hematocrit (HCT) and hemoglobin (HGB). The user has not changed the percentage of 'tolerated' missing values, and therefore the default of 50% is used.
The original data is summarized below. <br> <br>
<style>
table {
font-family: arial, sans-serif;
border-collapse: collapse;
width: 100%;
}
td, th {
border: 1px solid
text-align: left;
padding: 8px;
}
tr:nth-child(2) {
background-color:
}
tr:nth-child{
background-color:
}
tr:first-child{
background-color:
}
</style>
<table>
<tr>
<th colspan='1'> </th>
<th colspan='3'>HCT</th>
<th colspan='3'>HGB</th>
</tr>
<tr>
<th>Subject</th>
<th>Visit 1</th>
<th>Visit 2</th>
<th>Visit 3</th>
<th>Visit 1</th>
<th>Visit 2</th>
<th>Visit 3</th>
</tr>
<tr>
<th> 1 </th>
<th> 42.8 </th>
<th> <font color='
<th> <font color='
<th> 13.8</th>
<th> 13.8</th>
<th> 14.1 </th>
</tr>
<tr>
<th>2</th>
<th> 41.2 </th>
<th> <font color='
<th> 42.2</th>
<th> 16.2</th>
<th> 15.8</th>
<th> 16.4</th>
</tr>
<tr>
<th>3</th>
<th> <font color='
<th> 40.9 </th>
<th> 40.7</th>
<th> <font color='
<th> 14.3</th>
<th> 13.3</th>
</tr>
</table>
<br>
<i> Which visits will be omitted for each of the two laboratory parameters? </i>
<ul>
<li>Visit 2 will be automatically omitted for HCT since more than 50% of the values are missing. The visits 1 and 3 remain for HCT and will be used in the analyses. </li>
<li> No visit will be omitted for HGB. At maximum 1/3 of the values are missing, thus all three visits will be saved for HGB.
Visits which are not automatically deleted will be referred to as 'considered ' visits in the following.
</li></ul>
<i> Which subjects will be omitted from the analyses for each of the two laboratory parameters? </i>
<ul>
<li> Subject 1 will not be used for the analysis of HCT because this subject has a missing value at the considered visit 3. In contrast, subject 1 has no missing value for any of the considered visits 1, 2 and 3, and is therefore included in the analysis of HGB.
</li>
<li> Subject 2 is included in both the analyses of HCT and HGB because it has no missing values for any of the considered visits (note that visit 2 has been omitted for HCT).
</li>
<li> Subject 3 is excluded for both HCT and HGB because it has a missing value at any of the considered visit.
</li></ul>
"
)
)
),
shinydashboard::tabItem(tabName = "qual",
shiny::fluidPage(
shiny::conditionalPanel(condition = "output.flag == false",
shiny::HTML(
"<img src = 'www/BAY_eLaborator_Logo.svg'
alt = 'Graphic cannot be displayed'
width = '682'
height = '286'>"
),
h2(
"is a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data
from different perspectives."
),
br(),
tags$div(
HTML(
paste(
"<i class='fa fa-file-upload'></i> ",
tags$span(style = "font-size:150%",
"Upload your",
tags$span(style = "color:
" by using the 'Data Upload'-tab in the task bar on the left.
Select the file format and click
the 'Browse...'-button.",
sep = ""
)
)
)
),
tags$div(
HTML(
paste(
"<i class= 'fa fa-file'></i> ",
tags$span(style = "font-size:150%","Click the 'Data Manual'-tab for the required format and structure for laboratory data file.")
)
)
),
tags$div(
HTML(
paste(
"<i class='fa fa-info'></i> ",
tags$span(
style = "font-size:150%"," If you want to access information on the elaborator, click the 'Information'-tab.", sep = ""
)
)
)
)
),
shiny::conditionalPanel(condition = "output.flag == true",
shinydashboard::box(
width = NULL,
title = span(shiny::tagList(' ',icon("cogs"))),
background = 'black',
solidHeader = TRUE,
collapsible = TRUE,
shiny::fluidRow(
shiny::column(2,
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Font size", icon("question")))),
title = "Adapt font size. Set font size to 0 to exclude any text.",
placement = "top",
expanded = TRUE
),
shiny::sliderInput(
inputId = 'cex.trend',
label = '',
min = 0,
max = 5,
value = 0,
step = 0.5
)
),
shiny::column(2,
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Choose method for defining stability", icon("question")))),
title = "You can specify a tolerated difference in which a change in two adjacent lab values are considered stable ('='). This tolerated difference can be derived as a (small) percentage of the interquartile range (IQR), the range or the reference range. The IQR and the range is evaluated at the first visit across all treatment groups.",
placement = "bottom",
expanded = TRUE
),
shinyWidgets::prettyRadioButtons(
inputId = 'method',
label = ' ',
choices = c(
'Interquartile Range' = 'InQuRa',
'Range' = 'Range',
'Reference Range' = 'Reference Range'
),
selected = "InQuRa",
status = "warning"
)
),
shiny::column(2,
bsplus::bs_embed_tooltip(tag = h4(span(shiny::tagList("Select percentage", icon("question")))),
title = "Select a percent value in the method chosen in order to derive the critical boundary. If set to 0, then adjacent lab values must be exactly equal in order to be considered stable.",
placement = "top",
expanded = TRUE
),
shiny::sliderInput(
inputId = 'percent',
label = "",
min = 0,
max = 20,
value = 0,
step = 0.5
)
),
shiny::column(2,
bsplus::bs_embed_tooltip(tag = h4(span(shiny::tagList("Select a color scale", icon("question")))),
title = "Select your favorite color scale used for highlighting frequent patterns.", placement = "top", expanded = TRUE
),
shinyWidgets::pickerInput(
inputId = 'select.pal1',
label = "",
choices = names(colChoice),
selected = names(colChoice)[1],
multiple = FALSE,
options = list(
`live-search` = TRUE,
`style`='background: btn-warning',
`header`='Select item'
)
),
shiny::plotOutput('prev.pal1', height = '20px')
),
shiny::column(width = 2, offset = 4,
shiny::helpText(
HTML('<p style="color:white"> You can minimize/maximize this window with the -/+ button on the top right of the panel </p>')
)
)
)
),
shiny::conditionalPanel(condition = "output.ai == true",
shinydashboard::box(
width = NULL,
title = span(shiny::tagList('', icon("sort-alpha-down"),'Dendrogram - (Click on the + symbol to open)')),
solidHeader = TRUE,
background = 'black',
collapsible = TRUE,
collapsed = TRUE,
shiny::fluidRow(
shiny::column(12,
shiny::plotOutput(
'dendro_2',
height = "250px"
)
)
)
)
),
shiny::conditionalPanel(condition = "output.flag == true",
shiny::fluidRow(
shiny::column(2,
shiny::actionButton(
inputId = "apply_qual_plot",
label = paste0('Create Plots'),
icon = icon("object-group")
),
shiny::uiOutput('cont2')
),
shiny::column(5, offset = 2,
shiny::uiOutput('cont2_text')
)
)
),
shiny::uiOutput('tab2', width = 'auto'),
shiny::uiOutput('legendpanel'),
shiny::uiOutput('hoverpanel2')
)
)
),
shinydashboard::tabItem(
tabName = "rvbp",
shiny::fluidPage(
shiny::conditionalPanel(condition = "output.flag == false",
shiny::HTML(
"<img src = 'www/BAY_eLaborator_Logo.svg'
alt = 'Graphic cannot be displayed'
width = '682'
height = '286'>"
),
h2(
"is a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data
from different perspectives."
