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continentality <- function(tmax, tmin, tempScale = 1) {
res <- tmax - tmin
res <- res / tempScale
names(res) <- 'continentality'
return(res)
} |
summary.l2.reg <-
function (object, ...)
{
cat ("\n Call: \n")
print (object$call)
cat ("\n")
cat ("\n Feature Matrix \n")
print (object$X)
cat ("\n")
cat ("\n Outcome for Cases \n")
print (object$Y)
cat ("\n")
cat ("\n Residuals \n")
print (object$residual)
cat ("\n")
cat ("\n Sum of Residuals \n")
print (object$L2)
cat ("\n")
cat ("
cat ("
cat ("\n Lambda used:", object$lambda, "\n")
cat ("\n")
cat ("\n Intercept: \n")
print (object$intercept)
cat ("\n Esimtated Coefficients: \n")
print (object$estimate)
cat ("\n")
cat ("\n Number of Active Variables: \n")
print (object$nonzeros)
cat ("\n")
cat ("\n Selected Variables with Nonzero Coefficients: \n")
print (rownames(object$X)[object$selected+1])
} |
context("Information value")
test_that("test normal function", {
X <- mtcars$gear
Y <- mtcars$am
expect_error(ExpInfoValue(X, Y))
})
test_that("test output object", {
X <- mtcars$gear
Y <- mtcars$am
ivvaue <- ExpInfoValue(X, Y, valueOfGood = 1)
expect_output(str(ivvaue), "List of 2")
expect_equal(ivvaue$`Information values`, 0.44)
}) |
library(h2o)
h2o.init()
iris.hex = h2o.uploadFile(path = system.file("extdata", "iris_wheader.csv", package="h2o"), destination_frame = "iris.hex")
summary(iris.hex)
iris.rf = h2o.randomForest(y = 5, x = c(1,2,3,4), training_frame = iris.hex, ntrees = 50, max_depth = 100)
print(iris.rf) |
chkSpectra <- function(spectra, confirm = FALSE) {
UseMethod("chkSpectra")
} |
do.lapeig <- function(X, ndim=2, type=c("proportion",0.1),
symmetric=c("union","intersect","asymmetric"),
preprocess=c("null","center","scale","cscale","whiten","decorrelate"),
weighted=FALSE, kernelscale=1.0){
aux.typecheck(X)
n = nrow(X)
p = ncol(X)
ndim = as.integer(ndim)
if (!check_ndim(ndim,p)){stop("* do.lapeig : 'ndim' is a positive integer in [1,
nbdtype = type
if (missing(symmetric)){
nbdsymmetric = "union"
} else {
nbdsymmetric = match.arg(symmetric)
}
if (missing(preprocess)){
algpreprocess = "null"
} else {
algpreprocess = match.arg(preprocess)
}
wflag = weighted
if (!is.logical(wflag)){
stop("* do.lapeig : 'weighted' flag should be a logical input.")
}
t = kernelscale
if (!is.numeric(t)||is.na(t)||(t<=0)){
stop("* do.lapeig : 'kernelscale' is a positive real value.")
}
if (t==Inf){
wflag = FALSE
}
tmplist = aux.preprocess.hidden(X,type=algpreprocess,algtype="nonlinear")
trfinfo = tmplist$info
pX = tmplist$pX
n = nrow(pX)
p = ncol(pX)
nbdstruct = aux.graphnbd(pX,method="euclidean",
type=nbdtype,symmetric=nbdsymmetric)
if (wflag==FALSE){
W = matrix(as.double(nbdstruct$mask),nrow=nrow(nbdstruct$mask))
W = (W+t(W))/2
} else {
W = exp((-(nbdstruct$mask*nbdstruct$dist)^2)/t)
idxnan = which(is.nan(W))
W[idxnan] = 0
diag(W) = 0
W = (W+t(W))/2
}
embedding = method_eigenmaps(W);
eigvals = embedding$eigval
eigvecs = embedding$eigvec
result = list()
result$Y = eigvecs[,2:(ndim+1)]
result$eigvals = eigvals[2:(ndim+1)]
trfinfo$algtype = "nonlinear"
result$trfinfo = trfinfo
return(result)
} |
context("Making plots")
library(MOFA2)
filepath <- system.file("extdata", "model.hdf5", package = "MOFA2")
test_mofa2 <- load_model(filepath)
test_that("plot data overview works", {
expect_silent(p <- plot_data_overview(test_mofa2))
})
test_that("plot data heatmap", {
expect_silent(p <- plot_data_heatmap(test_mofa2, view = 1, factor = 1, silent = TRUE))
})
test_that("plot data scatter", {
expect_silent(p <- plot_data_scatter(test_mofa2, view = 1, factor = 1))
})
test_that("plot data ASCII in terminal", {
expect_error(plot_ascii_data(test_mofa2), NA)
})
test_that("plot weights heatmap", {
expect_silent(p <- plot_weights_heatmap(test_mofa2, view = 1, silent = TRUE))
})
test_that("plot weights", {
expect_silent(p <- plot_weights(test_mofa2, view = 1, factors = 1:2))
expect_silent(p <- plot_weights(test_mofa2, factors = 1))
})
test_that("plot top weights", {
expect_silent(p <- plot_top_weights(test_mofa2, view = 1, factors = 1))
})
test_that("plot factor values", {
expect_silent(p <- plot_factor(test_mofa2))
})
test_that("plot factor values", {
expect_silent(p <- plot_factors(test_mofa2, factors = 1:2))
})
test_that("plot factors correlation", {
expect_error({plot_factor_cor(test_mofa2); dev.off()}, NA)
}) |
model_parameters.selection <- function(model,
ci = .95,
component = c("all", "selection", "outcome", "auxiliary"),
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...) {
component <- match.arg(component)
out <- .model_parameters_generic(
model = model,
ci = ci,
bootstrap = bootstrap,
iterations = iterations,
component = component,
merge_by = c("Parameter", "Component"),
standardize = standardize,
exponentiate = exponentiate,
p_adjust = p_adjust,
...
)
attr(out, "object_name") <- deparse(substitute(model), width.cutoff = 500)
out
}
p_value.selection <- function(model, component = c("all", "selection", "outcome", "auxiliary"), ...) {
component <- match.arg(component)
s <- summary(model)
rn <- row.names(s$estimate)
estimates <- as.data.frame(s$estimate, row.names = FALSE)
params <- data.frame(
Parameter = rn,
p = estimates[[4]],
Component = "auxiliary",
stringsAsFactors = FALSE,
row.names = NULL
)
params$Component[s$param$index$betaS] <- "selection"
params$Component[s$param$index$betaO] <- "outcome"
if (component != "all") {
params <- params[params$Component == component, , drop = FALSE]
}
insight::text_remove_backticks(params, verbose = FALSE)
}
standard_error.selection <- function(model, component = c("all", "selection", "outcome", "auxiliary"), ...) {
component <- match.arg(component)
s <- summary(model)
rn <- row.names(s$estimate)
estimates <- as.data.frame(s$estimate, row.names = FALSE)
params <- data.frame(
Parameter = rn,
SE = estimates[[2]],
Component = "auxiliary",
stringsAsFactors = FALSE,
row.names = NULL
)
params$Component[s$param$index$betaS] <- "selection"
params$Component[s$param$index$betaO] <- "outcome"
if (component != "all") {
params <- params[params$Component == component, , drop = FALSE]
}
insight::text_remove_backticks(params, verbose = FALSE)
}
simulate_model.selection <- function(model, iterations = 1000, component = c("all", "selection", "outcome", "auxiliary"), ...) {
component <- match.arg(component)
out <- .simulate_model(model, iterations, component = component, effects = "fixed")
class(out) <- c("parameters_simulate_model", class(out))
attr(out, "object_name") <- .safe_deparse(substitute(model))
out
}
ci.selection <- ci.default
degrees_of_freedom.selection <- function(model, ...) {
s <- summary(model)
s$param$df
} |
plot.interv_multiple <- function(x, ...){
timser <- x$fit_H0$ts
intervention <- x$interventions
plot(timser, type="n", main="Time series with detected interventions", xlab="Time", ylab="Value", ...)
if(nrow(intervention)==0){
lines(timser)
legend("topleft", legend="No interventions detected ", bg="white")
}else{
lines(x=time(timser)[seq(along=timser)], y=x$ts_cleaned[[nrow(intervention)]], lty="dashed", col="blue")
abline(v=time(timser)[intervention[, "tau"]], col="red")
lines(timser)
legend("topleft", legend=paste("Intervention ", rownames(intervention), ": tau=", intervention[,"tau"], ", delta=", intervention[,"delta"], ", size=", round(intervention[,"size"],2), " ", sep=""), bg="white")
}
} |
x2weight.covMcd <-
function(xMat)
{
if(dim(xMat)[2]==3)
{
tmc = qchisq(0.95, ncol(xMat[,-1]))
cov1 = cov.rob(xMat[,-1], method="mcd")
dist = mahalanobis(xMat[,-1], cov1$center, cov1$cov)
return(ifelse(dist<tmc, 1, tmc/dist))
} else {
tmc1 = qchisq(0.95, 2)
tmc2 = qchisq(0.95, ncol(xMat[,-1]) - 2)
imrnr = dim(xMat)[2]
part1 = c(1 + which(abs(cor(xMat[,imrnr], xMat[,-c(1,imrnr)])) == min(abs(cor(xMat[,imrnr], xMat[,-c(1,imrnr)])))), imrnr)
part2 = c(-1, -part1)
cov1 = cov.rob(xMat[, part1], method="mcd")
dist = mahalanobis(xMat[, part1], cov1$center, cov1$cov)
weight1 = ifelse(dist < tmc1, 1, tmc1 / dist)
cov2 = cov.rob(xMat[, part2], method="mcd")
dist = mahalanobis(as.matrix(xMat[, part2]), cov2$center, cov2$cov)
weight2 = ifelse(dist < tmc2, 1, tmc2 / dist)
xwght = weight1 * weight2
return(xwght)
}
} |
ibeta = function(x,a,b){ stats::pbeta(x,a,b)*beta(a,b) }
G = function(r, v, q){
p=v/(v+q^2)
res = ifelse(q>0 | (r %% 2 != 0),
ibeta(p,0.5*(v-r),0.5*(r+1)),
2*beta(0.5*(v-r),0.5*(r+1)) - ibeta(p,0.5*(v-r),0.5*(r+1)))
return(res)
}
e_trunct_onesided = function(q,v,r){
v^(0.5*r) * G(r,v,q)/G(0,v,q)
}
e_trunct = function(a,b,df,r){
mult=ifelse(a>0 & b>0,-1,1)
aa = ifelse(a>0 & b>0, -b, a)
bb = ifelse(a>0 & b>0, -a, b)
mult^r * ifelse(aa==bb,aa^r,
(stats::pt(aa,df,lower.tail=FALSE)*e_trunct_onesided(aa,df,r) -
stats::pt(bb,df,lower.tail=FALSE)*e_trunct_onesided(bb,df,r))/(stats::pt(bb,df)-stats::pt(aa,df)))
} |
list_projects <- function() {
.Deprecated("bq_projects", package = "bigrquery")
bq_projects()
} |
testthat::context(desc="Test getLogBoxsize() function")
testthat::test_that(desc="Test, if getLogBoxsize throws errors/warnings on wrong arguments",
{
testthat::expect_error(object=getLogBoxsize())
testthat::expect_error(object=getLogBoxsize("aaa"))
testthat::expect_error(getLogBoxsize(list(1,2)))
testthat::expect_warning(getLogBoxsize(0))
testthat::expect_warning(getLogBoxsize(c(1,2,-3)))
testthat::expect_warning(getLogBoxsize(c(1,2,3)))
})
testthat::test_that(desc="Test, if getLogBoxsize returns results of same length as input",
{
testthat::expect_equivalent(object=length(getLogBoxsize(2)),expected=1)
length <- 1+rpois(1,10)
input <- rpois(length,30)
testthat::expect_equivalent(object=length(getLogBoxsize(input)),expected=length)
})
testthat::test_that(desc="Test, if getLogBoxsize returns appropriate results",
{
testthat::expect_equivalent(object=getLogBoxsize(1),expected=0.009950331)
}) |
if (gargle:::secret_can_decrypt("bigrquery")) {
json <- gargle:::secret_read("bigrquery", "bigrquery-testing.json")
bq_auth(path = rawToChar(json))
} |
"skewt.axis" <-
function(BROWN = "brown", GREEN = "green", redo = FALSE, ...)
{
tmr <- function(w, p)
{
c1 <- 0.049864645499999999
c2 <- 2.4082965000000001
c3 <- 7.0747499999999999
c4 <- 38.9114
c5 <- 0.091499999999999998
c6 <- 1.2035
x <- log10((w * p)/(622. + w))
tmrk <- 10^(c1 * x + c2) - c3 + c4 * ((10.^(c5 * x) - c6)^
2.)
tmrk - 273.14999999999998
}
tda <- function(o, p)
{
ok <- o + 273.14999999999998
tdak <- ok * ((p * 0.001)^0.28599999999999998)
tdak - 273.14999999999998
}
par(pty = "s", ... )
ymax <- skewty(1050)
ymin <- skewty(100)
xmin <- skewtx(-33, skewty(1050))
xmax <- skewtx(50, skewty(1000))
kinkx <- skewtx(5, skewty(400))
xc <- c(xmin, xmin, xmax, xmax, kinkx, kinkx, xmin)
yc <- c(ymin, ymax, ymax, skewty(625), skewty(400), ymin, ymin)
plot(xc, yc, type = "l", axes = FALSE, xlab = "", ylab = "", lwd =
0.10000000000000001)
ypos <- skewty(1050)
degc <- ((seq(-20, 100, by = 20) - 32) * 5)/9
axis(1, at = skewtx(degc, ypos), labels = seq(-20, 100, by = 20), pos
= ymax)
mtext(side = 1, line = 1, "Temperature (F)")
pres <- c(1050, 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100)
NPRES <- length(pres)
xpl <- rep(xmin, times = NPRES)
xpr <- c(xmax, xmax, xmax, xmax, skewtx(20, skewty(500)), kinkx, kinkx,
kinkx, kinkx, kinkx, kinkx)
y <- skewty(pres)
segments(xpl, y, xpr, y, col = BROWN, lwd = 0.10000000000000001, lty =
2)
ypos <- skewty(pres[2:NPRES])
axis(2, at = ypos, labels = pres[2:NPRES], pos = xmin)
mtext(side = 2, line = 1.5, "P (hPa)")
temp <- seq(from = -100, to = 50, by = 10)
NTEMP <- length(temp)
lendt <- rep(1050, NTEMP)
inds <- seq(1, length(temp))[temp < -30]
exponent <- (132.18199999999999 - (xmin - 0.54000000000000004 * temp[
inds])/0.90691999999999995)/44.061
lendt[inds] <- 10^exponent
rendt <- rep(100, NTEMP)
inds <- seq(1, length(temp))[(temp >= -30) & (temp <= 0)]
exponent <- (132.18199999999999 - (kinkx - 0.54000000000000004 * temp[
inds])/0.90691999999999995)/44.061
rendt[inds] <- 10^exponent
inds <- seq(1, length(temp))[temp > 30]
exponent <- (132.18199999999999 - (xmax - 0.54000000000000004 * temp[
inds])/0.90691999999999995)/44.061
rendt[inds] <- 10^exponent
rendt[temp == 10] <- 430
rendt[temp == 20] <- 500
rendt[temp == 30] <- 580
yr <- skewty(rendt)
xr <- skewtx(temp, yr)
yl <- skewty(lendt)
xl <- skewtx(temp, yl)
segments(xl, yl, xr, yr, col = BROWN, lwd = 0.10000000000000001)
text(xr[8:NTEMP], yr[8:NTEMP], labels = paste(" ", as.character(temp[
8:NTEMP])), srt = 45, adj = 0, col = BROWN)
mixrat <- c(20, 12, 8, 5, 3, 2, 1)
NMIX <- length(mixrat)
yr <- skewty(440.)
tmix <- tmr(mixrat[1], 440.)
xr <- skewtx(tmix, yr)
yl <- skewty(1000.)
tmix <- tmr(mixrat[1], 1000.)
xl <- skewtx(tmix, yl)
segments(xl, yl, xr, yr, lty = 2, col = GREEN, lwd =
0.10000000000000001)
yl <- skewty(1025.)
xl <- skewtx(tmix, yl)
text(xl, yl, labels = as.character(mixrat[1]), col = GREEN, srt = 55,
adj = 0.5, cex = 0.75)
yr <- skewty(rep(400., NMIX - 1))
tmix <- tmr(mixrat[2:NMIX], 400.)
xr <- skewtx(tmix, yr)
yl <- skewty(rep(1000., NMIX - 1))
tmix <- tmr(mixrat[2:NMIX], 1000.)
xl <- skewtx(tmix, yl)
segments(xl, yl, xr, yr, lty = 2, col = GREEN, lwd =
0.10000000000000001)
yl <- skewty(rep(1025., NMIX - 1))
xl <- skewtx(tmix, yl)
text(xl, yl, labels = as.character(mixrat[2:NMIX]), col = GREEN, srt =
55, adj = 0.5, cex = 0.75)
theta <- seq(from = -30, to = 170, by = 10)
NTHETA <- length(theta)
lendth <- rep(100, times = NTHETA)
lendth[1:8] <- c(880, 670, 512, 388, 292, 220, 163, 119)
rendth <- rep(1050, times = NTHETA)
rendth[9:NTHETA] <- c(1003, 852, 728, 618, 395, 334, 286, 245, 210,
180, 155, 133, 115)
for(itheta in 1:NTHETA) {
p <- seq(from = lendth[itheta], to = rendth[itheta], length =
200)
sy <- skewty(p)
dry <- tda(theta[itheta], p)
sx <- skewtx(dry, sy)
lines(sx, sy, lty = 1, col = BROWN)
}
p <- seq(from = 1050, to = 240, by = -10)
npts <- length(p)
sy <- skewty(p)
sx <- double(length = npts)
if(redo) {
pseudo <- c(32, 28, 24, 20, 16, 12, 8)
NPSEUDO <- length(pseudo)
holdx <- matrix(0, nrow = npts, ncol = NPSEUDO)
holdy <- matrix(0, nrow = npts, ncol = NPSEUDO)
for(ipseudo in 1:NPSEUDO) {
for(ilen in 1:npts) {
moist <- satlft(pseudo[ipseudo], p[ilen])
sx[ilen] <- skewtx(moist, sy[ilen])
}
inds <- (sx < xmin)
sx[inds] <- NA
sy[inds] <- NA
holdx[, ipseudo] <- sx
holdy[, ipseudo] <- sy
}
}
else {
holdx <- skewt.data$pseudox
holdy <- skewt.data$pseudoy
pseudo <- skewt.data$pseudo
NPSEUDO <- skewt.data$NPSEUDO
}
for(ipseudo in 1:NPSEUDO) {
sx <- holdx[, ipseudo]
sy <- holdy[, ipseudo]
lines(sx, sy, lty = 1, col = GREEN)
moist <- satlft(pseudo[ipseudo], 230)
labely <- skewty(230)
labelx <- skewtx(moist, labely)
if (labelx > xmin)
text(labelx, labely, labels = as.character(pseudo[ipseudo]),
col = GREEN, adj = 0.5, cex = 0.75)
moist <- satlft(pseudo[ipseudo], 1100)
labely <- skewty(1100)
labelx <- skewtx(moist, labely)
text(labelx, labely, labels = as.character(pseudo[ipseudo]),
col = GREEN, adj = 0.5, cex = 0.75)
}
invisible(list(pseudox=holdx, pseudoy=holdy, pseudo=pseudo,
NPSEUDO=NPSEUDO, plt=par()$plt))
} |
.pickands.fun.uni<-function(t, rho=0.5, alpha=c(1,1)) {
if(t==0 && t==1){return(1)}
a <- lgamma(alpha-rho)-lgamma(alpha)
kappa <- exp((log(1-t)+a[2])/rho - log(exp((log(1-t)+a[2])/rho)+exp((log(t)+a[1])/rho)))
exp(log(1-t)+ pbeta(kappa, alpha[1]-rho, alpha[2],log.p=TRUE))+
exp(log(t)+ pbeta(1-kappa, alpha[2]-rho,alpha[1],log.p=TRUE))
}
.pickands.fun<-Vectorize(.pickands.fun.uni, vectorize.args=c("t"))
.pickands.dir.uni<-function(t,alpha,rho){
if(t==0 && t==1){return(1)}
k1=lbeta(alpha[1]+rho,alpha[2])
k2=lbeta(alpha[1],alpha[2]+rho)
kappa<-exp((1/rho)*(log(1-t)+k1)-log(exp((1/rho)*(log(t)+k2))+exp((1/rho)*(k1+log(1-t)))))
exp(log(1-t)+ pbeta(kappa, alpha[1], alpha[2]+rho,log.p=T))+
exp(log(t)+ pbeta(1-kappa, alpha[2],alpha[1]+rho,log.p=T))
}
.pickands.dir<-Vectorize(.pickands.dir.uni, "t")
pickands.liouv<-function(t, rho=0.5, alpha=c(1,1),CDA=c("C","S")){
if(missing(CDA)==TRUE){
CDA="C"
warning("Setting default to Liouville CDA. Use CDA=`S` for the Dirichlet model")
}
if(any(t<0) || any(t>1)){
stop("t must be a vector whose elements are between 0 and 1")
}
if(any(rho<=0)){
stop("rho must be positive")
}
if(length(rho)>1){
stop("Argument `rho' must be of length 1")
}
if(CDA=="C" && any(c(rho >= min(alpha), rho <= 0))){
stop("`rho must be between (0,min(alpha))")
}
if(any(alpha<=0)){
stop("alpha must be positive")
}
if(length(alpha)!=2){
stop("Not implemented except in the bivariate case")
}
if(CDA=="C"){
return(.pickands.fun(t,rho=rho,alpha=alpha))
} else if(CDA=="S"){
return(.pickands.dir(t,rho=rho,alpha=alpha))
} else{
return(NA)
}
}
pickands.plot<-function(rho, alpha, plot.new=T,CDA=c("C","S"), tikz=F, ...){
if(missing(CDA)==TRUE){
CDA="C"
warning("Setting default to Liouville CDA. Use CDA=`S` for the Dirichlet model")
}
if(length(rho)!=1){stop("rho must be 1-dimensional")}
if(length(alpha)!=2){
stop("Not implemented beyond bivariate case")
}
if(plot.new==TRUE){
plot.new()
plot.window(c(0,1), c(0.5,1))
axis(side=2, at=seq(0.5,1,by=0.1), pos=0,las=2,tck=0.01)
axis(side=1, at=seq(0,1,by=0.1), pos=0.5,las=0,tck=0.01)
lines(c(0,0.5),c(1,0.5),lty=3,col="gray")
lines(c(0.5,1),c(0.5,1),lty=3,col="gray")
lines(c(0,1),c(1,1),lty=3,col="gray")
if(tikz==TRUE){
mtext("$t$", side=1, line=2)
mtext("$\\mathrm{A}(t)$", side=2, line=2)
} else{
mtext(expression(t), side=1, line=2)
mtext(expression(A(t)), side=2, line=2)
}
}
x = seq(0,1,by=0.001)
if(CDA=="C"){
lines(x=x,y=c(1,.pickands.fun(x[-c(1,length(x))],alpha=alpha, rho=rho),1),type="l",...)
}
if(CDA=="S"){
lines(x=x,y=.pickands.dir(x,alpha=alpha,rho=rho),type="l",...)
}
}
K.plot <- function(data, add=F, ...){
if(is.null(dim(data)) || ncol(data)<2){
stop("Invalid input matrix")
}
H <- function(data){
n <- dim(data)[1]
d <- dim(data)[2]
out <- rep(NA,n)
tmp <- paste("(data[,",1:d,"] <= data[i,",1:d,"])")
cmnd <- parse(text=paste(tmp,collapse=" & "))
for (i in 1:n){
subb <- sum(eval(cmnd))-1
out[i] <- subb/(n-1)
}
out
}
W <- function(n, d){
fun <- function(w,i,d){
K0 <- w+w*rowSums(sapply(1:(d-1), function(k){
exp(k*log(-log(w))-lgamma(k+1))
}))
dK0 <- exp((d-1)*log(-log(w))-lgamma(d))
exp(log(w)+log(dK0)+(i-1)*log(K0)+(n-i)*log(1-K0))
}
sapply(1:n, function(i){
n*choose(n-1,i-1)*integrate(fun,lower=0,upper=1,i=i,d=d,subdivisions=10000,
rel.tol=.Machine$double.eps^0.001)$value
})
}
n <- nrow(data)
d <- ncol(data)
W <- W(n, d)
Hs <- sort(H(data))
seq <- seq(from=0.01,to=1,by=0.01)
if(n < 200) {
if(add==F){
plot(W,Hs,xlim=c(0,1),ylim=c(0,1),xlab="Independence",ylab="Data",pch=4,cex=0.8)
lines(c(0,1),c(0,1),lty=2)
K0 <- seq-seq*log(seq)
lines(c(0,seq),c(0,K0),lty=2)
lines(c(1,0),c(0,0),lty=2)
} else{
points(W,Hs,xlim=c(0,1),ylim=c(0,1),pch=4,cex=0.8, ...)
}
} else {
ind <- matrix(runif(n*d),nrow=n,ncol=d)
Wind <- sort(H(ind))
if(add==F){
plot(Wind,Hs,xlim=c(0,1),ylim=c(0,1),xlab="Independence",ylab="Data",pch=4,cex=0.5)
lines(c(0,1),c(0,1),lty=2)
K0 <- seq-seq*log(seq)
lines(c(0,seq),c(0,K0),lty=2)
lines(c(1,0),c(0,0),lty=2)
} else{
points(Wind, Hs,xlim=c(0,1),ylim=c(0,1),pch=4,cex=0.5, ...)
}
}
}
hmvevdliouv <- function(w, alpha, rho, CDA=c("C","S"), logdensity=FALSE){
if(any(w<0) || any(w>1)){
stop("w must contain elements between 0 and 1")
}
w <- as.matrix(w)
if(ncol(w)!=length(alpha)){
w <- cbind(w, 1-rowSums(w))
} else{
if(rowSums(w)!=rep(1, nrow(w))){
stop("Components must sum to 1")
}
}
if(length(rho)>1){
rho <- rho[1]
warning("Using only first coordinate of rho")
}
if(rho<=0){
CDA <- "C"
rho <- -rho
}
if(CDA=="C" && rho> min(alpha)){
stop("`abs(rho)` must be smaller than `min(alpha)`")
}
if(any(alpha<=0)){
stop("alpha must be positive")
}
if(missing(CDA)){
warning("Invalid input for argument `CDA`. Defaulting to `C`, the `negdir` family")
CDA = "C"
}
if(!(CDA %in% c("C","S"))){
stop("Invalid input for argument `CDA`")
}
h <- apply(w, 1, function(wrow){ switch(CDA,
C=negdirspecdens(dat=t(as.matrix(wrow)), param=c(alpha, rho), d=length(alpha), transform=FALSE),
S=dirspecdens(dat=t(as.matrix(wrow)),param=c(alpha, rho), d=length(alpha), transform=FALSE)
)})
if(logdensity){
return(h)
} else{
return(exp(h))
}
} |
traitglm = function( L, R, Q=NULL, family="negative.binomial", formula = NULL, method="manyglm", composition=FALSE, col.intercepts = TRUE, ... )
{
L = as.data.frame(L)
allargs <- match.call(expand.dots = FALSE)
dots <- allargs$...
if( "best" %in% names(dots) )
best <- dots$best
else
best = "1se"
if( "plot" %in% names(dots) )
plot <- dots$plot
else
plot=TRUE
if( "prop.test" %in% names(dots) )
prop.test <- dots$prop.test
else
prop.test = 0.2
if( "n.split" %in% names(dots) )
n.split <- dots$n.split
else
n.split=10
if( "seed" %in% names(dots) )
seed <- dots$seed
else
seed=NULL
if( "show.progress" %in% names(dots) )
show.progress <- dots$show.progress
else
show.progress = FALSE
if( "get.fourth" %in% names(dots) )
get.fourth <- dots$get.fourth
else
get.fourth = TRUE
deactive <- c("best", "plot", "prop.test", "n.split", "seed", "show.progress", "get.fourth")
deactivate <- (1:length(dots))[names(dots) %in% deactive ]
for (i in length(deactivate):1)
dots[ deactivate[i] ]<-NULL
dots <- lapply( dots, eval, parent.frame() )
n.sites = dim(L)[1]
n.spp = dim(L)[2]
if(is.null(formula))
R.des = get_polys(R)
else
R.des = list(X=R)
if(is.null(Q))
{
cat(paste("No traits matrix entered, so will fit SDMs with different env response for each spp","\n"))
Q.des = list(X=data.frame(as.factor(names(L))),X.squ=NULL,var.type="factor")
}
else
{
if(is.null(formula))
Q.des = get_polys(Q)
else
Q.des = list(X=Q)
}
any.penalty = method=="cv.glm1path" || method=="glm1path"
marg.penalty = TRUE
X.des = get_design( R.des, Q.des, names(L), formula=formula, marg.penalty=marg.penalty, composition = composition, col.intercepts = col.intercepts, any.penalty=any.penalty, get.fourth=get.fourth )
X = X.des$X
l <- as.vector(as.matrix(L))
if( method=="cv.glm1path" || method=="glm1path" )
{
if( "block" %in% names(dots) )
blockID <- dots$block
else
blockID = 1:n.sites
block = factor(rep(blockID,n.spp))
ft = do.call(glm1path,c(list(y=l, X=X, family=family, penalty = X.des$penalty), k=log(n.sites), dots))
if( method=="cv.glm1path" )
ft = do.call(cv.glm1path,c(list(object=ft, block=block, best=best, plot=plot, prop.test=prop.test, n.split=n.split,
seed=seed, show.progress=show.progress), dots))
id.use = which(ft$lambdas==ft$lambda)
ft$deviance = -2*ft$logL[id.use]
ft$phi = ft$glm1$phi
if(ft$df[1]==1)
null.deviance = ft$logL[1]
}
else
ft = do.call( method, c(list(formula=l~., family=family, data=data.frame(X)), dots) )
ft$fourth.corner = matrix( coef(ft)[X.des$is.4th.corner], length(X.des$names.Q), length(X.des$names.R) )
ft$fourth.corner = provideDimnames(ft$fourth,base=list(X.des$names.Q,X.des$names.R))
ft$R.des = R.des
ft$Q.des = Q.des
ft$any.penalty = any.penalty
ft$L = L
ft$scaling = X.des$scaling
ft$call=match.call()
if(is.null(formula)==FALSE & get.fourth==FALSE)
ft$formula = X.des$formula
else
ft$formula = formula
class(ft)=c("traitglm",class(ft))
return( ft )
}
get_design = function( R.des, Q.des, L.names, formula = formula, marg.penalty=TRUE, composition = FALSE, col.intercepts = TRUE, any.penalty=TRUE, scaling=NULL, get.fourth=TRUE )
{
n.sites = dim(R.des$X)[1]
n.spp = length(L.names)
is.scaling.given = is.null(scaling)==F
if(col.intercepts==TRUE)
{
spp = rep(L.names,each=n.sites)
spp = as.factor(spp)
mod = as.formula("~spp-1")
X.spp = model.matrix(mod)
if(marg.penalty==FALSE || any.penalty==FALSE)
X.spp = X.spp[,-1]
X.spp[X.spp==0] = -1
if(is.scaling.given==F)
{
scaling = list()
X.spp = scale(X.spp)
scaling$spp$center = attr(X.spp,"scaled:center")
scaling$spp$scale = attr(X.spp,"scaled:scale")
}
if(is.scaling.given)
X.spp = scale(X.spp,center=scaling$spp$center, scale=scaling$spp$scale)
X.spp = cbind(1,X.spp)
}
else
{
X.spp = as.matrix( rep(1,n.sites*n.spp) )
if(is.scaling.given==F)
scaling = list()
}
if(composition==TRUE)
{
site = rep(dimnames(R.des$X)[[1]],n.spp)
site = as.factor(site)
mod = as.formula("~site-1")
X.site = model.matrix(mod)
if(marg.penalty==FALSE || any.penalty==FALSE)
X.site = X.site[,-1]
X.site[X.site==0] = -1
if(is.scaling.given==F)
{
X.site = scale(X.site)
scaling$site$center = attr(X.site,"scaled:center")
scaling$site$scale = attr(X.site,"scaled:scale")
}
if(is.scaling.given)
X.site = scale(X.site,center=scaling$site$center, scale=scaling$site$scale)
}
else
X.site = X.spp[,0]
if(is.null(formula))
{
X.R = X.spp[,0]
if( is.null(R.des$X.squ) )
{
R.small = R.des$X
var.type= R.des$var.type
is.lin.small = rep( TRUE,NCOL(R.des$X) )
}
else
{
R.small = cbind( R.des$X, R.des$X.squ )
var.type= c( R.des$var.type, rep("quantitative",dim(R.des$X.squ)[2]) )
is.lin.small = c( rep( TRUE,NCOL(R.des$X) ), rep( FALSE, NCOL(R.des$X.squ) ) )
}
names.R = c()
is.lin.R= c()
for( iR in 1:NCOL(R.small) )
{
R.i = rep( R.small[,iR], times=n.spp )
mod = as.formula("~0+R.i")
mm = model.matrix(mod)
names.i = dimnames(R.small)[[2]][iR]
if(var.type[iR]=="factor")
{
if(any.penalty==TRUE)
names.i = paste(dimnames(R.small)[[2]][iR], levels(R.small[,iR]),sep="")
else
{
mm = mm[,-1]
names.i = paste(dimnames(R.small)[[2]][iR], dimnames(contrasts(R.small[,iR]))[[2]],sep="")
}
}
names.R = c( names.R, names.i )
is.lin.R= c( is.lin.R, rep( is.lin.small[iR] , dim(mm)[2] ) )
X.R = cbind( X.R, mm )
}
dimnames(X.R)[[2]]=names.R
if(is.scaling.given==F)
{
X.R = scale(X.R)
scaling$R$center = attr(X.R,"scaled:center")
scaling$R$scale = attr(X.R,"scaled:scale")
}
if(is.scaling.given)
X.R = scale(X.R,center=scaling$R$center, scale=scaling$R$scale)
X.Q = X.spp[,0]
if( is.null(Q.des$X.squ) )
{
Q.small = Q.des$X
var.type= Q.des$var.type
is.lin.small = rep( TRUE,NCOL(Q.des$X) )
}
else
{
Q.small = cbind( Q.des$X, Q.des$X.squ )
var.type= c( Q.des$var.type, rep("quantitative",dim(Q.des$X.squ)[2]) )
is.lin.small = c( rep( TRUE,NCOL(Q.des$X) ), rep( FALSE, NCOL(Q.des$X.squ) ) )
}
names.Q = c()
is.lin.Q= c()
for( iQ in 1:NCOL(Q.small) )
{
Q.i = rep( Q.small[,iQ], each=n.sites )
mod = as.formula("~0+Q.i")
mm = model.matrix(mod)
names.i = dimnames(Q.small)[[2]][iQ]
if(var.type[iQ]=="factor")
{
if(any.penalty==TRUE)
names.i = paste(dimnames(Q.small)[[2]][iQ], levels(Q.small[,iQ]),sep="")
else
{
mm = mm[,-1]
names.i = paste(dimnames(Q.small)[[2]][iQ], dimnames(contrasts(Q.small[,iQ]))[[2]],sep="")
}
}
names.Q = c( names.Q, names.i )
is.lin.Q= c( is.lin.Q, rep( is.lin.small[iQ] , dim(mm)[2] ) )
X.Q = cbind( X.Q, mm )
}
dimnames(X.Q)[[2]]=names.Q
if(is.scaling.given==F)
{
X.Q = scale(X.Q)
scaling$Q$center = attr(X.Q,"scaled:center")
scaling$Q$scale = attr(X.Q,"scaled:scale")
}
if(is.scaling.given)
X.Q = scale(X.Q, center=scaling$Q$center, scale=scaling$Q$scale)
if(get.fourth==TRUE)
{
X.RQ = X.spp[,0]
n.R = sum(is.lin.R)
n.Q = sum(is.lin.Q)
ref.R = rep(1:n.R,each=n.Q)
ref.Q = rep(1:n.Q,n.R)
X.RQ = as.matrix( X.R[,ref.R] * X.Q[,ref.Q] )
dimnames(X.RQ)[[2]] = paste(dimnames(X.R)[[2]][ref.R], dimnames(X.Q)[[2]][ref.Q], sep=":")
if(is.scaling.given==F)
{
X.RQ = scale(X.RQ)
scaling$RQ$center = attr(X.RQ,"scaled:center")
scaling$RQ$scale = attr(X.RQ,"scaled:scale")
}
if(is.scaling.given)
X.RQ = scale(X.RQ,center=scaling$RQ$center, scale=scaling$RQ$scale)
}
else
X.RQ=X.R[,0]
if(any.penalty)
{
if(composition==TRUE)
X = cbind(X.spp,X.site,X.R,X.Q,X.RQ)
else
X = cbind(X.spp,X.R,X.Q,X.RQ)
}
else
{
if(composition==TRUE)
X = cbind(X.spp,X.site,X.RQ)
else
X = cbind(X.spp,X.R,X.RQ)
}
n.X = dim(X)[2]
if(get.fourth==TRUE)
is.4th.corner = c( rep(F,n.X-n.R*n.Q), rep(T,n.R*n.Q) )
else
is.4th.corner = rep(F,n.X)
names.R = dimnames(X.R)[[2]][is.lin.R]
names.Q = dimnames(X.Q)[[2]][is.lin.Q]
}
else
{
rowReps = rep(1:n.sites, times=n.spp)
R.i = data.frame(R.des$X[rowReps,])
names(R.i) = names(R.des$X)
colReps = rep(1:n.spp, each=n.sites)
Q.i = data.frame(Q.des$X[colReps,])
names(Q.i) = names(Q.des$X)
X = model.matrix(formula,cbind(R.i,Q.i))
tt = terms(formula)
facts = attr(tt,"factors")
which.env = charmatch(names(R.des$X),dimnames(facts)[[1]])
which.env = which.env[is.na(which.env)==FALSE]
which.trait = charmatch(names(Q.des$X),dimnames(facts)[[1]])
which.trait = which.trait[is.na(which.trait)==FALSE]
if(length(which.env)==1)
is.env = facts[which.env,]>0
else
{
if(length(which.env)==0)
is.env = logical(0)
else
is.env = apply(facts[which.env,],2,sum)>0
}
if(length(which.trait)==1)
is.trait = facts[which.trait,]>0
else
{
if(length(which.trait)==0)
is.trait = logical(0)
else
is.trait = apply(facts[which.trait,],2,sum)>0
}
is.4th.term = is.env & is.trait
if(get.fourth==TRUE)
is.4th.corner = is.4th.term[attr(X,"assign")]
else
is.4th.corner = rep( FALSE, length(attr(X,"assign")) )
if(attr(tt,"intercept")==1)
{
names.Q = dimnames(X)[[2]][c(FALSE,is.4th.corner)]
X = as.matrix(X[,-1])
}
else
names.Q = dimnames(X)[[2]][is.4th.corner]
names.R = "coef"
if(get.fourth==FALSE)
{
X = as.matrix(X[,-which(is.4th.term==TRUE)])
is.4th.corner = c()
if(sum(is.4th.term==FALSE)>0)
{
formula = paste0(dimnames(facts)[[2]][is.4th.term==FALSE],collapse="+")
formula = as.formula(paste0("~",formula))
}
else
formula=as.formula("~1")
}
X = cbind(X.spp,X.site,X)
is.4th.corner = c(rep(FALSE, dim(X.spp)[2]+dim(X.site)[2]), is.4th.corner)
}
if(any.penalty)
{
penalty = c( 0, rep(1,dim(X)[2]-1) )
if (col.intercepts==TRUE & marg.penalty==FALSE)
penalty = c( rep( 0,dim(X.spp)[2]+dim(X.site)[2] ), rep( 1, dim(X)[2]-dim(X.spp)[2]-dim(X.site)[2] ) )
}
else
penalty = NULL
return(list(X=X, is.4th.corner=is.4th.corner, names.R=names.R, names.Q=names.Q, penalty=penalty, any.penalty=any.penalty, scaling=scaling, formula=formula) )
} |
library(dplyr)
library(httr)
test_that("okresy", {
skip_on_cran()
Sys.setenv("NETWORK_UP" = FALSE)
expect_message(okresy(), "internet")
Sys.setenv("NETWORK_UP" = TRUE)
Sys.setenv("AWS_UP" = FALSE)
expect_message(okresy(), "source")
Sys.setenv("AWS_UP" = TRUE)
expect_true(is.data.frame(okresy()))
expect_true(is.data.frame(okresy("low")))
expect_true(is.data.frame(okresy("high")))
expect_s3_class(okresy(), "sf")
expect_s3_class(okresy("high"), "sf")
expect_s3_class(okresy("low"), "sf")
expect_equal(nrow(okresy()), 77)
expect_equal(nrow(okresy("low")), 77)
expect_equal(nrow(okresy("high")), 77)
expect_equal(st_crs(okresy("low"))$input, "EPSG:4326")
expect_equal(st_crs(okresy("high"))$input, "EPSG:4326")
expect_true(all(st_is_valid(okresy("high"))))
expect_true(all(st_is_valid(okresy("low"))))
expect_equal(colnames(okresy()), c("KOD_OKRES", "KOD_LAU1", "NAZ_LAU1", "KOD_KRAJ",
"KOD_CZNUTS3", "NAZ_CZNUTS3", "geometry"))
expect_equal(colnames(okresy("low")),
colnames(okresy("high")))
expect_error(okresy("bflm"))
expect_true(object.size(okresy("low")) < object.size(okresy("high")))
}) |
ggplot_na_intervals <- function(x,
number_intervals = NULL,
interval_size = NULL,
measure = "percent",
color_missing = "indianred2",
color_existing = "steelblue",
alpha_missing = 0.8,
alpha_existing = 0.3,
title = "Missing Values per Interval",
subtitle = "Amount of NA and non-NA for successive intervals",
xlab = "Time Lapse (Interval Size: XX)",
ylab = NULL,
color_border = "white",
theme = ggplot2::theme_linedraw()) {
data <- x
if (any(class(data) == "tbl_ts")) {
data <- as.vector(as.data.frame(data)[, 2])
}
else if (any(class(data) == "tbl")) {
data <- as.vector(as.data.frame(data)[, 1])
}
if (!is.null(dim(data)[2]) && dim(data)[2] > 1) {
stop("x is not univariate. The function only works with univariate
input for x. For data types with multiple variables/columns only input
the column you want to plot as parameter x.")
}
if (!is.null(dim(data)[2])) {
data <- data[, 1]
}
data <- as.vector(data)
if (!is.numeric(data)) {
stop("Input x is not numeric")
}
missindx <- is.na(data)
if (all(missindx)) {
stop("Input data consists only of NAs. At least one non-NA numeric value is needed
for creating a meaningful ggplot_na_distribution plot)")
}
if (is.null(number_intervals)) {
number_intervals <- grDevices::nclass.Sturges(data)
}
if (!is.null(interval_size)) {
breaks <- seq(from = 0, to = length(data) - 1, by = interval_size)
breaks <- c(breaks, length(data))
}
else {
breaks <- seq(from = 0, to = length(data) - 1, by = floor(length(data) / number_intervals))
breaks <- c(breaks, length(data))
}
binwidth <- breaks[2]
color_missing <- ggplot2::alpha(color_missing, alpha_missing)
color_existing <- ggplot2::alpha(color_existing, alpha_existing)
if ( (!is.null(subtitle)) && (subtitle == "Amount of NA and non-NA for successive intervals")) {
subtitle <- paste0("Amount of <b style='color:", color_missing, ";' >NA</b>
and <b style='color:", color_existing, "' >non-NA</b>
for successive intervals")
}
if (is.null(ylab)) {
ifelse(measure == "percent", ylab <- "Percent", ylab <- "Count")
}
if (xlab == "Time Lapse (Interval Size: XX)") {
xlab <- paste("Time Lapse (Interval Size:", binwidth, ")")
}
index <- seq_along(data)
miss <- as.factor(is.na(data))
df <- data.frame(index, miss)
gg <- ggplot2::ggplot(df, ggplot2::aes(index, fill = miss)) +
ggplot2::scale_fill_manual(
values = c(color_existing, color_missing),
labels = c("NAs", "non-NAs")
) +
theme +
ggplot2::theme(
legend.position = "none",
legend.title = ggplot2::element_blank(),
plot.subtitle = ggtext::element_markdown(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor.x = ggplot2::element_blank(),
) +
ggplot2::scale_x_continuous(expand = c(0, 0)) +
ggplot2::labs(title = title, subtitle = subtitle) +
ggplot2::xlab(xlab) +
ggplot2::ylab(ylab)
count <- NULL
if (measure == "percent") {
gg <- gg + ggplot2::stat_bin(ggplot2::aes(y = ggplot2::after_stat(count / binwidth)),
col = color_border, breaks = breaks, closed = "right"
) +
ggplot2::scale_y_continuous(expand = c(0, 0), labels = function(x) paste0(x*100, "%"))
}
else {
gg <- gg + ggplot2::stat_bin(ggplot2::aes(y = ggplot2::after_stat(count)),
col = color_border, breaks = breaks, closed = "right"
) +
ggplot2::scale_y_continuous(expand = c(0, 0))
}
return(gg)
} |
cluster_freedman_lane <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdx, qr_mm, prdy){
colSums(qr.fitted(qr_rdx, prdy)^2)/colSums(qr.resid(qr_mm, prdy)^2)* (NROW(prdy)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdx, qr_mm, prdy){
as.numeric(qr.coef(qr_rdx, prdy))/sqrt(colSums(qr.resid(qr_mm, prdy)^2)/sum(rdx^2)) * sqrt(NROW(args$y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
rdy <- qr.resid(qr_d, args$y)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
funT(qr_rdx = qr_rdx, qr_mm = qr_mm, prdy = Pmat_product(x = rdy,P = pi,type = type))})
return(out)}
cluster_kennedy <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdx, qr_mm, prdy){
colSums(qr.fitted(qr_rdx, prdy)^2)/colSums(qr.resid(qr_rdx, prdy)^2)* (NROW(prdy)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdx, qr_mm, prdy){
as.numeric(qr.coef(qr_rdx, prdy))/sqrt(colSums(qr.resid(qr_rdx, prdy)^2)/sum(rdx^2)) * sqrt(NROW(args$y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
rdy <- qr.resid(qr_d, args$y)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
funT(qr_rdx = qr_rdx, qr_mm = qr_mm, prdy = Pmat_product(x = rdy,P = pi,type = type))})
return(out)}
cluster_terBraak <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdx, qr_mm, pry){
colSums(qr.fitted(qr_rdx, pry)^2)/colSums(qr.resid(qr_mm, pry)^2)* (NROW(pry)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdx, qr_mm, pry){
as.numeric(qr.coef(qr_rdx, pry))/sqrt(colSums(qr.resid(qr_mm, pry)^2)/sum(rdx^2)) * sqrt(NROW(args$y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
rdy <- qr.resid(qr_d, args$y)
rmmy <- qr.resid(qr_mm, args$y)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
funT(qr_rdx = qr_rdx, qr_mm = qr_mm, pry = Pmat_product(x = rmmy,P = pi,type = type))})
out[,1] = funT(qr_rdx = qr_rdx, qr_mm = qr_mm, pry = rdy)
return(out)}
cluster_manly <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdx, qr_mm, py){
colSums(qr.fitted(qr_rdx, py)^2)/colSums(qr.resid(qr_mm, py)^2)* (NROW(py)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdx, qr_mm, py){
as.numeric(qr.coef(qr_rdx, py))/sqrt(colSums(qr.resid(qr_mm, py)^2)/sum(rdx^2)) * sqrt(NROW(args$y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
qr_1 <- qr(rep(1,NROW(args$y)))
r1y <- qr.resid(qr_1,args$y)
h1y <- qr.fitted(qr_1,args$y)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
funT(qr_rdx = qr_rdx, qr_mm = qr_mm, py = Pmat_product(x = r1y,P = pi,type = type)+h1y)
})
return(out)}
cluster_draper_stoneman <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdpx, qr_pmm, y, qr_mm, qr_rdx, rdpx){
colSums(qr.fitted(qr_rdpx, y)^2)/colSums(qr.resid(qr_pmm, y)^2)* (NROW(y)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdpx, qr_pmm, y, qr_mm, qr_rdx, rdpx){
as.numeric(qr.coef(qr_rdpx, y))/sqrt(colSums(qr.resid(qr_pmm, y)^2)/sum(rdpx^2)) * sqrt(NROW(y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
px = Pmat_product(x = args$mm[,select_x, drop=F],P =pi,type = type)
rdpx = qr.resid(qr_d,px)
qr_rdpx = qr(rdpx)
qr_pmm = qr(cbind(args$mm[,!select_x, drop=F],px))
funT(qr_rdpx = qr_rdpx, qr_pmm = qr_pmm, y = args$y, qr_mm = qr_mm, qr_rdx = qr_rdx, rdpx = rdpx)})
return(out)}
cluster_dekker <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_rdprx, ry, qr_mm,qr_rdx,rdprx){
colSums(qr.fitted(qr_rdprx, ry)^2)/colSums(qr.resid(qr_rdprx, ry)^2)* (NROW(ry)-qr_mm$rank)/(qr_rdx$rank)}
},
"t" = {funT = function(qr_rdprx, ry, qr_mm,qr_rdx,rdprx){
as.numeric(qr.coef(qr_rdprx, ry))/sqrt(colSums(qr.resid(qr_rdprx, ry)^2)/sum(rdprx^2)) * sqrt(NROW(ry)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign"))) %in% args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_rdx <- qr(rdx)
ry = qr.resid(qr_d,args$y)
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
rdprx = qr.resid(qr_d,Pmat_product(x = rdx,P = pi,type = type))
qr_rdprx = qr(rdprx)
funT(qr_rdprx = qr_rdprx, ry = ry, qr_mm = qr_mm, qr_rdx = qr_rdx, rdprx = rdprx)})
return(out)}
cluster_huh_jhun <- function(args){
switch(args$test,
"fisher"= {funT = function(qr_vx, qr_mm, pvy, rdx){
colSums(qr.fitted(qr_vx, pvy)^2)/colSums(qr.resid(qr_vx, pvy)^2)* (NROW(args$y)-qr_mm$rank)/(qr_vx$rank)}
},
"t" = {funT = function(qr_vx, qr_mm, pvy, rdx){
as.numeric(qr.coef(qr_vx, pvy))/sqrt(colSums(qr.resid(qr_vx, pvy)^2)/sum(rdx^2)) * sqrt(NROW(args$y)-qr_mm$rank)}
})
select_x <- c(1:length(attr(args$mm,"assign")))%in%args$colx
qr_mm <- qr(args$mm)
qr_d <- qr(args$mm[,!select_x, drop = F])
rdx <- qr.resid(qr_d, args$mm[, select_x, drop = F])
qr_o= qr(args$rnd_rotation[1:(NROW(args$y)-qr_d$rank),1:(NROW(args$y)-qr_d$rank)])
omega = qr.Q(qr_o)%*%diag(sign(diag(qr.R(qr_o))))
qcd = qr.Q(qr_d,complete = T)[,-c(1:qr_d$rank),drop=F]
v = omega%*%t(qcd)
vx <- v%*%(args$mm[,select_x, drop = F])
qr_vx <-qr(vx)
vy <- v%*%args$y
type = attr(args$P,"type")
out = apply(args$P,2,function(pi){
funT(qr_vx = qr_vx, qr_mm = qr_mm, pvy = Pmat_product(x = vy,P = pi,type = type), rdx = rdx)})
return(out)} |
expect_invisible <- function(call, label = NULL) {
lab <- label %||% expr_label(enexpr(call))
vis <- withVisible(call)
expect(
identical(vis$visible, FALSE),
sprintf("%s does not return invisibly", lab)
)
invisible(vis$value)
}
expect_visible <- function(call, label = NULL) {
lab <- label %||% expr_label(enexpr(call))
vis <- withVisible(call)
expect(
identical(vis$visible, TRUE),
sprintf("%s does not invisibly", lab)
)
invisible(vis$value)
} |
approx2 <- function(
x,
fill=NULL,
n=length(x),
quiet=FALSE,
...
)
{
if(all(is.na(x)))
{
if(!quiet) warning("There are no non-NA values in x.")
return(rep(NA,n))
}
L <- length(x)
if(is.na(x[1])) x[1] <- if(is.null(fill)) head(x[!is.na(x)],1) else fill(x, na.rm=TRUE)
if(is.na(x[L])) x[L] <- if(is.null(fill)) tail(x[!is.na(x)],1) else fill(x, na.rm=TRUE)
approx(x, n=n, ...)$y
} |
modif_canevas <-
function(xmin,xmax,ymin,ymax,projproj,nbfonds)
{
canevas<-balises_qgis()[[1]]
modifs=data.frame(c("XXXMIN","XXXMAX","YYYMIN","YYYMAX","PROJECTIONPROJET","NOMBREDEFONDS"),stringsAsFactors=F)
modifs[1,2]=xmin
modifs[2,2]=xmax
modifs[3,2]=ymin
modifs[4,2]=ymax
modifs[5,2]=projproj
modifs[6,2]=nbfonds
modifs[,2]=as.character(modifs[,2])
qgs=modification(canevas,modifs)
return(qgs)
} |
matching <- function(
x,
p = 2,
match_between = NULL,
match_within = NULL,
match_extreme_first = TRUE,
target_group = NULL,
sort_output = TRUE
) {
input_validation_matching(x, p, match_between, match_within, match_extreme_first, target_group)
data <- to_matrix(x)
match_between <- merge_into_one_variable(match_between)
target_group <- get_target_group(data, match_between, target_group)
if (argument_exists(match_within)) {
cl <- match_within(data, p, match_between, match_within, match_extreme_first, target_group)
} else {
cl <- nn_centroid_clustering(data, p, match_between, match_extreme_first, target_group)
}
if (sort_output) {
return(sort_by_objective(cl, data))
}
cl
}
get_target_group <- function(data, match_between, target_group) {
if (!argument_exists(match_between)) {
return(FALSE)
}
tab <- table(match_between)
if (!argument_exists(target_group)) {
if (all(tab == tab[1])) {
return(FALSE)
}
return(which.min(tab))
}
if (target_group == "smallest") {
return(which.min(tab))
} else if (target_group == "diverse") {
return(which.max(diversity_objective_by_group(match_between, data)))
} else if (target_group == "none") {
return(FALSE)
} else {
stop("argument `target_group` must be one of 'smallest', 'diverse' or 'none'")
}
}
match_within <- function(data, p, match_between, match_within, match_extreme_first, target_group) {
match_within <- merge_into_one_variable(match_within)
N <- nrow(data)
cl <- rep(NA, N)
c <- length(unique(match_within))
for (i in 1:c) {
tmp_data <- subset_data_matrix(data, match_within == i)
cl_tmp <- nn_centroid_clustering(
tmp_data,
p,
match_between[match_within == i],
match_extreme_first
)
cl[match_within == i] <- ifelse(is.na(cl_tmp), NA, paste0(cl_tmp, "_", i))
}
to_numeric(cl)
}
sort_by_objective <- function(cl, data, N) {
N <- nrow(data)
selected <- (1:N)[!is.na(cl)]
cl_sub <- cl[selected]
cl_sub <- order_cluster_vector(cl_sub)
objectives <- diversity_objective_by_group(cl_sub, subset_data_matrix(data, selected))
N <- length(cl_sub)
one <- data.frame(match = cl_sub, order_matches = 1:length(cl_sub))
two <- data.frame(match = order(objectives), objective_id = 1:length(objectives))
merged <- merge(one, two)
new <- rep(NA, N)
new[merged$order_matches] <- merged$objective_id
cl[!is.na(cl)] <- new
cl
} |
tbr_binom <- function(.tbl, x, tcolumn, unit = "years", n, alpha = 0.05) {
.tbl <- .tbl %>%
arrange(!! rlang::enquo(tcolumn)) %>%
mutate("temp" := purrr::map(row_number(),
~tbr_binom_window(x = !! rlang::enquo(x),
tcolumn = !! rlang::enquo(tcolumn),
unit = unit,
n = n,
alpha = alpha,
i = .x))) %>%
tidyr::unnest(.data$temp)
.tbl <- tibble::as_tibble(.tbl)
return(.tbl)
}
tbr_binom_window <- function(x, tcolumn, unit = "years", n, i, alpha) {
u <- (c("years", "months", "weeks", "days", "hours", "minutes", "seconds"))
if (!unit %in% u) {
stop("unit must be one of ", paste(u, collapse = ", "))
}
window <- open_window(x, tcolumn, unit = unit, n, i)
df <- tibble(window) %>%
summarise(n = n(), successes = as.integer(sum(window)))
results <- binom_ci(x = df$successes, n = df$n, alpha = alpha, return.df = TRUE)
return(results)
}
binom_ci <- function(x, n, alpha = 0.05,
method = c("wilson","exact","asymptotic"),
return.df = FALSE)
{
method <- match.arg(method)
bc <- function(x, n, alpha, method)
{
nu1 <- 2 * (n - x + 1)
nu2 <- 2 * x
ll <- if (x > 0)
x/(x + qf(1 - alpha/2, nu1, nu2) * (n - x + 1))
else
0
nu1p <- nu2 + 2
nu2p <- nu1 - 2
pp <- if (x < n)
qf(1 - alpha/2, nu1p, nu2p)
else
1
ul <- ((x + 1) * pp)/(n - x + (x + 1) * pp)
zcrit <- -qnorm(alpha/2)
z2 <- zcrit * zcrit
p <- x/n
cl <- (p + z2/2/n + c(-1, 1) * zcrit *
sqrt((p * (1 - p) + z2/4/n)/n))/(1 + z2/n)
if (x == 1)
cl[1] <- -log(1 - alpha)/n
if (x == (n - 1))
cl[2] <- 1 + log(1 - alpha)/n
asymp.lcl <- x/n - qnorm(1 - alpha/2) *
sqrt(((x/n) * (1 - x/n))/n)
asymp.ucl <- x/n + qnorm(1 - alpha/2) * sqrt(((x/n) * (1 - x/n)
)/n)
res <- rbind(c(ll, ul), cl, c(asymp.lcl, asymp.ucl))
res <- cbind(rep(x/n, 3), res)
switch(method,
wilson = res[2, ],
exact = res[1, ],
asymptotic = res[3, ])
}
if ((length(x) != length(n)) & length(x) == 1)
x <- rep(x, length(n))
if ((length(x) != length(n)) & length(n) == 1)
n <- rep(n, length(x))
mat <- matrix(ncol = 3, nrow = length(x))
for (i in 1:length(x))
mat[i, ] <- bc(x[i], n[i], alpha = alpha, method = method)
dimnames(mat) <- list(rep("", dim(mat)[1]),
c("PointEst", "Lower", "Upper"))
if (return.df)
mat <- as.data.frame(mat, row.names = NULL)
mat
} |
library(nlsr)
fnDeriv(quote(1 + x + y), c("x", "y"))
nlsDeriv( ~ log(x), "x")
nlsDeriv( ~ log(x, base=3), "x" )
nlsDeriv( ~ exp(x), "x")
nlsDeriv( ~ sin(x), "x")
nlsDeriv( ~ cos(x), "x")
nlsDeriv( ~ tan(x), "x")
nlsDeriv( ~ sinh(x), "x")
nlsDeriv( ~ cosh(x), "x")
nlsDeriv( ~ sqrt(x), "x")
nlsDeriv( ~ pnorm(q), "q")
nlsDeriv( ~ dnorm(x, mean), "mean")
nlsDeriv( ~ asin(x), "x")
nlsDeriv( ~ acos(x), "x")
nlsDeriv( ~ atan(x), "x")
nlsDeriv( ~ gamma(x), "x")
nlsDeriv( ~ lgamma(x), "x")
nlsDeriv( ~ digamma(x), "x")
nlsDeriv( ~ trigamma(x), "x")
nlsDeriv( ~ psigamma(x, deriv = 5), "x")
nlsDeriv( ~ x*y, "x")
nlsDeriv( ~ x/y, "x")
nlsDeriv( ~ x^y, "x")
nlsDeriv( ~ (x), "x")
nlsDeriv( ~ +x, "x")
nlsDeriv( ~ -x, "x")
nlsDeriv( ~ abs(x), "x")
nlsDeriv( ~ sign(x), "x")
nlsSimplify(quote(+(a+b)))
nlsSimplify(quote(-5))
nlsSimplify(quote(--(a+b)))
nlsSimplify(quote(exp(log(a+b))))
nlsSimplify(quote(exp(1)))
nlsSimplify(quote(log(exp(a+b))))
nlsSimplify(quote(log(1)))
nlsSimplify(quote(!TRUE))
nlsSimplify(quote(!FALSE))
nlsSimplify(quote((a+b)))
nlsSimplify(quote(a + b + 0))
nlsSimplify(quote(0 + a + b))
nlsSimplify(quote((a+b) + (a+b)))
nlsSimplify(quote(1 + 4))
nlsSimplify(quote(a + b - 0))
nlsSimplify(quote(0 - a - b))
nlsSimplify(quote((a+b) - (a+b)))
nlsSimplify(quote(5 - 3))
nlsSimplify(quote(0*(a+b)))
nlsSimplify(quote((a+b)*0))
nlsSimplify(quote(1L * (a+b)))
nlsSimplify(quote((a+b) * 1))
nlsSimplify(quote((-1)*(a+b)))
nlsSimplify(quote((a+b)*(-1)))
nlsSimplify(quote(2*5))
nlsSimplify(quote((a+b) / 1))
nlsSimplify(quote((a+b) / (-1)))
nlsSimplify(quote(0/(a+b)))
nlsSimplify(quote(1/3))
nlsSimplify(quote((a+b) ^ 1))
nlsSimplify(quote(2^10))
nlsSimplify(quote(log(exp(a), 3)))
nlsSimplify(quote(FALSE && b))
nlsSimplify(quote(a && TRUE))
nlsSimplify(quote(TRUE && b))
nlsSimplify(quote(a || TRUE))
nlsSimplify(quote(FALSE || b))
nlsSimplify(quote(a || FALSE))
nlsSimplify(quote(if (TRUE) a+b))
nlsSimplify(quote(if (FALSE) a+b))
nlsSimplify(quote(if (TRUE) a+b else a*b))
nlsSimplify(quote(if (FALSE) a+b else a*b))
nlsSimplify(quote(if (cond) a+b else a+b))
nlsSimplify(quote(--(a+b)))
modelformula <- y ~ ms * b1/(1 + b2 * exp(-b3 * tt))
pvec <- c(b1=1, b2=1, b3=1)
cat("model2rjfunx: modelformula = ")
print(modelformula)
print(class(modelformula))
if (length(modelformula) == 2) {
residexpr <- modelformula[[2]]
} else if (length(modelformula) == 3) {
residexpr <- call("-", modelformula[[3]], modelformula[[2]])
} else stop("Unrecognized formula")
if (is.null(names(pvec)))
names(pvec) <- paste0("p", seq_along(pvec))
residexpr1 <- nlsDeriv( ~ residexpr, names(pvec))
cat("residexpr1:\n")
print(residexpr1)
residexpr2 <- fnDeriv(residexpr, names(pvec))
cat("residexpr2:\n")
print(residexpr2) |
test_that("attaches the butcher class", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
fit <- butcher::butcher(fit)
expect_s3_class(fit, "butchered_workflow")
})
test_that("fails if not a fitted workflow", {
skip_if_not_installed("butcher")
expect_error(butcher::butcher(workflow()))
})
test_that("can axe the call", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
fit <- butcher::axe_call(fit)
expect_identical(fit$fit$fit$fit$call, rlang::expr(dummy_call()))
})
test_that("can axe the fitted bits", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
fit <- butcher::axe_fitted(fit)
expect_identical(fit$fit$fit$fit$fitted.values, numeric())
})
test_that("axing the data removes the outcomes/predictors", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
expect_s3_class(fit$pre$mold$outcomes, "tbl_df")
expect_s3_class(fit$pre$mold$predictors, "tbl_df")
fit <- butcher::axe_data(fit)
expect_null(fit$pre$mold$outcomes)
expect_null(fit$pre$mold$predictors)
})
test_that("axing the env - recipe", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
rec <- recipes::recipe(mpg ~ cyl + disp, mtcars)
rec <- recipes::step_center(rec, cyl, disp)
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_recipe(wf, rec)
fit <- fit(wf, mtcars)
fit <- butcher::axe_env(fit)
expect_s3_class(fit$pre$actions$recipe$recipe, "butchered_recipe")
})
test_that("axing the env - formula", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
fit <- butcher::axe_env(fit)
expect_s3_class(fit$pre$actions$formula$formula, "butchered_formula")
})
test_that("axing the env - variables", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_variables(wf, mpg, c(cyl, disp))
fit <- fit(wf, mtcars)
axed <- butcher::axe_env(fit)
expect_s3_class(axed, "butchered_workflow")
original_outcomes <- fit$pre$actions$variables$variables$outcomes
original_predictors <- fit$pre$actions$variables$variables$predictors
axed_outcomes <- axed$pre$actions$variables$variables$outcomes
axed_predictors <- axed$pre$actions$variables$variables$predictors
expect_s3_class(axed_outcomes, "quosure")
expect_s3_class(axed_predictors, "quosure")
expect_false(identical(original_outcomes, axed_outcomes))
expect_false(identical(original_predictors, axed_predictors))
})
test_that("can still predict after butcher - recipe", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
rec <- recipes::recipe(mpg ~ cyl + disp, mtcars)
rec <- recipes::step_center(rec, cyl, disp)
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_recipe(wf, rec)
fit <- fit(wf, mtcars)
axed <- butcher::axe_env(fit)
expect_identical(
predict(fit, mtcars),
predict(axed, mtcars)
)
})
test_that("can still predict after butcher - formula", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_formula(wf, mpg ~ cyl + disp)
fit <- fit(wf, mtcars)
axed <- butcher::axe_env(fit)
expect_identical(
predict(fit, mtcars),
predict(axed, mtcars)
)
})
test_that("can still predict after butcher - variables", {
skip_if_not_installed("butcher")
model <- parsnip::linear_reg()
model <- parsnip::set_engine(model, "lm")
wf <- workflow()
wf <- add_model(wf, model)
wf <- add_variables(wf, mpg, c(cyl, disp))
fit <- fit(wf, mtcars)
axed <- butcher::axe_env(fit)
expect_identical(
predict(fit, mtcars),
predict(axed, mtcars)
)
}) |
rxodeTest(
{
context("Test PythonFsum")
test_that("Fsum", {
et <- eventTable() %>%
add.sampling(0)
rx <- RxODE({
s1 <- sum(1e100, 1.0, -1e100, 1e-100, 1e50, -1.0, -1e50)
s2 <- sum(2.0^53, -0.5, -2.0^-54)
s3 <- sum(2.0^53, 1.0, 2.0^-100)
s4 <- sum(2.0^53 + 10.0, 1.0, 2.0^-100)
s5 <- sum(2.0^53 - 4.0, 0.5, 2.0^-54)
s6 <- sum(1e16, 1., 1e-16)
s7 <- sum(a, b, c, d)
s8 <- sum(1e100, 1, -1e100, 1)
s9 <- sum(R_pow(prod(2, 3), 2), 6)
})
s <- rxSolve(rx,
params = c(
a = 1e16 - 2.,
b = 1. - 2.^-53,
c = -(1e16 - 2.),
d = -(1. - 2.^-53)
), et,
sumType = "fsum"
)
expect_identical(s$s1, 1e-100)
expect_identical(s$s2, 2.0^53 - 1.0)
expect_identical(s$s3, 2.0^53 + 2.0)
expect_identical(s$s4, 2.0^53 + 12.0)
expect_identical(s$s5, 2.0^53 - 3.0)
expect_identical(s$s6, 10000000000000002.0)
expect_identical(s$s7, 0.0)
expect_identical(s$s8, 2.0)
expect_error(rxSetSum("c"))
expect_error(rxSetProd("double"))
})
},
test = "cran"
) |
library(pipenostics)
test_that("water's function errs in water states paths", {
expect_equal(
ps_t(),
c(.353658941e-2, .263889776e1, .123443146e2),
tolerance = 1e-8
)
expect_equal(
cp1_tp(),
c(.417301218, .401008987, 0.465580682)*10,
tolerance = 1e-8
)
expect_equal(
v1_tp(),
c(.100215168e-2, .971180894e-3, .120241800e-2),
tolerance = 1e-8
)
expect_equal(
fric_romeo(2118517, c(0, 70e-3/1, 7e-3/1)),
fric_buzelli(2118517, c(0, 70e-3/1, 7e-3/1)),
tolerance = 1e-3
)
expect_equal(
fric_romeo(2118517, c(0, 70e-3/1, 7e-3/1)),
fric_vatankhan(2118517, c(0, 70e-3/1, 7e-3/1)),
tolerance = 1e-3
)
expect_equal(
all(
fric_romeo(2118517, c(0, 70e-3/1, 7e-3/1)) < .2 &
fric_romeo(2118517, c(0, 70e-3/1, 7e-3/1)) > 0
),
TRUE
)
with(pipenostics:::r12t4, {
expect_equal(
dynvisc(temperature, density),
dynvisc,
tolerance = 1e-8
)
})
}) |
pcmvtruncnorm <- function(
lowerY, upperY,
mean, sigma, lower, upper,
dependent.ind, given.ind, X.given,
...
) {
params <- condtMVN(
mean = mean,
sigma = sigma,
lower = lower,
upper = upper,
dependent.ind = dependent.ind,
given.ind = given.ind, X.given = X.given,
init = 0
)
d <- length(lowerY)
if (length(lowerY) != length(dependent.ind))
stop("Error: `lowerY` refers to the lower probability of Y|X and must equal to length of dependent.ind.", call. = FALSE)
if (length(upperY) != length(dependent.ind))
stop("Error: `upperY` refers to the upper probability of Y|X and must equal to length of dependent.ind.", call. = FALSE)
when.equal <- lowerY == upperY
if (identical(lowerY, upperY)) {
warning("Warning: lowerY and upperY are equal in all dimenseions. Returning zero")
prob <- 0
return(prob)
} else if (0 < sum(when.equal) & sum(when.equal) < d) {
stop("Error: lowerY is equal to upperY in at least one dimension; tmvtnorm does not calculate the CDF",
call. = FALSE)
} else if (any(lowerY > upperY)) {
stop("Error: lowerY is higher than to upperY in at least one dimension. Did you mean to switch them?", call. = FALSE)
}
if (length(dependent.ind) == 1) {
message("univariate CDF: using truncnorm::ptruncnorm")
prob <-
truncnorm::ptruncnorm(
upperY,
mean = params$condMean,
sd = params$condVar,
a = params$condLower,
b = params$condUpper
) -
truncnorm::ptruncnorm(
lowerY,
mean = params$condMean,
sd = params$condVar,
a = params$condLower,
b = params$condUpper
)
} else {
prob <- tmvtnorm::ptmvnorm(
lowerY, upperY,
mean = params$condMean,
sigma = params$condVar,
lower = params$condLower,
upper = params$condUpper,
...
)
}
return(prob)
} |
cat("doExtras is ", simsalapar:::doExtras(), "\n",
"nCores4test(): ", simsalapar:::nCores4test(), "\n", sep="") |
check_arg_test_phevis <- function(train_param,
df_test,
surparam,
model,
START_DATE,
PATIENT_NUM,
ENCOUNTER_NUM){
if(!is.character(START_DATE)) stop("START_DATE should be character")
if(length(START_DATE) != 1) stop("START_DATE should be of length 1")
if(!is.character(PATIENT_NUM)) stop("PATIENT_NUM should be character")
if(length(PATIENT_NUM) != 1) stop("PATIENT_NUM should be of length 1")
if(!is.character(ENCOUNTER_NUM)) stop("ENCOUNTER_NUM should be character")
if(length(ENCOUNTER_NUM) != 1) stop("ENCOUNTER_NUM should be of length 1")
if(!is.data.frame(df_test)) stop("df_test should be a data.frame object")
if(!is.list(train_param)) stop("train_param should be a list")
if(!is.list(surparam)) stop("surparam should be a list")
if(!is.list(model)) stop("model should be a list")
message("-- Arguments passed check -> PheVis begins --")
return(NULL)
} |
library(datamart)
test_strxxcrypt <- function() {
print(strencrypt(""))
print(strdecrypt(""))
print(strdecrypt(strencrypt("abc")))
print(strencrypt("abc"))
}
test_strxxcrypt() |
trim_model <- function(model, maxit = 0, return_loglik=FALSE, zerotol=1e-8, verbose = TRUE, ...){
ll_original <- logLik(model)
model_original <- model
if(inherits(model, "hmm")){
if(model$n_channels==1){
if(!(any(model$initial_probs < zerotol & model$initial_probs > 0) ||
any(model$transition_probs < zerotol & model$transition_probs > 0)
|| any(model$emission_probs < zerotol & model$emission_probs > 0))){
if(verbose)
print("Nothing to trim.")
if(return_loglik){
return(list(model=model,loglik=logLik(model)))
} else return(model)
}
model$initial_probs[model$initial_probs < zerotol] <- 0
model$initial_probs <- model$initial_probs/sum(model$initial_probs)
model$transition_probs[model$transition_probs < zerotol] <- 0
model$transition_probs <- model$transition_probs/rowSums(model$transition_probs)
model$emission_probs[model$emission_probs < zerotol] <- 0
model$emission_probs <- model$emission_probs/rowSums(model$emission_probs)
if(!is.finite(ll0 <- logLik(model))){
warning("Trimming resulted in non-finite log-likelihood; returning the original model. Try changing the zerotol parameter.")
return(model_original)
}
if (!isTRUE(all.equal(ll0,ll_original)) && ll0 < ll_original) {
warning("Trimming resulted model with smaller log-likelihood; returning the original model. ")
return(model_original)
}
if(maxit > 0){
for(ii in 1:maxit){
fit <- fit_model(model, ...)
ll <- fit$logLik
if(ll > ll0){
model <- fit$model
ll0 <- ll
} else break
if(!(any(model$initial_probs < zerotol & model$initial_probs > 0) ||
any(model$transition_probs < zerotol & model$transition_probs > 0)
|| any(model$emission_probs < zerotol & model$emission_probs > 0)))
break
model$initial_probs[model$initial_probs < zerotol] <- 0
model$initial_probs <- model$initial_probs/sum(model$initial_probs)
model$transition_probs[model$transition_probs < zerotol] <- 0
model$transition_probs <- model$transition_probs/rowSums(model$transition_probs)
model$emission_probs[model$emission_probs < zerotol] <- 0
model$emission_probs <- model$emission_probs/rowSums(model$emission_probs)
}
}
} else {
if(!(any(model$initial_probs < zerotol & model$initial_probs > 0) ||
any(model$transition_probs < zerotol & model$transition_probs > 0)
|| any(sapply(model$emission_probs,function(x) any(x < zerotol & x > 0))))){
if(verbose)
print("Nothing to trim.")
if(return_loglik){
return(list(model=model,loglik=logLik(model)))
} else return(model)
}
model$initial_probs[model$initial_probs < zerotol] <- 0
model$initial_probs <- model$initial_probs/sum(model$initial_probs)
model$transition_probs[model$transition_probs < zerotol] <- 0
model$transition_probs <- model$transition_probs/rowSums(model$transition_probs)
for(i in 1:model$n_channels){
model$emission_probs[[i]][model$emission_probs[[i]] < zerotol] <- 0
model$emission_probs[[i]] <- model$emission_probs[[i]]/
rowSums(model$emission_probs[[i]])
}
if(!is.finite(ll0 <- logLik(model))){
warning("Trimming resulted in non-finite log-likelihood; returning the original model. Try changing the zerotol parameter.")
return(model_original)
}
if (!isTRUE(all.equal(ll0,ll_original)) && ll0 < ll_original) {
warning("Trimming resulted model with smaller log-likelihood; returning the original model. ")
return(model_original)
}
if(maxit > 0){
for(ii in 1:maxit){
fit <- fit_model(model, ...)
ll <- fit$logLik
if(ll > ll0){
model <- fit$model
ll0 <- ll
} else break
if(!(any(model$initial_probs < zerotol & model$initial_probs > 0) ||
any(model$transition_probs < zerotol & model$transition_probs > 0)
|| any(sapply(model$emission_probs,function(x) any(x < zerotol & x > 0)))))
break
model$initial_probs[model$initial_probs < zerotol] <- 0
model$initial_probs <- model$initial_probs/sum(model$initial_probs)
model$transition_probs[model$transition_probs < zerotol] <- 0
model$transition_probs <- model$transition_probs/rowSums(model$transition_probs)
for(i in 1:model$n_channels){
model$emission_probs[[i]][model$emission_probs[[i]] < zerotol] <- 0
model$emission_probs[[i]] <- model$emission_probs[[i]]/
rowSums(model$emission_probs[[i]])
}
}
}
}
}else if(inherits(model, "mhmm")){
if(model$n_channels==1){
if(!(any(unlist(model$initial_probs) < zerotol & unlist(model$initial_probs) > 0) ||
any(unlist(model$transition_probs) < zerotol & unlist(model$transition_probs) > 0)
|| any(unlist(model$emission_probs) < zerotol & unlist(model$emission_probs) > 0))){
if(verbose)
print("Nothing to trim.")
if(return_loglik){
return(list(model=model,loglik=logLik(model)))
} else return(model)
}
for(m in 1:model$n_clusters){
model$initial_probs[[m]][model$initial_probs[[m]] < zerotol] <- 0
model$initial_probs[[m]] <- model$initial_probs[[m]]/sum(model$initial_probs[[m]])
model$transition_probs[[m]][model$transition_probs[[m]] < zerotol] <- 0
model$transition_probs[[m]] <- model$transition_probs[[m]]/rowSums(model$transition_probs[[m]])
model$emission_probs[[m]][model$emission_probs[[m]] < zerotol] <- 0
model$emission_probs[[m]] <- model$emission_probs[[m]]/rowSums(model$emission_probs[[m]])
}
if(!is.finite(ll0 <- logLik(model))){
warning("Trimming resulted in non-finite log-likelihood; returning the original model. Try changing the zerotol parameter.")
return(model_original)
}
if (!isTRUE(all.equal(ll0,ll_original)) && ll0 < ll_original) {
warning("Trimming resulted model with smaller log-likelihood.")
return(model_original)
}
if(maxit > 0){
for(ii in 1:maxit){
fit <- fit_model(model, ...)
ll <- fit$logLik
if(ll > ll0){
model <- fit$model
ll0 <- ll
} else break
if(!(any(unlist(model$initial_probs) < zerotol & unlist(model$initial_probs) > 0) ||
any(unlist(model$transition_probs) < zerotol & unlist(model$transition_probs) > 0)
|| any(unlist(model$emission_probs) < zerotol & unlist(model$emission_probs) > 0)))
break
for(m in 1:model$n_clusters){
model$initial_probs[[m]][model$initial_probs[[m]] < zerotol] <- 0
model$initial_probs[[m]] <- model$initial_probs[[m]]/sum(model$initial_probs[[m]])
model$transition_probs[[m]][model$transition_probs[[m]] < zerotol] <- 0
model$transition_probs[[m]] <- model$transition_probs[[m]]/rowSums(model$transition_probs[[m]])
model$emission_probs[[m]][model$emission_probs[[m]] < zerotol] <- 0
model$emission_probs[[m]] <- model$emission_probs[[m]]/rowSums(model$emission_probs[[m]])
}
}
}
} else {
if(!(any(unlist(model$initial_probs) < zerotol & unlist(model$initial_probs) > 0) ||
any(unlist(model$transition_probs) < zerotol & unlist(model$transition_probs) > 0)
|| any(unlist(model$emission_probs) < zerotol & unlist(model$emission_probs) > 0))){
if(verbose)
print("Nothing to trim.")
if(return_loglik){
return(list(model=model,loglik=logLik(model)))
} else return(model)
}
for(m in 1:model$n_clusters){
model$initial_probs[[m]][model$initial_probs[[m]] < zerotol] <- 0
model$initial_probs[[m]] <- model$initial_probs[[m]]/sum(model$initial_probs[[m]])
model$transition_probs[[m]][model$transition_probs[[m]] < zerotol] <- 0
model$transition_probs[[m]] <- model$transition_probs[[m]]/rowSums(model$transition_probs[[m]])
for(i in 1:model$n_channels){
model$emission_probs[[m]][[i]][model$emission_probs[[m]][[i]] < zerotol] <- 0
model$emission_probs[[m]][[i]] <- model$emission_probs[[m]][[i]]/
rowSums(model$emission_probs[[m]][[i]])
}
}
if(!is.finite(ll0 <- logLik(model))){
warning("Trimming resulted in non-finite log-likelihood; returning the original model. Try changing the zerotol parameter.")
return(model_original)
}
if (!isTRUE(all.equal(ll0,ll_original)) && ll0 < ll_original) {
warning("Trimming resulted model with smaller log-likelihood.")
return(model_original)
}
if(maxit > 0){
for(ii in 1:maxit){
fit <- fit_model(model, ...)
ll <- fit$logLik
if(ll > ll0){
model <- fit$model
ll0 <- ll
} else break
if(!(any(unlist(model$initial_probs) < zerotol & unlist(model$initial_probs) > 0) ||
any(unlist(model$transition_probs) < zerotol & unlist(model$transition_probs) > 0)
|| any(unlist(model$emission_probs) < zerotol & unlist(model$emission_probs) > 0)))
break
for(m in 1:model$n_clusters){
model$initial_probs[[m]][model$initial_probs[[m]] < zerotol] <- 0
model$initial_probs[[m]] <- model$initial_probs[[m]]/sum(model$initial_probs[[m]])
model$transition_probs[[m]][model$transition_probs[[m]] < zerotol] <- 0
model$transition_probs[[m]] <- model$transition_probs[[m]]/rowSums(model$transition_probs[[m]])
for(i in 1:model$n_channels){
model$emission_probs[[m]][[i]][model$emission_probs[[m]][[i]] < zerotol] <- 0
model$emission_probs[[m]][[i]] <- model$emission_probs[[m]][[i]]/
rowSums(model$emission_probs[[m]][[i]])
}
}
}
}
}
}else{
stop("An object of class hmm or mhmm required.")
}
if (verbose) {
if(maxit > 0)
print(paste(ii,"iteration(s) used."))
if(ll0 > ll_original){
print(paste("Trimming improved log-likelihood, ll_trim-ll_orig =", signif(ll0-ll_original, 3)))
}
}
if(return_loglik){
list(model=model,loglik=ll0)
} else model
} |
create_trend <- function(data,
metric,
hrvar = "Organization",
mingroup = 5,
return = "plot",
legend_title = "Hours"){
required_variables <- c("Date",
metric,
"PersonId")
data %>%
check_inputs(requirements = required_variables)
if(is.null(hrvar)){
data <- totals_col(data)
hrvar <- "Total"
}
clean_nm <- us_to_space(metric)
myTable <-
data %>%
mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
rename(group = !!sym(hrvar)) %>%
select(PersonId, Date, group, !!sym(metric)) %>%
group_by(group) %>%
mutate(Employee_Count = n_distinct(PersonId)) %>%
filter(Employee_Count >= mingroup)
myTable <-
myTable %>%
group_by(Date, group) %>%
summarize(Employee_Count = mean(Employee_Count, na.rm = TRUE),
!!sym(metric) := mean(!!sym(metric), na.rm = TRUE))
myTable_plot <- myTable %>% select(Date, group, !!sym(metric))
myTable_return <- myTable_plot %>% tidyr::spread(Date, !!sym(metric))
plot_object <-
myTable_plot %>%
ggplot(aes(x = Date , y = group , fill = !!sym(metric))) +
geom_tile(height=.5) +
scale_x_date(position = "top") +
scale_fill_gradientn(name = legend_title,
colours = c("steelblue4",
"aliceblue",
"white",
"mistyrose1",
"tomato1")) +
theme_wpa_basic() +
theme(axis.line.y = element_blank(), axis.title.y = element_blank()) +
labs(title = clean_nm,
subtitle = paste("Hotspots by", tolower(camel_clean(hrvar)))) +
xlab("Date") +
ylab(hrvar) +
labs(caption = extract_date_range(data, return = "text"))
if(return == "table"){
myTable_return
} else if(return == "plot"){
plot_object
} else {
stop("Please enter a valid input for `return`.")
}
} |
KellyRatio <-
function (R, Rf = 0, method = "half")
{
R = checkData(R)
if(!is.null(dim(Rf)))
Rf = checkData(Rf)
kr <- function (R, Rf, method)
{
xR = Return.excess(R, Rf)
KR = mean(xR, na.rm=TRUE)/StdDev(R, na.rm=TRUE)^2
if (method == "half") {
KR = KR/2
}
return(KR)
}
result = sapply(R, kr, Rf = Rf, method = method)
dim(result) = c(1,NCOL(R))
colnames(result) = colnames(R)
rownames(result) = "Kelly Ratio"
return (result)
} |
test_that("can parse versions", {
out <- .rlang_downstream_parse_deps(c("foo (>= 1.0)"))
expect_equal(out, list(
c(pkg = "foo", min = "1.0")
))
out <- .rlang_downstream_parse_deps(c("foo (>= 1.0)", "bar (>= 2.0.0)"))
expect_equal(out, list(
c(pkg = "foo", min = "1.0"),
c(pkg = "bar", min = "2.0.0")
))
expect_error(
.rlang_downstream_parse_deps("foo"),
"Parsing error"
)
expect_error(
.rlang_downstream_parse_deps("foo (1.0)"),
"Parsing error"
)
expect_error(
.rlang_downstream_parse_deps("foo (< 1.0)"),
"Can only check `>=` requirements"
)
})
test_that("can check downstream versions", {
local_interactive(FALSE)
ok_deps <- .rlang_downstream_parse_deps(c(
"base (>= 1.0)",
"utils (>= 1.1)"
))
expect_no_warning(
expect_true(
.rlang_downstream_check(
pkg = "rlang",
pkg_ver = "0.5.0",
deps = ok_deps,
info = "Consequences.",
env = env(checked = FALSE)
)
)
)
bad_deps <- .rlang_downstream_parse_deps(c(
"base (>= 1.0)",
"utils (>= 100.10)"
))
expect_snapshot({
(expect_warning({
expect_false(
.rlang_downstream_check(
pkg = "rlang",
pkg_ver = "0.5.0",
deps = bad_deps,
info = "Consequences.",
env = env(checked = FALSE)
)
)
NULL
}))
})
missing_deps <- .rlang_downstream_parse_deps(c(
"base (>= 1.0)",
"foobar (>= 100.10)"
))
expect_no_warning({
expect_true(
.rlang_downstream_check(
pkg = "rlang",
pkg_ver = "0.5.0",
deps = missing_deps,
info = "Consequences.",
env = env(checked = FALSE)
)
)
NULL
})
})
test_that("setting `rlib_downstream_check` disables the check", {
local_options(rlib_downstream_check = FALSE)
local_interactive(FALSE)
bad_deps <- .rlang_downstream_parse_deps(c(
"base (>= 1.0)",
"utils (>= 100.10)"
))
expect_no_warning(
expect_null(
.rlang_downstream_check(
pkg = "rlang",
pkg_ver = "0.5.0",
deps = bad_deps,
info = "Consequences.",
env = env(checked = FALSE)
)
)
)
})
test_that("check_downstream() saves status in global env", {
local_interactive(TRUE)
local_options("rlang:::no_downstream_prompt" = TRUE)
bad_deps <- .rlang_downstream_parse_deps(c(
"base (>= 1.0)",
"utils (>= 100.10)"
))
key <- as.character(runif(1))
check <- function() {
.rlang_downstream_check(
pkg = "rlang",
pkg_ver = "0.5.0",
deps = bad_deps,
info = "Consequences.",
deps_key = key,
env = env(checked = FALSE)
)
}
expect_warning(expect_false(check()))
expect_no_warning(expect_null(check()))
}) |
context("palettes")
library(ggplot2)
test_that("trek_pal returns as expected", {
expect_error(trek_pal("a"), "Invalid palette name.")
x <- trek_pal()
expect_is(x, "character")
expect_equal(length(x), 35)
expect_equal(length(trek_pal("lcars_series")), 31)
expect_equal(trek_pal("starfleet", reverse = TRUE), rev(trek_pal("starfleet")))
})
test_that("view_trek_pals returns as expected", {
file <- file.path(tempdir(), "test-plot.png")
png(file)
expect_is(p <- view_trek_pals(), "NULL")
expect_is(view_trek_pals(c("starfleet", "starfleet2")), "NULL")
dev.off()
unlink(file, recursive = TRUE, force = TRUE)
})
test_that("trek scale functions return as expected", {
p <- ggplot(diamonds, aes(carat, stat(count), color = cut, fill = cut)) +
geom_density(position = "fill")
expect_is(p + scale_color_trek("andorian") +
scale_fill_trek("klingon"), "ggplot")
p <- ggplot(diamonds, aes(carat, price)) + geom_bin2d()
expect_is(p + scale_fill_trek("klingon", discrete = FALSE), "ggplot")
p <- ggplot(diamonds, aes(carat, price)) +
geom_density_2d(aes(color = ..level..))
expect_is(p + scale_color_trek("andorian", discrete = FALSE), "ggplot")
})
test_that("LCARS helpers return as expected", {
expect_equal(length(lcars_colors()), 31)
expect_equal(length(lcars_2357()), 9)
expect_equal(length(lcars_2357("tanoi")), 1)
expect_equal(as.character(lcars_2357(c("a", "tanoi"))), c(NA, "
expect_equal(length(lcars_2369()), 8)
expect_equal(length(lcars_2369("tanoi")), 1)
expect_equal(as.character(lcars_2369(c("a", "melrose"))), c(NA, "
expect_equal(length(lcars_2375()), 8)
expect_equal(length(lcars_2375("tanoi")), 1)
expect_equal(as.character(lcars_2375(c("a", "danub"))), c(NA, "
expect_equal(length(lcars_2379()), 8)
expect_equal(length(lcars_2379("tanoi")), 1)
expect_equal(as.character(lcars_2379(c("a", "husk"))), c(NA, "
x <- lcars_pals()
expect_is(x, "list")
expect_equal(length(x), 12)
expect_equal(lcars_pal(), as.character(lcars_2357()))
expect_equal(lcars_pal("2369"), as.character(lcars_2369()))
expect_equal(lcars_pal("2375"), as.character(lcars_2375()))
expect_equal(lcars_pal("2379"), as.character(lcars_2379()))
expect_error(lcars_pal(c("2357", "2365")), "Invalid LCARS palette name.")
expect_error(lcars_pal("a"), "Invalid LCARS palette name.")
expect_equal(lcars_pal(reverse = TRUE), rev(lcars_pal()))
expect_is(lcars_colors_pal("2357"), "function")
expect_is(lcars_colors_pal("2357", reverse = TRUE), "function")
expect_is(lcars_colors_pal("blue-bell"), "function")
})
test_that("lcars scale functions return as expected", {
p <- ggplot(diamonds, aes(carat, stat(count), color = cut, fill = cut)) +
geom_density(position = "fill")
expect_is(p + scale_fill_lcars("2357") + scale_color_lcars("2357"), "ggplot")
expect_is(p + scale_fill_lcars1("atomic-tangerine", dark = TRUE,
reverse = TRUE) +
scale_color_lcars1("atomic-tangerine", dark = TRUE,
reverse = TRUE), "ggplot")
expect_is(p + scale_fill_lcars2("pale-canary", "danub", reverse = TRUE) +
scale_color_lcars2("pale-canary", "danub", reverse = TRUE),
"ggplot")
p <- ggplot(diamonds, aes(carat, stat(count), color = cut, fill = cut)) +
geom_density(position = "fill")
expect_is(p + scale_fill_lcars2("pale-canary", "danub") +
scale_color_lcars2("pale-canary", "danub"), "ggplot")
expect_is(p + scale_fill_lcars2("pale-canary", "danub", divergent = TRUE) +
scale_fill_lcars2("pale-canary", "danub", divergent = TRUE),
"ggplot")
expect_is(
p +
scale_fill_lcars2("pale-canary", "danub", dark = TRUE, divergent = TRUE) +
scale_fill_lcars2("pale-canary", "danub", dark = TRUE, divergent = TRUE),
"ggplot")
p <- ggplot(diamonds, aes(carat, price)) + geom_bin2d()
expect_is(p + scale_fill_lcars("2357", discrete = FALSE), "ggplot")
expect_is(p + scale_fill_lcars1("rust", discrete = FALSE), "ggplot")
expect_is(p + scale_fill_lcars2("rust", "danub", discrete = FALSE), "ggplot")
p <- ggplot(diamonds, aes(carat, price)) +
geom_density_2d(aes(color = ..level..))
expect_is(p + scale_color_lcars("2357", discrete = FALSE), "ggplot")
expect_is(p + scale_color_lcars1("tanoi", discrete = FALSE), "ggplot")
expect_is(p + scale_color_lcars2("tanoi", "rust", discrete = FALSE), "ggplot")
}) |
find_parameters.BGGM <- function(x,
component = c("correlation", "conditional", "intercept", "all"),
flatten = FALSE,
...) {
component <- match.arg(component)
l <- switch(component,
"correlation" = list(correlation = colnames(get_parameters(x, component = "correlation"))),
"conditional" = list(conditional = colnames(get_parameters(x, component = "conditional"))),
"intercept" = list(intercept = colnames(x$Y)),
"all" = list(
intercept = colnames(x$Y),
correlation = colnames(get_parameters(x, component = "correlation")),
conditional = colnames(get_parameters(x, component = "conditional"))
)
)
l <- .compact_list(l)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.BFBayesFactor <- function(x,
effects = c("all", "fixed", "random"),
component = c("all", "extra"),
flatten = FALSE,
...) {
conditional <- NULL
random <- NULL
extra <- NULL
effects <- match.arg(effects)
component <- match.arg(component)
if (.classify_BFBayesFactor(x) == "correlation") {
conditional <- "rho"
} else if (.classify_BFBayesFactor(x) %in% c("ttest1", "ttest2")) {
conditional <- "Difference"
} else if (.classify_BFBayesFactor(x) == "meta") {
conditional <- "Effect"
} else if (.classify_BFBayesFactor(x) == "proptest") {
conditional <- "p"
} else if (.classify_BFBayesFactor(x) == "linear") {
posteriors <- as.data.frame(suppressMessages(
BayesFactor::posterior(x, iterations = 20, progress = FALSE, index = 1, ...)