),
br(),
tags$div(
HTML(
paste(
"<i class='fa fa-file-upload'></i> ",
tags$span(style = "font-size:150%",
"Upload your",
tags$span(style = "color:
" by using the 'Data Upload'-tab in the task bar on the left.
Select the file format and click
the 'Browse...'-button.",
sep = ""
)
)
)
),
tags$div(
HTML(
paste(
"<i class= 'fa fa-file'></i> ",
tags$span(style = "font-size:150%","Click the 'Data Manual'-tab for the required format and structure for laboratory data file.")
)
)
),
tags$div(
HTML(
paste(
"<i class='fa fa-info'></i> ",
tags$span(style = "font-size:150%"," If you want to access information on the elaborator, click the 'Information'-tab.", sep = "")
)
)
)
),
shiny::conditionalPanel(condition = "output.flag == true",
shinydashboard::box(
width = NULL,
title = span(shiny::tagList('', icon("cogs"))),
background = 'black',
solidHeader = TRUE,
collapsible = TRUE,
shiny::column(2,
bsplus::use_bs_popover(),
bsplus::use_bs_tooltip(),
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Font size", icon("question")))),
title = "Adapt font size. Set font size to 0 to suppress any text.",
placement = "top",
expanded = TRUE
),
shiny::sliderInput(
inputId = 'cex.rvbp',
label = '',
min = 0,
max = 5,
value = 0,
step = 0.5
)
),
shiny::column(2,
bsplus::use_bs_popover(),
bsplus::use_bs_tooltip(),
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Definition of abnormal values", icon("question")))),
title = "Select how to define abnormal values based on the upper limit of normal (ULN) and lower limit of normal (LLN).",
placement = "top",
expanded = TRUE
),
shinyWidgets::prettyRadioButtons(
inputId = "criterion",
label = tags$div(tags$h4("")),
choices = c(
"above ULN OR below LLN" = "within",
"above ULN" = "greater",
"below LLN" = "less"
),
selected = "within",
status = "warning"
)
),
shiny::column(2,
bsplus::use_bs_popover(),
bsplus::use_bs_tooltip(),
bsplus::bs_embed_tooltip(
tag = h4(span(shiny::tagList("Factor multiplied with ULN or LLN", icon("question")))),
title = "Define abnormal values in terms of ULN or LLN multiplied with a positive value. E.g. the factor 2
means that abnormal values are defined as values above 2xULN and/or below 2xLLN.",
placement = "top",
expanded = TRUE
),
shiny::numericInput(
inputId = "abnormal_values_factor",
label = "",
value = 1,
min = 0,
step = 0.1
)
),
shiny::column(width = 2, offset = 4,
shiny::helpText(HTML('<p style="color:white"> You can minimize/maximize this window with the -/+ button on the top right of the panel </p>'))
)
),
shiny::conditionalPanel(
condition = "output.ai == true",
shinydashboard::box(
width = NULL,
title = span(shiny::tagList('',icon("sort-alpha-down"),'Dendrogram - (Click on the + symbol to open)')),
solidHeader = TRUE,
background = 'black',
collapsible = TRUE,
collapsed = TRUE,
shiny::fluidRow(
shiny::column(12,
shiny::plotOutput(
outputId = 'dendro_3',
height = "250px"
)
)
)
)
),
shiny::fluidRow(
shiny::column(12,
shiny::conditionalPanel(condition = "input.abnormal_values_factor >= 0 && input.abnormal_values_factor != undefined && output.flag == true",
shiny::fluidRow(
shiny::column(2,
shiny::actionButton(
inputId = "apply_ref_plot",
label = paste0('Create Plots'),
icon = icon("object-group")
),
shiny::uiOutput('cont3')
),
shiny::column(5, offset = 2,
shiny::uiOutput('cont3_text')
)
)
),
shiny::conditionalPanel(condition = "input.abnormal_values_factor < 0 || input.abnormal_values_factor == undefined",
HTML('<p style="color:
),
shiny::uiOutput('tab3', width = 'auto'),
shiny::uiOutput('hoverpanel3')
)
)
)
)
)
),
tags$script(
HTML("$('body').addClass('sidebar-mini');")
),
tags$head(
tags$style(
HTML(
" h1 {font-family: 'Arial';line-height: 1.1;color:
)
)
),
tags$script(
HTML(
'$(document).ready(function() {
$("header").find("nav").append(\' <h4 style="color:white"> A New Perspective on Laboratory Data </h4>\');
})'
)
),
tags$style(
type = 'text/css',
".selectize-dropdown-content {max-height: 50px;}"
),
tags$style(
type = 'text/css', ".selectize-input { background-color:
)
)
) |
NULL
dleftparetolognormal <- function(x, shape1 = 1.5, meanlog = -0.5, sdlog = 0.5, log = FALSE) {
d <- shape1 * x^(shape1 - 1) * exp((-shape1 * meanlog + ((-shape1)^2 * sdlog^2) / 2)) * pnorm(((log(x) - meanlog + shape1 * sdlog^2) / sdlog), lower.tail = FALSE)
if (log) {
d <- log(d)
}
return(d)
}
pleftparetolognormal <- function(q, shape1 = 1.5, meanlog = -0.5, sdlog = 0.5, lower.tail = TRUE, log.p = FALSE) {
P1 <- pnorm(((log(q) - meanlog) / sdlog))
P2 <- q^(shape1) * exp(-shape1 * meanlog + ((-shape1)^2 * sdlog^2) / 2) * pnorm(((log(q) - meanlog + shape1 * sdlog^2) / sdlog), lower.tail = FALSE)
P2[pnorm(((log(q) - meanlog + shape1 * sdlog^2) / sdlog), lower.tail = FALSE) == 0] <- 0
p <- P1 + P2
p <- if (lower.tail) {
p
} else {
1 - p
}
p <- if (log.p) {
log(p)
} else {
p
}
return(p)
}