))
params <- colnames(posteriors)
vars <- find_variables(x, effects = "all", verbose = FALSE)
interactions <- find_interactions(x)
dat <- get_data(x, verbose = FALSE)
if ("conditional" %in% names(vars)) {
conditional <- unlist(lapply(vars$conditional, function(i) {
if (is.factor(dat[[i]])) {
sprintf("%s-%s", i, levels(dat[[i]]))
} else {
sprintf("%s-%s", i, i)
}
}))
}
if ("conditional" %in% names(interactions)) {
for (i in interactions$conditional) {
conditional <- c(conditional, params[grepl(paste0("^\\Q", i, "\\E"), params)])
}
}
if ("random" %in% names(vars)) {
random <- unlist(lapply(vars$random, function(i) {
if (is.factor(dat[[i]])) {
sprintf("%s-%s", i, levels(dat[[i]]))
} else {
sprintf("%s-%s", i, i)
}
}))
}
extra <- setdiff(params, c(conditional, random))
}
elements <- .get_elements(effects, component = component)
l <- lapply(.compact_list(list(conditional = conditional, random = random, extra = extra)), .remove_backticks_from_string)
l <- .compact_list(l[elements])
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.MCMCglmm <- function(x,
effects = c("all", "fixed", "random"),
flatten = FALSE,
...) {
sc <- summary(x)
effects <- match.arg(effects)
l <- .compact_list(list(
conditional = rownames(sc$solutions),
random = rownames(sc$Gcovariances)
))
.filter_parameters(l,
effects = effects,
flatten = flatten,
recursive = FALSE
)
}
find_parameters.mcmc.list <- function(x, flatten = FALSE, ...) {
l <- list(conditional = colnames(x[[1]]))
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.bamlss <- function(x,
flatten = FALSE,
component = c("all", "conditional", "location", "distributional", "auxiliary"),
parameters = NULL,
...) {
component <- match.arg(component)
cn <- colnames(as.data.frame(unclass(x$samples)))
ignore <- grepl("(\\.alpha|logLik|\\.accepted|\\.edf)$", cn)
cond <- cn[grepl("^(mu\\.p\\.|pi\\.p\\.)", cn) & !ignore]
sigma <- cn[grepl("^sigma\\.p\\.", cn) & !ignore]
smooth_terms <- cn[grepl("^mu\\.s\\.(.*)(\\.tau\\d+|\\.edf)$", cn)]
alpha <- cn[grepl("\\.alpha$", cn)]
elements <- .get_elements(effects = "all", component = component)
l <- .compact_list(list(
conditional = cond,
smooth_terms = smooth_terms,
sigma = sigma,
alpha = alpha
)[elements])
l <- .filter_pars(l, parameters)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.brmsfit <- function(x,
effects = "all",
component = "all",
flatten = FALSE,
parameters = NULL,
...) {
effects <- match.arg(effects, choices = c("all", "fixed", "random"))
component <- match.arg(component, choices = c("all", .all_elements()))
fe <- colnames(as.data.frame(x))
pattern <- c("^[A-z]_\\d\\.\\d\\.(.*)")
fe <- fe[!grepl(pattern, fe, perl = TRUE)]
is_mv <- NULL
fe <- fe[!grepl("^Intercept", fe)]
cond <- fe[grepl("^(b_|bs_|bsp_|bcs_)(?!zi_)(.*)", fe, perl = TRUE)]
zi <- fe[grepl("^(b_zi_|bs_zi_|bsp_zi_|bcs_zi_)", fe, perl = TRUE)]
rand <- fe[grepl("(?!.*__(zi|sigma|beta))(?=.*^r_)", fe, perl = TRUE) & !grepl("^prior_", fe, perl = TRUE)]
randzi <- fe[grepl("^r_(.*__zi)", fe, perl = TRUE)]
rand_sd <- fe[grepl("(?!.*_zi)(?=.*^sd_)", fe, perl = TRUE)]
randzi_sd <- fe[grepl("^sd_(.*_zi)", fe, perl = TRUE)]
rand_cor <- fe[grepl("(?!.*_zi)(?=.*^cor_)", fe, perl = TRUE)]
randzi_cor <- fe[grepl("^cor_(.*_zi)", fe, perl = TRUE)]
simo <- fe[grepl("^simo_", fe, perl = TRUE)]
car_struc <- fe[fe %in% c("car", "sdcar")]
smooth_terms <- fe[grepl("^sds_", fe, perl = TRUE)]
priors <- fe[grepl("^prior_", fe, perl = TRUE)]
sigma <- fe[grepl("^sigma_", fe, perl = TRUE) | grepl("sigma", fe, fixed = TRUE)]
randsigma <- fe[grepl("^r_(.*__sigma)", fe, perl = TRUE)]
beta <- fe[grepl("beta", fe, fixed = TRUE)]
randbeta <- fe[grepl("^r_(.*__beta)", fe, perl = TRUE)]
mix <- fe[grepl("mix", fe, fixed = TRUE)]
shiftprop <- fe[grepl("shiftprop", fe, fixed = TRUE)]
dispersion <- fe[grepl("dispersion", fe, fixed = TRUE)]
auxiliary <- fe[grepl("(shape|phi|precision|_ndt_)", fe)]
sigma <- setdiff(sigma, c(cond, rand, rand_sd, rand_cor, randsigma, car_struc, "prior_sigma"))
beta <- setdiff(beta, c(cond, rand, rand_sd, randbeta, rand_cor, car_struc))
auxiliary <- setdiff(auxiliary, c(cond, rand, rand_sd, rand_cor, car_struc))
l <- .compact_list(list(
conditional = cond,
random = c(rand, rand_sd, rand_cor, car_struc),
zero_inflated = zi,
zero_inflated_random = c(randzi, randzi_sd, randzi_cor),
simplex = simo,
smooth_terms = smooth_terms,
sigma = sigma,
sigma_random = randsigma,
beta = beta,
beta_random = randbeta,
dispersion = dispersion,
mix = mix,
shiftprop = shiftprop,
auxiliary = auxiliary,
priors = priors
))
elements <- .get_elements(effects = effects, component = component)
elements <- c(elements, "priors")
if (is_multivariate(x)) {
rn <- names(find_response(x))
l <- lapply(rn, function(i) {
if (.obj_has_name(l, "conditional")) {
conditional <- l$conditional[grepl(sprintf("^(b_|bs_|bsp_|bcs_)\\Q%s\\E_", i), l$conditional)]
} else {
conditional <- NULL
}
if (.obj_has_name(l, "random")) {
random <- l$random[grepl(sprintf("__\\Q%s\\E\\[", i), l$random) |
grepl(sprintf("^sd_(.*)\\Q%s\\E\\_", i), l$random) |
grepl("^cor_", l$random) |
l$random %in% c("car", "sdcar")]
} else {
random <- NULL
}
if (.obj_has_name(l, "zero_inflated")) {
zero_inflated <- l$zero_inflated[grepl(sprintf("^(b_zi_|bs_zi_|bsp_zi_|bcs_zi_)\\Q%s\\E_", i), l$zero_inflated)]
} else {
zero_inflated <- NULL
}
if (.obj_has_name(l, "zero_inflated_random")) {
zero_inflated_random <- l$zero_inflated_random[grepl(sprintf("__zi_\\Q%s\\E\\[", i), l$zero_inflated_random) |
grepl(sprintf("^sd_(.*)\\Q%s\\E\\_", i), l$zero_inflated_random) |
grepl("^cor_", l$zero_inflated_random)]
} else {
zero_inflated_random <- NULL
}
if (.obj_has_name(l, "simplex")) {
simplex <- l$simplex
} else {
simplex <- NULL
}
if (.obj_has_name(l, "sigma")) {
sigma <- l$sigma[grepl(sprintf("^sigma_\\Q%s\\E$", i), l$sigma)]
} else {
sigma <- NULL
}
if (.obj_has_name(l, "beta")) {
beta <- l$beta[grepl(sprintf("^beta_\\Q%s\\E$", i), l$sigma)]
} else {
beta <- NULL
}
if (.obj_has_name(l, "dispersion")) {
dispersion <- l$dispersion[grepl(sprintf("^dispersion_\\Q%s\\E$", i), l$dispersion)]
} else {
dispersion <- NULL
}
if (.obj_has_name(l, "mix")) {
mix <- l$mix[grepl(sprintf("^mix_\\Q%s\\E$", i), l$mix)]
} else {
mix <- NULL
}
if (.obj_has_name(l, "shape") || .obj_has_name(l, "precision")) {
aux <- l$aux[grepl(sprintf("^(shape|precision)_\\Q%s\\E$", i), l$aux)]
} else {
aux <- NULL
}
if (.obj_has_name(l, "smooth_terms")) {
smooth_terms <- l$smooth_terms
} else {
smooth_terms <- NULL
}
if (.obj_has_name(l, "priors")) {
priors <- l$priors
} else {
priors <- NULL
}
pars <- .compact_list(list(
conditional = conditional,
random = random,
zero_inflated = zero_inflated,
zero_inflated_random = zero_inflated_random,
simplex = simplex,
smooth_terms = smooth_terms,
sigma = sigma,
beta = beta,
dispersion = dispersion,
mix = mix,
priors = priors,
auxiliary = aux
))
.compact_list(pars[elements])
})
names(l) <- rn
is_mv <- "1"
} else {
l <- .compact_list(l[elements])
}
l <- .filter_pars(l, parameters, !is.null(is_mv) && is_mv == "1")
attr(l, "is_mv") <- is_mv
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.bayesx <- function(x,
component = c("all", "conditional", "smooth_terms"),
flatten = FALSE,
parameters = NULL,
...) {
cond <- rownames(stats::coef(x))
smooth_terms <- rownames(x$smooth.hyp)
l <- .compact_list(list(
conditional = cond,
smooth_terms = smooth_terms
))
l <- .filter_pars(l, parameters)
component <- match.arg(component)
elements <- .get_elements(effects = "all", component)
l <- .compact_list(l[elements])
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.stanreg <- function(x,
effects = c("all", "fixed", "random"),
component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"),
flatten = FALSE,
parameters = NULL,
...) {
fe <- colnames(as.data.frame(x))
cond <- fe[grepl("^(?!(b\\[|sigma|Sigma))", fe, perl = TRUE) & .grep_non_smoothers(fe)]
rand <- fe[grepl("^b\\[", fe, perl = TRUE)]
rand_sd <- fe[grepl("^Sigma\\[", fe, perl = TRUE)]
smooth_terms <- fe[grepl("^smooth_sd", fe, perl = TRUE)]
sigma <- fe[grepl("sigma", fe, fixed = TRUE)]
auxiliary <- fe[grepl("(shape|phi|precision)", fe)]
cond <- setdiff(cond, auxiliary)
l <- .compact_list(list(
conditional = cond,
random = c(rand, rand_sd),
smooth_terms = smooth_terms,
sigma = sigma,
auxiliary = auxiliary
))
l <- .filter_pars(l, parameters)
effects <- match.arg(effects)
component <- match.arg(component)
elements <- .get_elements(effects, component)
l <- .compact_list(l[elements])
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.bcplm <- function(x,
flatten = FALSE,
parameters = NULL,
...) {
l <- .filter_pars(list(conditional = dimnames(x$sims.list[[1]])[[2]]), parameters)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.stanmvreg <- function(x,
effects = c("all", "fixed", "random"),
component = c("all", "conditional", "sigma"),
flatten = FALSE,
parameters = NULL,
...) {
fe <- colnames(as.data.frame(x))
rn <- names(find_response(x))
cond <- fe[grepl("^(?!(b\\[|sigma|Sigma))", fe, perl = TRUE) & .grep_non_smoothers(fe) & !grepl("\\|sigma$", fe, perl = TRUE)]
rand <- fe[grepl("^b\\[", fe, perl = TRUE)]
sigma <- fe[grepl("\\|sigma$", fe, perl = TRUE) & .grep_non_smoothers(fe)]
l <- .compact_list(list(
conditional = cond,
random = rand,
sigma = sigma
))
if (.obj_has_name(l, "conditional")) {
x1 <- sub("(.*)(\\|)(.*)", "\\1", l$conditional)
x2 <- sub("(.*)(\\|)(.*)", "\\3", l$conditional)
l.cond <- lapply(rn, function(i) {
list(conditional = x2[which(x1 == i)])
})
names(l.cond) <- rn
} else {
l.cond <- NULL
}
if (.obj_has_name(l, "random")) {
x1 <- sub("b\\[(.*)(\\|)(.*)", "\\1", l$random)
x2 <- sub("(b\\[).*(.*)(\\|)(.*)", "\\1\\4", l$random)
l.random <- lapply(rn, function(i) {
list(random = x2[which(x1 == i)])
})
names(l.random) <- rn
} else {
l.random <- NULL
}
if (.obj_has_name(l, "sigma")) {
l.sigma <- lapply(rn, function(i) {
list(sigma = "sigma")
})
names(l.sigma) <- rn
} else {
l.sigma <- NULL
}
l <- mapply(c, l.cond, l.random, l.sigma, SIMPLIFY = FALSE)
l <- .filter_pars(l, parameters, is_mv = TRUE)
effects <- match.arg(effects)
component <- match.arg(component)
elements <- .get_elements(effects, component)
l <- lapply(l, function(i) .compact_list(i[elements]))
attr(l, "is_mv") <- "1"
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.sim.merMod <- function(x,
effects = c("all", "fixed", "random"),
flatten = FALSE,
parameters = NULL,
...) {
fe <- colnames(.get_armsim_fixef_parms(x))
re <- colnames(.get_armsim_ranef_parms(x))
l <- .compact_list(list(
conditional = fe,
random = re
))
l <- .filter_pars(l, parameters)
effects <- match.arg(effects)
elements <- .get_elements(effects, component = "all")
l <- .compact_list(l[elements])
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.sim <- function(x, flatten = FALSE, parameters = NULL, ...) {
l <- .filter_pars(
list(conditional = colnames(.get_armsim_fixef_parms(x))),
parameters
)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.mcmc <- function(x, flatten = FALSE, parameters = NULL, ...) {
l <- .filter_pars(list(conditional = colnames(x)), parameters)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.bayesQR <- function(x, flatten = FALSE, parameters = NULL, ...) {
l <- .filter_pars(list(conditional = x[[1]]$names), parameters)
if (flatten) {
unique(unlist(l))
} else {
l
}
}
find_parameters.stanfit <- function(x,
effects = c("all", "fixed", "random"),
flatten = FALSE,
parameters = NULL,
...) {
fe <- colnames(as.data.frame(x))
cond <- fe[grepl("^(?!(b\\[|sigma|Sigma|lp__))", fe, perl = TRUE) & .grep_non_smoothers(fe)]
rand <- fe[grepl("^b\\[", fe, perl = TRUE)]
l <- .compact_list(list(
conditional = cond,
random = rand
))
l <- .filter_pars(l, parameters)
effects <- match.arg(effects)
elements <- .get_elements(effects, component = "all")
l <- .compact_list(l[elements])
if (flatten) {
unique(unlist(l))
} else {
l
}
} |
team.and.score.filters = function(x,...){
if(!all(c("scores","teams") %in% names(x))) stop("team.and.score.filters function requires a list with scores and teams data frames.\n", call.=FALSE)
team.data = x$teams
scores=x$scores
names(team.data)=tolower(names(team.data))
names(scores)=tolower(names(scores))
if(!any(names(team.data)=="name")) stop("The teams data frame requires a column named \"name\" for the team (display) name.\n",call.=FALSE)
if(!all(c("home.team","away.team") %in% names(scores))) stop("The scores data frame requires columns named \"home.team\" and \"away.team\".\n",call.=FALSE)
cc = names(team.data)=="name"
all.team.names=team.data[,cc]
nteams = length(all.team.names)
extra=list(...)
names(extra)=tolower(names(extra))
if(any(duplicated(names(extra)))){
duplicated.extra=names(extra)[duplicated(names(extra))]
stop(paste("There are duplicated column names passed in for filtering. Names are case insensitive.\nThe duplicated names are:",paste(duplicated.extra,collapse=", ")),call.=FALSE)
}
if(any(!(names(extra) %in% c(names(team.data),names(scores)))) ){
cat("Extra arguments (besides team and venue) passed in should correspond to columns in the team or match files.\n")
cat("The following extra arguments are not in the team or match files (names are not case sensitive): ")
bad.vals = names(extra)[!(names(extra) %in% c(names(team.data),names(scores)))]
cat(paste(bad.vals,collapse=", "))
cat("\n")
stop(call.=FALSE)
}
if(any((names(extra) %in% names(team.data)) & (names(extra) %in% names(scores))) ){
cat("You cannot filter on names that appear as column names in BOTH the team file and match file.\n")
cat("The following appear as column names in both team or match files (names are case sensitive): ")
bad.vals = names(extra)[(names(extra) %in% names(team.data)) & (names(extra) %in% names(scores))]
cat(paste(bad.vals,collapse=", "))
cat("\n")
stop(call.=FALSE)
}
extra.team = extra[names(extra) %in% names(team.data)]
extra.scores = extra[names(extra) %in% names(scores)]
extra.team.filter = extra.team
for(j in names(extra.team)){
cc = names(team.data)==j
if(identical(tolower(extra.team.filter[[j]]),"all")){
extra.team.filter[[j]] = unique(as.vector(as.matrix(team.data[,cc])))
}
if(all(!(extra.team.filter[[j]] %in% unique(as.vector(as.matrix(team.data[,cc])))))){
cat(paste("None of the values passed in for ", j, " are in the team file.\n",sep=""))
cat("Exiting since nothing would be printed.\n")
stop(call.=FALSE)
}
if(any(!(extra.team.filter[[j]] %in% unique(as.vector(as.matrix(team.data[,cc])))))){
bad.vals = extra.team.filter[[j]][!(extra.team.filter[[j]] %in% unique(as.vector(as.matrix(team.data[,cc]))))]
cat(paste("FYI: values ", paste(bad.vals, collapse=",")," passed in for ", j, " are not found in the ", j," column of the ", as.character(match.call()$x),"$teams dataframe.", sep=""))
}
}
extra.scores.filter = extra.scores
for(j in names(extra.scores)){
cc = names(scores)==j
if(identical(tolower(extra.scores.filter[[j]]),"all")){
extra.scores.filter[[j]] = unique(as.vector(as.matrix(scores[,cc])))
}
if(all(!(extra.scores.filter[[j]] %in% unique(as.vector(as.matrix(scores[,cc])))))){
cat(paste("None of the values passed in for ", j, " are in the ", as.character(match.call()$x),"$scores dataframe.\n",sep=""))
cat("Exiting since nothing would be printed.\n")
stop(call.=FALSE)
}
if(any(!(extra.scores.filter[[j]] %in% unique(as.vector(as.matrix(scores[,cc])))))){
bad.vals = extra.scores.filter[[j]][!(extra.scores.filter[[j]] %in% unique(as.vector(as.matrix(scores[,cc]))))]
cat(paste("FYI: values ", paste(bad.vals, collapse=",")," passed in for ", j, " are not found in the ", j," column of the ", as.character(match.call()$x),"$scores dataframe.", sep=""))
cat("\nProceeding using the other values.\n\n")
}
}
team.filter = "all"
if(identical(tolower(team.filter),"all")){
cc = tolower(names(team.data))=="name"
team.filter = unique(as.vector(as.matrix(team.data[,cc])))
}
tmp.fun=function(x,y){ any(x %in% y) }
include.extra.team = rep(TRUE,nteams)
for(j in names(extra.team)){
filt=extra.team.filter[[j]]
include.extra.team = include.extra.team & (team.data[[j]] %in% filt)
}
include.extra.scores = rep(TRUE,nteams)
label.extra.scores = list()
for(j in names(extra.scores)){
label.extra.scores[[j]]=list()
label.extra.scores[[j]][all.team.names]=""
teams.in.this.filter = list()
for(i in extra.scores.filter[[j]]){
rows.to.include = scores[scores[[j]]==i,c("home.team", "away.team")]
teams.in.this.filter[[i]] = unique(as.vector(as.matrix(rows.to.include)))
label.extra.scores[[j]][ teams.in.this.filter[[i]] ]=lapply(label.extra.scores[[j]][teams.in.this.filter[[i]]],paste,i,sep=",")
}
label.extra.scores[[j]]=lapply(label.extra.scores[[j]],str_sub,2)
teams.in.this.filter=unique(unlist(teams.in.this.filter))
include.extra.scores = include.extra.scores & (all.team.names %in% teams.in.this.filter)
}
cc = tolower(names(team.data))=="name"
include.team = apply(team.data[,cc,drop=FALSE], 1, tmp.fun, team.filter)
include.teams=all.team.names[(include.team & include.extra.scores & include.extra.team)]
scores.filter = names(extra)[names(extra) %in% names(scores)]
include.scores=scores$home.team %in% include.teams | scores$away.team %in% include.teams
for(sfil in scores.filter){
include.scores=include.scores & (scores[[sfil]] %in% extra[[sfil]])
}
return(list(include.teams=include.teams, include.scores=include.scores))
} |
thorn <- function(
shader,
width = NULL, height = NULL,
elementId = NULL
) {
x = list(
shader = match.arg(shader, c(
"thorn",
"thorn-color",
"ikeda",
"biomorph1",
"biomorph2",
"biomorph3",
"sweet",
"apollony",
"smoke",
"plasma"
))
)
htmlwidgets::createWidget(
name = "thorn",
x,
width = width,
height = height,
package = "thorn",
elementId = elementId
)
}
thornOutput <- function(outputId, width = "100%", height = "100%"){
htmlwidgets::shinyWidgetOutput(
outputId, "thorn", width, height, package = "thorn"
)
}
renderThorn <- function(expr, env = parent.frame(), quoted = FALSE) {
if (!quoted) { expr <- substitute(expr) }
htmlwidgets::shinyRenderWidget(expr, thornOutput, env, quoted = TRUE)
} |
library(amap)
url = 'https://docs.google.com/spreadsheets/d/1PWWoMqE5o3ChwJbpexeeYkW6p4BHL9hubVb1fkKSBgA/edit
library(gsheet)
data = as.data.frame(gsheet2tbl(url))
str(data)
head(data)
names(data)
summary(data)
colnames(data)
class(data$Age)
apply(data, 2, FUN= class)
dim(data)
head(data)
summary(data)
names(data)
k1 <- amap::Kmeans(data[,-1],centers=3, iter.max = 200,nstart = 1, method = c("euclidean"))
k1$centers
k1$size
k1$withinss
k1$cluster
k1$centers
k1$cluster[9000:9800]
table(k1$cluster)
k1$size
data_clus_2 <- data[ k1$cluster == 2,]
(data_clus_2)
mean(data_clus_2$Age)
data_clus_2$Cust_id
write.csv(data_clus_2[,1], file = "./data/data_clus_2.csv") |
survfit.rpsftm <- function(object, ...){
if(class(object)[2] != "coxph"){
stop(paste( "No applicable method for 'survfit' applied to an object of class", class(object)[2],"\n")
)
}else{
coxfit <- attr( object$ans$f.root, "fit")
survival::survfit(coxfit, ...=...)
}
} |
expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(list(structure(c(0.445983387275159, 0.0291424961297979, 0.305722673636889, 0.0640910333172597, 6.1841587262516e-05, 0.000608774190997193, 0.00533346072966287, 1.87468589092225, 0.00776943250876635, 0.00695873604736988), .Names = c(\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\")), 0), \"any\")"));
.Internal(`is.vector`(argv[[1]], argv[[2]]));
}, o=expected); |
rray_flip <- function(x, axis) {
axis <- vec_cast(axis, integer())
validate_axis(axis, x)
res <- rray__flip(x, as_cpp_idx(axis))
vec_cast_container(res, x)
} |
knitr::opts_chunk$set(echo = TRUE)
set.seed(1)
X <- cbind(1, rnorm(100))
theta_true <- c(1, 1, 1)
y <- X %*% theta_true[1:2] + rnorm(100)
metropolis <- function(y, X, theta0, S, n_iter, n_burnin, adapt = FALSE) {
p <- length(theta0)
theta <- matrix(NA, n_iter, p)
accept <- numeric(n_iter)
mu <- X %*% theta0[1:(p - 1)]
posterior <- sum(dnorm(y, mean = mu, sd = theta0[p], log = TRUE))
theta[1, ] <- theta0
for (i in 2:n_iter){
u <- rnorm(p)
theta_prop <- theta[i - 1, ] + S %*% u
if (theta_prop[p] > 0) {
mu <- X %*% theta_prop[1:(p - 1)]
posterior_prop <- sum(dnorm(y, mean = mu, sd = theta_prop[p], log = TRUE))
acceptance_prob <- min(1, exp(posterior_prop - posterior))
if (runif(1) < acceptance_prob) {
accept[i] <- 1
theta[i, ] <- theta_prop
posterior <- posterior_prop
}else{
theta[i, ] <- theta[i - 1, ]
}
} else {
theta[i, ] <- theta[i - 1, ]
acceptance_prob <- 0
}
if(adapt & i <= n_burnin) {
S <- ramcmc::adapt_S(S, u, acceptance_prob, i - 1)
}
}
list(theta = theta[(n_burnin + 1):n_iter, ], S = S,
acceptance_rate = sum(accept[(n_burnin + 1):n_iter]) / (n_iter - n_burnin))
}
mcmc <- metropolis(y, X, c(0, 0, 1), diag(1, 3), 1e4, 1e4/2)
mcmc_adapt <- metropolis(y, X, c(0, 0, 1), diag(1, 3), 1e4, 1e4/2, adapt = TRUE)
mcmc$acceptance_rate
mcmc_adapt$acceptance_rate
mcmc_adapt$S
hist(mcmc$theta[, 2], main = "theta_2")
hist(mcmc_adapt$theta[, 2], main = "theta_2")
acf(mcmc$theta)
acf(mcmc_adapt$theta) |
rcircmix<-function(n,model=NULL,dist=NULL,param=NULL){
if (!is.numeric(n)) stop("argument 'n' must be numeric")
if (is.null(model) & is.null(dist)) stop("No model specified")
if (!is.null(dist)){
if (length(param)<3 | length(param)>4) stop ("Length of argument 'param' must be 3 or 4")
for (i in 1:length(param)){
if (length(dist)!=length(param[[i]])) stop ("Length of the objects of the list 'param' must be equal to the length of argument 'dist'")
}
if (sum(param[[1]])!=1){
warning ("Proportions must sum 1. Proportions were normalized by the sum")
ndist<-length(dist)
param[[1]]<-param[[1]]/sum(param[[1]])
}
}
if (!is.null(model)){
if (!is.numeric(model)) stop("argument 'model' must be numeric")
if (!any(model==1:20)) stop("Value specified for argument 'model' is not valid")
if (model==1){
dist<-"unif"
param <- list(p=1,mu=0,con=0)
}else if (model==2){
dist<-"vm"
param <- list(p=1,mu=pi,con=1)
}else if (model==3){
dist<-"wn"
param <- list(p=1,mu=pi,con=0.9)
}else if (model==4){
dist<-"car"
param <- list(p=1,mu=pi,con=0.5)
}else if (model==5){
dist<-"wc"
param <- list(p=1,mu=pi,con=0.8)
}else if (model==6){
dist<-"wsn"
param <- list(p=1,mu=pi,con=1,sk=20)
}else if (model==7){
dist<-c("vm","vm")
param <- list(p=c(1/2,1/2),mu=c(0,pi),con=c(4,4))
}else if (model==8){
dist<-c("vm","vm")
param <- list(p=c(1/2,1/2),mu=c(2,4),con=c(5,5))
}else if (model==9){
dist<-c("vm","vm")
param <- list(p=c(1/4,3/4),mu=c(0,pi/sqrt(3)),con=c(2,2))
}else if (model==10){
dist<-c("vm","wc")
param <- list(p=c(4/5,1/5),mu=c(pi,4*pi/3),con=c(5,0.9))
}else if (model==11){
dist<-c("vm","vm","vm")
param <- list(p=c(1/3,1/3,1/3),mu=c(pi/3,pi,5*pi/3),con=c(6,6,6))
}else if (model==12){
dist<-c("vm","vm","vm")
param <- list(p=c(2/5,1/5,2/5),mu=c(pi/2,pi,3*pi/2),con=c(4,4,4))
}else if (model==13){
dist<-c("vm","vm","vm")
param <- list(p=c(2/5,2/5,1/5),mu=c(0.5,3,5),con=c(6,6,24))
}else if (model==14){
dist<-c("vm","vm","vm","vm")
param <- list(p=c(1/4,1/4,1/4,1/4),mu=c(0,pi/2,pi,3*pi/2),con=c(12,12,12,12))
}else if (model==15){
dist<-c("vm","wc","wn","wsn")
param <- list(p=c(1/4,3/10,1/4,1/5),mu=c(pi+2,pi-1,pi+0.5,6),con=c(3,0.6,0.9,1),sk=c(0,0,0,1))
}else if (model==16){
dist<-c("vm","vm","vm","vm","vm")
param <- list(p=c(1/5,1/5,1/5,1/5,1/5),mu=c(pi/5,3*pi/5,pi,7*pi/5,9*pi/5),con=c(18,18,18,18,18))
}else if (model==17){
dist<-c("car","wc")
param <- list(p=c(2/3,1/3),mu=c(pi,pi),con=c(0.5,0.9),sk=c(0,0))
}else if (model==18){
dist<-c("vm","vm","vm","vm")
param <- list(p=c(1/2,1/6,1/6,1/6),mu=c(pi,pi-0.8,pi,pi+0.8),con=c(1,30,30,30))
}else if (model==19){
dist<-c("vm","vm","vm","vm","vm")
param <- list(p=c(4/9,5/36,5/36,5/36,5/36),mu=c(2,4,3.5,4,4.5),con=c(3,3,50,50,50))
}else if (model==20){
dist<-c("wc","wc","wsn","wsn")
param <- list(p=c(1/6,1/6,1/3,1/3),mu=c(3*pi/4,7*pi/4,0,pi),con=c(0.9,0.9,0.7,0.7),sk=c(0,0,20,20))
}
}
p <- param[[1]]
ind <- as.numeric(cut(runif(n), c(0, cumsum(p)), include.lowest=TRUE))
pos <- split(seq_len(n), ind)
nms <- names(pos)
result <- rep(NA, n)
for (i in seq_along(pos)){
j <- as.numeric(nms[i])
distribution <- dist[j]
position <- pos[[i]]
npos<-length(position)
if (distribution=="unif"){
result[position] <- rcircularuniform(npos)
}else if (distribution=="vm"){
result[position] <- rvonmises(npos,mu=circular(param[[2]][j]),kappa=param[[3]][j])
}else if (distribution=="car"){
result[position] <- rcardioid(npos, mu=circular(param[[2]][j]), rho=param[[3]][j])
}else if (distribution=="wc"){
result[position] <- rwrappedcauchy(npos,mu=circular(param[[2]][j]),rho=param[[3]][j])
}else if (distribution=="wn"){
result[position] <- rwrappednormal(npos,mu=circular(param[[2]][j]),rho=param[[3]][j])
}else if (distribution=="wsn"){
result[position] <- rwsn(npos,xi=circular(param[[2]][j]),eta=param[[3]][j],lambda=param[[4]][j])
}
}
return(circular(result))
} |
plot.regsubsets<-function(x,labels=obj$xnames,main=NULL,
scale=c("bic","Cp","adjr2","r2"),
col=gray(seq(0,0.9,length=10)),...){
obj<-x
lsum<-summary(obj)
par(mar=c(7,5,6,3)+0.1)
nmodels<-length(lsum$rsq)
np<-obj$np
propscale<-FALSE
sscale<-pmatch(scale[1],c("bic","Cp","adjr2","r2"),nomatch=0)
if (sscale==0)
stop(paste("Unrecognised scale=",scale))
if (propscale)
stop(paste("Proportional scaling only for probabilities"))
yscale<-switch(sscale,lsum$bic,lsum$cp,lsum$adjr2,lsum$rsq)
up<-switch(sscale,-1,-1,1,1)
index<-order(yscale*up)
colorscale<- switch(sscale,
yscale,yscale,
-log(pmax(yscale,0.0001)),-log(pmax(yscale,0.0001)))
image(z=t(ifelse(lsum$which[index,],
colorscale[index],NA+max(colorscale)*1.5)),
xaxt="n",yaxt="n",x=(1:np),y=1:nmodels,xlab="",ylab=scale[1],col=col)
laspar<-par("las")
on.exit(par(las=laspar))
par(las=2)
axis(1,at=1:np,labels=labels)
axis(2,at=1:nmodels,labels=signif(yscale[index],2))
if (!is.null(main))
title(main=main)
box()
invisible(NULL)
} |
hysteresis_plot <- function(dataframe,
datetime,
q,
ssc,
base_font_size = 12,
legend = "bottom", ...) {
stopifnot("Table must be of class 'data.frame'" = "data.frame" %in% class(dataframe))
if (missing(datetime)) {
q <- dplyr::enquo(q)
ssc <- dplyr::enquo(ssc)
dataframe %>%
tidyr::drop_na(!!q, !!ssc) %>%
dplyr::mutate(Limb = ifelse(dplyr::row_number() %in% c(1:which.max(!!q)),
"Rising limb",
"Falling limb"
)) %>%
ggplot2::ggplot(ggplot2::aes(x = !!q, y = !!ssc)) +
ggplot2::geom_path(arrow = ggplot2::arrow(
length = ggplot2::unit(3, "mm"),
ends = "last"
)) +
ggplot2::geom_point(aes(color = Limb),
size = 2
) +
ggplot2::labs(
x = expression(italic("Q") * "," ~ m^3 %.% s^-1),
y = expression(italic("SSC") * "," ~ g %.% m^-3),
color = ""
) +
ggplot2::theme(
legend.background = ggplot2::element_blank(),
legend.key = ggplot2::element_blank(),
legend.position = legend,
strip.background = ggplot2::element_blank()
)
} else {
q <- dplyr::enquo(q)
datetime <- dplyr::enquo(datetime)
ssc <- dplyr::enquo(ssc)
dataframe %>%
dplyr::arrange(!!datetime) %>%
tidyr::drop_na(!!q, !!ssc) %>%
dplyr::mutate(Limb = ifelse(dplyr::row_number() %in% c(1:which.max(!!q)),
"Rising limb", "Falling limb"
)) %>%
ggplot2::ggplot(ggplot2::aes(x = !!q, y = !!ssc)) +
ggplot2::geom_path(arrow = ggplot2::arrow(
length = unit(3, "mm"),
ends = "last"
)) +
ggplot2::geom_point(aes(color = Limb),
size = 2
) +
ggplot2::labs(
x = expression(italic("Q") * "," ~ m^3 %.% s^-1),
y = expression(italic("SSC") * "," ~ g %.% m^-3),
color = ""
) +
ggplot2::theme(
legend.background = ggplot2::element_blank(),
legend.key = ggplot2::element_blank(),
legend.position = legend,
strip.background = ggplot2::element_blank()
)
}
} |
centipede_plot <- function(x, spp, minN2 = 1, mult = 1) {
stopifnot(inherits(x, "WA"))
N2 <- Hill.N2(spp) %>%
enframe(name = "Taxon", value = "n2")
opt_tol <- coef(x) %>%
as_tibble(rownames = "Taxon") %>%
verify(has_all_names("Optima", "Tolerances")) %>%
inner_join(N2, by = "Taxon") %>%
filter(.data$n2 >= minN2) %>%
mutate(
Taxon = factor(.data$Taxon),
Taxon = fct_reorder(.data$Taxon, .data$Optima),
ymin = .data$Optima - .data$Tolerances * mult,
ymax = .data$Optima + .data$Tolerances * mult
)
g <- ggplot(opt_tol, aes(x = .data$Taxon, y = .data$Optima,
ymin = .data$ymin, ymax = .data$ymax)) +
geom_errorbar() +
geom_point() +
coord_flip()
return(g)
} |
BinaryVectorCheck <- function (x)
{
for (i in 1:length(x)) if (!((x[i] == 0) | (x[i] == 1)))
return(FALSE)
return(TRUE)
} |
NULL
"CFT15" |
plain_tweets <- function(x) {
if (is.data.frame(x)) {
if (has_name_(x, "text")) {
x$text <- plain_tweets_(x$text)
} else {
stop("Couldn't find \"text\" variable.", call. = FALSE)
}
} else if (is.list(x)) {
if (has_name_(x, "text")) {
x$text <- plain_tweets_(x$text)
} else {
stop("Couldn't find \"text\" variable.", call. = FALSE)
}
} else {
x <- plain_tweets_(x)
}
x
}
plain_tweets_ <- function(x) {
if (is.factor(x)) {
x <- as.character(x)
}
stopifnot(is.character(x))
x <- rm_links(x)
x <- rm_linebreaks(x)
x <- rm_fancy_spaces(x)
x <- rm_fancy_apostrophes(x)
x <- rm_amp(x)
x <- enc2ascii(x)
trim_ws(x)
}
rm_fancy_apostrophes <- function(x) gsub(intToUtf8(8217), "'", x)
rm_fancy_spaces <- function(x) {
gsub("\\t", " ", gsub(intToUtf8(65039), " ", x))
}
rm_links <- function(x) {
x <- gsub("\\s?https?[[:graph:]]", "", x)
gsub("\\s?\\b[[:graph:]]+(\\.com|\\.net|\\.gov|\\.io|\\.org)\\b", "", x)
}
rm_linebreaks <- function(x, y = " ") {
gsub("\\n", y, x)
}
enc2ascii <- function(x, y = "") {
iconv(x, to = "ascii", sub = y)
}
rm_amp <- function(x, y = "&") {
if (is.null(y)) {
y <- ""
}
gsub("&", y, x)
}
trim_ws <- function(x) {
x <- gsub("[ ]{2,}", " ", x)
gsub("^[ ]+|[ ]+$", "", x)
} |
summary.bootnet <- function(
object,
graph,
statistics = c("edge", "intercept", "strength", "closeness", "betweenness","distance"),
perNode = FALSE,
rank = FALSE,
tol = sqrt(.Machine$double.eps),
...
){
if (length(unique(object$sampleTable$graph)) > 1 && missing(graph)){
stop("Argument 'graph' can not be missing when multiple graphs have been estimated.")