qleftparetolognormal <- function(p, shape1 = 1.5, meanlog = -0.5, sdlog = 0.5, lower.tail = TRUE, log.p = FALSE) {
p <- if (log.p) {
exp(p)
} else {
p
}
p <- if (lower.tail) {
p
} else {
1 - p
}
q <- suppressWarnings(uniroot(function(q, shape1, meanlog, sdlog) {
pleftparetolognormal(q, shape1, meanlog, sdlog) - p
}, interval = c(1e-6, 1), extendInt = c("yes"), shape1, meanlog, sdlog)$root)
return(q)
}
qleftparetolognormal <- Vectorize(qleftparetolognormal, vectorize.args = "p")
mleftparetolognormal <- function(r = 0, truncation = 0, shape1 = 1.5, meanlog = -0.5, sdlog = 0.5, lower.tail = TRUE) {
truncation <- as.vector(truncation)
I1 <- -shape1 * exp(-shape1 * meanlog + 1 / 2 * (shape1^2 * sdlog^2)) * (truncation^(r + shape1)) / (r + shape1) * (1 - pnorm(((log(truncation) - meanlog + shape1 * sdlog^2) / sdlog)))
I2 <- (shape1 * (exp((r) * meanlog + ((r) * sdlog)^2 / 2)) / (r + shape1) * (1 - pnorm((log(truncation) - (meanlog - shape1 * sdlog^2)) / sdlog - sdlog * ((r) + shape1))))
I1[is.na(I1)] <- 0
moment <- I1 + I2
notrunc_I1 <- -shape1 * exp(-shape1 * meanlog + 1 / 2 * (shape1^2 * sdlog^2)) * (0^(r + shape1)) / (r + shape1) * (1 - pnorm(((log(0) - meanlog + shape1 * sdlog^2) / sdlog)))
notrunc_I2 <- (shape1 * (exp((r) * meanlog + ((r) * sdlog)^2 / 2)) / (r + shape1) * (1 - pnorm((log(0) - (meanlog - shape1 * sdlog^2)) / sdlog - sdlog * ((r) + shape1))))
notrunc_I1[is.na(notrunc_I1)] <- 0
notrunc_moment <- notrunc_I1 + notrunc_I2
if (lower.tail) {
moment <- notrunc_moment - moment
}
return(moment)
}
rleftparetolognormal <- function(n, shape1 = 1.5, meanlog = -0.5, sdlog = 0.5) {
r <- exp(meanlog + sdlog * rnorm(n) - rexp(n) / shape1)
return(r)
}
leftparetolognormal_plt <- function(shape1 = 1.5, meanlog = -0.5, sdlog = 0.5, a = 1, b = 1, inv = FALSE) {
if (!inv) {
b <- 1 / b
a <- (1 / a)^b
}
newmeanlog <- (meanlog - log(a)) / b
newshape1 <- shape1 * b
newsdlog <- sdlog / b
return_list <- list(coefficients = c(shape1 = newshape1, meanlog = newmeanlog, sdlog = newsdlog))
return(return_list)
}
leftparetolognormal.mle <- function(x, lower = c(1e-10, 1e-10), upper = c(Inf, Inf), start = NULL) {
y <- log(x)
n <- length(y)
logl <- function(x, shape1, meanlog, sdlog, n) {
n * log(shape1) + sum(log(dnorm((y - meanlog) / sdlog))) +
sum(log(pnorm((shape1 * sdlog + (y - meanlog) / sdlog), lower.tail = FALSE) / dnorm((shape1 * sdlog + (y - meanlog) / sdlog))))
}
mle <- function(par, y, n) {
shape1 <- par[1]
meanlog <- mean(y) + 1 / shape1
sdlog <- par[2]
nlogl <- -logl(x = y, shape1 = shape1, meanlog = meanlog, sdlog = sdlog, n = n)
return(nlogl)
}
if (is.null(start)) {
start <- c(shape1 = 2, sdlog = sd(log(x)))
}
start <- start[c("shape1", "sdlog")]
optim.out <- suppressWarnings(nlminb(as.numeric(start), mle, y = y, n = n, lower = lower, upper = upper, control = list(maxit = 1e5)))
par <- optim.out$par
shape1 <- par[1]
meanlog <- mean(y) + 1 / shape1
sdlog <- par[2]
convergence <- ifelse(optim.out$convergence == 0, TRUE, FALSE)
return_list <- list(coefficients = c(meanlog = meanlog, sdlog = sdlog, shape1 = shape1), convergence = convergence, n = n, np = 3)
return(return_list)
} |
require(ltm)
infoGPCM <- ltm:::infoGPCM
probs <- ltm:::probs
count.rows <- function(x) {
if (is.matrix (x) && (dim (x) [2] == 1))
x <- as.vector (x)
order.x <- do.call(order,as.data.frame(x))
if (is.vector (x)) {
equal.to.previous <-
x[tail(order.x,-1)] == x[head(order.x,-1)]
} else {
equal.to.previous <-
rowSums(x[tail(order.x,-1),] != x[head(order.x,-1),])==0
}
indices <- split (order.x, cumsum (c (TRUE, !equal.to.previous)))
if (is.vector (x)) {
x <- x [sapply (indices, function (x) x [[1]]), drop = FALSE]
} else {
x <- x [sapply (indices, function (x) x [[1]]), ,drop = FALSE]
}
data.frame (counts = sapply (indices, length) , ind = I(indices), x)
}
infoprobs <- function (betas, z) {
cpr <- cprobs(betas, z)
ipr <- iprobs(betas, z)
sum.cprs <- lapply(cpr, function(x) {
nr <- nrow(x)
if(ncol(x) == 1){
t((1 - c(x[1, ], x[-nr, ] + x[-1, ]))^2)
} else if(ncol(x) > 1){
t((1 - rbind(x[1, ], x[-nr, ] + x[-1, ]))^2)
}
})
betas. <- sapply(betas, function(x) x[length(x)])
for (i in 1:length(betas)) sum.cprs[[i]] <- betas.[i]^2 * ipr[[i]] * sum.cprs[[i]]
do.call(cbind, lapply(sum.cprs, rowSums))
}
cprobs <- function (betas, z, eps = .Machine$double.eps^(1/3)){
lapply(betas, function(x, z) {
nx <- length(x)
out <- plogis(x[-nx] - matrix(x[nx] * z, nx - 1, length(z),
TRUE))
if (any(ind <- out == 1))
out[ind] <- 1 - eps
if (any(ind <- out == 0))
out[ind] <- eps
rbind(out, 1)
}, z = z)
}
iprobs =
function (betas, z)
{
n <- length(z)
gammas <- lapply(betas, function(x) {
nx <- length(x)
cbind(plogis(matrix(x[-nx], n, nx - 1, TRUE) - x[nx] * z), 1)
})
lapply(gammas, function(x) {
nc <- ncol(x)
if(n==1){
c(x[, 1], x[, 2:nc] - x[, 1:(nc - 1)])
} else if(n>1){
cbind(x[, 1], x[, 2:nc] - x[, 1:(nc - 1)])
}
})
}
inf.func.ltm <-
function (object, items = NULL, ...)