}
if (!missing(graph)){
object$sampleTable <- object$sampleTable[object$sampleTable$graph %in% graph,]
object$bootTable <- object$bootTable[object$bootTable$graph %in% graph,]
}
naTo0 <- function(x){
x[is.na(x)] <- 0
x
}
if (rank){
object$bootTable$value <- object$bootTable$rank_avg
object$sampleTable$value <- object$sampleTable$rank_avg
object$bootTable$value_min <- object$bootTable$rank_min
object$sampleTable$rank_min <- object$sampleTable$rank_min
object$bootTable$value_max <- object$bootTable$rank_max
object$sampleTable$value_max <- object$sampleTable$rank_max
} else {
object$bootTable$value_min <- object$bootTable$value
object$sampleTable$rank_min <- object$sampleTable$value
object$bootTable$value_max <- object$bootTable$value
object$sampleTable$value_max <- object$sampleTable$value
}
if (!object$type %in% c("person","node")){
if (object$type == "jackknife"){
N <- object$sampleSize
tab <- object$bootTable %>%
dplyr::filter(.data[['type']] %in% statistics) %>%
dplyr::left_join(object$sampleTable %>% dplyr::select(.data[['type']],.data[['id']],.data[['node1']],.data[['node2']],sample = .data[['value']]), by=c("id","type","node1","node2")) %>%
dplyr::mutate(PS = N*.data[['sample']] - (N-1)*.data[['value']]) %>%
dplyr::group_by(.data[['type']], .data[['node1']], .data[['node2']], .data[['id']]) %>%
dplyr::summarize(
mean = mean(.data[['value']]),
sample = mean(.data[['PS']]),
var = (1/(N-1)) * sum((.data[['PS']] - .data[['value']])^2),
CIlower = .data[['sample']] - 2 * sqrt(.data[['var']]/N),
CIupper = .data[['sample']] + 2 * sqrt(.data[['var']]/N)
)%>%
dplyr::select(.data[['type']], .data[['id']], .data[['node1']], .data[['node2']], .data[['sample']], .data[['mean']], .data[['CIlower']], .data[['CIupper']])
} else {
tab <- object$bootTable %>%
dplyr::filter(.data[['type']] %in% statistics) %>%
dplyr::group_by(.data[['type']], .data[['node1']], .data[['node2']], .data[['id']]) %>%
dplyr::summarize(
mean = mean(.data[['value']],na.rm=TRUE),
var = var(.data[['value']],na.rm=TRUE),
sd = sd(.data[['value']],na.rm=TRUE),
prop0 = mean(abs(.data[['value']]) < tol) %>% naTo0,
q2.5 = quantile(.data[['value_min']], 2.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q97.5 = quantile(.data[['value_max']], 97.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q2.5_non0 = quantile(.data[['value_min']][!abs(.data[['value_min']]) < tol], 2.5/100, na.rm = TRUE, type = 6) %>% naTo0,
mean_non0 = mean(.data[['value']][!abs(.data[['value']]) < tol], na.rm = TRUE) %>% naTo0,
q97.5_non0 = quantile(.data[['value_max']][!abs(.data[['value_max']]) < tol], 97.5/100, na.rm = TRUE, type = 6) %>% naTo0,
var_non0 = var(.data[['value']][!abs(.data[['value']]) < tol],na.rm=TRUE) %>% naTo0,
sd_non0 = sd(.data[['value']][!abs(.data[['value']]) < tol],na.rm=TRUE) %>% naTo0
) %>%
dplyr::left_join(object$sampleTable %>% dplyr::select(.data[['type']],.data[['id']],.data[['node1']],.data[['node2']],sample = .data[['value']]), by=c("id","type","node1","node2")) %>%
dplyr::mutate(
CIlower = .data[['sample']]-2*.data[['sd']], CIupper = .data[['sample']] + 2*.data[['sd']],
CIlower_non0 = .data[['mean_non0']] - 2*.data[['sd_non0']], CIupper_non0 = .data[['mean_non0']] + 2*.data[['sd_non0']]) %>%
dplyr::select(.data[['type']], .data[['id']], .data[['node1']], .data[['node2']], .data[['sample']], .data[['mean']], .data[['sd']], .data[['CIlower']], .data[['CIupper']],
.data[['q2.5']], .data[['q97.5']], .data[['q2.5_non0']], .data[['mean_non0']], .data[['q97.5_non0']], .data[['var_non0']], .data[['sd_non0']], .data[['prop0']])
}
} else {
tab <- object$bootTable %>%
dplyr::filter(.data[['type']] %in% statistics) %>%
dplyr::left_join(object$sampleTable %>% dplyr::select(.data[['type']],.data[['id']],.data[['node1']],.data[['node2']],sample = .data[['value']]), by=c("id","type","node1","node2"))
if (perNode){
tab <- tab %>% group_by(.data[['id']], .data[['type']], .data[['nNode']], .data[['nPerson']]) %>%
dplyr::summarize(
mean = mean(.data[['value']],na.rm=TRUE),
var = var(.data[['value']],na.rm=TRUE),
sd = sd(.data[['value']],na.rm=TRUE),
q1 = quantile(.data[['value']],1/100, na.rm = TRUE, type = 6) %>% naTo0,
q2.5 = quantile(.data[['value']], 2.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q5 = quantile(.data[['value']], 5/100, na.rm = TRUE, type = 6) %>% naTo0,
q25 = quantile(.data[['value']], 25/100, na.rm = TRUE, type = 6) %>% naTo0,
q50 = quantile(.data[['value']], 50/100, na.rm = TRUE, type = 6) %>% naTo0,
q75 = quantile(.data[['value']], 75/100, na.rm = TRUE, type = 6) %>% naTo0,
q95 = quantile(.data[['value']], 95/100, na.rm = TRUE, type = 6) %>% naTo0,
q97.5 = quantile(.data[['value']], 97.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q99 = quantile(.data[['value']], 99/100, na.rm = TRUE, type = 6) %>% naTo0,
prop0 = mean(abs(.data[['value']]) < tol),
q2.5_non0 = quantile(.data[['value']][!abs(.data[['value']]) < tol], 2.5/100, na.rm = TRUE, type = 6) %>% naTo0,
mean_non0 = mean(.data[['value']][!abs(.data[['value']]) < tol], na.rm = TRUE) %>% naTo0,
q97.5_non0 = quantile(.data[['value']][!abs(.data[['value']]) < tol], 97.5/100, na.rm = TRUE, type = 6) %>% naTo0,
var_non0 = var(.data[['value']][!abs(.data[['value']]) < tol],na.rm=TRUE) %>% naTo0,
sd_non0 = sd(.data[['value']][!abs(.data[['value']]) < tol],na.rm=TRUE) %>% naTo0
) %>% mutate(
CIlower = .data[['mean']] - 2*.data[['sd']], CIupper = .data[['mean']] + 2*.data[['sd']],
CIlower_non0 = .data[['mean_non0']] - 2*.data[['sd_non0']], CIupper_non0 = .data[['mean_non0']] + 2*.data[['sd_non0']]
) %>% arrange(.data[['nNode']],.data[['nPerson']])
} else {
tab <- tab %>% group_by(.data[['name']], .data[['type']], .data[['nNode']], .data[['nPerson']]) %>%
summarize(cor = suppressWarnings(cor(.data[['value']],sample, use = "pairwise.complete.obs"))) %>%
dplyr::group_by(.data[['nNode']], .data[['nPerson']], .data[['type']]) %>%
dplyr::summarize(
mean = mean(.data[['cor']],na.rm=TRUE),
var = var(.data[['cor']],na.rm=TRUE),
sd = sd(.data[['cor']],na.rm=TRUE),
q1 = quantile(.data[['cor']],1/100, na.rm = TRUE, type = 6) %>% naTo0,
q2.5 = quantile(.data[['cor']], 2.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q5 = quantile(.data[['cor']], 5/100, na.rm = TRUE, type = 6) %>% naTo0,
q25 = quantile(.data[['cor']], 25/100, na.rm = TRUE, type = 6) %>% naTo0,
q50 = quantile(.data[['cor']], 50/100, na.rm = TRUE, type = 6) %>% naTo0,
q75 = quantile(.data[['cor']], 75/100, na.rm = TRUE, type = 6) %>% naTo0,
q95 = quantile(.data[['cor']], 95/100, na.rm = TRUE, type = 6) %>% naTo0,
q97.5 = quantile(.data[['cor']], 97.5/100, na.rm = TRUE, type = 6) %>% naTo0,
q99 = quantile(.data[['cor']], 99/100, na.rm = TRUE, type = 6) %>% naTo0
) %>% arrange(.data[['nNode']], .data[['nPerson']])
}
}
return(tab)
} |
context("names")
test_that("", {
x <- c("---", b=2, c=3)
y <- c(c=3)
out <- iSEE:::.setdiffWithNames(x, y)
expect_identical(out, c("---", b = "2"))
}) |
sort_loop_index <- function(loop_list){
sorted_loop_list <- loop_list
sorted_loops <- lapply(sorted_loop_list$loop,function(x){
ind_min <- which.min(x)
if (ind_min>1){
return(x[c(ind_min:length(x),(2:ind_min))])
} else {
return(x)
}
})
sorted_loop_list$loop <- sorted_loops
return(sorted_loop_list)
}
find_edge <- function(loop_list,source_node,target_node){
vec_is_edge <- vapply(loop_list$loop,function(x){
source_ind <- which(x==source_node)
if (length(source_ind)==0){
return(FALSE)
} else {
if (target_node==x[source_ind[1]+1]){
return(TRUE)
} else {
return(FALSE)
}
}},logical(1))
return(which(vec_is_edge))
}
loop_summary <- function(loop_list,column_val='length'){
res_mat <- table(loop_list$length)
loop_tab <- data.frame(
matrix(0,ncol=as.numeric(names(res_mat))[length(names(res_mat))],nrow=3),
row.names=c('all','pos','neg'))
loop_tab[1,as.numeric(row.names(res_mat))] <- res_mat
res_mat_temp <- table(loop_list$length[loop_list$sign==1])
loop_tab[2,as.numeric(row.names(res_mat_temp))] <- res_mat_temp
res_mat_temp <- table(loop_list$length[loop_list$sign==-1])
loop_tab[3,as.numeric(row.names(res_mat_temp))] <- res_mat_temp
colnames(loop_tab) <- paste0('len_',1:length(loop_tab))
if (column_val == 'sign') {
loop_tab <- t(loop_tab[2:3,])
}
return(loop_tab)
}
compare_loop_list <- function(loop_list_a, loop_list_b){
sorted_loop_list_a <- sort_loop_index(loop_list_a)
sorted_loop_list_b <- sort_loop_index(loop_list_b)
inds_a_in_b <- match(sorted_loop_list_a$loop,sorted_loop_list_b$loop)
ind_a_notin <- which(is.na(inds_a_in_b))
ind_a_in <- which(!is.na(inds_a_in_b))
logvec_a_id <- sorted_loop_list_a$sign[ind_a_in] == sorted_loop_list_b$sign[inds_a_in_b[ind_a_in]]
return(list('ind_a_id' = ind_a_in[logvec_a_id],
'ind_a_switch' = ind_a_in[!logvec_a_id],
'ind_a_notin' = ind_a_notin,
'ind_b_id' = inds_a_in_b[ind_a_in[logvec_a_id]],
'ind_b_switch' = inds_a_in_b[ind_a_in[!logvec_a_id]]))
} |
f_weekday <- function(x, distinct = FALSE, ...) {
UseMethod('f_weekday')
}
f_weekday.default <- function(x, distinct = FALSE, ...) {
if (distinct){
locs <- match(
gsub("(^.)(.)(.+)", "\\U\\1\\L\\2", as.character(x), perl = TRUE),
short_weekdays_key
)
return(names(short_weekdays_key)[locs])
}
toupper(gsub("(^.)(.+)", "\\1", as.character(x)))
}
f_weekday.numeric <- function(x, distinct = FALSE, ...) {
if (distinct) return(names(short_weekdays_key)[x])
c("S", "M", "T", "W", "T", "F", "S")[x]
}
f_weekday.Date <- function(x, distinct = FALSE, ...) {
if (distinct){
locs <- match(
gsub("(^.)(.)(.+)", "\\U\\1\\L\\2", weekdays(x), perl = TRUE),
short_weekdays_key
)
return(names(short_weekdays_key)[locs])
}
toupper(gsub("(^.)(.+)", "\\1", weekdays(x)))
}
f_weekday.POSIXt <- function(x, distinct = FALSE, ...) {
if (distinct){
locs <- match(
gsub("(^.)(.)(.+)", "\\U\\1\\L\\2", weekdays(x), perl = TRUE),
short_weekdays_key
)
return(names(short_weekdays_key)[locs])
}
toupper(gsub("(^.)(.+)", "\\1", weekdays(x)))
}
f_weekday.hms <- function(x, distinct = FALSE, ...) {
f_weekday.POSIXt(as.POSIXct(x))
}
ff_weekday <- function(distinct = FALSE, ...) {
function(x) {f_weekday(x, distinct = distinct)}
}
short_weekdays_key <- structure(c("Su", "Mo", "Tu", "We", "Th", "Fr", "Sa"), .Names = c("Su",
"M", "T", "W", "Th", "F", "S"))
f_weekday_name <- function(x, ...) {
UseMethod('f_weekday_name')
}
f_weekday_name.default <- function(x, ...) {
gsub("(^.)(.+)", "\\U\\1\\L\\2", as.character(x), perl = TRUE)
}
f_weekday_name.numeric <- function(x, ...) {
constant_weekdays[x]
}
f_weekday_name.Date <- function(x, ...) {
weekdays(x)
}
f_weekday_name.POSIXt <- function(x, ...) {
weekdays(x)
}
f_weekday_name.hms <- function(x, ...) {
f_weekday_name.POSIXt(as.POSIXct(x))
}
ff_weekday_name <- function(...) {
function(x) {f_weekday_name(x)}
}
f_weekday_abbreviation <- function(x, ...) {
UseMethod('f_weekday_abbreviation')
}
f_weekday_abbreviation.default <- function(x, ...) {
gsub("(^.)(.{2})", "\\U\\1\\L\\2", as.character(x), perl = TRUE)
}
f_weekday_abbreviation.numeric <- function(x, ...) {
constant_weekdays_abbreviation[x]
}
f_weekday_abbreviation.Date <- function(x, ...) {
substring(weekdays(x), 1, 3)
}
f_weekday_abbreviation.POSIXt <- function(x, ...) {
substring(weekdays(x), 1, 3)
}
f_weekday_abbreviation.hms <- function(x, ...) {
f_weekday_abbreviation.POSIXt(as.POSIXct(x))
}
ff_weekday_abbreviation <- function(...) {
function(x) {f_weekday_abbreviation(x)}
} |
context("stats-smooth.spline")
skip_if_not_installed("modeltests")
library(modeltests)
fit <- smooth.spline(mtcars$wt, mtcars$mpg)
test_that("smooth.spline tidier arguments", {
check_arguments(glance.smooth.spline)
check_arguments(augment.smooth.spline)
})
test_that("glance.smooth.spline", {
gl <- glance(fit)
check_glance_outputs(gl)
})
test_that("augment.smooth.spline", {
check_augment_function(
aug = augment.smooth.spline,
model = fit,
data = mtcars,
strict = FALSE
)
}) |
library(methods)
library(ggplot2)
library(grid)
library(gridExtra)
args <- commandArgs(trailingOnly=T)
outdir <- 'out'
if (length(args) < 2) {
cat('Error: please supply at least two commits to generate comparison.\n')
quit()
}
read.stats <- function(rvm, version, timestamp) {
filename <- paste(outdir, '/', rvm, '-', version, '.csv', sep='')
if (file.exists(filename)) {
df <- read.csv(filename)
df$rvm <- rvm
df$version <- version
df$timestamp <- strptime(timestamp, "%Y-%m-%d %H:%M:%S")
df
}
}
merge.stats <- function(prev, rvm, version, timestamp) {
if (is.data.frame(prev)) {
rbind(prev, read.stats(rvm, version, timestamp))
} else {
read.stats(rvm, version, timestamp)
}
}
versions <- read.csv(paste(outdir, '/versions', sep=''), header=F)
colnames(versions) <- c('commit', 'timestamp')
versions <- subset(versions, commit %in% args)
cr <- NA
cr.jit <- NA
rho <- NA
rho.jit <- NA
for (i in seq_len(nrow(versions))) {
commit <- versions$commit[i]
timestamp <- versions$timestamp[i]
cr <- merge.stats(cr, 'cr', commit, timestamp)
cr.jit <- merge.stats(cr.jit, 'cr-jit', commit, timestamp)
rho <- merge.stats(rho, 'rho', commit, timestamp)
rho.jit <- merge.stats(rho.jit, 'rho-jit', commit, timestamp)
}
cols <- c('benchmark', 'rvm', 'version', 'timestamp', 'time')
report <- rbind(cr[cols], cr.jit[cols], rho[cols], rho.jit[cols])
report <- report[order(report$timestamp),]
report$version <- factor(report$version, unique(report$version))
report$time <- report$time / 1000
pdf('comparison.pdf')
for (vm in unique(report$rvm)) {
report.subset <- subset(report, rvm == vm)
print(ggplot(report.subset, aes(x=benchmark, y=time, group=version, fill=version)) +
geom_bar(stat='identity', width=0.5, position=position_dodge(0.5)) +
expand_limits(y=0) +
labs(title=paste('Version Comparison for RVM', vm), y='time (seconds)') +
theme(axis.text.x=element_text(angle=90, hjust=1)))
}
invisible(dev.off()) |
identify_column <-
function(std_name, alt_names, header) { return(which(header %in% c(alt_names[ which(alt_names[ ,1]==std_name), 2], std_name))) } |
print.funreg <- function(x, digits=4, show.fits=FALSE, ...) {
stopifnot(class(x)=="funreg");
summary1 <- summary(x,digits=digits);
cat("funreg Functional Regression\n\n");
cat("Call:\n");
print(x$call.info);
cat("\n");
cat("Intercept estimate: ");
cat(x$intercept.estimate.uncentered);
cat("\n");
if (!is.null(x$other.covariates.estimate)) {
cat("Subject-level coefficients:\n");
print(summary1$subject.level.covariates.table,...);
}
if (show.fits) {
cat("Functional coefficients:\n");
print(summary1$functional.covariates.table,...);
}
if (!show.fits) {
cat("\nTo view functional coefficients, use print(...,show.fits=TRUE)");
cat(" or plot(...).\n\n");
}
} |
GetNetworkGeneMappingResultTable<-function(mSetObj=NA){
load_rsqlite()
mSetObj <- .get.mSet(mSetObj);
qvec <- mSetObj$dataSet$gene;
enIDs <- mSetObj$dataSet$gene.name.map$hit.values;
match.state<-mSetObj$dataSet$gene.name.map$match.state;
if(length(qvec) > 0){
if(mSetObj$dataSet$q.type.gene == "kos"){
hit.kos <- mSetObj$dataSet$kos.name.map
} else{
hit.kos <- doGene2KONameMapping(enIDs)
}
match.state[is.na(hit.kos) & match.state!=0] <- 2
match.state[!is.na(hit.kos) & match.state==0] <- 2
} else{
mSetObj$dataSet$q.type.gene = ""
hit.kos = NULL
}
pre.style<-NULL;
post.style<-NULL;
if(mSetObj$dataSet$q.type.gene == "name"){
no.prestyle<-"<strong style=\"background-color:yellow; font-size=125%; color=\"black\">";
no.poststyle<-"</strong>";
} else{
nokos.prestyle<-"<strong style=\"background-color:lightgrey; font-size=125%; color=\"black\">";
nokos.poststyle<-"</strong>";
no.prestyle<-"<strong style=\"background-color:red; font-size=125%; color=\"black\">";
no.poststyle<-"</strong>";
}
html.res<-matrix("", nrow=length(qvec), ncol=6);
csv.res<-matrix("", nrow=length(qvec), ncol=6);
colnames(csv.res)<-c("Query", "Entrez", "Symbol", "KO", "Name", "Comment");
org.code <- mSetObj$org;
sqlite.path <- paste0(url.pre, org.code, "_genes.sqlite");
con <- .get.sqlite.con(sqlite.path); ;
gene.db <- dbReadTable(con, "entrez")
hit.inx <- match(enIDs, gene.db[, "gene_id"]);
hit.values<-mSetObj$dataSet$gene.name.map$hit.values;
mSetObj$dataSet$gene.name.map$hit.inx <- hit.inx;
mSetObj$dataSet$gene.name.map$hit.kos <- hit.kos;
hit.kos[is.na(hit.kos)] <- "";
if(length(qvec) > 0){
for (i in 1:length(qvec)){
if(match.state[i]==1){
pre.style<-"";
post.style="";
}else if(match.state[i]==2){
pre.style<-nokos.prestyle;
post.style<-nokos.poststyle;
}else{
pre.style<-no.prestyle;
post.style<-no.poststyle;
}
hit <-gene.db[hit.inx[i], ,drop=F];
html.res[i, ]<-c(paste(pre.style, qvec[i], post.style, sep=""),
paste(ifelse(match.state[i]==0 || is.na(hit$gene_id),"-", paste("<a href=http://www.ncbi.nlm.nih.gov/gene/", hit$gene_id, " target='_blank'>",hit$gene_id,"</a>", sep="")), sep=""),
paste(ifelse(match.state[i]==0 || is.na(hit$symbol), "-", paste("<a href=http://www.ncbi.nlm.nih.gov/gene/", hit$gene_id, " target='_blank'>", hit$symbol,"</a>", sep="")), sep=""),
paste(ifelse(is.na(hit.kos[i]), "-", paste("<a href=http://www.ncbi.nlm.nih.gov/gene/", hit$gene_id, " target='_blank'>", hit.kos[i],"</a>", sep="")), sep=""),
paste(ifelse(match.state[i]==0 || is.na(hit$name),"-", paste("<a href=http://www.ncbi.nlm.nih.gov/gene/", hit$gene_id, " target='_blank'>",hit$name,"</a>", sep="")), sep=""),
ifelse(match.state[i]!=1,"View",""));
csv.res[i, ]<-c(qvec[i],
ifelse(match.state[i]==0, "NA", hit$gene_id),
ifelse(match.state[i]==0, "NA", hit$symbol),
ifelse(is.na(hit.kos[i]), "NA", hit.kos[i]),
ifelse(match.state[i]==0, "NA", hit$name),
match.state[i]);
}
}
mSetObj$dataSet$gene.map.table <- csv.res;
fast.write.csv(csv.res, file="gene_name_map.csv", row.names=F);
dbDisconnect(con);
if(.on.public.web){
.set.mSet(mSetObj)
return(as.vector(html.res));
}else{
return(.set.mSet(mSetObj));
}
}
PrepareNetworkData <- function(mSetObj=NA){
mSetObj <- .get.mSet(mSetObj);
if(!is.null(mSetObj$dataSet$gene.mat)){
gene.mat <- mSetObj$dataSet$gene.mat;
enIDs <- mSetObj$dataSet$gene.name.map$hit.values;
kos <- mSetObj$dataSet$gene.name.map$hit.kos;
rownames(gene.mat) <- enIDs;
na.inx <- is.na(kos);
gene.mat.clean <- gene.mat[!na.inx, ,drop=F];
kos.clean <- kos[!na.inx]
gene.names <- rownames(gene.mat.clean)
gene.mat.clean <- RemoveDuplicates(gene.mat.clean);
if(nrow(gene.mat.clean) < length(kos.clean)){
mSetObj$dataSet$gene.name.map$hit.kos <- kos.clean[!duplicated(gene.names)]
} else{
mSetObj$dataSet$gene.name.map$hit.kos <- kos.clean
}
AddMsg(paste("A total of ", nrow(gene.mat.clean), "unique genes were uploaded."));
if(!exists("pathinteg.imps", where = mSetObj$dataSet)){
mSetObj$dataSet$pathinteg.imps <- list();
}
mSetObj$dataSet$pathinteg.imps$gene.mat <- gene.mat.clean;
done <- 1;
}
if(!is.null(mSetObj$dataSet$gene.mat)){
if(mSetObj$dataSet$q.type.gene == "kos"){
rownames(gene.mat) <- kos
gene.mat <- RemoveDuplicates(gene.mat);
mSetObj$dataSet$gene.name.map$hit.kos <- rownames(gene.mat)
AddMsg(paste("A total of ", nrow(gene.mat), "unique KOs were uploaded."));
if(!exists("pathinteg.imps", where = mSetObj$dataSet)){
mSetObj$dataSet$pathinteg.imps <- list();
}
mSetObj$dataSet$pathinteg.imps$kos.mat <- gene.mat;
done <- 1;
} else{
mSetObj$dataSet$pathinteg.imps$kos.mat <- mSetObj$dataSet$pathinteg.imps$gene.mat;
}
}
if((!is.null(mSetObj$dataSet$cmpd.mat) || (!is.null(mSetObj$dataSet$cmpd)))){
nm.map <- GetFinalNameMap(mSetObj);
my.ids <- ifelse(is.na(nm.map$kegg), paste("unmapped", rownames(nm.map), sep = "_"), nm.map$kegg);
mSetObj$dataSet$orig.var.ids <- my.ids;
valid.inx <- !(is.na(nm.map$kegg)| duplicated(nm.map$kegg));
cmpd.vec <- nm.map$query[valid.inx];
kegg.id <- nm.map$kegg[valid.inx];
cmpd.mat <- mSetObj$dataSet$cmpd.mat;
if(is.null(mSetObj$dataSet$cmpd.mat)){
cmpd <- as.matrix(mSetObj$dataSet$cmpd);
cmpd.mat <- cbind(cmpd, rep("0", nrow(cmpd)))
}
hit.inx <- match(cmpd.vec, rownames(cmpd.mat));
cmpd.mat <- cmpd.mat[hit.inx, ,drop=F];
rownames(cmpd.mat) <- kegg.id;
cmpd.mat <- RemoveDuplicates(cmpd.mat);
AddMsg(paste("A total of ", nrow(cmpd.mat), "unique compounds were found."));
mSetObj$dataSet$pathinteg.imps$cmpd.mat <- cmpd.mat;
done <- 1;
}
return(.set.mSet(mSetObj));
}
PrepareQueryJson <- function(mSetObj=NA){
mSetObj <- .get.mSet(mSetObj);
kos <- mSetObj$dataSet$gene.name.map$hit.kos
expr.mat <- mSetObj$dataSet$pathinteg.imps$kos.mat
kos <- cbind(kos, expr.mat)
cmpds.expr <- mSetObj$dataSet$pathinteg.imps$cmpd.mat
cmpds <- cbind(rownames(cmpds.expr), cmpds.expr)
enrich.type <- "hyper";
gene.mat <- list()
if(length(kos) > 0){
dataSet.gene <- PerformMapping(kos, "ko")
if(length(dataSet.gene)==0){
return(0);
}
if(enrich.type == "hyper"){
exp.vec <- dataSet.gene$data[,1];
}else{
genemat <- as.data.frame(t(otu_table(dataSet.gene$norm.phyobj)));
exp.vec <- rep(2, ncol(genemat));
names(exp.vec) <- colnames(genemat);
}
gene.mat <- MapKO2KEGGEdges(exp.vec);
}
cmpd.mat <- list()
if(length(cmpds) > 1){
dataSet.cmpd <- PerformMapping(cmpds, "cmpd")
if(length(dataSet.cmpd)==0){
return(0);
}
if(enrich.type == "hyper"){
exp.vec <- dataSet.cmpd$data[,1];
}else{
genemat <- as.data.frame(t(otu_table(dataSet.cmpd$norm.phyobj)));
exp.vec <- rep(2, ncol(genemat));
names(exp.vec) <- colnames(genemat);
}
cmpd.mat <- MapCmpd2KEGGNodes(exp.vec);
}
if(length(cmpd.mat) != 0 && length(gene.mat) != 0){
edge.mat <- as.data.frame(rbind(as.matrix(cmpd.mat), as.matrix(gene.mat)));
dataSet <<- MergeDatasets(dataSet.cmpd, dataSet.gene);
idtype <<- "gene&cmpd";
} else if(length(cmpd.mat) != 0){
edge.mat <- cmpd.mat;
dataSet <<- dataSet.cmpd;
idtype <<- "cmpd";
} else{
edge.mat <- gene.mat;
dataSet <<- dataSet.gene;
idtype <<- "gene";
}
row.names(edge.mat) <- eids <- rownames(edge.mat);
query.ko <- edge.mat[,1];
net.orig <- edge.mat[,2];
query.res <- edge.mat[,3];
names(query.res) <- eids;
json.mat <- RJSONIO::toJSON(query.res, .na='null');
sink("network_query.json");
cat(json.mat);
sink();
return(.set.mSet(mSetObj));
}
doGene2KONameMapping <- function(enIDs){
if(.on.public.web){
ko.dic <- .readDataTable("../../libs/network/ko_dic.csv");
}else{
ko.dic <- .readDataTable("https://www.metaboanalyst.ca/resources/libs/network/ko_dic.csv");
}
ko.dic.enIDs <- as.integer(ko.dic[, "Entrez_hsa"])
ko.dic.enIDs[is.na(ko.dic.enIDs)] <- -1
hit.inx <- match(as.integer(enIDs), ko.dic.enIDs);
kos <- ko.dic[hit.inx, "KO"];
na.inx <- is.na(kos);
kos[na.inx] <- NA
return(kos);
}
MatchQueryOnKEGGMap <- function(query, ko.map){
hits <- lapply(query,
function(x) {
as.character(unique(ko.map$edge[ko.map$queryid%in%unlist(x)]));
}
);
return(hits)
}
Save2KEGGJSON <- function(hits.query, res.mat, file.nm, hits.all){
resTable <- data.frame(Pathway=rownames(res.mat), res.mat);
AddMsg("Functional enrichment analysis was completed");
if(!exists("ko.edge.map")){
if(.on.public.web){
ko.edge.path <- paste("../../libs/network/ko_edge.csv", sep="");
ko.edge.map <- .readDataTable(ko.edge.path);
}else{
ko.edge.path <- paste("https://www.metaboanalyst.ca/resources/libs/network/ko_edge.csv", sep="");
download.file(ko.edge.path, destfile = "ko_edge.csv", method="libcurl", mode = "wb")
ko.edge.map <- .readDataTable("ko_edge.csv");
}
ko.edge.map <- ko.edge.map[ko.edge.map$net=="ko01100",];
ko.edge.map <<- ko.edge.map;
}
hits.edge <- list();
hits.node <- list();
hits.edge.all <- list();
hits.node.all <- list();
if(idtype == "gene"){
ko.map <- ko.edge.map;
colnames(ko.map) <- c("queryid", "edge", "net")
hits.edge <- MatchQueryOnKEGGMap(hits.query, ko.map)
hits.inx <- unlist(lapply(hits.edge, length))>0;
hits.edge.all <- MatchQueryOnKEGGMap(hits.all, ko.map)
hits.inx.all <- unlist(lapply(hits.edge.all, length))>0;
}else if(idtype == "cmpd"){
ko.map <- ko.node.map.global;
colnames(ko.map) <- c("queryid", "edge", "net")
hits.node <- MatchQueryOnKEGGMap(hits.query, ko.map)
hits.inx <- unlist(lapply(hits.node, length))>0;
hits.node.all <- MatchQueryOnKEGGMap(hits.all, ko.map)
hits.inx.all <- unlist(lapply(hits.node.all, length))>0;
}else{
ko.map1 <- ko.edge.map;
colnames(ko.map1) <- c("queryid", "edge", "net"); rownames(ko.map1)<-NULL;
hits.edge <- MatchQueryOnKEGGMap(hits.query, ko.map1)
hits.edge.all <- MatchQueryOnKEGGMap(hits.all, ko.map1)
ko.map2 <- ko.node.map.global;
colnames(ko.map2) <- c("queryid", "edge", "net"); rownames(ko.map2)<-NULL;
hits.node <- MatchQueryOnKEGGMap(hits.query, ko.map2)
hits.node.all <- MatchQueryOnKEGGMap(hits.all, ko.map2)
ko.map <- rbind(ko.map1, ko.map2)
hits.both <- MatchQueryOnKEGGMap(hits.query, ko.map)
hits.inx <- unlist(lapply(hits.both, length))>0;
hits.both <- MatchQueryOnKEGGMap(hits.all, ko.map)
hits.inx.all <- unlist(lapply(hits.both, length))>0;
}
hits.query <- hits.query[hits.inx]; hits.all <- hits.all[hits.inx.all];
resTable <- resTable[hits.inx, ];
fun.pval = resTable$Pval; if(length(fun.pval) ==1) { fun.pval <- matrix(fun.pval) };
hit.num = resTable$Hits; if(length(hit.num) ==1) { hit.num <- matrix(hit.num) };
fun.ids <- as.vector(current.setids[names(hits.query)]); if(length(fun.ids) ==1) { fun.ids <- matrix(fun.ids) };
rm.ids <- which(is.na(fun.ids))
if(length(rm.ids) != 0){
fun.ids <- fun.ids[-rm.ids]
fun.pval <- fun.pval[-rm.ids]
hit.num <- hit.num[-rm.ids]
hits.query <- hits.query[-rm.ids]
}
expr = as.list(dataSet$data)
names(expr) <- rownames(dataSet$data)
json.res <- list(
expr.mat = expr,
hits.query = hits.query,
hits.edge = hits.edge,
hits.node = hits.node,
hits.all = hits.all,
hits.edge.all = hits.edge.all,
hits.node.all = hits.node.all,
path.id = fun.ids,
fun.pval = fun.pval,
hit.num = hit.num
);
json.mat <- RJSONIO::toJSON(json.res, .na='null');
json.nm <- paste(file.nm, ".json", sep="");
sink(json.nm)
cat(json.mat);
sink();
fun.hits <<- hits.query;
fun.pval <<- resTable[,5];
hit.num <<- resTable[,4];
csv.nm <- paste(file.nm, ".csv", sep="");
fast.write.csv(resTable, file=csv.nm, row.names=F);
}
LoadKEGGKO_lib<-function(category){
if(category == "module"){
kegg.anot <- .get.my.lib("ko_modules.qs", "network");
current.setlink <- kegg.anot$link;
current.mset <- kegg.anot$sets$"Pathway module";
}else{
kegg.anot <- .get.my.lib("ko_pathways.qs", "network");
current.setlink <- kegg.anot$link;
current.mset <- kegg.anot$sets$Metabolism;
}
if(!exists("ko.edge.map")){
if(.on.public.web){
ko.edge.path <- paste("../../libs/network/ko_edge.csv", sep="");
ko.edge.map <<- .readDataTable(ko.edge.path);
}else{
ko.edge.path <- paste("https://www.metaboanalyst.ca/resources/libs/network/ko_edge.csv", sep="");
download.file(ko.edge.path, destfile = "ko_edge.csv", method="libcurl", mode = "wb")
ko.edge.map <<- .readDataTable("ko_edge.csv");
}
}
kos.01100 <- ko.edge.map$gene[ko.edge.map$net == "ko01100"];
current.mset <- lapply(current.mset,
function(x) {
as.character(unique(x[x %in% kos.01100]));
}
);
mset.ln <- lapply(current.mset, length);
current.mset <- current.mset[mset.ln > 0];
set.ids<- names(current.mset);
names(set.ids) <- names(current.mset) <- kegg.anot$term[set.ids];
current.setlink <<- current.setlink;
current.setids <<- set.ids;
current.geneset <<- current.mset;
}
.preparePhenoListSeeds <- function(mSetObj, table.nm){
if(.on.public.web){
libs.path <<- "../../libs/";
}else{
libs.path <<- "https://www.metaboanalyst.ca/resources/libs/";
}
table.nm <<- table.nm;
mSetObj <- .get.mSet(mSetObj);
cmpds <- rownames(mSetObj$dataSet$pathinteg.imps$cmpd.mat);
seed.compounds <- cmpds;
seed.expr.compounds <- as.vector(mSetObj$dataSet$pathinteg.imps$cmpd.mat[,1]);
genes <- rownames(mSetObj$dataSet$pathinteg.imps$gene.mat);
if(length(genes) == 0){
seed.genes <- c();
seed.expr.genes <- c();
} else {
seed.genes <- genes;
seed.expr.genes <- as.vector(mSetObj$dataSet$pathinteg.imps$gene.mat[,1]);
}
if((table.nm == "metabo_phenotypes") || (table.nm == "metabo_metabolites")){
seed.graph <<- seed.compounds;
seed.expr <<- seed.expr.compounds;
} else {
seed.graph <<- c(seed.compounds, seed.genes);
seed.expr <<- c(seed.expr.compounds, seed.expr.genes);
}
list(
genes = genes,
cmpds = cmpds
);
}
doKOFiltering <- function(ko.vec, type){
if(.on.public.web){
ko.dic <- .readDataTable("../../libs/network/ko_dic.csv");
}else{
ko.dic <- .readDataTable("https://www.metaboanalyst.ca/resources/libs/network/ko_dic.csv");
}
hit.inx <- match(ko.vec, ko.dic$KO);
return(ko.dic$KO[hit.inx]);
}
MapKO2KEGGEdges<- function(kos, net="ko01100"){
if(!exists("ko.edge.map")){
if(.on.public.web){
ko.edge.path <- paste("../../libs/network/ko_edge.csv", sep="");
ko.edge.map <<- .readDataTable(ko.edge.path);
}else{
ko.edge.path <- paste("https://www.metaboanalyst.ca/resources/libs/network/ko_edge.csv", sep="");
ko.edge.map <<- .readDataTable(ko.edge.path);
}
}
all.hits <- ko.edge.map$gene %in% names(kos) & ko.edge.map$net == net;
my.map <- ko.edge.map[all.hits, ];
q.map <- data.frame(gene=names(kos), expr=as.numeric(kos));
dat <- merge(my.map, q.map, by="gene");
dup.inx <- duplicated(dat[,2]);
dat <- dat[!dup.inx,];
rownames(dat) <- dat[,2];
return(dat[,-2]);
}
MapCmpd2KEGGNodes <- function(cmpds, net="ko01100"){
lib <- "hsa_kegg.qs"
if(!exists("ko.node.map.global")){
if(.on.public.web){
pathway.lib <- qs::qread(paste("../../libs/mummichog/", lib, sep=""));
}else{
if(!file.exists(lib)){
path.url <- paste("https://www.metaboanalyst.ca/resources/libs/mummichog/", lib, sep="")
download.file(path.url, destfile = lib, method="libcurl", mode = "wb")
pathway.lib <- qs::qread(lib);
}else{
pathway.lib <- qs::qread(lib);
}
}
pathways <- pathway.lib$pathways;
names(pathways$cpds) <- pathways$name
current.cmpd.set <<- pathways$cpds;
if(.on.public.web){
ko.pathway.names <- .readDataTable(paste("../../libs/network/ko01100_compounds_ids.csv", sep=""));
}else{
ko.pathway.names <- .readDataTable(paste("https://www.metaboanalyst.ca/resources/libs/network/ko01100_compounds_ids.csv", sep=""));
}
ko.node.map.global <<- data.frame(cmpd = ko.pathway.names[,1], edge = ko.pathway.names[,2], net = rep("ko01100", nrow(ko.pathway.names)))
}
all.hits <- ko.node.map.global$cmpd %in% names(cmpds) & ko.node.map.global$net == net;
my.map <- ko.node.map.global[all.hits, ];
q.map <- data.frame(cmpd=names(cmpds), expr=as.numeric(cmpds));
dat <- merge(my.map, q.map, by="cmpd");
dup.inx <- duplicated(dat[,2]);
dat <- dat[!dup.inx,];
rownames(dat) <- dat[,2];
return(dat[,-2]);
}
PerformMapping <- function(inputIDs, type){
dataSet <- list();
dataSet$orig <- inputIDs;
data.mat <- as.matrix(inputIDs);
if(dim(data.mat)[2] == 1){
data.only <- 1;
data.mat <- cbind(data.mat, rep(1, nrow(data.mat)));
}else {
data.only <- 0;
data.mat <- data.mat[,1:2];
}
if(!is.matrix(data.mat)){
data.mat <- as.matrix(t(data.mat))
}
rownames(data.mat) <- data.mat[,1];
data.mat <- data.mat[,-1, drop=F];
dataSet$id.orig <- data.mat;
dataSet$data.only <- data.only;
data.mat <- RemoveDuplicates(data.mat, "sum", quiet=F);
dataSet$id.uniq <- data.mat;
if(type == "ko"){
kos <- doKOFiltering(rownames(data.mat), type);
if(sum(!is.na(kos)) < 2){
AddErrMsg("Less than two hits found in the database. ");
dataSet <- list();
return(dataSet);
}
rownames(data.mat) <- kos;
gd.inx <- (!is.na(kos)) & data.mat[,1] > -Inf;
data.mat <- data.mat[gd.inx, ,drop=F];
AddMsg(paste("A total of unqiue", nrow(data.mat), "KO genes were mapped to KEGG network!"));
}
dataSet$id.mapped <- dataSet$data <- data.mat;
return(dataSet);
}
MergeDatasets <- function(dataSet1, dataSet2){
dataSet <- list();
dataSet$orig <- c(dataSet1$orig, dataSet2$orig);
dataSet$id.orig <- rbind(dataSet1$id.orig, dataSet2$id.orig)
dataSet$id.uniq <- rbind(dataSet1$id.uniq, dataSet2$id.uniq)
dataSet$data <- rbind(dataSet1$data, dataSet2$data)
dataSet$id.mapped <- rbind(dataSet1$id.mapped, dataSet2$id.mapped)
return(dataSet);
} |
"LongDat_cont_master_table" |
DLR <- function(basemodel, augmentedmodel, diseasestatus,
dataset, clustervar=NULL, alpha=0.05) {
d <- diseasestatus
x <- formula(basemodel)
v <- formula(augmentedmodel)
dat <- dataset
dat[,d] <- as.numeric(as.character(dat[,d]))
xterms <- paste(attr(terms(x),"term.labels"),collapse="+")
yterms <- paste(setdiff(attr(terms(v),"term.labels"),attr(terms(x),"term.labels")),collapse="+")
v <- formula(paste("~",paste(xterms,yterms,sep="+")))
if (!is.null(clustervar)) dat$subjid <- dat[,clustervar] else dat$subjid <- 1:nrow(dat)
datdup <- rbind(dat,dat)
datdup$indicator <- rep(1:0,each=nrow(dat))
x.modelmatrix <- model.matrix(x, datdup)
colnames(x.modelmatrix) <- make.names(gsub(".Intercept.","intercept",paste("x",colnames(x.modelmatrix),sep=".")))
dim.x <- ncol(x.modelmatrix)
v.modelmatrix <- model.matrix(v, datdup)
colnames(v.modelmatrix) <- make.names(gsub(".Intercept.","intercept",paste("v",colnames(v.modelmatrix),sep=".")))