{
if (!class(object) %in% c("grm", "gpcm", "ltm", "rasch",
"tpm"))
stop("'object' must inherit from either class 'grm', class 'gpcm', class 'ltm', class 'rasch' or class 'tpm'.\n")
p <- ncol(object$X)
itms <- if (!is.null(items)) {
if (!is.numeric(items) && length(items) > p)
stop("'items' should be a numeric vector of maximum length ",
p, ".\n")
if (any(!items %in% 1:p))
stop("'items' should contain numbers in: ", paste(1:p,
collapse = ", "), " indicating the items.\n")
items
}
else 1:p
if (class(object) == "ltm" && (object$ltst$factors > 1 |
any(unlist(object$ltst[c("inter", "quad.z1", "quad.z2")]))))
stop("Information is currently computed only for the one-factor two-parameter logistic model.\n")
f <- function(z) {
switch(class(object),
grm = rowSums(infoprobs(object$coefficients, z)[, itms, drop = FALSE]),
gpcm = rowSums(infoGPCM(object$coefficients, z, object$IRT.param)[, itms, drop = FALSE]),
ltm = {
betas <- object$coefficients
Z <- cbind(1, z)
mat <- t(t(plogis(Z %*% t(betas)) * (1 - plogis(Z %*% t(betas)))) * betas[, 2]^2)
rowSums(mat[, itms, drop = FALSE])
}, rasch = {
betas <- object$coefficients
Z <- cbind(1, z)
mat <- betas[1, 2]^2 * plogis(Z %*% t(betas)) * (1 - plogis(Z %*% t(betas)))
rowSums(mat[, itms, drop = FALSE])
}, tpm = {
thetas <- object$coefficients
Z <- cbind(1, z)
betas <- thetas[, 2:3]
cs <- plogis(thetas[, 1]) * object$max.guessing
pi. <- plogis(Z %*% t(betas))
cs <- matrix(cs, nrow(Z), p, TRUE)
pi <- cs + (1 - cs) * pi.
pqr <- pi * (1 - pi) * (pi./pi)^2
mat <- t(t(pqr) * betas[, 2]^2)
rowSums(mat[, itms, drop = FALSE])
})
}
f
}
Jeffreys <- function(ltm.obj=NULL, inf.mat=NULL, inf.func=NULL, return="prior", spl.method="natural", range.int=c(-Inf, Inf)){
if(sum(is.null(ltm.obj), is.null(inf.mat), is.null(inf.func)) != 2)
stop("One of ltm.obj, inf.mat, or inf.func must be supplied.\n")
int.lower <- range.int[1]
int.upper <- range.int[2]
if(((class(ltm.obj) == "grm") | (class(ltm.obj) == "gpcm")) & any(abs(range.int) > 50)){
if(int.lower < -50) { int.lower <- -50 }
if(int.upper > 50) { int.upper <- 50 }
}
if(!is.null(inf.func)){
Jp.nc <- integrate(function(x){sqrt(inf.func(x))}, int.lower, int.upper)$val
} else if(!is.null(inf.mat)){
inf.func.interp <- splinefun(inf.mat[,1], inf.mat[,2], method=spl.method)
inf.func <- function(theta){
out <- inf.func.interp(theta)
out[out < 0] <- 0
out
}
Jp.nc <- integrate(function(x){sqrt(inf.func(x))}, int.lower, int.upper)$val
} else if(!is.null(ltm.obj)){
inf.func <- inf.func.ltm(ltm.obj)
Jp.nc <- integrate(function(x){sqrt(inf.func(x))}, int.lower, int.upper)$val
}
Jp <- function(theta){
sqrt(inf.func(theta)) / Jp.nc
}
switch(return,
prior = Jp,
nc = Jp.nc,
both = list(prior = Jp, nc = Jp.nc)
)
}
rJeffreys <- function(n, prior, range.int=c(-Inf, Inf)){
int.lower = range.int[1]
int.upper = range.int[2]
Jpinv.min <- function(xval, prob){ abs(prob - integrate(prior, int.lower, xval)$val) }
Jpinv <- function(unvar){ nlminb(qnorm(unvar), Jpinv.min, prob=unvar, lower=int.lower, upper=int.upper)$par }
sapply(runif(n), Jpinv)
}
logL.ltm <- function(z, dat, i, ltm.obj){
switch(class(ltm.obj),
ltm = { logL <- function (z, dat, ltm.obj, i) {
betas = ltm.obj$coef
y = dat[i, ]
Z <- c(1, z)
names(Z) <- c("(Intercept)", "z1")
Z <- Z[match(colnames(betas), names(Z))]
pr <- probs(c(betas %*% Z))
sum(dbinom(y, 1, pr, log = TRUE), na.rm = TRUE)
}
},
rasch = { logL <- function (z, dat, ltm.obj, i) {
betas = ltm.obj$coef
y = dat[i, ]
pr <- probs(c(betas %*% c(1, z)))
sum(dbinom(y, 1, pr, log = TRUE), na.rm = TRUE)
}
},
tpm = { logL <- function (z, dat, ltm.obj, i) {
thetas = ltm.obj$coef
betas <- thetas[, 2:3]
y = dat[i, ]
cs <- plogis(thetas[, 1]) * ltm.obj$max.guessing
pr <- cs + (1 - cs) * probs(c(betas %*% c(1, z)))
sum(dbinom(y, 1, pr, log = TRUE), na.rm = TRUE)
}
},
gpcm = { if(min(dat) < 1) {stop("Responses in data must be coded 1, 2, 3, ...")}
logL <- function (z, dat, ltm.obj, i) {
betas = ltm.obj$coefficients
p = length(betas)
y = dat[i, ]
log.prs <- ltm:::crf.GPCM(betas, z, IRT.param = ltm.obj$IRT.param, log = TRUE)
log.pxz <- numeric(p)
for (j in 1:p) {
log.pxz[j] <- if (!is.na(y[j])) log.prs[[j]][y[j]] else 0
}
if(any(!is.finite(log.pxz))){
log.pxz[log.pxz == -Inf] = log(.Machine$double.xmin)
log.pxz[log.pxz == Inf] = log(.Machine$double.xmax)
}
sum(log.pxz, na.rm = TRUE)
}
},
grm = { if(min(dat) < 1) {stop("Responses in data must be coded 1, 2, 3, ...")}
logL <- function (z, dat, ltm.obj, i) {
betas = ltm.obj$coefficients
p = length(betas)
y = dat[i, ]
gammas <- lapply(betas, function (x) {
n <- length(z)
nx <- length(x)
c(plogis(matrix(x[-nx], n, nx - 1, TRUE) - x[nx] * z), 1)
})
log.prs <- lapply(gammas, function (x) {
nc <- length(x)
prs <- c(x[1], x[2:nc] - x[1:(nc - 1)])