dim.v <- ncol(v.modelmatrix)
z <- data.frame((1 - datdup$indicator) * x.modelmatrix, datdup$indicator * v.modelmatrix)
datdup <- cbind(datdup,z)
datdup <- datdup[order(datdup$subjid),]
gee_xv <- gee(as.formula(paste("d ~ ", paste(colnames(z),collapse="+"),"-1")),
data=datdup, corstr="independence",family=binomial, id=datdup$subjid, maxiter=100, tol=1e-5)
coefs <- coef(gee_xv)
robvcov <- gee_xv$robust.variance
coef_logDLR <- coefs[colnames(v.modelmatrix)]
coef_logDLR[1:dim.x] <- coef_logDLR[1:dim.x] - coefs[colnames(x.modelmatrix)]
names(coef_logDLR) <- substr(names(coef_logDLR),3,100)
I.x.matrix <- diag(1,dim.x)
Z.x.matrix <- diag(0,nrow=dim.x, ncol=dim.v - dim.x)
I.v.matrix <- diag(1,dim.v - dim.x)
Z.v.matrix <- diag(0,ncol=dim.x, nrow=dim.v - dim.x)
A.matrix <- rbind(cbind(-I.x.matrix,I.x.matrix,Z.x.matrix),cbind(Z.v.matrix,Z.v.matrix,I.v.matrix))
robSE_logDLR <- sqrt(diag(A.matrix %*% robvcov %*% t(A.matrix)))
robZstat_logDLR <- coef_logDLR/robSE_logDLR
pvalues <- 2 * pnorm(abs(robZstat_logDLR), lower.tail = FALSE)
lowerci_logDLR <- coef_logDLR - qnorm(1-alpha/2) * robSE_logDLR
upperci_logDLR <- coef_logDLR + qnorm(1-alpha/2) * robSE_logDLR
logPreTestResults <- summary(gee_xv)$coefficients[colnames(x.modelmatrix),,drop=F]
logPostTestResults <- summary(gee_xv)$coefficients[colnames(v.modelmatrix),,drop=F]
logDLRResults <- data.frame(coefs=coef_logDLR,robustSE=robSE_logDLR,Zstat=robZstat_logDLR,pvalue=pvalues,lowerCI=lowerci_logDLR,upperCI=upperci_logDLR)
xoneyone <- rep(1,ncol(v.modelmatrix))
xoneyzero <- c(rep(1,ncol(x.modelmatrix)),rep(0,ncol(v.modelmatrix)-ncol(x.modelmatrix)))
logDLR_pos <- coef_logDLR %*% xoneyone
logDLR_neg <- coef_logDLR %*% xoneyzero
DLRy_pos <- exp(logDLR_pos)
DLRy_neg <- exp(logDLR_neg)
SE_logDLR_pos <- sqrt(xoneyone %*% A.matrix %*% robvcov %*% t(A.matrix) %*% xoneyone)
SE_logDLR_neg <- sqrt(xoneyzero %*% A.matrix %*% robvcov %*% t(A.matrix) %*% xoneyzero)
lowerCI_DLRy_pos <- exp(logDLR_pos - qnorm(1-alpha/2) * SE_logDLR_pos)
upperCI_DLRy_pos <- exp(logDLR_pos + qnorm(1-alpha/2) * SE_logDLR_pos)
lowerCI_DLRy_neg <- exp(logDLR_neg - qnorm(1-alpha/2) * SE_logDLR_neg)
upperCI_DLRy_neg <- exp(logDLR_neg + qnorm(1-alpha/2) * SE_logDLR_neg)
DLR_results <- data.frame(rbind(c(DLRy_pos,lowerCI_DLRy_pos,upperCI_DLRy_pos),
c(DLRy_neg,lowerCI_DLRy_neg,upperCI_DLRy_neg)))
colnames(DLR_results) <- c("DLR","lowerCI","upperCI")
rownames(DLR_results) <- c("DLR(Y=1|X=1)","DLR(Y=0|X=1)")
return(list("logPreTestModel" = logPreTestResults,
"logPostTestModel" = logPostTestResults,
"logDLRModel" = logDLRResults,
"DLR" = DLR_results))
} |
context("test-Gapfill")
test_that("Gapfill demo",{
data <- ndvi[,,2:4,2:4]
expect_message(fill <- Gapfill(data)$fill,
"data has 3969 values: 3301 \\(83%\\) observed\n 668 \\(17%\\) missing\n 668 \\(17%\\) to predict\n")
ref <- c(0.5174, 0.4835, 0.4992, 0.4933, 0.5401, 0.5348, 0.5484, 0.5189,
0.5161, 0.5044, 0.5449, 0.5391, 0.5376, 0.5401, 0.5599, 0.5784,
0.5606, 0.5794, 0.5618, 0.549, 0.5527, 0.528, 0.5639, 0.5186,
0.5327, 0.5046, 0.5117, 0.5283, 0.5359, 0.5265, 0.5484, 0.5316,
0.5475, 0.5387, 0.5026, 0.5554, 0.5165, 0.5469, 0.5327, 0.5173,
0.5194, 0.5205, 0.5114, 0.5049, 0.6921, 0.6417, 0.6258, 0.5182,
0.5268, 0.5145, 0.5119, 0.6025, 0.6023, 0.6451, 0.6608, 0.6474,
0.1218, 0.6017, 0.5877, 0.5938, 0.5896, 0.5986, 0.5624, 0.5225,
0.6061, 0.6154, 0.6206, 0.538, 0.5798, 0.5975, 0.483, 0.3553,
0.106, 0.1356, 0.5996, 0.6154, 0.6068, 0.6003, 0.5892, 0.5627,
0.4892, 0.4969, 0.5023, 0.4835, 0.5152, 0.5015, 0.5026, 0.5634,
0.589, 0.5513, 0.5566, 0.5488, 0.498, 0.5086, 0.5359, 0.4947,
0.513, 0.5157, 0.5154, 0.5118, 0.5147, 0.4978, 0.5092, 0.5193,
0.517, 0.53, 0.5105, 0.5714, 0.5994, 0.5891, 0.5479, 0.5, 0.4909,
0.5048, 0.5172, 0.5265, 0.5139, 0.5419, 0.5306, 0.5454, 0.5418,
0.5147, 0.51, 0.5225, 0.4955, 0.5066, 0.4783, 0.55, 0.5435, 0.5617,
0.5341, 0.5305, 0.575, 0.5799, 0.6121, 0.5891, 0.5742, 0.5238,
0.5259, 0.5112, 0.5139, 0.5094, 0.4944, 0.4971, 0.5021, 0.5086,
0.521, 0.5484, 0.5463, 0.5306, 0.5619, 0.5345, 0.5191, 0.474,
0.4882, 0.5132, 0.5639, 0.5304, 0.5082, 0.5171, 0.5433, 0.5166,
0.5481, 0.5154, 0.4917, 0.5335, 0.5291, 0.501, 0.4922, 0.5021,
0.5027, 0.5031, 0.4912, 0.4912, 0.5, 0.4662, 0.513, 0.5086, 0.5439,
0.534, 0.5404, 0.56, 0.5291, 0.5154, 0.5328, 0.4734, 0.4885,
0.48, 0.503, 0.4896, 0.4752, 0.4971, 0.5097, 0.5115, 0.5576,
0.4911, 0.5161, 0.5591, 0.5358, 0.5429, 0.4597, 0.4793, 0.4903,
0.509, 0.5074, 0.4967, 0.4847, 0.4967, 0.5123, 0.4974, 0.5013,
0.498, 0.5123, 0.5082, 0.5395, 0.5772, 0.4417, 0.4627, 0.452,
0.4895, 0.4881, 0.4886, 0.5082, 0.5334, 0.5033, 0.4896, 0.4862,
0.4904, 0.5129, 0.5154, 0.5434, 0.5611, 0.5449, 0.5651, 0.515,
0.4517, 0.4686, 0.4963, 0.4941, 0.5121, 0.522, 0.5307, 0.5221,
0.4562, 0.5129, 0.5382, 0.5382, 0.5125, 0.4859, 0.5577, 0.5401,
0.5024, 0.543, 0.515, 0.4834, 0.5006, 0.4966, 0.5089, 0.5131,
0.5121, 0.4965, 0.4476, 0.4553, 0.4488, 0.4939, 0.5068, 0.4816,
0.483, 0.4852, 0.5604, 0.5286, 0.4978, 0.5539, 0.531, 0.5184,
0.5112, 0.5125, 0.5014, 0.4891, 0.4968, 0.4632, 0.4674, 0.4323,
0.4288, 0.4335, 0.4388, 0.4708, 0.5581, 0.5765, 0.6183, 0.5994,
0.6213, 0.6343, 0.6095, 0.6081, 0.6284, 0.6953, 0.6971, 0.6904,
0.6906, 0.6709, 0.6804, 0.6813, 0.69, 0.6829, 0.681, 0.6687,
0.6595, 0.7472, 0.692, 0.6792, 0.6774, 0.6672, 0.6771, 0.6724,
0.6667, 0.6676, 0.6674, 0.6769, 0.6811, 0.6715, 0.6882, 0.6779,
0.6735, 0.6752, 0.6834, 0.6688, 0.6972, 0.6797, 0.7078, 0.6988,
0.7209, 0.336, 0.6807, 0.6741, 0.6751, 0.6575, 0.6856, 0.7248,
0.6562, 0.5671, 0.4942, 0.3961, 0.5152, 0.661, 0.6663, 0.6503,
0.6672, 0.6625, 0.6682, 0.6961, 0.7043, 0.6805, 0.6782, 0.7169,
0.7082, 0.6794, 0.6649, 0.6733, 0.676, 0.6746, 0.6721, 0.681,
0.6737, 0.69, 0.6868, 0.6754, 0.6798, 0.6817, 0.6833, 0.6735,
0.6673, 0.6746, 0.6547, 0.6617, 0.6418, 0.7021, 0.7069, 0.6906,
0.6809, 0.6682, 0.666, 0.6676, 0.6589, 0.6573, 0.662, 0.6732,
0.6802, 0.6941, 0.69, 0.6952, 0.6819, 0.6755, 0.6791, 0.6933,
0.6756, 0.6596, 0.6847, 0.6759, 0.6777, 0.6811, 0.6598, 0.6426,
0.673, 0.6887, 0.7013, 0.6828, 0.6744, 0.6813, 0.7273, 0.7482,
0.6751, 0.6795, 0.7043, 0.6786, 0.6592, 0.6593, 0.6797, 0.6816,
0.6676, 0.6782, 0.6639, 0.6624, 0.673, 0.6689, 0.6691, 0.6715,
0.672, 0.6438, 0.6362, 0.6569, 0.6612, 0.6698, 0.6828, 0.6929,
0.7029, 0.6924, 0.6832, 0.64, 0.6497, 0.6659, 0.6754, 0.6755,
0.6948, 0.6884, 0.6702, 0.6922, 0.6795, 0.6759, 0.66, 0.6622,
0.6749, 0.671, 0.6814, 0.6834, 0.6829, 0.6841, 0.6581, 0.6946,
0.684, 0.6775, 0.683, 0.6867, 0.6764, 0.6618, 0.6656, 0.6814,
0.6764, 0.6935, 0.7244, 0.7547, 0.684, 0.6912, 0.7071, 0.7163,
0.711, 0.7157, 0.7234, 0.7245, 0.7238, 0.7137, 0.5642, 0.5782,
0.4921, 0.5331, 0.6679, 0.6728, 0.6439, 0.6262, 0.6396, 0.6421,
0.6514, 0.6543, 0.6587, 0.6606, 0.6716, 0.7027, 0.7399, 0.7617,
0.7475, 0.4389, 0.5384, 0.3841, 0.4055, 0.3929, 0.7325, 0.7033,
0.7022, 0.7005, 0.7518, 0.7401, 0.6875, 0.6867, 0.6853, 0.6947,
0.6957, 0.6844, 0.6741, 0.6972, 0.7087, 0.7049, 0.7015, 0.6993,
0.7212, 0.7298, 0.7366, 0.7053, 0.6669, 0.6736, 0.6943, 0.697,
0.6984, 0.6981, 0.6864, 0.6899, 0.6765, 0.662, 0.6898, 0.6937,
0.7049, 0.7017, 0.7418, 0.6769, 0.6605, 0.68, 0.6936, 0.6996,
0.7174, 0.6985, 0.6757, 0.6779, 0.7019, 0.7186, 0.702, 0.7, 0.6772,
0.6902, 0.6946, 0.6884, 0.6837, 0.6968, 0.6928, 0.6931, 0.6961,
0.6947, 0.6813, 0.6831, 0.6896, 0.708, 0.7152, 0.7186, 0.7083,
0.7128, 0.6656, 0.6731, 0.694, 0.7164, 0.7052, 0.6951, 0.7041,
0.6942, 0.7171, 0.7012, 0.692, 0.6777, 0.6901, 0.7004, 0.6946,
0.6999, 0.7086, 0.6993, 0.6618, 0.7309, 0.7093, 0.6983, 0.709,
0.7094, 0.6975, 0.6831, 0.6993, 0.7102, 0.7004, 0.6995, 0.7146,
0.6473, 0.5199, 0.6292, 0.6086, 0.6381, 0.5974, 0.6101, 0.6164,
0.5966, 0.6076, 0.6109, 0.6173, 0.6029, 0.6106, 0.6315, 0.6254,
0.6099, 0.6166, 0.6455, 0.6316, 0.5838, 0.5751, 0.596, 0.6728,
0.6825, 0.6727, 0.6577, 0.671, 0.6674, 0.6474, 0.6824, 0.693,
0.6301, 0.6577, 0.7057, 0.6963, 0.6478, 0.6178, 0.6104, 0.6186,
0.6454, 0.6531, 0.6392, 0.6525, 0.6456, 0.6525, 0.664, 0.6513,
0.6696, 0.6544, 0.6067, 0.6874, 0.6637, 0.6503, 0.6646, 0.6642,
0.649, 0.6285, 0.6314, 0.6094, 0.6637, 0.6511, 0.6503, 0.6672)
expect_equal(fill[is.na(data)], ref, tolerance=1e-4)
})
test_that("Gapfill prediction interval",{
toFill <- c(1741L, 1785L, 1809L, 1816L, 1887L, 1888L, 1919L, 1939L, 2107L,
2124L, 2126L, 2131L, 2132L, 2133L, 2159L, 2193L, 2283L, 2405L,
2466L, 2513L, 2577L, 2741L, 2845L, 2990L, 3600L, 3601L, 3611L,
3724L, 3778L, 3858L, 3861L, 3932L, 4653L, 5035L, 5107L, 5108L,
5180L, 5222L, 5224L, 5293L, 5349L, 5359L, 5443L, 5482L, 5527L,
5573L, 5582L, 5596L, 6694L, 6846L)
ref.pred <- c(0.6886, 0.4716, 0.479, 0.4279, 0.4646, 0.4759, 8e-04, 0.4867,
0.4318, 0.4212, 0.4203, 0.4293, 0.4298, 0.4185, 0.4326, 0.375,
0.523, 0.5198, 0.5667, 0.5264, 0.5278, 0.661, 0.6445, 0.6923,
0.4971, 0.497, 0.4831, 0.4545, 0.4485, 0.4865, 0.5192, 0.4862,
0.6623, 0.4371, 0.6952, 0.716, 0.7008, 0.6843, 0.7131, 0.424,
0.4868, 0.4562, 0.5438, 0.5163, 0.5147, 0.5202, 0.5253, 0.4763,
0.6889, 0.6511)
ref.lo <- c(0.6216, 0.4392, 0.4692, 0.3491, 0.4379, 0.4497, -0.033, 0.4805,
0.393, 0.366, 0.3878, 0.3685, 0.4085, 0.3905, 0.3905, 0.3445,
0.4228, 0.4016, 0.4761, 0.3975, 0.3869, 0.5172, 0.5142, 0.5422,
0.4349, 0.4352, 0.4264, 0.4152, 0.3589, 0.3983, 0.453, 0.4398,
0.5544, 0.3885, 0.5982, 0.6452, 0.6219, 0.6032, 0.6369, 0.4061,
0.4505, 0.4196, 0.4826, 0.4235, 0.4239, 0.4255, 0.4541, 0.3685,
0.6579, 0.6234)
ref.up <- c(0.7454, 0.623, 0.6886, 0.6118, 0.6393, 0.6381, 0.4627, 0.6513,
0.5874, 0.5897, 0.5754, 0.5818, 0.5819, 0.5772, 0.5882, 0.545,
0.7036, 0.7153, 0.7819, 0.7058, 0.7106, 0.7216, 0.701, 0.7356,
0.6144, 0.6304, 0.6089, 0.5857, 0.5892, 0.5982, 0.632, 0.6451,
0.7387, 0.5842, 0.716, 0.7439, 0.7206, 0.704, 0.7373, 0.678,
0.6419, 0.6247, 0.7061, 0.6343, 0.6631, 0.6421, 0.62, 0.5826,
0.7577, 0.7112)
fill <- Gapfill(ndvi, nPredict = 3, subset = toFill, predictionInterval = TRUE)$fill
pred <- fill[toFill]
lo <- fill[toFill + length(ndvi)]
up <- fill[toFill + 2*length(ndvi)]
expect_identical(pred, fill[,,,,1][!is.na(fill[,,,,2])])
expect_identical(lo, fill[,,,,2][!is.na(fill[,,,,2])])
expect_identical(up, fill[,,,,3][!is.na(fill[,,,,3])])
expect_true(all(lo <= pred))
expect_true(all(pred <= up))
expect_equal(pred, ref.pred, tolerance = 1e-4)
expect_equal(lo, ref.lo, tolerance = 1e-4)
expect_equal(up, ref.up, tolerance = 1e-4)
})
test_that("Gapfill subset",{
data <- ndvi[,,2:3,2:3]
PredictMean <- function (a, i) mean(a, na.rm = TRUE)
ref <- structure(c(0.6279, 0.6241, 0.6228, 0.6215, 0.6211, 0.619, 0.6173,
0.6159, 0.6152, 0.6142, 0.615, 0.6134, 0.6125, 0.6096, 0.6072,
0.6063, 0.6061, 0.6045, 0.6039, 0.602, 0.6009, 0.6273, 0.6237,
0.6227, 0.6216, 0.6212, 0.6192, 0.6175, 0.6161, 0.6154, 0.6144,
0.6151, 0.6137, 0.6128, 0.6098, 0.6075, 0.6067, 0.6065, 0.605,
0.6044, 0.6026, 0.6016, 0.6284, 0.625, 0.624, 0.6228, 0.6223,
0.6203, 0.6187, 0.6173, 0.6166, 0.6156, 0.6163, 0.6151, 0.6143,
0.6115, 0.6093, 0.6084, 0.6082, 0.6065, 0.6057, 0.6039, 0.6028,
0.6282, 0.6251, 0.6242, 0.6231, 0.6226, 0.6206, 0.6191, 0.6178,
0.617, 0.6162, 0.6168, 0.6157, 0.615, 0.6123, 0.6102, 0.6095,
0.6094, 0.6076, 0.6068, 0.6051, 0.6041, 0.6266, 0.6239, 0.6232,
0.6223, 0.6221, 0.6203, 0.6189, 0.6176, 0.617, 0.6164, 0.6172,
0.6164, 0.6159, 0.6135, 0.6117, 0.611, 0.611, 0.6095, 0.6089,
0.6073, 0.6067, 0.6255, 0.623, 0.6225, 0.6216, 0.6211, 0.6195,
0.6182, 0.6172, 0.6168, 0.6163, 0.617, 0.6163, 0.616, 0.6139,
0.6122, 0.6117, 0.6117, 0.6103, 0.6097, 0.6083, 0.6077, 0.6244,
0.6221, 0.6215, 0.6206, 0.6199, 0.6183, 0.6172, 0.6163, 0.6159,
0.6156, 0.6165, 0.6159, 0.6157, 0.6138, 0.6123, 0.6118, 0.6119,
0.6103, 0.6098, 0.6083, 0.6078, 0.623, 0.621, 0.6203, 0.6194,
0.6189, 0.6174, 0.6164, 0.6157, 0.6153, 0.6153, 0.6161, 0.6158,
0.616, 0.6143, 0.6129, 0.6126, 0.6126, 0.6113, 0.6108, 0.6094,
0.6086, 0.6233, 0.6213, 0.6208, 0.6198, 0.6194, 0.618, 0.617,
0.6162, 0.6159, 0.6158, 0.6165, 0.6162, 0.6165, 0.6148, 0.6136,
0.6133, 0.6133, 0.6119, 0.6114, 0.6102, 0.6094, 0.6225, 0.6207,
0.6201, 0.6192, 0.6188, 0.6176, 0.6167, 0.6161, 0.6158, 0.6158,
0.6165, 0.6163, 0.6165, 0.615, 0.6139, 0.6136, 0.6136, 0.6123,
0.6118, 0.6109, 0.6103, 0.621, 0.6193, 0.6189, 0.6182, 0.6179,
0.617, 0.6162, 0.6157, 0.6155, 0.6155, 0.6163, 0.6161, 0.6163,
0.6149, 0.6138, 0.6136, 0.6136, 0.6124, 0.6122, 0.6116, 0.6113,
0.6211, 0.6192, 0.6187, 0.6179, 0.6175, 0.6165, 0.6158, 0.6152,
0.6149, 0.6149, 0.6156, 0.6154, 0.6157, 0.6143, 0.6132, 0.6128,
0.6128, 0.6114, 0.6113, 0.6104, 0.61, 0.6179, 0.6159, 0.6154,
0.6147, 0.6144, 0.6134, 0.6128, 0.6122, 0.6121, 0.6123, 0.6133,
0.6133, 0.6136, 0.6123, 0.6112, 0.6109, 0.6109, 0.6096, 0.6097,
0.6091, 0.6087, 0.6155, 0.6134, 0.6129, 0.6122, 0.6119, 0.6108,
0.6103, 0.6098, 0.6098, 0.6102, 0.6113, 0.6116, 0.6123, 0.611,
0.6096, 0.6093, 0.6093, 0.6081, 0.6083, 0.6076, 0.6072, 0.6131,
0.6108, 0.6103, 0.6095, 0.6092, 0.6081, 0.6076, 0.6073, 0.6074,
0.6079, 0.6093, 0.6098, 0.6107, 0.6093, 0.6079, 0.6075, 0.6076,
0.6064, 0.6066, 0.6059, 0.6057, 0.6123, 0.6101, 0.6099, 0.6093,
0.6091, 0.6079, 0.6074, 0.6071, 0.6071, 0.6077, 0.6092, 0.61,
0.6114, 0.6102, 0.609, 0.6085, 0.6087, 0.6073, 0.6076, 0.607,
0.6067, 0.6122, 0.6101, 0.6101, 0.6096, 0.6094, 0.6081, 0.6075,
0.6072, 0.6071, 0.6075, 0.6092, 0.6096, 0.6098, 0.609, 0.6083,
0.6081, 0.6088, 0.6077, 0.6081, 0.6076, 0.6069, 0.6154, 0.6129,
0.6126, 0.6121, 0.6117, 0.6103, 0.6093, 0.6083, 0.6081, 0.6085,
0.6098, 0.6107, 0.6113, 0.6107, 0.6102, 0.6092, 0.6078, 0.6058,
0.6065, 0.6056, 0.6046, 0.6158, 0.6167, 0.6176, 0.617, 0.6164,
0.6146, 0.6135, 0.6123, 0.6117, 0.6116, 0.6125, 0.6138, 0.6147,
0.6146, 0.6145, 0.6142, 0.6135, 0.6118, 0.6108, 0.6103, 0.6098,
0.6125, 0.6133, 0.6139, 0.6141, 0.614, 0.6143, 0.6148, 0.6151,
0.6154, 0.6167, 0.6172, 0.6189, 0.6203, 0.6206, 0.6209, 0.6211,
0.6211, 0.6204, 0.6206, 0.6214, 0.6226, 0.6109, 0.612, 0.6129,
0.6132, 0.6132, 0.6136, 0.6142, 0.6149, 0.6155, 0.6168, 0.6173,
0.6193, 0.6211, 0.6217, 0.6225, 0.6231, 0.6232, 0.6226, 0.6229,
0.6239, 0.6251), .Dim = c(21L, 21L))
ref <- abind::abind(ref, ref, along=3); ref <- abind::abind(ref, ref, along=4)
expect_equal(Gapfill(data, fnPredict=PredictMean, subset="all")$fill, ref,
check.attributes = FALSE, tolerance=1e-4)
ref_m <- data; ref_m[is.na(data)] <- ref[is.na(data)]
expect_equal(Gapfill(data, fnPredict=PredictMean, subset="missings")$fill, ref_m,
check.attributes = FALSE, tolerance=1e-4)
ref_o <- ref; ref_o[is.na(data)] <- NA
expect_equal(Gapfill(data, fnPredict=PredictMean, subset="observed")$fill, ref_o,
check.attributes = FALSE, tolerance=1e-4)
sub1 <- sample(seq_along(data), 100)
ref_sub1 <- data; ref_sub1[sub1] <- ref[sub1]
expect_equal(Gapfill(data, fnPredict=PredictMean, subset=sub1)$fill, ref_sub1,
check.attributes = FALSE, tolerance=1e-4)
sub2 <- array(FALSE, dim(data)); sub2[sample(seq_along(data), 100)] <- TRUE
ref_sub2 <- data; ref_sub2[sub2] <- ref[sub2]
expect_equal(Gapfill(data, fnPredict=PredictMean, subset=sub2)$fill, ref_sub2,
check.attributes = FALSE, tolerance=1e-4)
})
test_that("Gapfill args",{
expect_error(Gapfill(),
"argument \"data\" is missing, with no default")
expect_error(Gapfill(matrix(1)),
"identical\\(length\\(dim\\(data\\)\\), 4L\\) is not TRUE")
expect_error(Gapfill(array(TRUE)),
"identical\\(length\\(dim\\(data\\)\\), 4L\\) is not TRUE")
expect_error(Gapfill(array(1, c(1,2,3,4,5))),
"identical\\(length\\(dim\\(data\\)\\), 4L\\) is not TRUE")
data <- ndvi[,,1:2,1:2]
expect_error(Gapfill(data = data, fnSubset = 1),
"is.function\\(fnSubset\\) is not TRUE")
expect_error(Gapfill(data = data, fnSubset = function(x) x),
"all\\(names\\(fnSubset_formals\\)\\[1:3\\] == c\\(\"data\", \"mp\", \"i\"\\)\\) is not TRUE")
expect_error(Gapfill(data = data, fnSubset = function(data, mp, k) x),
"all\\(names\\(fnSubset_formals\\)\\[1:3\\] == c\\(\"data\", \"mp\", \"i\"\\)\\) is not TRUE")
expect_equal(Gapfill(data = data,
fnSubset = function(data, mp, i)
array(1, c(1,1,1,1)))$fill,
data)
expect_error(Gapfill(data = data, fnPredict = 1),
"is.function\\(fnPredict\\) is not TRUE")
expect_error(Gapfill(data = data, fnPredict = function(x) x),
"all\\(names\\(fnPredict_formals\\)\\[1:2\\] == c\\(\"a\", \"i\"\\)\\) is not TRUE")
expect_error(Gapfill(data = data, fnPredict = function(n, k) x),
"all\\(names\\(fnPredict_formals\\)\\[1:2\\] == c\\(\"a\", \"i\"\\)\\) is not TRUE")
expect_equal(Gapfill(data = data, fnPredict = function(a, i) NA)$fill,
data)
expect_error(Gapfill(data = data, iMax = TRUE),
"is.numeric\\(iMax\\) is not TRUE")
expect_error(Gapfill(data = data, iMax = c(1, 1)),
"identical\\(length\\(iMax\\), 1L\\) is not TRUE")
expect_error(Gapfill(data = data, iMax = -1),
"iMax >= 0L is not TRUE")
expect_error(Gapfill(data = data, nPredict = TRUE),
"is.numeric\\(nPredict\\) is not TRUE")
expect_error(Gapfill(data = data, nPredict = c(1, 1)),
"identical\\(length\\(nPredict\\), 1L\\) is not TRUE")
expect_error(Gapfill(data = data, nPredict = -1),
"nPredict >= 1L is not TRUE")
expect_error(Gapfill(data = data, subset = "a"),
"subset\\[1\\] == \"missings\" || \\(identical\\(dim\\(subset\\), dim\\(data\\)\\) && .... is not TRUE")
expect_error(Gapfill(data = data, subset = c(1, 1)),
"subset\\[1\\] == \"missings\" || \\(identical\\(dim\\(subset\\), dim\\(data\\)\\) && .... is not TRUE")
expect_error(Gapfill(data = data,
subset = c(8, 8)),
"Argument \"subset\" contains duplicated values.")
expect_equal(Gapfill(data = data, subset = 155)$fill[155],
0.1527, tolerance = 1e-3)
data2 <- data[1:5,1:5,,]
fill2_obs <- Gapfill(data = data2, fnPredict=function(a, i) mean(a, na.rm=TRUE),
subset = "observed")$fill
expect_equal(fill2_obs[!is.na(fill2_obs)], rep(mean(data2, na.rm=TRUE), sum(!is.na(data2))))
expect_equal(!is.na(fill2_obs), !is.na(data2))
fill2_all <- Gapfill(data = data2, fnPredict=function(a, i) mean(a, na.rm=TRUE),
subset = "all")$fill
expect_true(all(!is.na(fill2_all)))
expect_equal(fill2_all, array(mean(data2, na.rm=TRUE), dim(data2), dimnames=dimnames(data2)))
expect_error(Gapfill(data = data, clipRange = TRUE),
"is.numeric\\(clipRange\\) is not TRUE")
expect_error(Gapfill(data = data, clipRange = 1),
"identical\\(length\\(clipRange\\), 2L\\) is not TRUE")
expect_error(Gapfill(data = data, clipRange = c(5,4)),
"clipRange\\[1\\] < clipRange\\[2\\] is not TRUE")
expect_error(Gapfill(data = data, dopar = c(1)),
"is.logical\\(dopar\\) is not TRUE")
expect_error(Gapfill(data = data, dopar = c(TRUE, TRUE)),
"identical\\(length\\(dopar\\), 1L\\) is not TRUE")
expect_error(Gapfill(data = data, verbose = 1),
"is.logical\\(verbose\\) is not TRUE")
expect_error(Gapfill(data = data, verbose = c(TRUE, TRUE)),
"identical\\(length\\(verbose\\), 1L\\) is not TRUE")
expect_error(Gapfill(data = data, notExtistent = 1),
"is/are not used by fnSubset\\(\\) or fnPredict\\(\\)")
fnS <- function(data, mp, i, e) e
expect_error(Gapfill(data = data, fnSubset = fnS),
"argument \"e\" is missing, with no default")
}) |
mpsedist <- function (data, distr, start = NULL, fix.arg = NULL, optim.method = "default",
lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL,
silent = TRUE, gradient = NULL, ...)
{
if (!is.character(distr))
stop("distr must be a character string naming a distribution")
else distname <- distr
ddistname <- paste("d", distname, sep = "")
if (!exists(ddistname, mode = "function"))
stop(paste("The ", ddistname, " function must be defined"))
pdistname <- paste("p", distname, sep = "")
if (!exists(pdistname, mode = "function"))
stop(paste("The ", pdistname, " function must be defined"))
if (is.null(custom.optim))
optim.method <- match.arg(optim.method, c("default",
"Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN",
"Brent"))
start.arg <- start
if (is.vector(start.arg))
start.arg <- as.list(start.arg)
txt1 <- "data must be a numeric vector of length greater than 1 for non censored data"
if (!is.null(weights)) {
if (any(weights <= 0))
stop("weights should be a vector of numbers greater than 0")
if (length(weights) != NROW(data) + 1)
stop("weights should be a vector with a length equal to the observation number")
warning("weights are not taken into account in the default initial values")
}
if (is.vector(data)) {
cens <- FALSE
if (!(is.numeric(data) & length(data) > 1))
stop(txt1)
}
else {
stop("Maximum product of spacing estimation is not yet available for censored data.")
}
argpdistname <- names(formals(pdistname))
chfixstt <- checkparam(start.arg = start.arg, fix.arg = fix.arg,
argdistname = argpdistname, errtxt = NULL,
data10 = head(data, 10), distname = distname)
if (!chfixstt$ok)
stop(chfixstt$txt)
if (is.function(chfixstt$start.arg))
vstart <- unlist(chfixstt$start.arg(data))
else vstart <- unlist(chfixstt$start.arg)
if (is.function(fix.arg)) {
fix.arg.fun <- fix.arg
fix.arg <- fix.arg(data)
}
else fix.arg.fun <- NULL
if (distname == "unif") {
n <- length(data)
data <- sort(data)
par <- c(min = (n*data[1]-data[n])/(n-1), max = (n*data[n] - data[1])/(n-1))
par <- c(par[!names(par) %in% names(fix.arg)], unlist(fix.arg))
value <- unname(sum(log(diff(c(par["min"],data,par["max"])))) - (n+1)*log(par["max"]-par["min"]))
res <- list(estimate = par[!names(par) %in% names(fix.arg)], convergence = 0,
value = value,
loglik = .loglik(par[!names(par) %in% names(fix.arg)], fix.arg, data, ddistname),
hessian = NA, optim.function = NA, fix.arg = fix.arg)
return(res)
}
if (!cens && is.null(weights)) {
fnobj <- function(par, fix.arg, obs, pdistnam, ddistnam) {
obs <- sort(obs)
spacing <- diff(c(0, do.call(pdistnam, c(list(obs), as.list(par), as.list(fix.arg))), 1))
if(any(is.nan(spacing)))
return(NaN)
ind <- abs(spacing) < .epsilon
if(any(ind)){
aux <- c(obs[1],obs)[ind]
spacing[ind] <- do.call(ddistnam, c(list(aux), as.list(par), as.list(fix.arg)))
}
-sum(log(spacing))
}
}
else if (!cens && !is.null(weights)) {
fnobj <- function(par, fix.arg, obs, pdistnam, ddistnam) {
obs <- sort(obs)
spacing <- diff(c(0, do.call(pdistnam, c(list(obs), as.list(par), as.list(fix.arg))), 1))
if(any(is.nan(spacing)))
return(NaN)
ind <- abs(spacing) < .epsilon
if(any(ind)){
aux <- c(obs[1],obs)[ind]
spacing[ind] <- do.call(ddistnam, c(list(aux), as.list(par), as.list(fix.arg)))
}
-sum(weights * log(spacing))
}
}
owarn <- getOption("warn")
if (is.null(custom.optim)) {
hasbound <- any(is.finite(lower) | is.finite(upper))
if (optim.method == "default") {
meth <- ifelse(length(vstart) > 1, "Nelder-Mead",
"BFGS")
}
else meth <- optim.method
if (meth == "BFGS" && hasbound && is.null(gradient)) {
meth <- "L-BFGS-B"
txt1 <- "The BFGS method cannot be used with bounds without provided the gradient."
txt2 <- "The method is changed to L-BFGS-B."
warning(paste(txt1, txt2))
}
options(warn = ifelse(silent, -1, 0))
if (hasbound) {
if (!is.null(gradient)) {
opt.fun <- "constrOptim"
}
else {
if (meth == "Nelder-Mead")
opt.fun <- "constrOptim"
else if (meth %in% c("L-BFGS-B", "Brent"))
opt.fun <- "optim"
else {
txt1 <- paste("The method", meth, "cannot be used by constrOptim() nor optim() without gradient and bounds.")
txt2 <- "Only optimization methods L-BFGS-B, Brent and Nelder-Mead can be used in such case."
stop(paste(txt1, txt2))
}
}
if (opt.fun == "constrOptim") {
npar <- length(vstart)
lower <- as.double(rep_len(lower, npar))
upper <- as.double(rep_len(upper, npar))
haslow <- is.finite(lower)
Mat <- diag(npar)[haslow, ]
hasupp <- is.finite(upper)
Mat <- rbind(Mat, -diag(npar)[hasupp, ])
colnames(Mat) <- names(vstart)
rownames(Mat) <- paste0("constr", 1:NROW(Mat))
Bnd <- c(lower[is.finite(lower)], -upper[is.finite(upper)])
names(Bnd) <- paste0("constr", 1:length(Bnd))
initconstr <- Mat %*% vstart - Bnd
if (any(initconstr < 0))
stop("Starting values must be in the feasible region.")
opttryerror <- try(opt <- constrOptim(theta = vstart,
f = fnobj, ui = Mat, ci = Bnd, grad = gradient,
fix.arg = fix.arg, obs = data,
pdistnam = pdistname, ddistnam = ddistname,
hessian = !is.null(gradient), method = meth,
...), silent = TRUE)
if (!inherits(opttryerror, "try-error"))
if (length(opt$counts) == 1)
opt$counts <- c(opt$counts, NA)
}
else {
opttryerror <- try(opt <- optim(par = vstart,
fn = fnobj, fix.arg = fix.arg, obs = data,
pdistnam = pdistname, ddistnam = ddistname,
gr = gradient, hessian = TRUE,
method = meth, lower = lower, upper = upper,
...), silent = TRUE)
}
}
else {
opt.fun <- "optim"
opttryerror <- try(opt <- optim(par = vstart,
fn = fnobj, fix.arg = fix.arg, obs = data,
pdistnam = pdistname, ddistnam = ddistname,
gr = gradient, hessian = TRUE,
method = meth, lower = lower, upper = upper,
...), silent = TRUE)
}
options(warn = owarn)
if (inherits(opttryerror, "try-error")) {
warnings("The function optim encountered an error and stopped.")
if (getOption("show.error.messages"))
print(attr(opttryerror, "condition"))
return(list(estimate = rep(NA, length(vstart)),
convergence = 100, value=NA, loglik = NA, hessian = NA,
optim.function = opt.fun, fix.arg = fix.arg,
optim.method = meth, fix.arg.fun = fix.arg.fun,
counts = c(NA, NA)))
}
if (opt$convergence > 0) {
warnings("The function optim failed to converge, with the error code ",
opt$convergence)
}
if (is.null(names(opt$par)))
names(opt$par) <- names(vstart)
res <- list(estimate = opt$par, convergence = opt$convergence, value = -opt$value,
loglik = .loglik(opt$par, fix.arg, data, ddistname),
hessian = opt$hessian, optim.function = opt.fun,
fix.arg = fix.arg, optim.method = meth, fix.arg.fun = fix.arg.fun,
weights = weights, counts = opt$counts, optim.message = opt$message)
}
else {
options(warn = ifelse(silent, -1, 0))
opttryerror <- try(opt <- custom.optim(fn = fnobj,
fix.arg = fix.arg, obs = data,
pdistnam = pdistname, ddistnam = ddistname,
par = vstart, ...), silent = TRUE)
options(warn = owarn)
if (inherits(opttryerror, "try-error")) {
warnings("The customized optimization function encountered an error and stopped.")
if (getOption("show.error.messages"))
print(attr(opttryerror, "condition"))
return(list(estimate = rep(NA, length(vstart)),
convergence = 100, value = NA, loglik = NA, hessian = NA,
optim.function = custom.optim, fix.arg = fix.arg,
fix.arg.fun = fix.arg.fun, counts = c(NA, NA)))
}
if (opt$convergence > 0) {
warnings("The customized optimization function failed to converge, with the error code ",
opt$convergence)
}
argdot <- list(...)
method.cust <- argdot[argdot == "method"]
if (length(method.cust) == 0) {
method.cust <- NULL
}
if (is.null(names(opt$par)))
names(opt$par) <- names(vstart)
res <- list(estimate = opt$par, convergence = opt$convergence, value = -opt$value,
loglik = .loglik(opt$par, fix.arg, data, ddistname),
hessian = opt$hessian, optim.function = custom.optim,
fix.arg = fix.arg, method = method.cust, fix.arg.fun = fix.arg.fun,
weights = weights, counts = opt$counts, optim.message = opt$message)
}
return(res)
} |
LogL.pss1Ic2.aux<- function(parameters, X, Z, data, trace)
{
loglik1 <- function(param, X, Z, y,trace)
{
npar <-as.integer(length(param)-2)
beta<- as.double(param[1:(npar-1)])
bt<- as.double(param[1:(npar-1)])
rho<-as.double(param[npar])
y<- as.integer(y)
n <- as.integer(length(y))
x<-matrix(as.double(X),nrow=n,ncol=npar-1)
theta<- work<-as.double(rep(0,n))
logL <- as.double(0)
eta<-i.fit<- fit<-as.vector(npar-1)
eta<-x%*%beta
ui<-y-exp(eta)
pos.r2<-as.double(0)
names.Z <- dimnames(Z)[[2]]
names.X <- dimnames(X)[[2]]
for (i in 2:ncol(X))
{ if (!is.na(match(names.Z[2],names.X[i]))) pos.r2<-i }
m<-glm(as.numeric(y)~ offset(eta) + Z[,2], family=poisson, maxit=100)
b1i<-coef(m)[1]
b2i<-coef(m)[2]
beta[1]<-beta[1]+b1i
beta[pos.r2]<-beta[pos.r2]+b2i
y[is.na(y)]<-(-1)
y<- as.integer(y)
link <- as.integer(1)
m <- max(y)
fact <- rep(1, m + 1)
if(m > 0)
{for(i in 2:(m + 1))
fact[i] <- fact[i - 1] * (i - 1)}
fact <- as.double(fact)
bi<-c(b1i,b2i)
results <- .Fortran("psslik",logL,beta,rho,
npar,x,y,theta,work,n,fact,link,PACKAGE="cold")
i.fit<-x%*%beta
return(list(loglik=results[[1]], fit=i.fit, bi.est=bi))}
if(trace) cat(paste(format(parameters[length(parameters)-2], digit=4), collapse=" "), "\t")
if(trace) cat(paste(format(parameters[length(parameters)-1], digit=4), collapse=" "), "\t")
if(trace) cat(paste("\t",(format(parameters[length(parameters)], digit=4)), collapse=" "), "\t")
nparam<-length(parameters)
omega1<-as.double(parameters[nparam]-1)
omega2<-as.double(parameters[nparam])
ti.repl<-data[[1]]
cumti.repl<-cumsum(ti.repl)
n.cases<- length(ti.repl)
y<-data[[2]]
fitted<-as.double(rep(0,length(y)))
bi.estimate<-matrix(as.double(0),nrow=n.cases,ncol=2)
gi.estimate<-as.double(rep(0,length(n.cases)))
logL1<-as.double(0)
k1<-1
for (i in 1:n.cases)
{
k2<-cumti.repl[i]
z1<-loglik1(param=parameters,X=X[k1:k2,], Z=Z[k1:k2,], y=y[k1:k2],trace=trace)
fitted[k1:k2]<-z1$fit
bi.estimate[i,]<-z1$bi.est
k1<-k2+1
}
return(list(fit=fitted,bi.est=bi.estimate))} |
genCov_buildTrack <- function(name, bg_fill="black", text_fill="white",
border="black", size=10)
{
background <- geom_rect(colour=border, fill=bg_fill)
label <- geom_text(aes(x=.5, y=.5), label=name, colour=text_fill, angle=90,
size=size)
scale_x <- scale_x_continuous(expand=c(0,0))
scale_y <- scale_y_continuous(expand=c(0,0))
labels <- labs(x=NULL, y=NULL)
theme <- theme(axis.title.x=element_blank(), axis.text.x=element_blank(),
axis.ticks.x=element_blank(), axis.title.y=element_blank(),
axis.text.y=element_blank(), axis.ticks.y=element_blank(),
plot.margin=unit(c(0, 0, 0, 0), "null"),
axis.ticks.length=unit(0,"null"),
panel.spacing=unit(0,"null"))
label <- ggplot(mapping=aes(xmin=0, xmax=1, ymin=0, ymax=1)) +
background + label + theme + scale_x + scale_y + labels
return(label)
} |
superbarplot <- function(x,
names = 1:dim(x)[2],
names_height=NULL,
col = gray(seq(.8,.5,length=dim(x)[1]/2)), ...) {
plot.bar <- function(x,min,max,width=1,...) {
alpha <- (1-width)/2
polygon(x + c(alpha,alpha,1-alpha,1-alpha,alpha),
c(min,max,max,min,min),
...)
}
n = dim(x)[2]
m = dim(x)[1]
no.bars = dim(x)[1]/2
y.range = c(min(x),max(x))
x.range = c(1,n+1)
plot.new()
plot.window(xlim=x.range,ylim=y.range,
xaxt="n",
bty="n",ann=FALSE)
title(...)
for(i in 1:no.bars) {
for(j in 1:n) {
plot.bar(j,x[2*i-1,j],x[2*i,j],width=1 - i/(3*no.bars),col=col[i])
}
}
if(!is.null(names)) {
if(is.null(names_height)) {
f = par("yaxp")
names_height= f[1] + (f[2]-f[1])*(f[3]-1)/f[3]
}
text(0.5 + 1:n,rep(names_height,n),format(names))
}
}
|
NULL
.organizations$accept_handshake_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(HandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$accept_handshake_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$attach_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string")), TargetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$attach_policy_output <- function(...) {
list()
}
.organizations$cancel_handshake_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(HandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$cancel_handshake_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), RoleName = structure(logical(0), tags = list(type = "string")), IamUserAccessToBilling = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_account_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(CreateAccountStatus = structure(list(Id = structure(logical(0), tags = list(type = "string")), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), CompletedTimestamp = structure(logical(0), tags = list(type = "timestamp")), AccountId = structure(logical(0), tags = list(type = "string")), GovCloudAccountId = structure(logical(0), tags = list(type = "string")), FailureReason = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_gov_cloud_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), RoleName = structure(logical(0), tags = list(type = "string")), IamUserAccessToBilling = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_gov_cloud_account_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(CreateAccountStatus = structure(list(Id = structure(logical(0), tags = list(type = "string")), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), CompletedTimestamp = structure(logical(0), tags = list(type = "timestamp")), AccountId = structure(logical(0), tags = list(type = "string")), GovCloudAccountId = structure(logical(0), tags = list(type = "string")), FailureReason = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_organization_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(FeatureSet = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_organization_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Organization = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), FeatureSet = structure(logical(0), tags = list(type = "string")), MasterAccountArn = structure(logical(0), tags = list(type = "string")), MasterAccountId = structure(logical(0), tags = list(type = "string")), MasterAccountEmail = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), AvailablePolicyTypes = structure(list(structure(list(Type = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_organizational_unit_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ParentId = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_organizational_unit_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnit = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Content = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$create_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policy = structure(list(PolicySummary = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), AwsManaged = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Content = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$decline_handshake_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(HandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$decline_handshake_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$delete_organization_input <- function(...) {
list()
}
.organizations$delete_organization_output <- function(...) {
list()
}
.organizations$delete_organizational_unit_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnitId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$delete_organizational_unit_output <- function(...) {
list()
}
.organizations$delete_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$delete_policy_output <- function(...) {
list()
}
.organizations$deregister_delegated_administrator_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string")), ServicePrincipal = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$deregister_delegated_administrator_output <- function(...) {
list()
}
.organizations$describe_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_account_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Account = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Name = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Status = structure(logical(0), tags = list(type = "string")), JoinedMethod = structure(logical(0), tags = list(type = "string")), JoinedTimestamp = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_create_account_status_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(CreateAccountRequestId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_create_account_status_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(CreateAccountStatus = structure(list(Id = structure(logical(0), tags = list(type = "string")), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), CompletedTimestamp = structure(logical(0), tags = list(type = "timestamp")), AccountId = structure(logical(0), tags = list(type = "string")), GovCloudAccountId = structure(logical(0), tags = list(type = "string")), FailureReason = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_effective_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyType = structure(logical(0), tags = list(type = "string")), TargetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_effective_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(EffectivePolicy = structure(list(PolicyContent = structure(logical(0), tags = list(type = "string")), LastUpdatedTimestamp = structure(logical(0), tags = list(type = "timestamp")), TargetId = structure(logical(0), tags = list(type = "string")), PolicyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_handshake_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(HandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_handshake_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_organization_input <- function(...) {
list()
}
.organizations$describe_organization_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Organization = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), FeatureSet = structure(logical(0), tags = list(type = "string")), MasterAccountArn = structure(logical(0), tags = list(type = "string")), MasterAccountId = structure(logical(0), tags = list(type = "string")), MasterAccountEmail = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), AvailablePolicyTypes = structure(list(structure(list(Type = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_organizational_unit_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnitId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_organizational_unit_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnit = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$describe_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policy = structure(list(PolicySummary = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), AwsManaged = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Content = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$detach_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string")), TargetId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$detach_policy_output <- function(...) {
list()
}
.organizations$disable_aws_service_access_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServicePrincipal = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$disable_aws_service_access_output <- function(...) {
list()
}
.organizations$disable_policy_type_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RootId = structure(logical(0), tags = list(type = "string")), PolicyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$disable_policy_type_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Root = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), PolicyTypes = structure(list(structure(list(Type = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$enable_aws_service_access_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServicePrincipal = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$enable_aws_service_access_output <- function(...) {
list()
}
.organizations$enable_all_features_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$enable_all_features_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$enable_policy_type_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(RootId = structure(logical(0), tags = list(type = "string")), PolicyType = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$enable_policy_type_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Root = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), PolicyTypes = structure(list(structure(list(Type = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$invite_account_to_organization_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Target = structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), Notes = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$invite_account_to_organization_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshake = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$leave_organization_input <- function(...) {
list()
}
.organizations$leave_organization_output <- function(...) {
list()
}
.organizations$list_aws_service_access_for_organization_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_aws_service_access_for_organization_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(EnabledServicePrincipals = structure(list(structure(list(ServicePrincipal = structure(logical(0), tags = list(type = "string")), DateEnabled = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_accounts_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_accounts_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Accounts = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Name = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Status = structure(logical(0), tags = list(type = "string")), JoinedMethod = structure(logical(0), tags = list(type = "string")), JoinedTimestamp = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_accounts_for_parent_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ParentId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_accounts_for_parent_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Accounts = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Name = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Status = structure(logical(0), tags = list(type = "string")), JoinedMethod = structure(logical(0), tags = list(type = "string")), JoinedTimestamp = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_children_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ParentId = structure(logical(0), tags = list(type = "string")), ChildType = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_children_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Children = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_create_account_status_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(States = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_create_account_status_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(CreateAccountStatuses = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), AccountName = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), CompletedTimestamp = structure(logical(0), tags = list(type = "timestamp")), AccountId = structure(logical(0), tags = list(type = "string")), GovCloudAccountId = structure(logical(0), tags = list(type = "string")), FailureReason = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_delegated_administrators_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ServicePrincipal = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_delegated_administrators_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(DelegatedAdministrators = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Email = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Name = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Status = structure(logical(0), tags = list(type = "string")), JoinedMethod = structure(logical(0), tags = list(type = "string")), JoinedTimestamp = structure(logical(0), tags = list(type = "timestamp")), DelegationEnabledDate = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_delegated_services_for_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_delegated_services_for_account_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(DelegatedServices = structure(list(structure(list(ServicePrincipal = structure(logical(0), tags = list(type = "string")), DelegationEnabledDate = structure(logical(0), tags = list(type = "timestamp"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_handshakes_for_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Filter = structure(list(ActionType = structure(logical(0), tags = list(type = "string")), ParentHandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_handshakes_for_account_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshakes = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_handshakes_for_organization_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Filter = structure(list(ActionType = structure(logical(0), tags = list(type = "string")), ParentHandshakeId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_handshakes_for_organization_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Handshakes = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Parties = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), State = structure(logical(0), tags = list(type = "string")), RequestedTimestamp = structure(logical(0), tags = list(type = "timestamp")), ExpirationTimestamp = structure(logical(0), tags = list(type = "timestamp")), Action = structure(logical(0), tags = list(type = "string")), Resources = structure(list(structure(list(Value = structure(logical(0), tags = list(type = "string", sensitive = TRUE)), Type = structure(logical(0), tags = list(type = "string")), Resources = structure(logical(0), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_organizational_units_for_parent_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ParentId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_organizational_units_for_parent_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnits = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_parents_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ChildId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_parents_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Parents = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_policies_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Filter = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_policies_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policies = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), AwsManaged = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_policies_for_target_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(TargetId = structure(logical(0), tags = list(type = "string")), Filter = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_policies_for_target_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policies = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), AwsManaged = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_roots_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_roots_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Roots = structure(list(structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), PolicyTypes = structure(list(structure(list(Type = structure(logical(0), tags = list(type = "string")), Status = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_tags_for_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_tags_for_resource_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_targets_for_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string")), NextToken = structure(logical(0), tags = list(type = "string")), MaxResults = structure(logical(0), tags = list(type = "integer", box = TRUE))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$list_targets_for_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Targets = structure(list(structure(list(TargetId = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list")), NextToken = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$move_account_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string")), SourceParentId = structure(logical(0), tags = list(type = "string")), DestinationParentId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$move_account_output <- function(...) {
list()
}
.organizations$register_delegated_administrator_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string")), ServicePrincipal = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$register_delegated_administrator_output <- function(...) {
list()
}
.organizations$remove_account_from_organization_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(AccountId = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$remove_account_from_organization_output <- function(...) {
list()
}
.organizations$tag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), Tags = structure(list(structure(list(Key = structure(logical(0), tags = list(type = "string")), Value = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$tag_resource_output <- function(...) {
list()
}
.organizations$untag_resource_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(ResourceId = structure(logical(0), tags = list(type = "string")), TagKeys = structure(list(structure(logical(0), tags = list(type = "string"))), tags = list(type = "list"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$untag_resource_output <- function(...) {
list()
}
.organizations$update_organizational_unit_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnitId = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$update_organizational_unit_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(OrganizationalUnit = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$update_policy_input <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(PolicyId = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Content = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))
return(populate(args, shape))
}
.organizations$update_policy_output <- function(...) {
args <- c(as.list(environment()), list(...))