prs[prs == 0] = .Machine$double.eps
log(prs)
})
log.pxz <- numeric(p)
for (j in 1:p) {
log.pxz[j] <- if (!is.na(y[j])) log.prs[[j]][y[j]] else 0
}
sum(log.pxz, na.rm = TRUE)
}
}
)
Vectorize(logL, "z")(z=z, dat=dat, i=i, ltm.obj=ltm.obj)
}
iota <-
function(ltm.obj, logL.fun, fscore.obj=NULL, data=NULL, prior=NULL, theta0=NULL, range.int=c(-Inf, Inf), range.theta = c(-10, 10)){
if( (!is.null(fscore.obj) & !is.null(data)) | (is.null(fscore.obj) & is.null(data)) ){ stop("One of fscore.obj or data must be supplied.\n") }
if( !is.null(fscore.obj) & (class(fscore.obj) != "fscores") ){ stop("fscore.obj must be a fscores object.\n") }
if( (class(prior) != "function") | (is.null(prior)) ){ stop("A prior must be supplied in the form of a function.\n") }
if(any(!is.finite(range.theta))) {stop("range.theta must be finite.")}
int.lower = range.int[1]
int.upper = range.int[2]
if(((class(ltm.obj) == "grm") | (class(ltm.obj) == "gpcm")) & any(abs(range.int) > 10)){
if(int.lower < -10) { int.lower = -10 }
if(int.upper > 10) { int.upper = 10 }
}
if( missing(logL.fun) & missing(ltm.obj) ) {stop("A log-likelihood function or ltm object must be supplied.\n")}
if( missing(logL.fun) & (!missing(ltm.obj)) ) {
logL.fun <- function(z, dat, i){ logL.ltm(z=z, dat=dat, i=i, ltm.obj=ltm.obj) }
}
if(!is.null(data)){
data.unique = count.rows(data)
N = nrow(data)
N.u = nrow(data.unique)
p.xe = data.unique$counts / N
dat.mat = as.matrix(data.unique[,-c(1,2)])
} else if(!is.null(fscore.obj)){
N = sum(fscore.obj$score.dat$Obs)
N.u = nrow(fscore.obj$score.dat)
p.xe = fscore.obj$score.dat$Obs / N
dat.mat = as.matrix(fscore.obj$score.dat[,1:nrow(fscore.obj$coef)])
}
if( !is.null(theta0) ){
if( (mode(theta0) == "numeric") & (length(theta0) > 1) & (length(theta0) != N.u) ) {
stop("theta0 must be a scalar or equal in length to the number of unique response patterns.")
} else if( mode(theta0) == "character" ) {
if( length(theta0) > 1 ) {
stop("theta0 must be a numeric scalar or vector, \"max.prior\", or NULL\n")
} else if( (theta0 != "max.prior") ) {
stop("theta0 must be a numeric scalar or vector, \"max.prior\", or NULL\n")
}
}
}
I.x = vector(length=N.u)
p.x = vector(length=N.u)
dlogp.xt <- function(z, i){
logL.fun(z=z, dat=dat.mat, i=i)
}
dp.tx <- function(z, i){
exp(dlogp.xt(z=z, i=i))*prior(z)
}
dI.x <- function(z, i, p.x.i){
(dp.tx(z=z, i=i) / p.x.i) * (dlogp.xt(z=z, i=i) - log(p.x.i))
}
for(i in 1:N.u) {
p.x[i] = integrate(dp.tx, int.lower, int.upper, i=i)$val
I.x[i] = integrate(dI.x, int.lower, int.upper, i=i, p.x.i=p.x[i])$val
}
I = sum(I.x*p.xe)
if( (!is.null(theta0)) & (length(theta0) == 1) ){
if(theta0 == "max.prior"){ theta0 = optimize(prior, range.theta, maximum=T)$max }
logL0 = vector(length=N.u)
for(i in 1:N.u) {
logL0[i] = dlogp.xt(z=theta0, i=i)
}
} else if( (!is.null(theta0)) & (length(theta0) == N.u) ){
logL0 = vector(length=N.u)
for(i in 1:N.u) {
logL0[i] = dlogp.xt(z=theta0[i], i=i)
}
}
if( !is.null(data) ){
I.x.exp = vector(length=N)
p.x.exp = vector(length=N)
for(i in 1:N.u){
I.x.exp[data.unique$ind[[i]]] = I.x[i]
p.x.exp[data.unique$ind[[i]]] = p.x[i]
}
I.x = I.x.exp
p.x = p.x.exp
if( !is.null(theta0) ){
logL0.exp = vector(length=N)
for(i in 1:N.u){logL0.exp[data.unique$ind[[i]]] = logL0[i]}
logL0 = logL0.exp
}
}
if( is.null(theta0) ) {
list(I=I, I.x=I.x, p.x=p.x )
} else if( !is.null(theta0) ) {
list(I=I, I.x=I.x, p.x=p.x, logL0=logL0, logNL0.pval = 1-pchisq(-2*((logL0 - log(p.x))-I.x), df=1) )
}
}
iota.l <- function(x){
if(class(x) %in% c("ltm", "grm", "gpcm")){
nc = Jeffreys(x, return="nc")
} else if( class(x) == "numeric" ) {
nc = x
} else { stop("x must be an ltm object or a normalizing constant.\n") }
(1/2)*log(1/(2*pi*exp(1))) + log(nc)
}
iota.u <- function(prior, range.int=c(-Inf, Inf)){
if(class(prior) != "function"){ stop("prior must be a function.\n") }
int.lower = range.int[1]
int.upper = range.int[2]
Hint <- function(theta){
p.t = prior(theta)
integrand = prior(theta)*log(prior(theta))
integrand[p.t == 0] = 0
integrand
}
-integrate(Hint, int.lower, int.upper)$val
}
nmru <- function(ltm.obj, range.int=c(-Inf, Inf)){
int.lower = range.int[1]
int.upper = range.int[2]
if(!(class(ltm.obj) %in% c("ltm", "grm", "gpcm"))){ stop("x must be an ltm object.\n") }
Jp.obj = Jeffreys(ltm.obj, return="both")
prior = Jp.obj$prior
nc = Jp.obj$nc
iota.lower = iota.l(nc)
iota.upper = iota.u(prior, range.int=range.int)
list(val=iota.lower/iota.upper, iota.l=iota.lower, iota.u=iota.upper)
}
iota.c <-
function(ltm.obj, M=NULL, prior=NULL, logL.fun=NULL, rirm=NULL, range.int=c(-Inf, Inf)){
if(is.null(M)){stop("The number of Monte Carlo replications M must be supplied.\n")}
if((!is.null(prior)) & (class(prior) != "function") ){ stop("The reference prior must be supplied in the form of a function.\n") }
int.lower = range.int[1]
int.upper = range.int[2]
if(((class(ltm.obj) == "grm") | (class(ltm.obj) == "gpcm")) & any(abs(range.int) > 10)){
if(int.lower < -10) { int.lower = -10 }
if(int.upper > 10) { int.upper = 10 }
}
if( missing(ltm.obj) & (is.null(prior) | is.null(logL.fun) | is.null(rirm)) ) {
stop("A log-likelihood function, response generating function, and reference prior must be supplied in the absence of an ltm object.\n")
}
if( !missing(ltm.obj) ) {
prior = Jeffreys(ltm.obj=ltm.obj)
logL.fun <- function(z, dat, i){ logL.ltm(z=z, dat=dat, i=i, ltm.obj=ltm.obj) }
switch(class(ltm.obj),
ltm = { rirm <- function(M, m.theta) { rmvlogis(n=M, thetas=coef(ltm.obj), IRT=ltm.obj$IRT.param, z.vals=m.theta) } },
rasch = { rirm <- function(M, m.theta) { rmvlogis(n=M, thetas=coef(ltm.obj), IRT=ltm.obj$IRT.param, z.vals=m.theta) } },
gpcm = { rirm <- function(M, m.theta) { rmvordlogis(n=M, thetas=as.list(as.data.frame(t(coef(ltm.obj)))), IRT=ltm.obj$IRT.param, z.vals=m.theta, model=class(ltm.obj)) } },
grm = { rirm <- function(M, m.theta) { rmvordlogis(n=M, thetas=as.list(as.data.frame(t(coef(ltm.obj)))), IRT=ltm.obj$IRT.param, z.vals=m.theta, model=class(ltm.obj)) } }
)
}
m.theta = rJeffreys(M, prior, range.int=c(int.lower, int.upper))
data = rirm(M=M, m.theta=m.theta)
data.unique = count.rows(data)
N.u = nrow(data.unique)
dat.mat = as.matrix(data.unique[,-c(1,2)])
p.x = vector(length=M)
I.x = vector(length=M)
dlogp.xt <- function(z, i){
logL.fun(z=z, dat=dat.mat, i=i)
}
dp.tx <- function(z, i){
exp(dlogp.xt(z=z, i=i))*prior(z)
}
for(i in 1:N.u) {
p.x[data.unique$ind[[i]]] = integrate(dp.tx, int.lower, int.upper, i=i)$val
}
for(i in 1:M){
I.x[i] = logL.fun(z=m.theta[i], dat=data, i=i) - log(p.x[i])
}
list(I = mean(I.x), se.I = sd(I.x) / sqrt(M) , I.x=I.x, p.x=p.x)
}
|
marks = matrix(1:12, nrow=3)
marks
addmargins(marks)
addmargins(marks, FUN=sd)
addmargins(marks, FUN=list(sum, sd))
addmargins(marks, FUN=list(list(sum, sd), list(var, median)))
addmargins(marks, FUN=list(SUM=sum, StdDev = sd))
set.seed(1)
x <- sample( 1:7, 20, replace = TRUE)
y <- sample( 1:7, 20, replace = TRUE)
table(x,y)
tx <- addmargins( table(x, y) )
print(tx, zero.print = ".")
years <- sample(c("2015", "2016", "2017"), 177, replace = TRUE)
matches <- sample(c("test", "oneday"), 177, replace = TRUE)
teams <- sample(c("india", "pakistan", "england", "australia"), 177, replace = TRUE)
dfcricket = data.frame(years, matches, teams)
dfcricket
(A <- table(years, matches, teams))
(B = table(dfcricket))
addmargins(A)
addmargins(B)
ftable(A)
ftable(addmargins(A))
ftable(addmargins(A, c(1, 3),
FUN = list(Sum = sum, list(Min = min, Max = max))))
ftable(addmargins(A, c(3, 1),
FUN = list(list(Min = min, Max = max), Sum = sum)))
sqsm <- function(x) sum(x)^2/100
(C <- table(teams, matches))
apply(C, 2, sum)/100
sweep(C, 2, apply(C, 2, sum)/100, "/")
?sweep
round(sweep(C, 2, apply(C, 2, sum)/100, "/"), 1)
round(sweep(addmargins(C, 1, list(list(All = sum, N = sqsm))), 2,
apply(C, 2, sum)/100, "/"), 1)
round(sweep(addmargins(C, 2, list(list(All = sum, N = sqsm))), 1,
apply(C, 1, sum)/100, "/"), 1)
mC <- addmargins(C, 2, FUN = list(list(Total = sum)))
round(ftable(sweep(addmargins(mC, 1, list(list(All = sum, N = sqsm))), 2,
apply(mC, 2, sum)/100, "/")), 1)
(M = matrix( 1:12, ncol=3))
addmargins(M)
addmargins(M,margin = 1)
addmargins(M, FUN=mean)
addmargins(M, FUN=list(list(mean,sum), list(sd, median))) |
"xp.ind.inf.terms" <-
function(gamobj=NULL,
xlb = NULL,
ylb = NULL,
plot.ids=TRUE,
idscex=0.7,
ptscex=0.7,
prompt=TRUE,
...){
if(is.null(gamobj)){
gamobj <- check.gamobj()
if(is.null(gamobj)){
return()
} else {
}
} else {
c1 <- call("assign",pos=1, "current.gam", gamobj,immediate=T)
eval(c1)
}
if(length(names(coefficients(eval(parse(text="current.gam")))))==0){
cat("\nNo covariates found for this parameter\n")
return()
}
cook <- data.frame(dfbetas(eval(parse(text="current.gam")))^2)
cook <- cook[,-1]
xvals <- seq(length = length(cook[, 1]))
ylm <- range(cook)
ylmm <- diff(ylm)*0.05
ylm[1]= ylm[1]-ylmm
ylm[2]= ylm[2]+ylmm
if(any(is.null(eval(parse(text="current.gam$data$ID"))))){
ids <- "n"
} else {
ids <- eval(parse(text="current.gam$data$ID"))
}
plotList <- vector("list",length(cook[1,]))
for(i in 1:length(cook[1,])) {
title <- NULL
if(is.null(xlb)){
xlbb <- "Index number (ID)"
} else {
xlbb <- xlb
}
if(is.null(ylb)) {
ylbb <- paste(names(cook)[i])
} else {
ylbb <- ylb
}
xplot <- xyplot(cook[,i]~xvals,
ylab=ylbb,
xlab=xlbb,
ylim=ylm,
main=title,
aspect="fill",
ids = ids,
panel=
function(x,y,ids,...) {
if(!any(ids == "n")&& plot.ids==TRUE) {
addid(x,y,ids=ids,
idsmode=TRUE,
idsext =0.05,
idscex = idscex,
idsdir = "both")
} else {
panel.xyplot(x,y,cex=ptscex,col="black",...)