shape <- structure(list(Policy = structure(list(PolicySummary = structure(list(Id = structure(logical(0), tags = list(type = "string")), Arn = structure(logical(0), tags = list(type = "string")), Name = structure(logical(0), tags = list(type = "string")), Description = structure(logical(0), tags = list(type = "string")), Type = structure(logical(0), tags = list(type = "string")), AwsManaged = structure(logical(0), tags = list(type = "boolean"))), tags = list(type = "structure")), Content = structure(logical(0), tags = list(type = "string"))), tags = list(type = "structure"))), tags = list(type = "structure"))
return(populate(args, shape))
} |
"d2p" <- function (depth, lat=40)
{
ndepth <- length(depth)
nlat <- length(lat)
nmax <- max(ndepth, nlat)
if(ndepth < nmax){depth <- rep(depth[1], nmax)}
if(nlat < nmax){lat <- rep(lat[1], nmax)}
pi <- 3.141592654
pressure <- rep(NA,nmax)
for(i in (1:nmax)){
lat[i] <- abs(lat[i])
plat <- abs(lat[i]*pi)/180
d <- sin(plat)
c1 <- 5.92e-3 + d^2 * 5.25e-3
pressure[i] <- ((1-c1)-sqrt(((1-c1)^2)-(8.84e-6*depth[i]))) / 4.42e-6
}
return(pressure)
} |
parallelMap = function(fun, ..., more.args = list(), simplify = FALSE,
use.names = FALSE, impute.error = NULL, level = NA_character_,
show.info = NA) {
assertFunction(fun)
assertList(more.args)
assertFlag(simplify)
assertFlag(use.names)
if (!is.null(impute.error)) {
if (is.function(impute.error)) {
impute.error.fun = impute.error
} else {
impute.error.fun = function(x) impute.error
}
}
assertString(level, na.ok = TRUE)
assertFlag(show.info, na.ok = TRUE)
if (!is.na(level) && level %nin% unlist(getPMOption("registered.levels", list()))) {
stopf("Level '%s' not registered", level)
}
cpus = getPMOptCpus()
load.balancing = getPMOptLoadBalancing()
logging = getPMOptLogging()
reproducible = getPMOptReproducible()
logdir = ifelse(logging, getNextLogDir(), NA_character_)
if (isModeLocal() || !isParallelizationLevel(level) || getPMOptOnSlave()) {
if (!is.null(impute.error)) {
fun2 = function(...) {
res = try(fun(...), silent = getOption("parallelMap.suppress.local.errors"))
if (BBmisc::is.error(res)) {
res = list(try.object = res)
class(res) = "parallelMapErrorWrapper"
}
return(res)
}
} else {
fun2 = fun
}
assignInFunctionNamespace(fun, env = PKG_LOCAL_ENV)
res = mapply(fun2, ..., MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE)
} else {
iters = seq_along(..1)
showInfoMessage("Mapping in parallel%s: mode = %s; level = %s; cpus = %i; elements = %i.",
ifelse(load.balancing, " (load balanced)", ""), getPMOptMode(),
level, getPMOptCpus(), length(iters), show.info = show.info)
if (isModeMulticore()) {
more.args = c(list(.fun = fun, .logdir = logdir), more.args)
if (reproducible) {
old.seed = .Random.seed
old.rng.kind = RNGkind()
seed = sample(1:100000, 1)
rm(.Random.seed, envir = globalenv())
set.seed(seed, "L'Ecuyer-CMRG")
}
res = MulticoreClusterMap(slaveWrapper, ..., .i = iters,
MoreArgs = more.args, mc.cores = cpus,
SIMPLIFY = FALSE, USE.NAMES = FALSE)
if (reproducible) {
.Random.seed = old.seed
RNGkind(old.rng.kind[1], old.rng.kind[2], old.rng.kind[3])
}
} else if (isModeSocket() || isModeMPI()) {
more.args = c(list(.fun = fun, .logdir = logdir), more.args)
if (load.balancing) {
res = clusterMapLB(cl = NULL, slaveWrapper, ..., .i = iters,
MoreArgs = more.args)
} else {
res = clusterMap(cl = NULL, slaveWrapper, ..., .i = iters,
MoreArgs = more.args, SIMPLIFY = FALSE, USE.NAMES = FALSE)
}
} else if (isModeBatchJobs()) {
more.args = c(list(.fun = fun, .logdir = NA_character_), more.args)
suppressMessages({
reg = getBatchJobsReg()
asNamespace("BatchJobs")$dbRemoveJobs(reg, BatchJobs::getJobIds(reg))
BatchJobs::batchMap(reg, slaveWrapper, ..., more.args = more.args)
BatchJobs::submitJobs(reg, resources = getPMOptBatchJobsResources(), max.retries = 15)
ok = BatchJobs::waitForJobs(reg, stop.on.error = is.null(impute.error))
})
if (!is.na(logdir)) {
term = BatchJobs::findTerminated(reg)
fns = BatchJobs::getLogFiles(reg, term)
dests = file.path(logdir, sprintf("%05i.log", term))
file.copy(from = fns, to = dests)
}
ids = BatchJobs::getJobIds(reg)
ids.err = BatchJobs::findErrors(reg)
ids.exp = BatchJobs::findExpired(reg)
ids.done = BatchJobs::findDone(reg)
ids.notdone = c(ids.err, ids.exp)
msgs = rep("Job expired!", length(ids.notdone))
msgs[ids.err] = BatchJobs::getErrorMessages(reg, ids.err)
if (is.null(impute.error) && length(c(ids.notdone)) > 0) {
extra.msg = sprintf("Please note that remaining jobs were killed when 1st error occurred to save cluster time.\nIf you want to further debug errors, your BatchJobs registry is here:\n%s",
reg$file.dir)
onsys = BatchJobs::findOnSystem(reg)
suppressMessages(
BatchJobs::killJobs(reg, onsys)
)
onsys = BatchJobs::findOnSystem(reg)
if (length(onsys) > 0L) {
warningf("Still %i jobs from operation on system! kill them manually!", length(onsys))
}
if (length(ids.notdone) > 0L) {
stopWithJobErrorMessages(ids.notdone, msgs, extra.msg)
}
}
res = vector("list", length(ids))
res[ids.done] = BatchJobs::loadResults(reg, simplify = FALSE, use.names = FALSE)
res[ids.notdone] = lapply(msgs, function(s) impute.error.fun(simpleError(s)))
} else if (isModeBatchtools()) {
more.args = insert(more.args, list(.fun = fun, .logdir = NA_character_))
old = getOption("batchtools.verbose")
options(batchtools.verbose = FALSE)
on.exit(options(batchtools.verbose = old))
reg = getBatchtoolsReg()
if (nrow(reg$status) > 0L) {
batchtools::clearRegistry(reg = reg)
}
ids = batchtools::batchMap(fun = slaveWrapper, ..., more.args = more.args, reg = reg)
batchtools::submitJobs(ids = ids, resources = getPMOptBatchtoolsResources(), reg = reg)
ok = batchtools::waitForJobs(ids = ids, stop.on.error = is.null(impute.error), reg = reg)
if (!is.na(logdir)) {
x = batchtools::findStarted(reg = reg)
x$log.file = file.path(reg$file.dir, "logs", sprintf("%s.log", x$job.hash))
.mapply(function(id, fn) writeLines(batchtools::getLog(id, reg = reg), con = fn), x, NULL)
}
if (ok) {
res = batchtools::reduceResultsList(ids, reg = reg)
} else {
if (is.null(impute.error)) {
extra.msg = sprintf("Please note that remaining jobs were killed when 1st error occurred to save cluster time.\nIf you want to further debug errors, your batchtools registry is here:\n%s",
reg$file.dir)
batchtools::killJobs(reg = reg)
ids.notdone = batchtools::findNotDone(reg = reg)
stopWithJobErrorMessages(
inds = ids.notdone$job.id,
batchtools::getErrorMessages(ids.notdone, missing.as.error = TRUE, reg = reg)$message,
extra.msg)
} else {
res = batchtools::findJobs(reg = reg)
res$result = list()
ids.complete = batchtools::findDone(reg = reg)
ids.incomplete = batchtools::findNotDone(reg = reg)
res[ids.complete, data.table::`:=`("result", batchtools::reduceResultsList(ids.complete, reg = reg)), with = FALSE]
ids[ids.complete, data.table::`:=`("result", lapply(batchtools::getErrorMessages(ids.incomplete, reg = reg)$message, simpleError)), with = FALSE]
}
}
}
}
if (is.null(impute.error)) {
checkResultsAndStopWithErrorsMessages(res)
} else {
res = lapply(res, function(x) {
if (inherits(x, "parallelMapErrorWrapper")) {
impute.error.fun(attr(x$try.object, "condition"))
} else {
x
}
})
}
if (use.names && !is.null(names(..1))) {
names(res) = names(..1)
} else if (use.names && is.character(..1)) {
names(res) = ..1
} else if (!use.names) {
names(res) = NULL
}
if (isTRUE(simplify) && length(res) > 0L) {
res = simplify2array(res, higher = simplify)
}
options(parallelMap.nextmap = (getPMOptNextMap() + 1L))
return(res)
}
slaveWrapper = function(..., .i, .fun, .logdir = NA_character_) {
if (!is.na(.logdir)) {
options(warning.length = 8170L, warn = 1L)
.fn = file.path(.logdir, sprintf("%05i.log", .i))
.fn = file(.fn, open = "wt")
.start.time = as.integer(Sys.time())
sink(.fn)
sink(.fn, type = "message")
on.exit(sink(NULL))
}
options(parallelMap.on.slave = TRUE)
on.exit(options(parallelMap.on.slave = FALSE))
res = try(.fun(...))
if (BBmisc::is.error(res)) {
res = list(try.object = res)
class(res) = "parallelMapErrorWrapper"
}
if (!is.na(.logdir)) {
.end.time = as.integer(Sys.time())
print(gc())
message(sprintf("Job time in seconds: %i", .end.time - .start.time))
sink(NULL)
}
return(res)
}
assignInFunctionNamespace = function(fun, li = list(), env = new.env()) {
ee = environment(fun)
ns = ls(env)
for (n in ns) {
assign(n, get(n, envir = env), envir = ee)
}
ns = names(li)
for (n in ns) {
assign(n, li[[n]], envir = ee)
}
} |
library(relatable)
context("relation() reports properties of given mapping correctly for report_properties = TRUE")
test_that("report_properties can correctly determine properties of a given relation", {
A1 <- c("a", "b", "c")
A2 <- c("a", "a", "c", "d")
A3 <- c("a", "a", "c", "d", "e")
A4 <- c("a", "b", "c", "d")
B1 <- c(1, 2, 3)
B1.1 <- list("apple", c("banana", "berry"), list("cherry", "coconut"))
B2 <- c(1, 2, 2, 3, 4)
B3 <- c(1, 2, 2, 3)
B4 <- c(1, 2, 3, 4)
expect_message(relation(A1, B1, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: TRUE\nmax_one_x_per_y: TRUE")
expect_message(relation(A1, B1.1, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: TRUE\nmax_one_x_per_y: TRUE")
expect_message(relation(B1.1, A1, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: TRUE\nmax_one_x_per_y: TRUE")
expect_message(relation(A2, B2, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: FALSE\nmax_one_y_per_x: FALSE\nmax_one_x_per_y: FALSE")
expect_message(relation(A3, B3, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: FALSE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: FALSE\nmax_one_x_per_y: FALSE")
expect_message(relation(A3, B2, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: FALSE\nmax_one_x_per_y: FALSE")
expect_message(relation(A4, B2, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: FALSE\nmax_one_y_per_x: TRUE\nmax_one_x_per_y: FALSE")
expect_message(relation(A3, B4, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: FALSE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: FALSE\nmax_one_x_per_y: TRUE")
expect_message(relation(A2, B4, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: FALSE\nmax_one_x_per_y: TRUE")
expect_message(relation(B4, A2, atomic = FALSE, relation_type = NULL, report_properties = TRUE), "Relation properties:\nmin_one_y_per_x: TRUE\nmin_one_x_per_y: TRUE\nmax_one_y_per_x: TRUE\nmax_one_x_per_y: FALSE")
}) |
short_summary <- function(x) {
x <- summary(x)
cmat <- coef(x)
printCoefmat(cmat)
}
logisticfn <- function(x) exp(x) / (1 + exp(x))
curve(logisticfn, -5, 5, main="Logistic function")
points(0, logisticfn(0), pch=15, cex=2)
x1 <- rep(1:10, 2)
x2 <- rchisq(20, df=2)
y <- rnorm(20, mean=x1 + 2*x2, sd=2)
res.lm <- lm(y ~ x1 + x2)
short_summary(res.lm)
res.glm <- glm(y ~ x1 + x2, family="gaussian")
summary(res.glm)
babies.prem = subset(babies,
subset= gestation < 999 & wt1 < 999 & ht < 99 & smoke < 9,
select=c("gestation","smoke","wt1","ht"))
babies.prem$preemie = with(babies.prem, as.numeric(gestation < 7*37))
table(babies.prem$preemie)
babies.prem$BMI = with(babies.prem, (wt1/2.2) / (ht*2.54/100)^2)
hist(babies.prem$BMI)
res <- glm(preemie ~ factor(smoke) + BMI, family=binomial,
data=babies.prem)
short_summary(res)
library(MASS)
stepAIC(res, trace=0)
first.name <- gl(2, 2500, 5000, labels=c("yes", "no"))
offer <- gl(2, 1250, 5000, labels=c("yes", "no"))
opened <- c(rep(1:0, c(20, 1250-20)), rep(1:0, c(15, 1250-15)),
rep(1:0, c(17, 1250-17)), rep(1:0, c( 8, 1250-8)))
xtabs(opened ~ first.name + offer)
f <- function(x) rep(1:0, c(x, 1250-x))
opened <- c(sapply(c(20, 15, 17, 8), f))
res.glm <- glm(opened ~ first.name + offer, family="binomial")
short_summary(res.glm)
opened <- c(8,15,17,20)
opened.mat <- cbind(opened=opened, not.opened=1250 - opened)
opened.mat
offer <- c(0, 0, 1, 1)
first.name <- c(0, 1, 0, 1)
glm(opened.mat ~ first.name + offer, family="binomial")
plot(count ~ year, data=yellowfin)
f <- function(t, N, r, d) N*(exp(-r*(t-1952))*(1-d) + d)
res.yf <- nls(count ~ f(year, N, r, d), start=c(N=6, r=1/10, d=0.1),
data=yellowfin)
res.yf
plot(count ~ year, data=yellowfin)
title(main="Mean catch per 100 hooks")
curve(f(x, N=6, r=1/10, d=0.1), add=TRUE)
curve(f(x, N=6.02, r=0.0939, d=0.0539), add=TRUE, lty=2, lwd=2)
legend(1980, 6, legend=c("exploratory", "exponential"), lty=1:2)
logistic <- function(t, Y, k, t0, m) Y * (1 + exp(-k * (t-t0)))^(-1)
richards <- function(t, Y, k, t0, m) Y * (1 - exp(-k * (t-t0)))^m
plot(jitter(size) ~ jitter(age,3), data=urchin.growth,
xlab="age", ylab="size", main="Urchin growth by age")
res.logistic <- nls(size ~ logistic(age, Y, k, t0),
start = c(Y=60, k=1, t0=2),
data = urchin.growth)
res.logistic
AIC(res.logistic)
res.logistic <- nls(size ~ logistic(age,Y,k,t0,m),
data = urchin.growth,
start = c(Y=53, k=1.393, t0=1.958, m=1))
res.richards <- nls(size ~ richards(age, Y, k, t0, m),
data = urchin.growth,
start = c(Y=53, k=0.5, t0=0, m=1))
res.richards
AIC(res.richards)
plot(jitter(size) ~ jitter(age,3), data=urchin.growth,
xlab="age", ylab="size", main="Urchin growth by age")
curve(logistic(x, Y=53.903, k=1.393, t0=1.958), add=TRUE)
curve(richards(x, Y=57.26, k=0.78, t0=-0.8587, m = 6.0636), add=TRUE, lty=2)
legend(5, 30, lty=1:2, legend=c("Logistic", "Richards"))
res <- glm(enjoyed ~ gender + age, data=tastesgreat,
family=binomial)
summary(res)
library(MASS)
stepAIC(glm(healthy ~ p + g, healthy, family=binomial))
library(MASS)
res.glm <- glm(low ~ age + lwt + smoke + ht + ui, data=birthwt,
family = binomial)
summary(res.glm)
stepAIC(res.glm, trace=0)
hfm = hall.fame$Hall.Fame.Membership != "not a member"
hfm <- hall.fame$Hall.Fame.Membership != "not a member"
res <- glm(hfm ~ BA + HR + hits + games, data=hall.fame,
family="binomial")
stepAIC(res, trace=0)
res.full <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
data = esoph, family = binomial())
res.add <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp,
data = esoph, family = binomial())
res.full <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
data = esoph, family = binomial())
res.add <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp,
data = esoph, family = binomial())
AIC(res.full)
AIC(res.add)
plot(circumference ~ age, data=Orange, subset = Tree == 1)
g <- function(t, Y, k, t_0) Y*( 1 + exp(-k*(t-t_0)))^(-1)
res.tree <- nls(circumference ~ g(age, Y, k, t0),
start=c(Y=150, k=1/300, t0=750),
data=Orange, subset=Tree == 1)
res.tree
age <- 0:1500
plot(circumference ~ age, data=Orange, subset = Tree == 1)
lines(age, predict(res.tree, data.frame(age=age)))
f <- function(t,Y,k,t0) Y * (1 + exp(-k*(t-t0)))^(-1)
plot(weight ~ Time, data=ChickWeight, subset= Chick == 1)
nls(weight ~ f(Time, Y, k, t0), start=c(Y=400, k=1/10, t0=20),
subset= Chick == 1, data=ChickWeight)
curve(f(x, Y=937, k=.08768, t0=35.22270), add=T)
library(MASS)
example(wtloss)
wtloss.fm
plot(Weight ~ Days, wtloss)
lines(predict(wtloss.fm) ~ Days, data = wtloss)
predict(wtloss.fm, newdata=data.frame(Days=365))
l <- function(t, a, b, k, t0) (a + b*t) * (1 - exp(-k*(t-t0)))
l1 <- function(t, a, k, t0) l(t, a, 0, k, t0)
res.l <- nls(length ~ l(age,a,b,k,t0), data=reddrum,
start=c(a=32,b=.25,k=.5,t0=0))
res.l1 <- nls(length ~ l1(age,a,k,t0), data=reddrum,
start <- c(a=32,k=.5,t0=0))
AIC(res.l)
AIC(res.l1)
year <- with(midsize, 2004-Year)
f <- function(x, N, r) N * exp(-r*x)
with(midsize, nls(Accord ~ f(year,N,r), start=c(N=Accord[1], r=1/5)))
with(midsize, nls(Camry ~ f(year,N,r), start=c(N=Camry[1], r=1/5)))
with(midsize, nls(Taurus ~ f(year,N,r), start=c(N=Taurus[1], r=1/5))) |
mxAlgebraObjective <- function(algebra, numObs = NA, numStats = NA) {
if (missing(algebra) || typeof(algebra) != "character") {
stop("Algebra argument is not a string (the name of the algebra)")
}
if (single.na(numObs)) {
numObs <- as.numeric(NA)
}
if (single.na(numStats)) {
numStats <- as.numeric(NA)
}
expectation <- NULL
fitfunction <- mxFitFunctionAlgebra(algebra, numObs, numStats)
msg <- paste("Objective functions like mxAlgebraObjective() have been deprecated in favor of expectation and fit functions.\n",
"Please use mxFitFunctionAlgebra(algebra = ...). See examples at help(mxFitFunctionAlgebra)")
warning(msg)
return(list(expectation = expectation, fitfunction = fitfunction))
} |
plot.BBSGoF <-
function(x, ...){
Adjusted.pvalues=x$Adjusted.pvalues
data=x$data
ss=seq(0.001,0.999,by=0.001)
plot(x$n.blocks,x$Tarone.pvalues,ylab="pvalue",ylim=c(0,1),xlim=c(x$kmin,x$kmax),xlab="blocks",main="Tarone's test");abline(h=0.05,col=2,lty=2)
rug(x$n.blocks)
par(ask=T)
plot(x$n.blocks,x$cor,ylab="cor",xlim=c(x$kmin,x$kmax),xlab="blocks",main="Within-block correlation")
rug(x$n.blocks)
par(ask=T)
plot(ss,dbeta(ss,x$beta.parameters[1],x$beta.parameters[2]),type="l",ylab="density",xlab="probability",main="Beta density"); abline(v=x$p,col=2,lty=2)
par(ask=T)
plot(x$n.blocks,x$effects,ylab="effects",xlab="blocks",xlim=c(x$kmin,x$kmax),main="Decision plot",ylim=c(min(x$effects)-5,x$SGoF));abline(h=x$SGoF,col=2,lty=2)
rug(x$n.blocks)
if(x$adjusted.pvalues==TRUE){
par(ask=T)
plot(sort(data),sort(Adjusted.pvalues),main="BB-SGoF Adjusted p-values",xlab="Unadjusted p-values",ylab="Adjusted p-values",xlim=c(0,1),ylim=c(0,1))
abline(0,1,lty=2,lwd=1.5)
par(ask=F)
}
} |
Gold_Wald_CIs_1xc <- function(n, alpha=0.05, printresults=TRUE) {
c0 <- length(n)
N <- sum(n)
pihat <- n / N
L <- rep(0, c0)
U <- rep(0, c0)
Scheffe <- qchisq(1 - alpha, c0 - 1)
for (i in 1:c0) {
L[i] = pihat[i] - sqrt(Scheffe * pihat[i] * (1-pihat[i]) / N)
U[i] = pihat[i] + sqrt(Scheffe * pihat[i] * (1-pihat[i]) / N)
}
if (printresults) {
print(sprintf('The Gold Wald simultaneous intervals'), quote=FALSE)
for (i in 1:c0) {
print(
sprintf(
' pi_%i: estimate = %6.4f (%6.4f to %6.4f)',
i, pihat[i], L[i], U[i]
),
quote=FALSE
)
}
}
res <- data.frame(lower=L, upper=U, estimate=pihat)
invisible(res)
} |
context("Mosaic Plot")
test_that("Mosaic Plot", {
set.seed(1619)
env = globalenv()
createCSEnvir(titanic
, strPreds = c("Class", "Age", "Sex", "Survived")
, env = env
)
createCSFunctions(env = env)
expect_true(mosaicPlot())
res = mosaicPlot(return.results = TRUE)
expect_list(res, len = 1)
expect_data_frame(res$long.contingency)
expect_equal(sum(res$long.contingency$Freq), 2201)
createCSEnvir(titanic
, strPreds = c("Age", "Sex", "Survived"), strResps = "Class"
, env = env
)
expect_true(mosaicPlot())
createCSEnvir(titanic
, strPreds = c("Class", "Age"), strResps = c("Survived", "Sex")
, env = env
)
expect_true(mosaicPlot())
titanic[, Survived := as.numeric(Survived)]
createCSEnvir(titanic
, strPreds = c("Class", "Age", "Sex"), strResps = c("Survived")
, env = env
)
expect_true(mosaicPlot())
data("titanic")
}) |
print.BinaryEPPM <-
function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\nCall:", deparse(x$call, width.cutoff = floor(getOption("width") *
0.85)), "", sep = "\n")
if ((x$converged)==FALSE) { cat("model did not converge\n")
} else {
if (length(x$coefficients$p.est)) {
cat(paste("Coefficients (model for p with ", x$link,
" link):\n", sep = ""))
print.default(format(x$coefficients$p.est, digits = digits),
print.gap = 2, quote = FALSE)
cat("\n")
}
else cat("No coefficients (in mean model)\n\n")
if (length(x$coefficients$scalef.est)) {
cat(paste("Coefficients (model for scale-factor with log link):\n", sep = ""))
print.default(format(x$coefficients$scalef.est,
digits = digits), print.gap = 2, quote = FALSE)
cat("\n")
}
else cat("No coefficients (in precision model)\n\n")
}
invisible(x) } |
context("Sample CTR")
test_that("sample_ctr returns correct shape", {
input_df <- tibble::tibble(
option_name = c("A", "B", "C"),
sum_impressions = c(10000, 10000, 10000),
sum_clicks = c(1000, 950, 1050)
)
n_options <- length(unique(input_df$option_name))
n_samples <- 150
expected_col_names <- c("option_name", "sum_impressions", "sum_clicks",
"sum_conversions", "beta_params", "samples")
output <- sample_ctr(input_df, priors = list(), n_samples = n_samples)
expect_true(is.data.frame(output))
expect_true(all(c("option_name", "samples") %in% colnames(output)))
expect_length(output$samples, n_options)
purrr::walk(output$samples, ~ expect_length(.x, n_samples))
expect_equal(colnames(output), expected_col_names)
}) |
`print.SII` <-
function (x, digits=3, ...)
{
cat("\n")
cat("SII:", round(x$sii, digits), "\n")
cat("\n")
} |
test_that("gradethis_setup() sets `grading_problem.type` and `grading_problem.message`", {
with_options(
list(
gradethis.grading_problem.type = NULL,
gradethis.grading_problem.message = NULL
), {
gradethis_setup(
grading_problem.type = "info",
grading_problem.message = "TEST PASS"
)
expect_equal(gradethis_settings$grading_problem.type(), "info")
expect_equal(gradethis_settings$grading_problem.message(), "TEST PASS")
}
)
})
test_that("gradethis_setup() issues warning for invalid `grading_problem.type`", {
with_options(list(gradethis.grading_problem.type = NULL), {
testthat::expect_message(
gradethis_setup(grading_problem.type = "bad"),
'Defaulting to "warning"'
)
expect_equal(
gradethis_settings$grading_problem.type(),
gradethis_default_options$grading_problem.type
)
})
with_options(list(gradethis.grading_problem.type = "PASS"), {
testthat::expect_message(
gradethis_setup(grading_problem.type = "bad"),
'Defaulting to "warning"'
)
})
})
test_that("gradethis_setup() sets default learnr options via chunk opts", {
local_knitr_opts_chunk()
with_knitr_opts_chunk(list(exercise.checker = "fail"), {
gradethis_setup(exercise.checker = "PASS")
expect_equal(knitr::opts_chunk$get("exercise.checker"), "PASS")
})
with_knitr_opts_chunk(list(exercise.checker = "fail"), {
gradethis_setup(exercise.checker = "PASS", exercise.timelimit = 42)
expect_equal(knitr::opts_chunk$get("exercise.checker"), "PASS")
expect_equal(knitr::opts_chunk$get("exercise.timelimit"), 42)
})
}) |
rowTabulates <- function(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = NA) {
if (is.integer(x)) {
} else if (is.logical(x)) {
} else if (is.raw(x)) {
} else {
stop(sprintf("Argument '%s' is not integer, logical, or raw: %s", "x", class(x)[1]))
}
if (!is.null(rows) && !is.null(cols)) x <- x[rows, cols, drop = FALSE]
else if (!is.null(rows)) x <- x[rows, , drop = FALSE]
else if (!is.null(cols)) x <- x[, cols, drop = FALSE]
if (is.null(values)) {
values <- as.vector(x)
values <- unique(values)
if (is.raw(values)) {
values <- as.integer(values)
values <- sort.int(values)
names <- sprintf("%x", values)
names <- paste("0x", names, sep = "")
values <- as.raw(values)
} else {
values <- sort.int(values, na.last = TRUE)
names <- as.character(values)
}
} else {
if (is.raw(values)) {
names <- sprintf("%x", as.integer(values))
names <- paste("0x", names, sep = "")
} else {
names <- as.character(values)
}
}
nbr_of_values <- length(values)
counts <- matrix(0L, nrow = nrow(x), ncol = nbr_of_values)
colnames(counts) <- names
na.rm <- anyMissing(x)
for (kk in seq_len(nbr_of_values)) {
counts[, kk] <- rowCounts(x, value = values[kk], na.rm = na.rm)
}
if (!is.na(useNames)) {
if (useNames) {
rownames <- rownames(x)
if (!is.null(rownames)) rownames(counts) <- rownames
} else {
rownames(counts) <- NULL
}
}
counts
}
colTabulates <- function(x, rows = NULL, cols = NULL, values = NULL, ..., useNames = NA) {
if (is.integer(x)) {
} else if (is.logical(x)) {
} else if (is.raw(x)) {
} else {
stop(sprintf("Argument '%s' is not integer, logical, or raw: %s", "x", class(x)[1]))
}
if (!is.null(rows) && !is.null(cols)) x <- x[rows, cols, drop = FALSE]
else if (!is.null(rows)) x <- x[rows, , drop = FALSE]
else if (!is.null(cols)) x <- x[, cols, drop = FALSE]
if (is.null(values)) {
values <- as.vector(x)
values <- unique(values)
if (is.raw(values)) {
values <- as.integer(values)
values <- sort.int(values)
names <- sprintf("%x", values)
names <- paste("0x", names, sep = "")
values <- as.raw(values)
} else {
values <- sort.int(values, na.last = TRUE)
names <- as.character(values)
}
} else {
if (is.raw(values)) {
names <- sprintf("%x", as.integer(values))
names <- paste("0x", names, sep = "")
} else {
names <- as.character(values)
}
}
transpose <- FALSE
if (!transpose) {
nbr_of_values <- length(values)
counts <- matrix(0L, nrow = ncol(x), ncol = nbr_of_values)
colnames(counts) <- names
na.rm <- anyMissing(x)
for (kk in seq_len(nbr_of_values)) {
counts[, kk] <- colCounts(x, value = values[kk], na.rm = na.rm)
}
}
if (!is.na(useNames)) {
if (useNames) {
colnames <- colnames(x)
if (!is.null(colnames)) rownames(counts) <- colnames
} else {
rownames(counts) <- NULL
}
}
counts
} |
getCells <- function(x) {
return(dimnames(x)[[1]])
}
`getCells<-` <- function(x, value) {
if (length(value) != ncells(x)) stop("Wrong number of cell names supplied!")