}
}
)
plotList[[i]] <- xplot
}
plotTitle <- paste("Inidividual influence (Cooks distance) on each GAM term\n",
"for ",
eval(parse(text="current.gam$pars")),
" (Run ",
eval(parse(text="current.gam$runno")), ")",
sep="")
obj <- xpose.multiple.plot(plotList,plotTitle,prompt,...)
return(obj)
} |
library(roxygen2)
options(repos = c(CRAN="http://cran.r-project.org"))
path <- "/home/ecor/Dropbox/R-packages"
pkg_name <- "RGENERATE"
pkg_dir <- paste(path,pkg_name,sep="/")
cran_pkg_path <- paste(path,"toCran",sep="/")
cran_pkg_dir <- paste(cran_pkg_path,pkg_name,sep="/")
roxygenize(pkg_dir,clean=TRUE)
oo <- installed.packages()
if (pkg_name %in% oo[,"Package"]) {
vv <-as.character(packageVersion(pkg_name))
vv1 <- as.character(packageVersion(pkg_name,lib.loc=path))
print(vv)
print(vv1)
if (compareVersion(vv1,vv)>=0) {
print("removing")
remove.packages(pkg_name)
install.packages(pkg_dir,type="source",repos=NULL)
}
} else {
install.packages(pkg_dir,type="source",repos=NULL)
}
toCran <- TRUE
if (toCran) {
system(paste("cp -R",pkg_dir,cran_pkg_path,sep=" "))
cran_pkg_hidden <- paste(cran_pkg_dir,".git*",sep="/")
system(paste("rm -rf",cran_pkg_hidden,sep=" "))
cran_pkg_unuseful <- paste(cran_pkg_dir,"LICENSE",sep="/")
system(paste("rm -rf",cran_pkg_unuseful,sep=" "))
cran_pkg_unuseful <- paste(cran_pkg_dir,"LICENSE",sep="/")
system(paste("rm -rf",cran_pkg_unuseful,sep=" "))
cran_pkg_unuseful <- paste(cran_pkg_dir,"Read-and-delete-me",sep="/")
system(paste("rm -rf",cran_pkg_unuseful,sep=" "))
cran_pkg_unuseful <- paste(cran_pkg_dir,"roxygenize*",sep="/")
system(paste("rm -rf",cran_pkg_unuseful,sep=" "))
toremove <- c(".DS_Store","inst/.DS_Store","inst/doc/.DS_Store","inst/doc/examples/.DS_Store","inst/doc/examples/snowstuff/.DS_Store","inst/template/.DS_Store","inst/template/friuli/.DS_Store", ".Rapp.history")
cran_pkg_unuseful <- paste(cran_pkg_dir,toremove,sep="/")
for (it in cran_pkg_unuseful) {
system(paste("rm -rf",it,sep=" "))
}
} |
with_mock <- function(mock, code) {
withr::with_options(list(httr2_mock = mock), code)
}
local_mock <- function(mock, env = caller_env()) {
withr::local_options(httr2_mock = mock, .local_envir = env)
} |
combplotp <- function(formula, data=NULL, subset, na.action=na.retain,
vnames=c('labels', 'names'),
includenone=FALSE, showno=FALSE,
maxcomb=NULL, minfreq=NULL, N=NULL,
pos=function(x) 1 * (tolower(x) %in%
c('true', 'yes', 'y', 'positive', '+', 'present', '1')),
obsname='subjects',
ptsize=35, width=NULL, height=NULL,
...) {
if (!requireNamespace("plotly"))
stop("This function requires the 'plotly' package.")
vnames <- match.arg(vnames)
frac <- markupSpecs$html$frac
fr2 <- function(a, b) paste0(frac(a, b), ' = ', round(a / b, 3))
Y <- if(missing(formula)) {
if(! missing(subset)) stop('subset not allowed if formula missing')
if(! length(data)) stop('data must be specified if formula missing')
data
} else {
if(!missing(subset) && length(subset))
model.frame(formula, data=data, subset=subset, na.action=na.action)
else
model.frame(formula, data=data, na.action=na.action)
}
labs <- if(vnames == 'names') structure(names(Y), names=names(Y))
else {
lbs <- sapply(Y, label)
ifelse(lbs == '', names(Y), lbs)
}
Y <- lapply(Y, pos)
m <- sapply(Y, sum, na.rm=TRUE)
Y <- Y[order(m)]
if(! length(N)) N <- length(Y[[1]])
f <- as.data.frame(table(Y, ...))
f <- f[f$Freq > 0, ]
p <- ncol(f) - 1
numcondpresent <- apply(f[, 1 : p], 1, function(u) sum(u == 1))
Nc <- sum(f$Freq[numcondpresent > 0])
if(! includenone && any(numcondpresent == 0))
f <- f[numcondpresent > 0, ]
mdesc <- sort(m)
mdesc <- 1 : length(mdesc)
names(mdesc) <- names(sort(m))
g <- function(x) {
i <- x > 0
ifelse(sum(i) == 1, mdesc[names(x)[i]], 0)
}
tiebr <- apply(f[, 1 : p], 1, g)
i <- order(-f$Freq, -tiebr)
f <- f[i, ]
if(length(maxcomb) && maxcomb < nrow(f)) f <- f[1 : maxcomb, ]
if(length(minfreq) && any(f$Freq < minfreq)) f <- f[f$Freq >= minfreq, ]
n <- nrow(f)
X <- as.matrix(1 * (f[, 1 : p] == '1'))
Freq <- f$Freq
x <- y <- present <- txt <- xn <- frq <- NULL
namx <- colnames(X)
for(i in 1 : n) {
x <- c(x, rep(i, p))
y <- c(y, 1 : p)
xi <- X[i, ]
present <- c(present, xi)
namespres <- if(! any(xi == 1)) 'none' else
paste(labs[namx][xi == 1], collapse='<br>')
k <- Freq[i]
tx <- paste0('<b>', namespres, '</b><br>',
'<br>Count: ', k,
'<br>Fraction of ', obsname, ': ', fr2(k, N),
'<br>Fraction of ', obsname, ' w/any cond: ', fr2(k, Nc))
txt <- c(txt, rep(tx, p))
xn <- c(xn, namx)
frq <- c(frq, rep(k, p))
}
txt <- paste0(txt, '<br>Fraction of ', obsname, ' w/', namx[y], ': ',
fr2(frq, m[namx[y]]))
hdc <- plotlyParm$heightDotchartb
if(! length(height)) height <- hdc(c(labs, '', ''), low=250, per=30)
if(! length(width)) {
w <- unlist(strsplit(labs, '<br>'))