if (ncells(x) == 0) return(x)
if (length(value) == 1) value <- list(value)
dimnames(x)[[1]] <- value
return(x)
} |
person_formats_female_ne_np = c(
'{{first_names_female}} {{last_names}}',
'{{first_names_female}} {{last_names}}',
'{{first_names_female}} {{last_names}}',
'{{first_names_female}} {{last_names}}',
'{{first_names_female}} {{last_names}}',
'{{prefixes_female}} {{first_names_female}} {{last_names}}'
)
person_formats_male_ne_np = c(
'{{first_names_male}} {{last_names}}',
'{{first_names_male}} {{last_names}}',
'{{first_names_male}} {{last_names}}',
'{{first_names_male}} {{last_names}}',
'{{first_names_male}} {{last_names}}',
'{{prefixes_male}} {{first_names_male}} {{last_names}}'
)
person_formats_ne_np = c(person_formats_male_ne_np, person_formats_female_ne_np)
person_first_names_female_ne_np = c(
"\u0905\u0902\u0917\u0941\u0930",
"\u0905\u091c\u093f\u0924\u093e",
"\u0905\u091e\u094d\u091c\u0928\u093e",
"\u0905\u0926\u093f\u0924\u0940",
"\u0905\u0928\u0927\u093e",
"\u0905\u0928\u093f\u0924\u093e",
"\u0905\u0928\u093f\u0924\u093e",
"\u0905\u0928\u093f\u0924\u093e",
"\u0905\u0928\u093f\u0924\u093e",
"\u0905\u0928\u0941",
"\u0905\u0928\u0941\u092a\u092e\u093e",
"\u0905\u0928\u0941\u0930\u093e",
"\u0905\u0928\u0941\u0936\u0941\u092f\u093e",
"\u0905\u0928\u094d\u091c\u0932\u0940",
"\u0905\u0928\u094d\n\u0928\u092a\u0942\u0930\u094d\u0923",
"\u0905\u092a\u0930\u094d\u0923\u093e",
"\u0905\u092e\u0930\u093e\u0935\u0924\u0940",
"\u0905\u092e\u093f\u0915\u093e",
"\u0905\u092e\u0943\u0924",
"\u0905\u092e\u0943\u0924\u093e",
"\u0905\u092e\u094d\u0935\u093f\u0915\u093e",
"\u0905\u092e\u094d\u0935\u093f\u0915\u093e",
"\u0905\u092e\u094d\u0935\u0940\u0915\u093e",
"\u0905\u0930\u0941\u0923\u093e",
"\u0905\u0930\u094d\u091a\u0928\u093e",
"\u0905\u0930\u094d\u091a\u0928\u093e",
"\u0905\u0930\u094d\u091a\u0928\u093e",
"\u0905\u0930\u094d\u091a\u0928\u093e",
"\u0905\u0938\u094d\n\u092e\u093f\u0924\u093e",
"\u0906\u091c\u094d\u091e\u093e",
"\u0906\u092d\u093e",
"\u0906\u092f\u0941\u0937\u093e",
"\u0906\u092f\u0941\u0937\u094d\n\u092e\u093e",
"\u0906\u0935\u0943\u0924\u093e",
"\u0906\u0936\u093e",
"\u0907\u091a\u094d\u091b\u093e",
"\u0907\u0928\u094d\u0926\u093f\u0930\u093e",
"\u0907\u0928\u094d\u0926\u093f\u0930\u093e",
"\u0907\u0928\u094d\u0926\u093f\u0930\u093e",
"\u0907\u0928\u094d\u0926\u093f\u0930\u093e",
"\u0907\u0928\u094d\u0926\u0940\u0930\u093e",
"\u0907\u0928\u094d\u0926\u0941",
"\u0907\u0928\u094d\u0926\u0941",
"\u0907\u0928\u094d\u0926\u094d\u0930",
"\u0907\u0928\u094d\u0926\u094d\u0930",
"\u0907\u0928\u094d\n\u0926\u094d\u0930",
"\u0907\u092d\u0928",
"\u0907\u092d\u093e",
"\u0907\u0936\u0941",
"\u0908\u0924\u093e\u0938\u093e",
"\u0908\u0928\u094d\u0926\u0941",
"\u0908\u0932\u093e",
"\u0908\u0936\u093e",
"\u0908\u0936\u093e",
"\u0908\u0936\u094d\n\u0935\u0930\u0940",
"\u0908\u0936\u094d\n\u0935\u0930\u0940",
"\u0908\u0936\u094d\n\u0935\u0930\u0940",
"\u0909\u0924\u094d\u0924\u0930\u093e",
"\u0909\u092a\u093e\u0938\u0928\u093e",
"\u0909\u092e\u093e",
"\u0909\u092e\u093e",
"\u0909\u092e\u093e",
"\u0909\u092e\u093e",
"\u0909\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0909\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0909\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0909\u0937\u093e",
"\u0909\u0937\u093e",
"\u0909\u0937\u093e",
"\u0909\u0937\u093e",
"\u0909\u0937\u093e",
"\u0909\u0937\u093e",
"\u090f\u0932\u093f\u0936\u093e",
"\u090f\u0932\u093f\u0938\u093e",
"\u090f\u0932\u093f\u0938\u093e",
"\u0910\u0930\u093f\u0915\u093e",
"\u0915\u092e\u0932\u093e",
"\u0915\u092e\u0932\u093e",
"\u0915\u092e\u0932\u093e",
"\u0915\u092e\u0932\u093e",
"\u0915\u092e\u0932\u093e",
"\u0915\u0930\u094d\u0938\u093e\u0919",
"\u0915\u0932\u094d\u092a\u0928\u093e",
"\u0915\u0935\u093f\u0924\u093e",
"\u0915\u0935\u093f\u0924\u093e",
"\u0915\u093e\u0928\u094d\u0924\u0940",
"\u0915\u093e\u0928\u094d\n\u0924\u093e",
"\u0915\u093e\u092e\u0928\u093e",
"\u0915\u093e\u0932\u0938\u093e\u0919\u094d\u0917",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0930\u0923",
"\u0915\u0941\u0938\u0941\u092e",
"\u0915\u0943\u0924\u093f\u0915\u093e",
"\u0915\u0943\u0937\u094d\u091f\u093f\u0928\u093e",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0947\u092e\u093e",
"\u0915\u0947\u0936\u0930\u0940",
"\u0915\u094b\u092e\u0932",
"\u0917\u0902\u0917\u093e",
"\u0917\u0902\u0917\u093e",
"\u0917\u0902\u0917\u093e",
"\u0917\u0923\u0947\u0936",
"\u0917\u0930\u0940\u092e\u093e",
"\u0917\u093e\u092f\u0924\u094d\u0930\u0940",
"\u0917\u0940\u0924\u093e",
"\u0917\u0940\u0924\u093e",
"\u0917\u0940\u0924\u093e",
"\u0917\u0940\u0924\u093e",
"\u0917\u094c\u0930\u0940",
"\u091a\u0923\u094d\u0921\u093f\u0915\u093e",
"\u091a\u0928\u094d\u0926\u093e",
"\u091a\u0928\u094d\u0926\u093e",
"\u091a\u0928\u094d\u0926\u093e",
"\u091a\u0928\u094d\u0926\u093e",
"\u091a\u0928\u094d\u0926\u094d\u0930\u092e\u093e\u092f\u093e",
"\u091a\u0928\u094d\u0926\u094d\u0930\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u091a\u0928\u094d\u0926\u094d\u0930\u093e",
"\u091a\u092e\u094d\u092a\u093e",
"\u091a\u093e\u0901\u0926\u0928\u0940",
"\u091a\u093f\u0928\u0940",
"\u091a\u093f\u0928\u0940",
"\u091a\u0941\u0928\u0941",
"\u091a\u0941\u0930\u0940\u0928\u093e\u0928\u0940",
"\u091b\u093f\u0930\u093f\u0919",
"\u091b\u093f\u0930\u093f\u0919",
"\u091b\u093f\u0930\u093f\u0919",
"\u091b\u0941\u0919\u0921\u094d\u092f\u093e\u0915",
"\u091b\u0947\u0924\u0928",
"\u091b\u094b\u0915\u094d\u092a\u093e",
"\u091c\u0924\u0928",
"\u091c\u0928\u0915",
"\u091c\u092e\u0941\u0928\u093e",
"\u091c\u092e\u0941\u0928\u093e",
"\u091c\u092f\u0936\u094d\u0930\u0940",
"\u091c\u0941\u0928\u093e",
"\u091c\u0941\u0928\u0941",
"\u091c\u0941\u0932\u0941\u092e",
"\u091c\u094d\u091e\u093e\u0928\u0939\u0947\u0930\u093e",
"\u091c\u094d\u091e\u093e\u0928\u0940",
"\u091c\u094d\u091e\u093e\u0928\u0941",
"\u091c\u094d\u092f\u093e\u0938\u094d\u092e\u0940\u0928",
"\u091d\u093f\u0928\u093e\u0932\u093e",
"\u091f\u093e\u0938\u0940",
"\u091f\u093f\u0928\u093e",
"\u0921\u093f\u0932\u0941",
"\u0921\u094b\u092e\u093e",
"\u0921\u094b\u0932\u0940",
"\u0921\u094b\u0932\u094d\u092e\u093e",
"\u0924\u093e\u0928\u093f\u092f\u093e",
"\u0924\u093e\u0930\u093e",
"\u0924\u093e\u0930\u093e",
"\u0924\u0941\u0932\u0938\u0940",
"\u0924\u0947\u091c\u0938\u094d\u0935\u0940",
"\u0924\u094b\u092f\u093e",
"\u0924\u094b\u0930\u0923",
"\u0925\u093f\u0928\u094d\u0932\u0947",
"\u0926\u092e\u092f\u0928\u094d\u0924\u093f",
"\u0926\u093f\u092a",
"\u0926\u093f\u092a\u093e",
"\u0926\u093f\u092a\u093e\u091e\u094d\n\u091c\u0932\u0940",
"\u0926\u093f\u092a\u093f\u0938\u093e",
"\u0926\u093f\u0932",
"\u0926\u0940\u0915\u094d\u0937\u093e",
"\u0926\u0941\u0930\u094d\u0917\u093e",
"\u0926\u0947\u091a\u0947\u0928",
"\u0926\u0947\u092c\u0915\u0940",
"\u0926\u0947\u0935\u0915\u0940",
"\u0926\u0947\u0935\u0940",
"\u0927\u0928\u092e\u093e\u092f\u093e",
"\u0927\u0928\u094d\u091c\u0941",
"\u0927\u0928\u094d\u0936\u094d\n\u0935\u0930\u0940",
"\u0927\u0930\u094d\u092e",
"\u0928\u0917\u093f\u0928\u093e",
"\u0928\u092e\u094d\u0930\u0924\u093e",
"\u0928\u093e\u0924\u0940",
"\u0928\u093e\u0928\u0941",
"\u0928\u093e\u0930\u0928",
"\u0928\u093e\u0930\u093e\u092f\u0923",
"\u0928\u093f\u0915\u093f\u0924\u093e",
"\u0928\u093f\u0915\u094d\u0937\u093e",
"\u0928\u093f\u0927\u0940",
"\u0928\u093f\u092d\u093e",
"\u0928\u093f\u092e\u093e",
"\u0928\u093f\u092e\u094d\u092e\u0940",
"\u0928\u093f\u092e\u094d\u092e\u0940",
"\u0928\u093f\u0930\u091c\u093e",
"\u0928\u093f\u0930\u093e",
"\u0928\u093f\u0930\u093e",
"\u0928\u093f\u0930\u0941",
"\u0928\u093f\u0930\u0941",
"\u0928\u093f\u0930\u094d\u092e\u0932",
"\u0928\u093f\u0930\u094d\u092e\u0932\u093e",
"\u0928\u093f\u0930\u094d\u092e\u0932\u093e",
"\u0928\u093f\u0932\u0941",
"\u0928\u093f\u0936\u0930\u0924",
"\u0928\u0940\u0930\u093e",
"\u092a\u0926\u092e",
"\u092a\u0926\u094d\n\u092e\u093e",
"\u092a\u0930\u0932\u093e",
"\u092a\u0932\u093f\u0938\u093e",
"\u092a\u0935\u093f\u0924\u094d\u0930\u093e",
"\u092a\u093e\u0930\u094d\u0935\u0924\u0940",
"\u092a\u093e\u0930\u094d\u0935\u0924\u0940",
"\u092a\u093f\u0928\u0941",
"\u092a\u0941\u091c\u0928",
"\u092a\u0941\u091c\u093e",
"\u092a\u0941\u091c\u093e",
"\u092a\u0941\u091c\u093e",
"\u092a\u0941\u0928",
"\u092a\u0941\u0937\u094d\u092a\u093e",
"\u092a\u0941\u0937\u094d\n\u0937\u093e",
"\u092a\u0942\u091c\u093e",
"\u092a\u0942\u0930\u094d\u0923",
"\u092a\u0942\u0930\u094d\u0923",
"\u092a\u0942\u0930\u094d\u0923\u092e\u093e\u092f\u093e",
"\u092a\u0947\u0928\u094d\u091c\u0940\u0932\u093e",
"\u092a\u094d\u0930\u0924\u093f\u0924\u093f",
"\u092a\u094d\u0930\u0924\u093f\u092d\u093e",
"\u092a\u094d\u0930\u0924\u093f\u092d\u093e",
"\u092a\u094d\u0930\u0924\u093f\u0938\u0930\u093e",
"\u092a\u094d\u0930\u092e\u093f\u0932\u093e",
"\u092a\u094d\u0930\u092e\u093f\u0932\u093e",
"\u092a\u094d\u0930\u0935\u093f\u0928\u093e",
"\u092a\u094d\u0930\u093f\u0924\u0940",
"\u092a\u094d\u0930\u093f\u092f\u093e",
"\u092a\u094d\u0930\u093f\u092f\u093e",
"\u092a\u094d\u0930\u093f\u092f\u093e",
"\u092a\u094d\u0930\u093f\u092f\u093e",
"\u092a\u094d\u0930\u093f\u092f\u093e",
"\u092a\u094d\u0930\u0947\u092e\u093e",
"\u092a\u094d\u0930\u0947\u0930\u0923\u093e",
"\u092a\u094d\u0930\u0947\u0930\u0923\u093e",
"\u092b\u0941\u092e\u093f\u0928\u0940",
"\u092b\u0941\u0932\u093e\u0935\u0924\u0940",
"\u092b\u094c\u091c\u093f\u092f\u093e",
"\u092c\u0928\u093f\u0924\u093e",
"\u092c\u0928\u094d\u0926\u0928\u093e",
"\u092c\u092c\u093f\u0924\u093e",
"\u092c\u0935\u093f\u0924\u093e",
"\u092c\u093f\u0923\u093e",
"\u092c\u093f\u0928\u093f\u0924\u093e",
"\u092c\u093f\u092e\u0932\u093e",
"\u092c\u0940\u0923\u093e",
"\u092c\u0941\u0926\u094d\u0927",
"\u092c\u0948\u0937\u094d\u0923\u0935\u0940",
"\u092d\u0917\u0935\u0924\u0940",
"\u092d\u0917\u0935\u0924\u0940",
"\u092d\u0917\u0935\u0924\u0940",
"\u092d\u0917\u0935\u0924\u0940",
"\u092d\u0917\u0935\u0924\u0940",
"\u092d\u0935\u093e\u0928\u0940",
"\u092d\u093e\u0930\u0924\u0940",
"\u092d\u093e\u0935\u0928\u093e",
"\u092d\u0941\u0935\u0928",
"\u092d\u094b\u091c\u0915\u0932\u093e",
"\u092e\u0902\u0917\u0932\u0940",
"\u092e\u091e\u094d\u091c\u0941",
"\u092e\u0923\u093f",
"\u092e\u0927\u0941",
"\u092e\u0928",
"\u092e\u0928",
"\u092e\u0928\u093f\u0932\u093e",
"\u092e\u0928\u093f\u0937\u093e",
"\u092e\u0928\u093f\u0937\u093e",
"\u092e\u0928\u0940\u0937\u093e",
"\u092e\u0928\u094d\u091c\u0941",
"\u092e\u0928\u094d\u091c\u0941",
"\u092e\u0928\u094d\u091c\u0941",
"\u092e\u0928\u094d\u0926\u0940\u0930\u093e",
"\u092e\u092e\u0924\u093e",
"\u092e\u092e\u0924\u093e",
"\u092e\u092f\u0919\u094d\u0916\u0941",
"\u092e\u0932\u094d\u0932\u0940\u0915\u093e",
"\u092e\u0932\u094d\u0932\u0940\u0915\u093e",
"\u092e\u0939\u093e\u0935\u0924\u0940",
"\u092e\u093e\u0927\u0941\u0930\u0940",
"\u092e\u093e\u0928\u0938\u0940",
"\u092e\u093f\u0919\u092e\u0930",
"\u092e\u093f\u0920\u0941",
"\u092e\u093f\u0928\u093e",
"\u092e\u093f\u0928\u093e",
"\u092e\u093f\u0928\u093e",
"\u092e\u093f\u0928\u093e",
"\u092e\u093f\u0928\u0941",
"\u092e\u093f\u0930\u093e",
"\u092e\u093f\u0930\u093e",
"\u092e\u093f\u0936\u094d\u0930\u0940",
"\u092e\u0940\u0928\u093e",
"\u092e\u0940\u0928\u093e",
"\u092e\u0940\u0930\u093e",
"\u092e\u0940\u0930\u093e",
"\u092e\u0941\u0928\u092e\u0941\u0928",
"\u092e\u0942\u0928",
"\u092e\u0947\u0928\u093e",
"\u092e\u0947\u0928\u094d\n\u0916\u0941",
"\u092e\u0947\u0930\u093f\u0928\u093e",
"\u092e\u0947\u0930\u093f\u0928\u093e",
"\u092e\u0948\u092f\u093e",
"\u092e\u094b\u0939\u093f\u0928\u0940",
"\u092f\u094b\u0919\u092e\u0940",
"\u0930\u0902\u091c\u0940\u0924\u093e",
"\u0930\u0915\u0940\u0932\u093e",
"\u0930\u091a\u093f\u0924\u093e",
"\u0930\u091c\u0928\u0940",
"\u0930\u091c\u0928\u0940",
"\u0930\u091c\u093f\u0924\u093e",
"\u0930\u0928\u094d\u091c\u0928\u093e",
"\u0930\u092c\u093f\u0928\u093e",
"\u0930\u092c\u0940\u0928\u093e",
"\u0930\u092e\u093e",
"\u0930\u092e\u093e",
"\u0930\u092e\u093e",
"\u0930\u092e\u093f\u0924\u093e",
"\u0930\u092e\u093f\u0924\u093e",
"\u0930\u092e\u093f\u0932\u093e",
"\u0930\u0935\u093f\u0928\u093e",
"\u0930\u0935\u093f\u0936\u094d\u0930\u0940",
"\u0930\u0936\u094d\u092e\u093f",
"\u0930\u0936\u094d\u092e\u093f",
"\u0930\u0936\u094d\n\u092e\u0940",
"\u0930\u093e\u091c\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0930\u093e\u091c\u094d\u092f\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0930\u093e\u0927\u093e",
"\u0930\u093e\u0927\u093f\u0915\u093e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093f\u0924\u093e",
"\u0930\u093f\u0924\u093e",
"\u0930\u093f\u0924\u093e",
"\u0930\u093f\u0924\u0941",
"\u0930\u093f\u092e\u093e",
"\u0930\u0940\u0924\u093e",
"\u0930\u0940\u0924\u093e",
"\u0930\u0940\u0924\u093e",
"\u0930\u0940\u0928\u093e",
"\u0930\u0941\u091c\u093e",
"\u0930\u0941\u0926\u094d\u0930",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u092a\u093e",
"\u0930\u0941\u0935\u093f\u0928\u093e",
"\u0930\u0947\u0916\u093e",
"\u0930\u0947\u0923\u0941",
"\u0930\u0947\u0935\u0924\u0940",
"\u0930\u094b\u091c\u093f\u0928\u093e",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\n\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\n\u092e\u0940",
"\u0932\u0932\u093f\u0924\u093e",
"\u0932\u093f\u0932\u093e\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0940\u0932\u093e",
"\u0932\u094d\u0939\u093e\u091c\u0940",
"\u0935\u0928\u094d\u0926\u093f\u0928\u0940",
"\u0935\u0930\u094d\u0937\u093e",
"\u0935\u0935\u0940",
"\u0935\u093f\u091c\u092f\u093e",
"\u0935\u093f\u091c\u092f\u093e",
"\u0935\u093f\u0926\u094d\u092f\u093e",
"\u0935\u093f\u0927\u094d\u092f\u093e",
"\u0935\u093f\u0928\u093e",
"\u0935\u093f\u0928\u093e",
"\u0935\u093f\u0928\u093f\u0924\u093e",
"\u0935\u093f\u0928\u093f\u0924\u093e",
"\u0935\u093f\u092d\u093e",
"\u0935\u093f\u092e\u0932\u093e",
"\u0935\u093f\u092e\u0932\u093e",
"\u0935\u093f\u092e\u0932\u093e",
"\u0935\u093f\u092e\u0932\u093e",
"\u0935\u093f\u0937\u094d\n\u0923\u0941",
"\u0935\u093f\u0937\u094d\n\u0923\u0941",
"\u0936\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0936\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0936\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0936\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0936\u0936\u0940",
"\u0936\u0936\u0940",
"\u0936\u0936\u0940",
"\u0936\u0936\u0940",
"\u0936\u093e\u0928\u094d\u0924\u093e",
"\u0936\u093e\u0928\u094d\u0924\u093f",
"\u0936\u093e\u0928\u094d\u0924\u0940",
"\u0936\u093e\u0928\u094d\u0924\u0940",
"\u0936\u093e\u0928\u094d\u0924\u0940",
"\u0936\u093e\u0928\u094d\u0924\u0940",
"\u0936\u093e\u0928\u094d\u0924\u0940",
"\u0936\u093f\u0916\u093e",
"\u0936\u093f\u0916\u093e",
"\u0936\u093f\u0932\u093e",
"\u0936\u093f\u0932\u0941",
"\u0936\u0940\u0932\u093e",
"\u0936\u0941\u092d\u0947\u091a\u094d\u091b\u093e",
"\u0936\u0941\u0936\u093f\u0932\u093e",
"\u0936\u0941\u0936\u093f\u0932\u093e",
"\u0936\u0941\u0938\u093f\u0932\u093e",
"\u0936\u094b\u092d\u093e",
"\u0936\u094b\u092d\u093e",
"\u0936\u094b\u092d\u093e",
"\u0936\u094d\u0930\u0926\u094d\u0927\u093e",
"\u0936\u094d\u0930\u0926\u094d\u0927\u093e",
"\u0936\u094d\u0930\u0940\u092a\u094d\u0930\u093e\u092a\u094d\n\u0924\u0940",
"\u0936\u094d\u0930\u0940\u092e\u0924\u0940",
"\u0936\u094d\u0930\u0940\u092f\u093e",
"\u0936\u094d\u0930\u0943\u0937\u094d\n\u091f\u093f",
"\u0936\u094d\u0930\u0947\u092f\u0936\u0940",
"\u0936\u094d\n\u092f\u093e\u092e\u093e",
"\u0936\u094d\n\u0935\u0947\u0924\u093e",
"\u0938\u0902\u0917\u093f\u0924\u093e",
"\u0938\u0902\u0917\u093f\u0924\u093e",
"\u0938\u0902\u0917\u0940\u0924\u093e",
"\u0938\u091a\u093f\u0924\u093e",
"\u0938\u091c\u0928\u093e",
"\u0938\u0924\u094d\u092f",
"\u0938\u0924\u094d\u092f\u0935\u094d\u0930\u0924\u093e",
"\u0938\u092a\u0928\u093e",
"\u0938\u092b\u0932\u0924\u093e",
"\u0938\u092c\u0928\u092e",
"\u0938\u092e\u093e\u0928\u0924\u093e",
"\u0938\u092e\u0940\u0928\u093e",
"\u0938\u092e\u094d\u092a\u0926\u093e",
"\u0938\u0930\u0932\u093e",
"\u0938\u0930\u0938\u094d\u0935\u0924\u0940",
"\u0938\u0930\u0938\u094d\u0935\u0924\u0940",
"\u0938\u0930\u0938\u094d\u0935\u0924\u0940",
"\u0938\u0930\u0938\u094d\u0935\u0924\u0940",
"\u0938\u0930\u0938\u094d\u0935\u0924\u0940",
"\u0938\u0930\u093f\u0924\u093e",
"\u0938\u0930\u093f\u0924\u093e",
"\u0938\u0930\u093f\u0924\u093e",
"\u0938\u0930\u093f\u0924\u093e",
"\u0938\u0930\u093f\u0924\u093e",
"\u0938\u0930\u0940\u0924\u093e",
"\u0938\u0930\u0940\u0924\u093e",
"\u0938\u0930\u094b\u091c",
"\u0938\u0930\u094b\u091c",
"\u0938\u0930\u094b\u091c\u093e",
"\u0938\u0930\u094d\u092e\u093f\u0932\u093e",
"\u0938\u0932\u093f\u0928\u093e",
"\u0938\u0932\u093f\u092e\u093e",
"\u0938\u0932\u094d\u092d\u093f\u092f\u093e",
"\u0938\u0935\u093f\u0924\u093e",
"\u0938\u0935\u093f\u0924\u093e",
"\u0938\u0935\u093f\u0924\u093e",
"\u0938\u0935\u093f\u0924\u093e",
"\u0938\u0935\u093f\u0924\u093e",
"\u0938\u0935\u093f\u0928\u093e",
"\u0938\u093e\u0907\u092e\u0941",
"\u0938\u093e\u0917\u0930",
"\u0938\u093e\u0928\u0941",
"\u0938\u093e\u0928\u0941",
"\u0938\u093e\u0928\u0941",
"\u0938\u093e\u0928\u094d\u0928\u093e\u0928\u0940",
"\u0938\u093e\u0928\u094d\u0928\u093e\u0928\u0940",
"\u0938\u093e\u0935\u093f\u0924\u094d\u0930\u0940",
"\u0938\u093e\u0935\u093f\u0924\u094d\u0930\u0940",
"\u0938\u093e\u0935\u093f\u0924\u094d\u0930\u0940",
"\u0938\u093f\u0924\u093e",
"\u0938\u093f\u0924\u093e",
"\u0938\u093f\u0930\u0941",
"\u0938\u0940\u0924\u093e",
"\u0938\u0941\u0915\u0943\u0924\u0940",
"\u0938\u0941\u0927\u093e",
"\u0938\u0941\u0927\u093e",
"\u0938\u0941\u0927\u093e",
"\u0938\u0941\u0927\u093e",
"\u0938\u0941\u0928",
"\u0938\u0941\u0928\u093e\u092e",
"\u0938\u0941\u0928\u093f\u0924\u093e",
"\u0938\u0941\u0928\u093f\u0924\u093e",
"\u0938\u0941\u0928\u093f\u0924\u093e",
"\u0938\u0941\u092a\u094d\u0930\u092d\u093e",
"\u0938\u0941\u092d\u0926\u094d\u0930\u093e",
"\u0938\u0941\u092e\u0928",
"\u0938\u0941\u092e\u093f\u0924\u094d\u0930\u093e",
"\u0938\u0941\u092e\u093f\u0924\u094d\u0930\u093e",
"\u0938\u0941\u092e\u093f\u0924\u094d\u0930\u093e",
"\u0938\u0941\u092e\u0948\u092f\u093e",
"\u0938\u0941\u0930\u0941\u091a\u0940",
"\u0938\u0941\u0930\u0947\u0928\u094d\n\u0926\u094d\u0930\u093e",
"\u0938\u0941\u0935\u0930\u094d\u0923\u093e",
"\u0938\u0941\u0936\u093f\u0932\u093e",
"\u0938\u0941\u0936\u093f\u0932\u093e",
"\u0938\u0941\u0936\u093f\u0932\u093e",
"\u0938\u0941\u0936\u093f\u0932\u093e",
"\u0938\u0941\u0937\u092e\u093e",
"\u0938\u0941\u0937\u094d\n\u092e\u093e",
"\u0938\u0941\u0937\u094d\n\u092e\u093e",
"\u0938\u0941\u0938\u093f\u0932\u093e",
"\u0938\u0943\u091c\u0928\u093e",
"\u0938\u0943\u091c\u0928\u093e",
"\u0938\u0943\u091c\u0928\u093e",
"\u0938\u094b\u0928\u0940",
"\u0938\u094b\u0928\u0941",
"\u0938\u094d\u092e\u0943\u0924\u0940",
"\u0938\u094d\u092e\u0943\u0924\u0940",
"\u0938\u094d\u0935\u0924\u093f",
"\u0938\u094d\u0935\u0947\u091a\u094d\n\u091b\u093e",
"\u0938\u094d\n\u0935\u0940\u0915\u0943\u0924\u0940",
"\u0939\u0928\u0940",
"\u0939\u0930\u093f",
"\u0939\u093f\u092e\u093e",
"\u0939\u093f\u0930\u093e",
"\u0939\u093f\u0930\u093e"
)
person_first_names_male_ne_np = c(
"\u0905\u0915\u094d\u0937\u092f",
"\u0905\u091a\u094d\u092f\u0941\u0924",
"\u0905\u091c\u092f",
"\u0905\u091c\u092f",
"\u0905\u0928\u0915",
"\u0905\u0928\u093f\u0930",
"\u0905\u0928\u093f\u0932",
"\u0905\u0928\u093f\u0932",
"\u0905\u0928\u093f\u0932",
"\u0905\u0928\u093f\u0932",
"\u0905\u0928\u093f\u0937",
"\u0905\u0928\u0941\u092a",
"\u0905\u0928\u0941\u092a",
"\u0905\u0928\u094b\u091c",
"\u0905\u092d\u093f\u0937\u0947\u0915",
"\u0905\u092d\u093f\u0937\u0947\u0915",
"\u0905\u092d\u093f\u0937\u0947\u0915",
"\u0905\u092e\u093f\u0924",
"\u0905\u092e\u093f\u0924",
"\u0905\u092e\u093f\u0924",
"\u0905\u092e\u093f\u0928",
"\u0905\u092e\u0943\u0924",
"\u0905\u092e\u0943\u0924",
"\u0905\u0930\u0941\u0923",
"\u0905\u0930\u0941\u0923",
"\u0905\u0930\u094d\u091c\u0941\u0928",
"\u0905\u0932\u0902\u0915\u093e\u0930",
"\u0905\u0935\u0932\u094b\u0915",
"\u0905\u0935\u093f\u0928\u093e\u0936",
"\u0905\u0935\u093f\u0936\u0947\u0915",
"\u0905\u0936\u094b\u0915",
"\u0905\u0936\u094b\u0915",
"\u0905\u0936\u094b\u0915",
"\u0905\u0936\u094b\u0915",
"\u0905\u0936\u094b\u0915",
"\u0905\u0936\u094b\u092c",
"\u0905\u0938\u093f\u0928",
"\u0905\u0938\u094b\u0915",
"\u0906\u0915\u093e\u0936",
"\u0906\u0924\u094d\u092e\u0947\u0936",
"\u0906\u0932\u094b\u0915",
"\u0906\u0932\u094b\u0915",
"\u0906\u0936\u092f",
"\u0906\u0936\u093f\u0937",
"\u0906\u0936\u093f\u0937",
"\u0906\u0936\u093f\u0937",
"\u0906\u0936\u093f\u0937",
"\u0906\u0936\u093f\u0937",
"\u0906\u0936\u0940\u0937",
"\u0908\u092c\u094d\u0930\u093e\u0939\u0940\u092e",
"\u0908\u0938\u0940",
"\u0909\u091c\u094d\u091c\u094d\u0935\u0932",
"\u0909\u0924\u094d\u0924\u092e",
"\u0909\u0924\u094d\u0924\u092e",
"\u0909\u0926\u094d\u0927\u0935",
"\u0909\u0926\u094d\u0927\u0935",
"\u0909\u092e\u0947\u0936",
"\u0909\u092e\u094d\u092e\u0947\u0926",
"\u090b\u0915\u0941",
"\u090b\u0936\u0941",
"\u090b\u0937\u093f",
"\u090b\u0937\u093f\u0915\u0947\u0938",
"\u090f\u0915",
"\u0913\u092e",
"\u0915\u092a\u093f\u0932",
"\u0915\u092e\u0932",
"\u0915\u092e\u0932",
"\u0915\u0930\u0928",
"\u0915\u0930\u0928",
"\u0915\u0930\u094d\u092e\u093e",
"\u0915\u0932\u094d\u092f\u093e\u0923",
"\u0915\u093e\u091c\u093f",
"\u0915\u093e\u091c\u0940",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0930\u0923",
"\u0915\u093f\u0936\u0928",
"\u0915\u093f\u0936\u094b\u0930",
"\u0915\u093f\u0936\u094b\u0930",
"\u0915\u093f\u0936\u094b\u0930",
"\u0915\u0940\u0930\u094d\u0924\u093f",
"\u0915\u0941\u092e\u093e\u0930",
"\u0915\u0941\u092e\u093e\u0930",
"\u0915\u0941\u0935\u0947\u0930",
"\u0915\u0941\u0936\u0932",
"\u0915\u0943\u091c\u0932",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\u0923",
"\u0915\u0943\u0937\u094d\u0923\u092e\u093e\u0928",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0943\u0937\u094d\n\u0923",
"\u0915\u0947\u0936\u0930",
"\u0915\u0947\u0936\u0935",
"\u0915\u0947\u0936\u0935",
"\u0915\u0947\u0936\u0935",
"\u0915\u0947\u0936\u0935\u0932\u093e\u0932",
"\u0915\u0947\u0938\u0930",
"\u0915\u094c\u0936\u0932",
"\u0916\u0917\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0916\u0921\u094d\u0917",
"\u0917\u0923\u0947\u0936",
"\u0917\u094b\u092a\u093e\u0932",
"\u0917\u094b\u092a\u093e\u0932",
"\u0917\u094b\u092a\u0940",
"\u0917\u094b\u0935\u093f\u0928\u094d\u0926",
"\u0917\u094b\u0935\u093f\u0928\u094d\u0926",
"\u0917\u094b\u0935\u093f\u0928\u094d\u0926",
"\u0917\u094c\u0924\u092e",
"\u091a\u0928\u094d\u0926",
"\u091a\u0928\u094d\u0926\u094d\u0930",
"\u091a\u0928\u094d\u0926\u094d\u0930\u0947\u0936",
"\u091a\u093f\u0930\u091e\u094d\u091c\u0940\u092c\u093f",
"\u091a\u093f\u0930\u0928\u091c\u0940\u0935\u0940",
"\u091a\u0948\u0924\u094d\u092f",
"\u091b\u0935\u093f",
"\u091b\u0947\u0935\u093e\u0919",
"\u091c\u0917\u0928\u093e\u0925",
"\u091c\u0917\u0928\u094d\u0928\u093e\u0925",
"\u091c\u0917\u0928\u094d\u0928\u093e\u0925",
"\u091c\u0917\u0935\u093f\u0930",
"\u091c\u092f\u0928\u094d\u0924",
"\u091c\u092f\u0928\u094d\u0926\u094d\u0930",
"\u091c\u092f\u0930\u093e\u092e",
"\u091c\u093f\u0924\u0947\u0928\u094d\u0926\u094d\u0930",
"\u091c\u0940\u092c\u0928",
"\u091c\u0941\u0917\u0932",
"\u091c\u094d\u091e\u093e\u0928",
"\u091c\u094d\u091e\u093e\u0928\u0941",
"\u091c\u094d\u091e\u093e\u0928\u0947\u0936\u094d\n\u0935\u0930",
"\u091c\u094d\u092f\u094b\u0924\u0940",
"\u091c\u094d\u092f\u094b\u0924\u0940",
"\u091c\u094d\n\u092f\u094b\u092d\u093e\u0928",
"\u091f\u0938\u0940",
"\u091f\u093e\u0938\u0940",
"\u091f\u093f\u0915\u093e",
"\u091f\u0947\u0915",
"\u0921\u093e.",
"\u0921\u093e.",
"\u0921\u093e.",
"\u0921\u0947\u0928\u093f\u0938",
"\u0924\u093f\u0930\u094d\u0925",
"\u0924\u0947\u091c\u0936\u094d\n\u0935\u0940",
"\u0924\u094b\u092a\u094d\u0932\u093e",
"\u0924\u094b\u0932\u093e\u0930\u093e\u092e",
"\u0924\u094d\u0930\u093f\u0930\u0924\u094d\n\u0928",
"\u0925\u094b\u0915\u0947\u0932",
"\u0926\u092e\u094b\u0926\u0930",
"\u0926\u0930\u094d\u0936\u0928",
"\u0926\u0935\u093e",
"\u0926\u093e\u0935\u093e",
"\u0926\u093f\u0917\u0935\u093f\u091c\u092f\u093e",
"\u0926\u093f\u0928\u0947\u0936",
"\u0926\u093f\u0928\u0947\u0938",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915",
"\u0926\u093f\u092a\u0915\u0938\u094d\u0935\u0930",
"\u0926\u093f\u092a\u0940\u0928",
"\u0926\u093f\u092a\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u093f\u092a\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u093f\u092a\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u093f\u092a\u0947\u0936",
"\u0926\u093f\u092a\u0947\u0936",
"\u0926\u093f\u0932\u093f\u092a",
"\u0926\u093f\u0935\u093e\u0915\u0930",
"\u0926\u0940\u092a\u0915",
"\u0926\u0940\u092a\u0947\u0936",
"\u0926\u0941\u0930\u094d\u0917\u093e",
"\u0926\u0947\u0935",
"\u0926\u0947\u0935\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u0947\u0935\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u0947\u0935\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0926\u0947\u0935\u0947\u0928\u094d\n\u0926\u094d\u0930",
"\u0926\u094d\u0935\u093e\u0930\u0940\u0915\u093e",
"\u0927\u0930\u094d\u092e\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0927\u093f\u0930\u091c",
"\u0927\u094d\u0930\u0941\u0935",
"\u0928\u092c\u093f\u0928",
"\u0928\u092c\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0928\u0930\u092a\u0932",
"\u0928\u0930\u092d\u0942\u092a\u093e\u0932",
"\u0928\u0930\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0928\u0930\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0928\u0935\u0930\u093e\u091c",
"\u0928\u0935\u093f\u0928",
"\u0928\u0935\u093f\u0928",
"\u0928\u093e\u0930\u093e\u092f\u0923",
"\u0928\u093e\u0930\u093e\u092f\u0923",
"\u0928\u093f\u092e\u0947\u0936",
"\u0928\u093f\u0930\u091c",
"\u0928\u093f\u0930\u091c",
"\u0928\u093f\u0930\u094b\u091c",
"\u0928\u093f\u0930\u094d\u092d\u092f",
"\u0928\u093f\u0930\u094d\u092e\u0932",
"\u0928\u093f\u0930\u094d\u092e\u0947\u0936",
"\u092a\u0902\u0915\u091c",
"\u092a\u0902\u091a",
"\u092a\u0935\u0928",
"\u092a\u0935\u0928",
"\u092a\u093e\u0930\u0938",
"\u092a\u093e\u0938\u093e\u0919\u094d\u0917",
"\u092a\u0941\u0930\u0941\u0937\u094b\u0924\u094d\u0924\u092e",
"\u092a\u0941\u0930\u094d\u0923\u092d\u0915\u094d\n\u0924",
"\u092a\u0941\u0932\u0915\u093f\u0924",
"\u092a\u0941\u0937\u094d\n\u092a",
"\u092a\u094d\u0930\u0915\u093e\u0936",
"\u092a\u094d\u0930\u0915\u093e\u0936",
"\u092a\u094d\u0930\u0915\u093e\u0936",
"\u092a\u094d\u0930\u0915\u093e\u0936",
"\u092a\u094d\u0930\u0915\u093e\u0938",
"\u092a\u094d\u0930\u091c\u0940\u0924",
"\u092a\u094d\u0930\u091c\u094d\u091e\u093e\u0928",
"\u092a\u094d\u0930\u091c\u094d\u0935\u0932",
"\u092a\u094d\u0930\u0926\u093f\u092a",
"\u092a\u094d\u0930\u0926\u094d\u092e\u0941\u092e\u094d\u0928",
"\u092a\u094d\u0930\u092b\u0941\u0932\u094d\u0932",
"\u092a\u094d\u0930\u092d\u0941",
"\u092a\u094d\u0930\u092e\u0947\u0936\u094d\n\u0935\u0930",
"\u092a\u094d\u0930\u092e\u094b\u0926",
"\u092a\u094d\u0930\u0932\u094d\n\u0939\u093e\u0926",
"\u092a\u094d\u0930\u0935\u093f\u0923",
"\u092a\u094d\u0930\u0935\u0947\u0936",
"\u092a\u094d\u0930\u0936\u093e\u0928\u094d\u0924",
"\u092a\u094d\u0930\u0936\u093e\u0928\u094d\u0928",
"\u092a\u094d\u0930\u0938\u0919\u094d\u0917",
"\u092a\u094d\u0930\u0947\u092e",
"\u092b\u0923\u093f\u0928\u094d\u0926\u094d\u0930",
"\u092c\u0926\u094d\u0930\u093f",
"\u092c\u0926\u094d\u0930\u0940",
"\u092c\u0926\u094d\u0930\u0940",
"\u092c\u0928\u0935\u093e\u0930\u0940",
"\u092c\u092c\u093f",
"\u092c\u092c\u093f",
"\u092c\u0932\u0915\u093f\u0938\u0928",
"\u092c\u0932\u0930\u093e\u092e",
"\u092c\u0932\u094d\u0932\u0941",
"\u092c\u0938\u0928\u094d\u0924",
"\u092c\u093e\u0938\u0941",
"\u092c\u093e\u0938\u0941",
"\u092c\u093f\u0915\u093e\u0936",
"\u092c\u093f\u0915\u093e\u0938",
"\u092c\u093f\u0930\u093e\u091f",
"\u092c\u0941\u0926\u094d\u0927\u093f",
"\u092c\u0941\u0927\u094d\u0926",
"\u092d\u0930\u0924",
"\u092d\u0930\u0924",
"\u092d\u0930\u0924",
"\u092d\u0935\u093f\u0928\u094d\n\u0926\u094d\u0930",
"\u092d\u093e\u0907",
"\u092d\u0941\u092e\u093e",
"\u092d\u0948\u092f\u093e",
"\u092d\u0948\u0930\u0935\u0932\u093e\u0932",
"\u092d\u094b\u0932\u093e",
"\u092e\u0902\u091c\u093f\u0932",
"\u092e\u0923\u0940",
"\u092e\u0923\u0940\u0930\u093e\u091c",
"\u092e\u0926\u0928",
"\u092e\u0926\u0928",
"\u092e\u0926\u0928",
"\u092e\u0928\u093f\u0937",
"\u092e\u0928\u0940\u0937",
"\u092e\u0928\u094b\u091c",
"\u092e\u0928\u094b\u091c",
"\u092e\u0928\u094b\u091c",
"\u092e\u0928\u094b\u091c",
"\u092e\u0928\u094b\u091c",
"\u092e\u0928\u094b\u0939\u0930",
"\u092e\u0928\u094b\u0939\u0930",
"\u092e\u0928\u094d\u0928\u093e",
"\u092e\u092f\u0941\u0936",
"\u092e\u0939\u0947\u0928\u094d\u0926\u094d\u0930",
"\u092e\u0939\u0947\u0936",
"\u092e\u0939\u0947\u0936",
"\u092e\u0939\u0947\u0936",
"\u092e\u0939\u0947\u0936",
"\u092e\u0939\u0947\u0936",
"\u092e\u093e\u0927\u0935",
"\u092e\u093e\u0927\u0935",
"\u092e\u093e\u0932\u091a\u0928\u094d\u0926",
"\u092e\u093f\u0924\u094d\u0930",
"\u092e\u093f\u0928\u0930\u093e\u091c",
"\u092e\u0941\u0915\u0941\u0928\u094d\u0926",
"\u092e\u0941\u0915\u0941\u0928\u094d\n\u0926",
"\u092e\u0941\u0915\u0947\u0936",
"\u092e\u0941\u0916\u094d\u092f\u093e",
"\u092e\u0941\u0930\u093e\u0930\u0940\u0932\u093e\u0932",
"\u092e\u0947\u0918",
"\u092e\u0948\u0924\u094d\u0930\u0940",
"\u092e\u094b\u0924\u0940",
"\u092e\u094b\u0924\u0940",
"\u092e\u094b\u0939\u0928",
"\u092e\u094b\u0939\u0928",
"\u092e\u094b\u0939\u0928",
"\u092f\u0936",
"\u092f\u093e\u092e",
"\u092f\u0941\u0935\u0930\u093e\u091c",
"\u092f\u094b\u0917\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u091c\u0924",
"\u0930\u0924\u0928",
"\u0930\u0924\u094d\n\u0928",
"\u0930\u092e\u0923",
"\u0930\u092e\u0947\u0936",
"\u0930\u092e\u0947\u0936",
"\u0930\u0935\u093f",
"\u0930\u0935\u093f",
"\u0930\u0935\u093f",
"\u0930\u0935\u093f\u0928",
"\u0930\u0935\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0930\u0935\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0930\u0935\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0930\u0935\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u0918\u0935",
"\u0930\u093e\u091c",
"\u0930\u093e\u091c\u0915\u0941\u092e\u093e\u0930",
"\u0930\u093e\u091c\u0928",
"\u0930\u093e\u091c\u0928",
"\u0930\u093e\u091c\u093f\u0935",
"\u0930\u093e\u091c\u0940\u0935",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0941",
"\u0930\u093e\u091c\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u091c\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u091c\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u091c\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u091c\u0947\u0936",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e",
"\u0930\u093e\u092e\u091a\u0928\u094d\u0926\u094d\u0930",
"\u0930\u093e\u092e\u091c\u093e\u0928",
"\u0930\u093e\u092e\u0936\u0930\u0923",
"\u0930\u093e\u0939\u0941\u0932",
"\u0930\u093e\u0939\u0941\u0932",
"\u0930\u093e\u094d\u091c\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0930\u0941\u092a\u0947\u0936",
"\u0930\u094b\u091c\u093f\u0928",
"\u0930\u094b\u091c\u0940\u0928",
"\u0930\u094b\u092e\u0947\u0928\u094d\n\u0926\u094d\u0930",
"\u0930\u094b\u0939\u0928",
"\u0930\u094c\u0928\u0915",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0923",
"\u0932\u0915\u094d\u0937\u094d\u092e\u0940",
"\u0932\u0915\u094d\u0937\u094d\n\u092e\u0923",
"\u0932\u0935",
"\u0932\u093e\u0932",
"\u0932\u093f\u091f\u0928",
"\u0935\u0938\u0928\u094d\u0924",
"\u0935\u093f\u0915\u093e\u0938",
"\u0935\u093f\u0915\u094d\u0930\u092e",
"\u0935\u093f\u091c\u092f",
"\u0935\u093f\u091c\u0947\u0936",
"\u0935\u093f\u0927\u094d\u092f\u093e\u092e\u093e\u0928",
"\u0935\u093f\u0928\u093f\u0932",
"\u0935\u093f\u0928\u094b\u0926",
"\u0935\u093f\u0928\u094b\u0926",
"\u0935\u093f\u0930\u092d\u0926\u094d\u0930",
"\u0935\u093f\u0930\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0935\u093f\u0935\u0947\u0915",
"\u0935\u093f\u0935\u0947\u0915",
"\u0935\u093f\u0935\u0947\u0915",
"\u0935\u093f\u0936\u094d\n\u0935",
"\u0935\u093f\u0937\u094d\u0923\u0941",
"\u0935\u093f\u0937\u094d\n\u0923\u0941",
"\u0935\u0941\u0937\u094d\n\u0915\u0930",
"\u0936\u0902\u0915\u0930",
"\u0936\u0902\u0915\u0930",
"\u0936\u0902\u0915\u0930",
"\u0936\u0902\u0915\u0930",
"\u0936\u0915\u094d\u0924\u093f",
"\u0936\u091a\u093f\u0928\u094d\u0926\u094d\u0930",
"\u0936\u0930\u0926",
"\u0936\u0930\u0926",
"\u0936\u0930\u0926",
"\u0936\u0936\u0940",
"\u0936\u093e\u0928\u094d\u0924",
"\u0936\u093e\u0939\u093f\u0926",
"\u0936\u093f\u0935",
"\u0936\u093f\u0935",
"\u0936\u0941\u0915\u094d\u0930",
"\u0936\u0941\u0936\u093e\u0928\u094d\u0924",
"\u0936\u0941\u0936\u093f\u0932",
"\u0936\u0947\u0932\u0947\u0928\u094d\n\u0926\u094d\u0930",
"\u0936\u0948\u0932\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0936\u0948\u0932\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0936\u0948\u0932\u0947\u0938",
"\u0936\u094d\u092f\u093e\u092e",
"\u0936\u094d\u0930\u0940\u091c\u0919\u094d\u0917",
"\u0936\u094d\u0930\u0940\u0935\u0924\u094d\u0938",
"\u0936\u094d\u0930\u0947\u092f\u0938",
"\u0936\u094d\n\u092f\u093e\u092e",
"\u0936\u094d\n\u092f\u093e\u092e\u0930\u093e\u091c",
"\u0938\u0902\u0915\u0930",
"\u0938\u0902\u091c\u092f",
"\u0938\u0902\u091c\u092f",
"\u0938\u0902\u091c\u093f\u0935",
"\u0938\u0902\u091c\u093f\u0935",
"\u0938\u0902\u091c\u0940\u092c",
"\u0938\u0902\u091c\u0940\u0935",
"\u0938\u0902\u0926\u093f\u092a",
"\u0938\u0917\u0941\u0928",
"\u0938\u091c\u0928",
"\u0938\u091c\u0947\u0928\u094d\n\u0926\u094d\u0930",
"\u0938\u0928\u094d\u091c\u092f",
"\u0938\u0928\u094d\u091c\u092f",
"\u0938\u0928\u094d\u091c\u0940\u092c",
"\u0938\u0928\u094d\u0924\u094b\u0937",
"\u0938\u092b\u0930\u093e\u091c",
"\u0938\u092e\u0941\u0928\u094d\n\u0926\u094d\u0930",
"\u0938\u092e\u094d\u092a\u0941\u0930\u094d\u0923",
"\u0938\u0930\u094b\u091c",
"\u0938\u0930\u094b\u091c",
"\u0938\u0930\u094b\u091c",
"\u0938\u0930\u094d\u0935\u0947\u0936",
"\u0938\u093e\u0917\u0930",
"\u0938\u093e\u0928\u0941",
"\u0938\u093f\u0926\u094d\u0927\u093f",
"\u0938\u0940\u0924\u093e\u0930\u093e\u092e",
"\u0938\u0941\u091c\u0928",
"\u0938\u0941\u091c\u0928",
"\u0938\u0941\u0926\u0928",
"\u0938\u0941\u0926\u0930\u094d\u0936\u0928",
"\u0938\u0941\u0927\u093f\u0930",
"\u0938\u0941\u0928\u093f\u0932",
"\u0938\u0941\u0928\u093f\u0932",
"\u0938\u0941\u0928\u093f\u0932",
"\u0938\u0941\u0928\u093f\u0932",
"\u0938\u0941\u0928\u094d\u0926\u0930",
"\u0938\u0941\u092e\u0928",
"\u0938\u0941\u092e\u0928",
"\u0938\u0941\u0930\u091c",
"\u0938\u0941\u0930\u091c",
"\u0938\u0941\u0930\u091c",
"\u0938\u0941\u0930\u0947\u0928",
"\u0938\u0941\u0930\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0938\u0941\u0930\u0947\u0928\u094d\n\u0926\u094d\u0930",
"\u0938\u0941\u0930\u0947\u0936",
"\u0938\u0941\u0930\u0947\u0936",
"\u0938\u0941\u0930\u0947\u0936",
"\u0938\u0941\u0930\u0947\u0936",
"\u0938\u0941\u0935\u0930\u094d\u0923",
"\u0938\u0941\u0935\u0930\u094d\u0923",
"\u0938\u0941\u0935\u094b\u0927",
"\u0938\u0941\u0936\u093e\u0928\u094d\u0924",
"\u0938\u0941\u0936\u093f\u0932",
"\u0938\u0941\u0936\u093f\u0932",
"\u0938\u0943\u091c\u0928",
"\u0938\u0947\u0916\u0930",
"\u0938\u0948\u092c\u0940",
"\u0938\u094b\u092e",
"\u0938\u094c\u0930\u092c",
"\u0938\u094c\u0930\u092d",
"\u0938\u094d\u0935\u093e\u0917\u0924",
"\u0938\f\u0902\u091c\u092f",
"\u0939\u0930\u093f",
"\u0939\u0930\u093f",
"\u0939\u0930\u093f",
"\u0939\u0930\u093f\u0939\u0930",
"\u0939\u0930\u094d\u0915",
"\u0939\u093f\u0930\u093e",
"\u0939\u093f\u0930\u093e",
"\u0939\u093f\u0930\u093e",
"\u0939\u093f\u0930\u0947\u0928\u094d\u0926\u094d\u0930",
"\u0939\u0947\u092e\u0928",
"\u0939\u0947\u092e\u0930\u093e\u091c"
)
person_first_names_ne_np = c(person_first_names_male_ne_np,
person_first_names_female_ne_np)
person_last_names_ne_np = c(
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0928\u0947\u092a\u093e\u0932",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0925\u093e\u092a\u093e",
"\u0932\u093e\u092e\u093e",
"\u0916\u0921\u094d\u0917\u0940",
"\u0936\u093e\u0939",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u092e\u093e\u0928\u0928\u094d\n\u0927\u0930",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0925\u093e\u092a\u093e",
"\u0930\u093f\u092e\u093e\u0932",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0938\u0930\u0940\u092f\u093e",
"\u0925\u093e\u092a\u093e",
"\u0926\u0941\u0917\u0932",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u092c\u0947\u0917\u093e\u092e\u0940",
"\u0938\u093f\u0902\u0939",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u092a\u0928\u094d\n\u0924",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u091c\u0948\u0928",
"\u0936\u093e\u0915\u094d\u092f",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0925\u093e\u092a\u093e",
"\u092a\u093e\u0923\u094d\n\u0921\u0947",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0930\u0947\u0917\u094d\u092e\u0940",
"\u0938\u0930\u093f\u092f\u093e",
"\u092a\u094b\u0916\u0930\u0947\u0932",
"\u0915\u093e\u0930\u094d\u0915\u0940",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u092d\u091f\u094d\u091f\u0930\u093e\u0908",
"\u092a\u094c\u0921\u094d\u092f\u093e\u0932",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0909\u092a\u093e\u0927\u094d\u092f\u093e\u092f",
"\u0930\f\u091c\u093f\u0924\u0915\u093e\u0930",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u092a\u093e\u0923\u094d\n\u0921\u0947",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u092e\u0941\u0938\u094d\n\u0932\u0940\u092e",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0915\u0915\u094d\u0937\u092a\u0924\u0940",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u092e\u093e\u0938\u094d\u0915\u0947",
"(\u0915\u094d\u0937\u0947\u0924\u094d\u0930\u0940)",
"\u0925\u093e\u092a\u093e",
"\u0938\u093e\u092a\u0915\u094b\u091f\u093e",
"\u092c\u094b\u0925\u0930\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092d\u0941\u0938\u093e\u0932",
"\u092a\u0928\u094d\u0924",
"(\u092a\u093e\u0923\u094d\u0921\u0947)",
"\u091f\u093f\u092c\u094d\u0930\u0947\u0935\u093e\u0932",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092c\u0947\u0917\u093e\u0928\u0940",
"\u092a\u0928\u0947\u0930\u0941",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0930\u093e\u0920\u094c\u0930",
"\u0917\u0941\u0930\u0941\u0919",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0932\u093e\u092e\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0925\u093e\u092a\u093e",
"\u0930\u093e\u0923\u093e",
"\u0926\u0941\u0917\u0921",
"\u092a\u094d\n\u092f\u093e\u0915\u0941\u0930\u0947\u0932",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u091c\u094b\u0936\u0940",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0938\u093f\u0932\u0935\u093e\u0932",
"\u0938\u093f\u0932\u094d\u0935\u093e\u0932",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u092a\u094c\u0921\u094d\u092f\u093e\u0932",
"\u0918\u093f\u092e\u093f\u0930\u0947",
"\u0915\u094d\u0937\u0947\u0924\u094d\u0930\u0940",
"\u0915\u0930\u094d\u092e\u093e\u091a\u093e\u0930\u094d\u092f",