longest <- w[which.max(nchar(w))]
nlongest <- nchar(longest)
width <- hdc(rep('X', n), per=23, low=450) + 8 * nlongest
}
P <- plotly::plot_ly(height=height, width=width)
yy <- 1 : p
P <- plotly::add_segments(P,
x = ~ rep(-2, p), xend = ~ rep(n, p),
y = ~ 1 : p, yend = ~ 1 : p,
color = I('gray80'), line=list(width=0.75),
hoverinfo='none', showlegend=FALSE)
P <- plotly::add_segments(P,
x = ~ 1 : n, xend = ~ 1 : n,
y = ~ rep(1, n), yend = ~ rep(p + 1.5, n),
color = I('gray80'), line=list(width=0.75),
hoverinfo='none', showlegend=FALSE)
P <- plotly::add_markers(P,
x = ~ x[present == 1],
y = ~ y[present == 1],
text = ~ txt[present == 1],
hoverinfo='text',
color=I('black'), size=I(ptsize),
showlegend=FALSE)
if(showno)
P <- plotly::add_markers(P,
x = ~ x[present == 0],
y = ~ y[present == 0],
hoverinfo='none',
color=I('gray90'), size=I(ptsize),
showlegend=FALSE)
relfreq <- m[namx] / max(m)
tmf <- paste0('<b>', labs[namx],
'</b><br><br>Marginal count: ', m[namx],
'<br>Fraction of ', obsname, ': ', fr2(m[namx], N),
'<br>Fraction of ', obsname, ' w/any cond: ', fr2(m[namx], Nc))
P <- plotly::add_segments(P,
x = ~ rep(0, p), xend= ~ -2 * relfreq,
y = ~ 1 : p, yend ~ 1 : p,
text = ~ tmf,
hoverinfo='text', color=I('blue'),
name='Marginal Counts',
showlegend=TRUE,
line=list(width=3)
)
relfreqc <- Freq / max(Freq)
nn <- 1 : n
xi <- X[i, ]
present <- c(present, xi)
namespres <- if(! any(xi == 1)) 'none' else
paste(labs[namx][xi == 1], collapse='<br>')
txtc <- character(n)
for(i in 1 : n) {
xi <- X[i, ]
txtc[i] <- if(! any(xi == 1)) 'none' else
paste(labs[namx][xi == 1], collapse='<br>')
}
txtc <- paste0('<b>', txtc, '</b>',
'<br><br>Count: ', Freq,
'<br>Fraction of ', obsname, ': ', fr2(Freq, N),
'<br>Fraction of ', obsname, ' w/any cond: ', fr2(Freq, Nc))
P <- plotly::add_segments(P,
x = ~ nn,
xend = ~ nn,
y = ~ rep(p + 0.5, n),
yend = ~ p + 0.5 + relfreqc,
text = ~ txtc,
hoverinfo='text', color=I('black'),
name='Combination Counts',
showlegend=TRUE, line=list(width=3))
P <- plotly::add_text(P,
x = ~ rep(n + 0.7, p), y = 1 : p,
text = ~ labs[namx],
textposition="middle right",
hoverinfo='none',
showlegend=FALSE)
P <- plotly::layout(P,
xaxis = list(title='', tickvals=1 : n,
range=c(-2, n + 0.4 * nlongest),
showgrid=FALSE,
showticklabels=FALSE, zeroline=FALSE),
yaxis = list(title='', tickvals=1 : p,
showgrid=FALSE,
showticklabels=FALSE),
legend= list(x=0.5, y=0, xanchor='center', yanchor='top', orientation='h'))
P
} |
BGWM.covar.estim <- function(sample, method=c("EE-m","MLE-m"), d, n, z0)
{
method <- match.arg(method)
method <- switch(method,
"EE-m" = 1,
"MLE-m" = 2)
V <- switch(method,
{
V <- BGWM.covar.EE(sample, d, n, z0)
V
},
{
V <- BGWM.covar.MLE(sample, d, n, z0)
V
})
dimnames(V) <- list( paste( "dist", rep(1:d,rep(d,d)), ".type", rep(1:d,d), sep="" ), paste( "type", 1:d, sep="" ) )
list(method=switch( method, "with Empirical Estimation of the means", "with Maximum Likelihood Estimation of the means" ), V=V )
}
BGWM.covar.EE <- function(y, d, n, z0)
{
y <- as.matrix(y)
if(length(d) != 1)
stop("'d' must be a number")
if(length(n) != 1)
stop("'n' must be a number")
if(length(z0) != d)
stop("'z0' must be a d-dimensional vector")
if(TRUE %in% (z0 < 0))
stop("'z0' must have positive elements")
if(is.matrix(y) == FALSE)
stop("'y' must be a matrix")
if(ncol(y) != d || nrow(y) < (n*d))
stop("'y' must have d columns and at least (n*d) rows")
if(n == 1)
stop("'n' must be greater than 1")
y <- y[1:(n*d),]
out <- matrix( rep( 0, (d*d*d) ), ncol=d )
Mn <- BGWM.mean.EE(y, d, n, z0)
for( i in 1:(n-1) )
{
if(i != 1)
Zi_1 <- apply( y[seq( (i-2)*d+1, (i-1)*d, 1 ),], 2, sum )
else
Zi_1 <- z0
Zi_1 <- rep( Zi_1, rep( d, d ) )
aux <- BGWM.mean.EE(y, d, i, z0) - Mn
out <- out + matrix( c( apply( aux, 1, tcrossprod ) ), ncol=d, byrow=TRUE ) * Zi_1
}
out <- out / n
out
}
BGWM.covar.MLE <- function(y, d, n, z0)
{
y <- as.matrix(y)
if(length(d) != 1)
stop("'d' must be a number")
if(length(n) != 1)
stop("'n' must be a number")
if(length(z0) != d)
stop("'z0' must be a d-dimensional vector")
if(TRUE %in% (z0 < 0))
stop("'z0' must have positive elements")
if(is.matrix(y) == FALSE)
stop("'y' must be a matrix")
if(ncol(y) != d || nrow(y) < (n*d))
stop("'y' must have d columns and at least (n*d) rows")
if(n == 1)
stop("'n' must be greater than 1")
y <- y[1:(n*d),]
out <- matrix( rep( 0, (d*d*d) ), ncol=d )
Mn <- BGWM.mean.MLE(y, d, n, z0)
for( i in 1:(n-1) )
{
if(i != 1)
aux2 <- aux2 + apply( y[seq( (i-2)*d+1, (i-1)*d, 1 ),], 2, sum )
else
aux2 <- z0
aux1 <- BGWM.mean.MLE(y, d, i, z0) - Mn
out <- out + matrix( c( apply( aux1, 1, tcrossprod ) ), ncol=d, byrow=TRUE ) * rep( aux2, rep( d, d ) )
}
out <- out / n
out
} |
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