"\u0915\u0947.\u0938\u0940.",
"\u0925\u093e\u092a\u093e",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0918\u093f\u092e\u093f\u0930\u0947",
"\u0921\u0902\u0917\u094b\u0932",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0916\u0924\u094d\u0930\u0940",
"\u0938\u093e\u0939\u0940",
"\u0925\u093e\u092a\u093e",
"\u092e\u0932\u094d\u0932",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0924\u093f\u0935\u093e\u0930\u0940",
"\u092e\u0932\u094d\u0932",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u092a\u094d\u0930\u091c\u093e\u092a\u0924\u093f",
"\u0938\u093f\u091f\u094c\u0932\u093e",
"\u0917\u093f\u0930\u0940",
"(\u0927\u0947\u0915\u0947)",
"\u0915\u0947.\u0938\u0940.",
"\u0936\u0930\u094d\u092e\u093e",
"\u0926\u0935\u093e\u0921\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092e\u0932\u094d\u0932",
"\u0938\u093f\u0932\u094d\u0935\u093e\u0932",
"\u0936\u093e\u0915\u094d\u092f",
"\u092a\u094d\u0930\u0927\u093e\u0928\u093e\u0919\u094d\u0917",
"\u0938\u0941\u0935\u093e\u0932",
"\u091c\u0948\u0928",
"\u0930\u093f\u092e\u093e\u0932",
"\u092a\u094c\u0921\u0947\u0932",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u091c\u094b\u0936\u0940",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u092a\u094c\u0921\u0947\u0932",
"\u0906\u091a\u093e\u0930\u094d\u092f",
"\u0924\u093f\u0935\u093e\u0930\u0940",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0926\u0941\u0917\u0921",
"\u0915\u0947.\u0938\u0940",
"\u0915\u0947.\u0938\u0940",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0930\u093e\u0923\u093e",
"\u0927\u0928\u093e\u0935\u0924",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0935\u091c\u094d\u0930\u093e\u091a\u093e\u0930\u094d\u092f",
"\u0915\u0915\u094d\u0937\u092a\u0924\u093f",
"\u092c\u0947\u0917\u093e\u0928\u0940",
"\u092d\u091f\u094d\u091f\u0930\u093e\u0908",
"\u092d\u0941\u091c\u0942",
"\u0918\u0932\u0947",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u092c\u0917\u093e\u0932\u0947",
"\u092a\u094b\u0916\u0930\u0947\u0932",
"\u0925\u093e\u092a\u093e",
"\u0925\u093e\u092a\u093e",
"\u092e\u0939\u0924\u094b",
"\u0932\u094b\u0939\u0928\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u093e\u0939\u0940",
"\u0909\u0915\u094d\u092f\u093e\u0935",
"\u0926\u0941\u0917\u0921",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0936\u0930\u094d\u092e\u093e",
"\u091c\u0948\u0928",
"\u0917\u0941\u0930\u0941\u0919",
"\u092c\u0938\u094d\u0928\u0947\u0924",
"\u0935\u091c\u094d\u0930\u093e\u091a\u093e\u0930\u094d\u092f",
"\u092e\u093e\u0928\u0928\u094d\n\u0927\u0930",
"\u092e\u0948\u0928\u093e\u0932\u0940",
"\u091a\u094c\u0932\u093e\u0917\u093e\u0908",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0938\u093f\u0902\u0939",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0905\u092e\u093e\u0924\u094d\u092f",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0915\u0947.\u0938\u0940",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"\u0932\u093e\u092e\u093e",
"\u092a\u094c\u0921\u0947\u0932",
"\u0936\u093e\u0939",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u0930\u094d\u092e\u093e",
"\u0936\u093e\u0915\u094d\u092f",
"\u092a\u0930\u093e\u091c\u0941\u0932\u0940",
"\u0936\u093e\u0939\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092a\u094c\u0921\u0947\u0932",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0938\u093f\u0902\u0939",
"\u092d\u091f\u094d\u091f\u0930\u093e\u0908",
"\u0938\u0941\u0928\u0941\u0935\u093e\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u091c\u0948\u0928",
"\u0936\u093e\u0939",
"\u0932\u094b\u0939\u0928\u0940",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0938\u093f\u0902\f\u0916\u0921\u093e",
"\u092a\u094d\u0930\u091c\u093e\u092a\u0924\u093f",
"\u092a\u094b\u0926\u094d\u0935\u093e\u0930",
"\u0915\u0947.\u0938\u0940",
"\u0936\u0930\u094d\u092e\u093e",
"\u0936\u0930\u094d\u092e\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0938\u093f\u0902\u0918\u0932",
"\u0915\u0947.\u0938\u0940.",
"\u0926\u0947\u0909\u091c\u093e",
"\u0926\u093e\u0939\u093e\u0932",
"\u0916\u0928\u093e\u0932",
"\u092a\u0928\u0947\u0930\u0941",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0917\u094b\u092f\u0932",
"\u091c\u094b\u0936\u0940",
"\u0905\u092e\u093e\u0924\u094d\n\u092f",
"\u0936\u0947\u0930\u094d\u092a\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u092a\u094d\u0930\u0927\u093e\u0928\u093e\u0919",
"\u092d\u093f\u092e\u0938\u0930\u093f\u092f\u093e",
"\u0938\u0941\u0935\u093e\u0932",
"\u0924\u0923\u094d\u0921\u0941\u0915\u093e\u0930",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0917\u0941\u0930\u093e\u0917\u093e\u0908",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u091a\u094d\u092f\u093e\u092e\u0947",
"\u0938\u0940",
"\u0917\u094c\u0924\u092e",
"\u091c\u094b\u0936\u0940",
"\u0936\u093e\u0939",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0917\u094c\u0924\u092e",
"\u0938\u093e\u0939",
"\u091c\u094b\u0936\u0940",
"\u0917\u0941\u0930\u0941\u0919",
"\u092e\u094b\u0915\u094d\u0924\u093e\u0928",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0938\u0941\u0935\u093e\u0932",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092a\u0928\u094d\u0924",
"\u0921\u0902\u0917\u094b\u0932",
"\u0909\u092a\u093e\u0927\u094d\n\u092f\u093e\u092f",
"\u092a\u094d\u0930\u0927\u093e\u0928",
"\u0916\u0928\u093e\u0932",
"(\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947)",
"\u092e\u093f\u0924\u094d\u0924\u0932",
"\u0936\u0930\u094d\u092e\u093e",
"\u0917\u0921\u0924\u094c\u0932\u093e",
"\u0917\u094b\u092f\u0932",
"\u0930\u0947\u0917\u094d\u092e\u0940",
"\u0916\u0921\u094d\u0917\u0940",
"\u0932\u094b\u0939\u0928\u0940",
"\u092a\u093e\u0923\u094d\u0921\u0947\u092f",
"\u0932\u094b\u0939\u0928\u0940",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0932\u093f\u092e\u094d\u092c\u0941",
"\u0936\u093e\u0939\u0940",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0921\u0902\u0917\u094b\u0932",
"\u0938\u093f\f\u0939",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0916\u0924\u094d\u0930\u0940",
"\u0939\u093e\u092f\u093e\u091c\u0941",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0916\u0928\u093e\u0932",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0925\u093e\u092a\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u093e\u0915\u094d\u092f",
"\u092a\u094d\u0930\u091c\u093e\u092a\u0924\u0940",
"\u0916\u0921\u094d\u0917\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u091c\u0948\u0928",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0917\u094c\u0924\u092e",
"\u0930\u0938\u093e\u092f\u0932\u0940",
"\u0916\u0921\u094d\u0917\u0940",
"\u0938\u093f\u0932\u0935\u093e\u0932",
"\u0936\u093e\u0939\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u093e\u0939\u0940",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u091c\u0948\u0928",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u091c\u094d\u091e\u0935\u093e\u0932\u0940",
"\u092d\u093f\u092e\u0938\u0930\u0940\u092f\u093e",
"\u0922\u0919\u094d\u0917\u0947\u0932",
"\u092c\u0938\u094d\u0928\u0947\u0924",
"\u092a\u094d\u092f\u093e\u0915\u0941\u0930\u0947\u0932",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0926\u0941\u0917\u0921",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0915\u0941\u0907\u0915\u0947\u0932",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u092a\u094c\u0921\u0947\u0932",
"\u0915\u0902\u0938\u093e\u0915\u093e\u0930",
"\u0926\u0941\u0917\u0921",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092d\u091f\u094d\u091f\u0930\u093e\u0908",
"\u0915\u094d\u0937\u0947\u0924\u094d\u0930\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0928\u093e\u0939\u091f\u093e",
"\u092c\u0938\u094d\u0928\u0947\u0924",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0915\u0947.\u0938\u0940.",
"\u0915\u0947.\u0938\u0940.",
"\u0917\u0941\u092a\u094d\n\u0924\u093e",
"\u092c\u091c\u094d\u0930\u093e\u091a\u093e\u0930\u094d\u092f",
"\u091c\u094b\u0936\u0940",
"\u091c\u094d\u091e\u0935\u093e\u0932\u0940",
"\u092a\u0928\u094d\u0924",
"\u0938\u093f\u0939\u0902",
"\u0917\u0941\u092a\u094d\n\u0924\u093e",
"\u091c\u094b\u0936\u0940",
"\u0936\u0930\u094d\u092e\u093e",
"\u0915\u0947.\u0938\u0940",
"\u0926\u0947\u0909\u091c\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0932\u094b\u0939\u0928\u0940",
"\u0930\u0947\u0917\u094d\n\u092e\u0940",
"\u0915\u094d\u0937\u0947\u0924\u094d\u0930\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u091a\u093e\u0932\u093f\u0938\u0947",
"\u0939\u093e\u092f\u091c\u0941",
"\u092e\u0928\u0928\u094d\u0927\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0915\u0947.\u0938\u0940.",
"\u0917\u0941\u0930\u0941\u0919",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0930\u093e\u0923\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0915\u093e\u092c\u0930\u093e",
"\u0936\u093e\u0939\u0940",
"\u0930\u093e\u0920\u094c\u0930",
"\u0909\u092a\u093e\u0927\u094d\u092f\u093e\u092f",
"\u0916\u0928\u093e\u0932",
"\u091a\u093e\u0932\u093f\u0938\u0947",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u092e\u0917\u0930",
"\u091c\u0948\u0928",
"\u092e\u0932\u094d\n\u0932",
"\u091c\u0948\u0928",
"\u0930\u093e\u0920\u0940",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u092a\u0928\u094d\n\u0924",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0925\u093e\u092a\u093e",
"\u092a\u094c\u0921\u0947\u0932",
"\u0938\u0941\u0935\u0947\u0926\u0940",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u092e\u0941\u0938\u0932\u092e\u093e\u0928",
"\u0915\u093f\u0932\u094d\u0932\u093e",
"\u092e\u094b\u0915\u094d\u0924\u093e\u0928",
"\u092c\u0938\u094d\u0928\u094d\u092f\u093e\u0924",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u092c\u091c\u094d\u0930\u093e\u091a\u093e\u0930\u094d\u092f",
"\u0926\u0947\u0909\u091c\u093e",
"\u0936\u093e\u0915\u094d\u092f",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0932\u0947\u0916\u0915",
"\u0916\u0921\u094d\u0915\u093e",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0915\u093e\u0930\u094d\u0915\u0940",
"\u0925\u093e\u092a\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092a\u0928\u094d\u0924",
"\u0930\u093e\u0908",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0932\u093e\u092e\u093e",
"\u0921\u0902\u0917\u094b\u0932",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u092a\u0928\u094d\n\u0924",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u093e\u0939",
"\u0932\u094b\u0939\u0928\u0940",
"\u0926\u0941\u0917\u0921",
"\u0932\u093e\u092e\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u093e\u0939",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0936\u093e\u0939",
"\u0909\u092a\u093e\u0927\u094d\u092f\u093e\u092f",
"\u0926\u0947\u0909\u091c\u093e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092c\u0947\u0917\u093e\u0928\u0940",
"\u0936\u0930\u094d\u092e\u093e",
"\u0917\u093f\u0930\u0940",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0925\u093e\u092a\u093e",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0906\u0932\u092e",
"\u0905\u0927\u093f\u0915\u093e\u0930\u0940",
"\u092e\u093e\u0928\u0928\u094d\n\u0927\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u092a\u093e\u0923\u094d\u0921\u0947",
"\u0930\u0938\u093e\u092f\u0932\u0940",
"\u0915\u0947.\u0938\u0940",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0922\u0915\u093e\u0932",
"\u0920\u0941\u0915\u0930\u0940",
"\u0922\u0941\u0919\u094d\u0917\u0947\u0932",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u092d\u091f\u094d\u091f\u0930\u093e\u0908",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0928\u0947\u092a\u093e\u0932",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u0917\u0941\u0930\u0941\u0919\u094d\u0917",
"\u0936\u093e\u0939",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0938\u0924\u094d\u092f\u093e\u0932",
"\u0938\u093f\u0902\u0939",
"\u092e\u093e\u0928\u0928\n\u0927\u0930",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0928\u0947\u092a\u093e\u0932\u0940",
"\u0936\u093e\u0939",
"\u0936\u0902\u0915\u0930",
"\u0938\u0941\u0928\u0941\u0935\u093e\u0930",
"\u0906\u0932\u092e",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0926\u0947\u0909\u091c\u093e",
"(\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920)",
"\u091c\u094d\u091e\u0935\u093e\u0932\u0940",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0916\u0921\u094d\u0917\u0940",
"\u092c\u091c\u094d\u0930\u093e\u091a\u093e\u0930\u094d\u092f",
"\u092a\u0928\u094d\u0924",
"\u0939\u093e\u092f\u091c\u0941",
"\u0936\u094d\u0930\u0947\u0937\u094d\u0920",
"\u0938\u093f\u0902\u0939",
"\u0917\u094c\u0924\u092e",
"\u092e\u093f\u0924\u094d\u0924\u0932",
"\u0920\u0915\u0941\u0930\u0940",
"\u0925\u093e\u092a\u093e",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"(\u0905\u092e\u093e\u0924\u094d\u092f)",
"\u0932\u093e\u092e\u093e",
"\u0938\u093f\u0902\u0939",
"\u0925\u093e\u092a\u093e",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0921\f\u0902\u0917\u094b\u0932",
"\u092e\u093e\u0928\u0928\u094d\u0927\u0930",
"\u0924\u0941\u0932\u093e\u0927\u0930",
"\u0930\u0938\u093e\u0907\u0932\u0940",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u091c\u0948\u0928",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0924\u093f\u0935\u093e\u0930\u0940",
"\u0936\u094d\u0930\u0947\u0937\u094d\n\u0920",
"\u0928\u094d\u092f\u094c\u092a\u093e\u0928\u0947",
"\u0924\u093f\u0935\u093e\u0930\u0940",
"\u091c\u0948\u0928",
"\u0922\u0941\u0919\u094d\u0917\u0947\u0932",
"\u092e\u0932\u094d\u0932",
"\u0917\u093f\u0930\u0940",
"\u0930\u093e\u091c\u0915\u0930\u094d\u0923\u093f\u0915\u093e\u0930",
"\u0925\u093e\u092a\u093e",
"\u0905\u0917\u094d\u0930\u0935\u093e\u0932",
"\u0938\u093f\u091f\u094c\u0932\u093e",
"\u0925\u093e\u092a\u093e",
"\u0915\u0930\u094d\u092e\u093e\u091a\u093e\u0930\u094d\u092f",
"\u0915\u093f\u0930\u093e\u0901\u0924",
"\u0938\u093f\u0902\u0939",
"\u0938\u093f\u0902\u0939",
"\u091c\u094b\u0936\u0940",
"\u0936\u093e\u0939\u0940",
"\u0926\u093e\u0939\u093e\u0932",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u092e\u0939\u0930\u094d\u091c\u0928",
"\u091c\u094b\u0936\u0940",
"\u0938\u093f\u0902\u0939",
"\u091c\u094b\u0936\u0940"
)
person_prefixes_female_ne_np = c("\u0936\u094d\u0930\u0940\u092e\u0924\u0940",
"\u0938\u0941\u0936\u094d\u0930\u0940")
person_prefixes_male_ne_np = c("\u0936\u094d\u0930\u0940",
"\u0936\u094d\u0930\u0940\u092e\u093e\u0928")
person_prefixes_ne_np <- c(person_prefixes_female_ne_np, person_prefixes_male_ne_np)
person_ne_np <- list(
first_names = person_first_names_ne_np,
first_names_female = person_first_names_female_ne_np,
first_names_male = person_first_names_male_ne_np,
last_names = person_last_names_ne_np,
prefixes_female = person_prefixes_female_ne_np,
prefixes_male = person_prefixes_male_ne_np,
prefixes = person_prefixes_ne_np
) |
imfill <- function(x=1,y=1,z=1,val=0,dim=NULL)
{
if (!is.null(dim))
{
x <- dim[1];y <- dim[2];z <- dim[3]
}
if (is.character(val))
{
val <- col2rgb(val)[,1]/255
}
if (length(val) == 1)
{
if (is.logical(val))
{
array(val,c(x,y,z,1)) %>% pixset
}
else
{
array(val,c(x,y,z,1)) %>% cimg
}
}
else
{
map(val,function(v) imfill(x,y,z,val=v)) %>% imappend("c")
}
}
imnoise <- function(x=1,y=1,z=1,cc=1,mean=0,sd=1,dim=NULL)
{
if (is.null(dim))
{
dim <- c(x,y,z,cc)
}
rnorm(prod(dim),mean=mean,sd=sd) %>% array(dim=dim) %>% cimg
}
as.cimg.function <- function(obj,width,height,depth=1,spectrum=1,standardise=FALSE,dim=NULL,...)
{
if (!is.null(dim))
{
width <- dim[1]
height <- dim[2]
depth <- dim[3]
spectrum <- dim[4]
}
fun <- obj
args <- formals(fun) %>% names
gr <- pixel.grid(dim=c(width,height,depth,spectrum),standardise=standardise,drop.unused=TRUE)
if (depth == 1)
{
if (setequal(args,c("x","y")))
{
val <- fun(x=gr$x,y=gr$y)
}
else if (setequal(args,c("x","y","cc")))
{
val <- fun(x=gr$x,y=gr$y,cc=gr$cc)
}
else{
stop("Input must be a function with arguments (x,y) or (x,y,cc)")
}
}
else
{
if (setequal(args,c("x","y","z")))
{
val <- fun(x=gr$x,y=gr$y,z=gr$z)
}
else if (setequal(args,c("x","y","z","cc")))
{
val <- fun(x=gr$x,y=gr$y,z=gr$z,cc=gr$cc)
}
else{
stop("Input must be a function with arguments (x,y,z) or (x,y,z,cc)")
}
}
dim(val) <- c(width,height,depth,spectrum)
cimg(val)
} |
spMisalignGLM <- function(formula, family="binomial", weights, data = parent.frame(), coords,
starting, tuning, priors, cov.model,
amcmc, n.samples,
verbose=TRUE, n.report=100, ...){
formal.args <- names(formals(sys.function(sys.parent())))
elip.args <- names(list(...))
for(i in elip.args){
if(! i %in% formal.args)
warning("'",i, "' is not an argument")
}
if(missing(formula)){stop("error: formula must be specified")}
if(is.list(formula) && is.list(coords)){
if(length(formula) != length(coords)){
stop("error: formula and coords are misspecified")
}
mod.dat <- mkMisalignYX(formula, data)
Y <- mod.dat[[1]]
X <- mod.dat[[2]]
misalign.n <- mod.dat[[3]]
misalign.p <- mod.dat[[4]]
x.names <- mod.dat[[5]]
m <- length(formula)
storage.mode(misalign.n) <- "integer"
storage.mode(misalign.p) <- "integer"
}else{
stop("error: formula is misspecified")
}
p <- ncol(X)
n <- nrow(X)
n.ltr <- m*(m+1)/2
storage.mode(Y) <- "double"
storage.mode(X) <- "double"
storage.mode(m) <- "integer"
storage.mode(p) <- "integer"
storage.mode(n) <- "integer"
if(!family %in% c("binomial","poisson"))
stop("error: family must be binomial or poisson")
if(missing(weights)){
weights <- rep(1, n)
}else if(is.list(weights) && length(weights) == m && all(sapply(weights, length) == misalign.n)){
weights<- do.call(c, weights)
}else{
stop(paste("error: weights must be a list length ", m," consisting of weight vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
storage.mode(weights) <- "integer"
n.batch <- 0
batch.length <- 0
accept.rate <- 0
is.amcmc <- TRUE
if(missing(amcmc)){
stop("error: amcmc must be be specified")
}else{
names(amcmc) <- tolower(names(amcmc))
if(!"n.batch" %in% names(amcmc)){stop("error: n.batch must be specified in amcmc list")}
n.batch <- amcmc[["n.batch"]]
if(!"batch.length" %in% names(amcmc)){stop("error: batch.length must be specified in amcmc list")}
batch.length <- amcmc[["batch.length"]]
if(!"accept.rate" %in% names(amcmc)){
warning("accept.rate was not specified in the amcmc list and was therefore set to the default 0.43")
accept.rate <- 0.43
}else{
accept.rate <- amcmc[["accept.rate"]]
}
}
storage.mode(is.amcmc) <- "integer"
storage.mode(n.batch) <- "integer"
storage.mode(batch.length) <- "integer"
storage.mode(accept.rate) <- "double"
if(missing(coords)){stop("error: coords must be specified")}
coords <- as.matrix(do.call(rbind, coords))
if(ncol(coords) != 2 || nrow(coords) != n){
stop("error: either the coords have more than two columns or then number of rows is different than data used in the model formula")
}
coords.D <- iDist(coords)
storage.mode(coords.D) <- "double"
if(missing(cov.model)){stop("error: cov.model must be specified")}
if(!cov.model%in%c("gaussian","exponential","matern","spherical"))
{stop("error: specified cov.model '",cov.model,"' is not a valid option; choose, from gaussian, exponential, matern, spherical.")}
beta.starting <- 0
A.starting <- 0
phi.starting <- 0
nu.starting <- 0
w.starting <- 0
if(missing(starting)){stop("error: starting value list for the parameters must be specified")}
names(starting) <- tolower(names(starting))
if("beta" %in% names(starting)){
beta.starting <- starting[["beta"]]
if(length(beta.starting) != p){stop(paste("error: starting values for beta must be of length ",p,sep=""))}
}else{
stop("error: beta must be specified in starting")
}
if(!"a" %in% names(starting)){stop("error: A must be specified in starting")}
A.starting <- starting[["a"]]
if(length(A.starting) != n.ltr){stop(paste("error: A must be of length ",n.ltr," in starting value list",sep=""))}
if(!"phi" %in% names(starting)){stop("error: phi must be specified in starting")}
phi.starting <- starting[["phi"]]
if(length(phi.starting) != m){stop(paste("error: phi must be of length ",m," in starting value list",sep=""))}
if(cov.model == "matern"){
if(!"nu" %in% names(starting)){stop("error: nu must be specified in starting")}
nu.starting <- starting[["nu"]]
if(length(nu.starting) != m){stop(paste("error: nu must be of length ",m," in starting value list",sep=""))}
}
if(!"w" %in% names(starting)){stop("error: w must be specified in starting value list")}
w.starting <- starting[["w"]]
if(is.numeric(w.starting) && length(w.starting) == 1){
w.starting <- rep(w.starting, n)
}else if(is.list(w.starting) && length(w.starting) == m && all(sapply(w.starting, length) == misalign.n)){
w.starting <- do.call(c, w.starting)
}else{
stop(paste("error: w in the starting value list must be a scalar of length 1 or a list of length ",m, " consisting of vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
storage.mode(beta.starting) <- "double"
storage.mode(phi.starting) <- "double"
storage.mode(A.starting) <- "double"
storage.mode(nu.starting) <- "double"
storage.mode(w.starting) <- "double"
beta.Norm <- 0
beta.prior <- "flat"
K.IW <- 0
nu.Unif <- 0
phi.Unif <- 0
if(missing(priors)) {stop("error: prior list for the parameters must be specified")}
names(priors) <- tolower(names(priors))
if("beta.norm" %in% names(priors)){
beta.Norm <- priors[["beta.normal"]]
if(!is.list(beta.Norm) || length(beta.Norm) != 2){stop("error: beta.Norm must be a list of length 2")}
if(length(beta.Norm[[1]]) != p ){stop(paste("error: beta.Norm[[1]] must be a vector of length, ",p, " with elements corresponding to betas' mean",sep=""))}
if(length(beta.Norm[[2]]) != p ){stop(paste("error: beta.Norm[[2]] must be a vector of length, ",p, " with elements corresponding to betas' sd",sep=""))}
beta.prior <- "normal"
}
if(!"k.iw" %in% names(priors)){stop("error: K.IW must be specified")}
K.IW <- priors[["k.iw"]]
if(!is.list(K.IW) || length(K.IW) != 2){stop("error: K.IW must be a list of length 2")}
if(length(K.IW[[1]]) != 1 ){stop("error: K.IW[[1]] must be of length 1 (i.e., the IW df hyperparameter)")}
if(length(K.IW[[2]]) != m^2 ){stop(paste("error: K.IW[[2]] must be a vector or matrix of length, ",m^2, ", (i.e., the IW scale matrix hyperparameter)",sep=""))}
if(!"phi.unif" %in% names(priors)){stop("error: phi.Unif must be specified")}
phi.Unif <- priors[["phi.unif"]]
if(!is.list(phi.Unif) || length(phi.Unif) != 2){stop("error: phi.Unif must be a list of length 2")}
if(length(phi.Unif[[1]]) != m){stop(paste("error: phi.Unif[[1]] must be a vector of length, ",m, "",sep=""))}
if(length(phi.Unif[[2]]) != m){stop(paste("error: phi.Unif[[2]] must be a vector of length, ",m, "",sep=""))}
if(any(phi.Unif[[2]]-phi.Unif[[1]] <= 0)){stop("error: phi.Unif has zero support")}
phi.Unif <- as.vector(t(cbind(phi.Unif[[1]],phi.Unif[[2]])))
if(cov.model == "matern"){
if(!"nu.unif" %in% names(priors)){stop("error: nu.Unif must be specified")}
nu.Unif <- priors[["nu.unif"]]
if(!is.list(nu.Unif) || length(nu.Unif) != 2){stop("error: nu.Unif must be a list of length 2")}
if(length(nu.Unif[[1]]) != m){stop(paste("error: nu.Unif[[1]] must be a vector of length, ",m, "",sep=""))}
if(length(nu.Unif[[2]]) != m){stop(paste("error: nu.Unif[[2]] must be a vector of length, ",m, "",sep=""))}
if(any(nu.Unif[[2]]-nu.Unif[[1]] <= 0)){stop("error: nu.Unif has zero support")}
nu.Unif <- as.vector(t(cbind(nu.Unif[[1]],nu.Unif[[2]])))
}
storage.mode(K.IW[[1]]) <- "double"; storage.mode(K.IW[[2]]) <- "double"
storage.mode(nu.Unif) <- "double"
storage.mode(phi.Unif) <- "double"
beta.tuning <- 0
phi.tuning <- 0
A.tuning <- 0
nu.tuning <- 0
w.tuning <- 0
if(!missing(tuning)){
names(tuning) <- tolower(names(tuning))
if(!"beta" %in% names(tuning)){stop("error: beta must be specified in tuning value list")}
beta.tuning <- tuning[["beta"]]
if(is.matrix(beta.tuning)){
if(nrow(beta.tuning) != p || ncol(beta.tuning) != p){
stop(paste("error: if beta tuning is a matrix, it must be of dimension ",p,sep=""))
}
if(is.amcmc){
beta.tuning <- diag(beta.tuning)
}
}else if(is.vector(beta.tuning)){
if(length(beta.tuning) != p){
stop(paste("error: if beta tuning is a vector, it must be of length ",p,sep=""))
}
if(!is.amcmc){
beta.tuning <- diag(beta.tuning)
}
}else{
stop("error: beta tuning is misspecified")
}
if(!"a" %in% names(tuning)){stop("error: A must be specified in tuning value list")}
A.tuning <- as.vector(tuning[["a"]])
if(length(A.tuning) != n.ltr){stop(paste("error: A must be of length ",n.ltr," in tuning value list",sep=""))}
if(!"phi" %in% names(tuning)){stop("error: phi must be specified in tuning value list")}
phi.tuning <- tuning[["phi"]]
if(length(phi.tuning) != m){stop(paste("error: phi must be of length ",m," in tuning value list",sep=""))}
if(cov.model == "matern"){
if(!"nu" %in% names(tuning)){stop("error: nu must be specified in tuning value list")}
nu.tuning <- tuning[["nu"]]
if(length(nu.tuning) != m){stop(paste("error: nu must be of length ",m," in tuning value list",sep=""))}
}
if(!"w" %in% names(tuning)){stop("error: w must be specified in tuning value list")}
w.tuning <- tuning[["w"]]
if(is.numeric(w.tuning) && length(w.tuning) == 1){
w.tuning <- rep(w.tuning, n)
}else if(is.list(w.tuning) && length(w.tuning) == m && all(sapply(w.tuning, length) == misalign.n)){
w.tuning <- do.call(c, w.tuning)
}else{
stop(paste("error: w in the tuning value list must be a scalar of length 1 or a list of length ",m, " consisting of vectors of lengths ", paste(misalign.n, collapse=" "), sep=""))
}
}else{
if(!is.amcmc){
stop("error: tuning value list must be specified")
}
beta.tuning <- rep(0.01,p)
phi.tuning <- rep(0.01,m)
A.tuning <- rep(0.01,m*(m-1)/2+m)
nu.tuning <- rep(0.01,m)
w.tuning <- rep(0.01,n)
}
storage.mode(beta.tuning) <- "double"
storage.mode(phi.tuning) <- "double"
storage.mode(A.tuning) <- "double"
storage.mode(nu.tuning) <- "double"
storage.mode(w.tuning) <- "double"
storage.mode(n.report) <- "integer"
storage.mode(verbose) <- "integer"
ptm <- proc.time()
out <- .Call("spGLMMisalign_AMCMC", Y, X, misalign.p, misalign.n, m, coords.D, family, weights,
beta.prior, beta.Norm,
K.IW, nu.Unif, phi.Unif,
phi.starting, A.starting, nu.starting, beta.starting, w.starting,
phi.tuning, A.tuning, nu.tuning, beta.tuning, w.tuning,
cov.model, n.batch, batch.length, accept.rate, verbose, n.report)
run.time <- proc.time() - ptm
out$p.beta.theta.samples <- mcmc(t(out$p.beta.theta.samples))
col.names <- rep("null",ncol(out$p.beta.theta.samples))
if(cov.model != "matern"){
col.names <- c(x.names, rep("K",n.ltr), paste("phi[",1:m,"]",sep=""))
}else{
col.names <- c(x.names, rep("K",n.ltr), paste("phi[",1:m,"]",sep=""), paste("nu[",1:m,"]",sep=""))
}
colnames(out$p.beta.theta.samples) <- col.names
AtA <- function(x, m){
A <- matrix(0, m, m)
A[lower.tri(A, diag=TRUE)] <- x
(A%*%t(A))[lower.tri(A, diag=TRUE)]
}
K.names <- paste("K[",matrix(apply(cbind(expand.grid(1:m,1:m)), 1, function(x) paste(x, collapse=",")),m,m)[lower.tri(matrix(0,m,m), diag=TRUE)],"]",sep="")
colnames(out$p.beta.theta.samples)[colnames(out$p.beta.theta.samples)%in%"K"] <- K.names
out$p.beta.theta.samples[,K.names] <- t(apply(out$p.beta.theta.samples[,K.names,drop=FALSE], 1, AtA, m))
out$weights <- weights
out$family <- family
out$Y <- Y
out$X <- X
out$m <- m
out$misalign.p <- misalign.p
out$misalign.n <- misalign.n
out$coords <- coords
out$x.names <- x.names
out$cov.model <- cov.model
out$run.time <- run.time
class(out) <- "spMisalignGLM"
out
} |
"ts_AR1_t" |
structure(list(url = "https://api.twitter.com/2/tweets/search/all?query=%23IchBinHanna%20place%3ABerlin&max_results=500&start_time=2021-06-01T00%3A00%3A00Z&end_time=2021-06-13T00%3A00%3A00Z&tweet.fields=attachments%2Cauthor_id%2Ccontext_annotations%2Cconversation_id%2Ccreated_at%2Centities%2Cgeo%2Cid%2Cin_reply_to_user_id%2Clang%2Cpublic_metrics%2Cpossibly_sensitive%2Creferenced_tweets%2Csource%2Ctext%2Cwithheld&user.fields=created_at%2Cdescription%2Centities%2Cid%2Clocation%2Cname%2Cpinned_tweet_id%2Cprofile_image_url%2Cprotected%2Cpublic_metrics%2Curl%2Cusername%2Cverified%2Cwithheld&expansions=author_id%2Centities.mentions.username%2Cgeo.place_id%2Cin_reply_to_user_id%2Creferenced_tweets.id%2Creferenced_tweets.id.author_id&place.fields=contained_within%2Ccountry%2Ccountry_code%2Cfull_name%2Cgeo%2Cid%2Cname%2Cplace_type",
status_code = 200L, headers = structure(list(date = "Sun, 13 Jun 2021 15:35:04 UTC",
server = "tsa_o", `content-type` = "application/json; charset=utf-8",
`cache-control` = "no-cache, no-store, max-age=0", `content-length` = "24084",
`x-access-level` = "read", `x-frame-options` = "SAMEORIGIN",
`content-encoding` = "gzip", `x-xss-protection` = "0",
`x-rate-limit-limit` = "300", `x-rate-limit-reset` = "1623598738",
`content-disposition` = "attachment; filename=json.json",
`x-content-type-options` = "nosniff", `x-rate-limit-remaining` = "292",
`strict-transport-security` = "max-age=631138519", `x-connection-hash` = "666b068b934aea113795347145b978a8aca3707fcb16212cb4ada53f1cc9822b"), class = c("insensitive",
"list")), all_headers = list(list(status = 200L, version = "HTTP/2",
headers = structure(list(date = "Sun, 13 Jun 2021 15:35:04 UTC",
server = "tsa_o", `content-type` = "application/json; charset=utf-8",
`cache-control` = "no-cache, no-store, max-age=0",
`content-length` = "24084", `x-access-level` = "read",
`x-frame-options` = "SAMEORIGIN", `content-encoding` = "gzip",
`x-xss-protection` = "0", `x-rate-limit-limit` = "300",
`x-rate-limit-reset` = "1623598738", `content-disposition` = "attachment; filename=json.json",
`x-content-type-options` = "nosniff", `x-rate-limit-remaining` = "292",
`strict-transport-security` = "max-age=631138519",
`x-connection-hash` = "666b068b934aea113795347145b978a8aca3707fcb16212cb4ada53f1cc9822b"), class = c("insensitive",
"list")))), cookies = structure(list(domain = c(".twitter.com",
".twitter.com"), flag = c(TRUE, TRUE), path = c("/", "/"),
secure = c(TRUE, TRUE), expiration = structure(c(1686667649,
1686667649), class = c("POSIXct", "POSIXt")), name = c("personalization_id",
"guest_id"), value = c("REDACTED", "REDACTED")), row.names = c(NA,
-2L), class = "data.frame"), content = charToRaw("{\"data\":[{\"entities\":{\"hashtags\":[{\"start\":57,\"end\":69,\"tag\":\"IchbinHanna\"}]},\"id\":\"1403438596695134209\",\"author_id\":\"14451880\",\"lang\":\"de\",\"created_at\":\"2021-06-11T19:46:50.000Z\",\"source\":\"Twitter for Android\",\"text\":\"was hat es mit den 12 jahren auf sich und wer ist hanna?
date = structure(1623598504, class = c("POSIXct", "POSIXt"
), tzone = "GMT"), times = c(redirect = 0, namelookup = 3.2e-05,
connect = 3.3e-05, pretransfer = 0.000118, starttransfer = 0.818008,
total = 0.820733)), class = "response") |
source("qq_plot_v7.R")
args <- commandArgs(TRUE)
root <- args[1]
gwas1<-read.table(paste(root, sep=""),head=T)
x1<-gwas1$P
pdf(paste(root,".QQ.pdf",sep=""),width=8,height=6)
qq.plot(x1, alpha=0.05, datatype="pvalue", scaletype="pvalue", df=1, plot.concentration.band=TRUE, one.sided=FALSE,frac=0.1, print=F, xat=NULL, yat=NULL, main=NULL, xlab=NULL, ylab=NULL, pch="x", cex=0.5, col="black")
text(x=12,y=4,paste("lambda--median=", format((median(qchisq(p=1-x1,df=1)))/0.4549,digits=3),sep=""))
dev.off() |
summary.mniptw <- function (object, ...){
nFits <- object$nFits
summaryList <- vector(mode = "list", length = nFits)
for (i in 1:nFits) {
summaryList[[i]] <- summary(object$psList[[i]], ...)
}
retObj <- list(summaryList = summaryList, nFit = object$nFit, uniqueTimes = object$uniqueTimes)
class(retObj) <- "summary.mniptw"
return(retObj)
} |
context("Testing mode difference functions")
run_mode_test <- function( dsin1, dsin2 , expect_diff ){
CALL <- match.call()
expect_equal(
identify_mode_differences(dsin1, dsin2) %>% nrow,
expect_diff,
info = str_c("dsin1 = " , as.character(CALL[2]) , "\ndsin2 = " , as.character(CALL[3])) ,
label = "identify_mode_differences returns a row count"
)
}
run_mode_test_with_N_obs <-function(N){
X_1col_int <- data_frame(
x = seq(1, N)
)
X_1col_num <- data_frame(
x = rnorm(N)
)
X_1col_num2 <- data_frame(
x = round(rnorm(N))
)
X_1col_char <- data_frame(
x = letters[1:N]
)
X_1col_char2 <- data_frame(
x = rep('cat', N)
)
X_1col_fact <- data_frame(
x = factor(letters[1:N])
)
X_1col_fact2 <- data_frame(
x = factor(rep(c('cat', 'dog'), c(floor(N/2), ceiling(N/2))))
)
X_1col_lgl <- data_frame(
x = rep(T, N)
)
X_1col_lgl2 <- data_frame(
x = rep(c(T,F), c(floor(N/2), ceiling(N/2)))
)
X_2col_numint <- data_frame(
x = X_1col_num$x,
y = X_1col_int$x
)
X_2col_charint <- data_frame(
x = X_1col_char$x,
y = X_1col_int$x
)
X_2col_intchar <- data_frame(
x = X_1col_int$x,
y = X_1col_char$x
)
X_2col_numchar <- data_frame(
x = X_1col_num$x,
y = X_1col_char$x
)
X_2col_numnum <- data_frame(
x = X_1col_num$x,
y = X_1col_num2$x
)
X_2col_numlgl <- data_frame(
x = X_1col_num$x,
y = X_1col_lgl$x
)
X_2col_numfact <- data_frame(
x = X_1col_num$x,
y = X_1col_fact$x
)
X_2col_charlgl <- data_frame(
x = X_1col_char$x,
y = X_1col_lgl$x
)
X_2col_charfact <- data_frame(
x = X_1col_char$x,
y = X_1col_fact$x
)
X_2col_charchar <- data_frame(
x = X_1col_char$x,
y = X_1col_char2$x
)
X_2col_lglfact <- data_frame(
x = X_1col_lgl$x,
y = X_1col_fact$x
)
X_2col_lgllgl <- data_frame(
x = X_1col_lgl$x,
y = X_1col_lgl$x
)
X_2col_factfact <- data_frame(
x = X_1col_fact$x,
y = X_1col_fact2$x
)
test_that( "Check comparision of equal objects",{
run_mode_test( X_1col_int , X_1col_int , 0 )
run_mode_test( X_1col_int , X_1col_fact , 0 )
run_mode_test( X_1col_int , X_1col_fact2 , 0 )
run_mode_test( X_1col_int , X_1col_num , 0 )
run_mode_test( X_1col_int , X_1col_num2 , 0 )
run_mode_test( X_1col_int , X_2col_factfact , 0 )
run_mode_test( X_1col_num , X_1col_num , 0 )
run_mode_test( X_1col_num , X_1col_num2 , 0 )
run_mode_test( X_1col_num , X_1col_fact , 0 )
run_mode_test( X_1col_num , X_1col_fact2 , 0 )
run_mode_test( X_1col_num2 , X_1col_fact , 0 )
run_mode_test( X_1col_num2 , X_1col_fact2 , 0 )
run_mode_test( X_1col_char , X_1col_char , 0 )
run_mode_test( X_1col_char , X_1col_char2 , 0 )
run_mode_test( X_1col_fact , X_1col_fact , 0 )
run_mode_test( X_1col_fact , X_1col_fact2 , 0 )
run_mode_test( X_1col_lgl , X_1col_lgl , 0 )
run_mode_test( X_1col_lgl , X_1col_lgl2 , 0 )
run_mode_test( X_2col_numnum , X_2col_numnum , 0 )
run_mode_test( X_2col_numchar , X_2col_numchar , 0 )
run_mode_test( X_2col_numlgl , X_2col_numlgl , 0 )
run_mode_test( X_2col_numfact , X_2col_numfact , 0 )
run_mode_test( X_2col_numnum , X_2col_numfact , 0 )
run_mode_test( X_2col_charlgl , X_2col_charlgl , 0 )
run_mode_test( X_2col_charfact , X_2col_charfact , 0 )
run_mode_test( X_2col_charchar , X_2col_charchar , 0 )
run_mode_test( X_2col_lglfact , X_2col_lglfact , 0 )
run_mode_test( X_2col_lgllgl , X_2col_lgllgl , 0 )
run_mode_test( X_2col_factfact , X_2col_factfact , 0 )
run_mode_test( X_2col_numint , X_2col_factfact , 0 )
run_mode_test( X_2col_numnum , X_2col_factfact , 0 )
})
test_that( "Check comparision of non equal objects aiming for differences of length 1",{
run_mode_test( X_1col_int , X_1col_char , 1 )
run_mode_test( X_1col_int , X_1col_char2 , 1 )
run_mode_test( X_1col_int , X_1col_lgl , 1 )
run_mode_test( X_1col_int , X_1col_lgl2 , 1 )
run_mode_test( X_1col_int , X_2col_charlgl , 1 )
run_mode_test( X_1col_num , X_1col_char , 1 )
run_mode_test( X_1col_num , X_1col_char2 , 1 )
run_mode_test( X_1col_num , X_1col_lgl , 1 )
run_mode_test( X_1col_num , X_1col_lgl2 , 1 )
run_mode_test( X_1col_num , X_2col_charlgl , 1 )
run_mode_test( X_1col_num2 , X_1col_char , 1 )
run_mode_test( X_1col_num2 , X_1col_char2 , 1 )
run_mode_test( X_1col_num2 , X_1col_lgl , 1 )
run_mode_test( X_1col_num2 , X_1col_lgl2 , 1 )
run_mode_test( X_1col_num2 , X_2col_charlgl , 1 )
run_mode_test( X_1col_char , X_1col_lgl , 1 )
run_mode_test( X_1col_char , X_1col_lgl2 , 1 )
run_mode_test( X_1col_char , X_1col_fact , 1 )
run_mode_test( X_1col_char , X_1col_fact2 , 1 )
run_mode_test( X_1col_char2 , X_1col_lgl , 1 )
run_mode_test( X_1col_char2 , X_1col_lgl2 , 1 )
run_mode_test( X_1col_char2 , X_1col_fact , 1 )
run_mode_test( X_1col_char2 , X_1col_fact2 , 1 )
run_mode_test( X_1col_lgl , X_1col_fact , 1 )
run_mode_test( X_1col_lgl , X_1col_fact2 , 1 )
run_mode_test( X_1col_lgl2 , X_1col_fact , 1 )
run_mode_test( X_1col_lgl2 , X_1col_fact2 , 1 )
run_mode_test( X_2col_numnum , X_2col_numchar , 1 )
run_mode_test( X_2col_numnum , X_2col_numlgl , 1 )
run_mode_test( X_2col_numchar , X_2col_numlgl , 1 )
run_mode_test( X_2col_numchar , X_2col_numfact , 1 )
run_mode_test( X_2col_numlgl , X_2col_numfact , 1 )
run_mode_test( X_2col_charchar , X_2col_charlgl , 1 )
run_mode_test( X_2col_charchar , X_2col_charfact , 1 )
run_mode_test( X_2col_lgllgl , X_2col_lglfact , 1 )
run_mode_test( X_2col_factfact , X_2col_intchar , 1 )
run_mode_test( X_2col_numnum , X_2col_charfact , 1 )
})
test_that( "Check comparision of non equal objects aiming for differences of length 2",{
run_mode_test( X_2col_numnum , X_2col_charlgl , 2 )
run_mode_test( X_2col_numnum , X_2col_charchar , 2 )
run_mode_test( X_2col_numnum , X_2col_lgllgl , 2 )
run_mode_test( X_2col_charchar , X_2col_lglfact , 2 )
run_mode_test( X_2col_charchar , X_2col_lgllgl , 2 )
run_mode_test( X_2col_charchar , X_2col_factfact , 2 )
run_mode_test( X_2col_lgllgl , X_2col_factfact , 2 )
})
}
run_mode_test_with_N_obs(1)
run_mode_test_with_N_obs(10)
run_mode_test_with_N_obs(100)
run_mode_test_with_N_obs(1000) |
context("link_ethoscope_metadata")
test_that("link_ethoscope_metadata with single file", {
dir <- scopr_example_dir()
test_file <- paste(dir, "ethoscope_results/029/E_029/2016-01-25_21-14-55/2016-01-25_21-14-55_029.db",sep="/")
out <- link_ethoscope_metadata(test_file)
expect_equal(nrow(out), 20)
expect_equal(unique(sapply(out$file_info, function(x) x$path)), test_file)
expect_equal(sort(out$region_id), 1:20)
out
})
test_that("link_ethoscope_metadata with date and machine name", {
dir <- paste0(scopr_example_dir(), "/ethoscope_results/")
query <- data.frame(machine_name = c("E_014", "E_014","E_029"),
date = c("2016-01-25", "2016-02-17","2016-01-25"),
time = c("21:46:14", NA, NA),
test=c(1,2,3)
)
out <- link_ethoscope_metadata(query, dir)
expect_equal(nrow(out), 60)
})
test_that("link_ethoscope_metadata with name of a text csv", {
dir <- paste0(scopr_example_dir(), "/ethoscope_results/")
query <- data.frame(machine_name = c("E_014", "E_014","E_029"),
date = c("2016-01-25", "2016-02-17","2016-01-25"),
time = c("21:46:14", NA, NA),
test=c(1,2,3)
)
file <- tempfile(fileext = ".txt")
write.csv(query, file, row.names = F)
out <- link_ethoscope_metadata(file, dir)
expect_equal(nrow(out), 60)
})
test_that("link_ethoscope_metadata with date, machine name, and ROIs", {
dir <- paste0(scopr_example_dir(), "/ethoscope_results/")
query <- data.frame(machine_name = c("E_014", "E_014","E_029"),
date = c("2016-01-25", "2016-02-17","2016-01-25"),
time = c("21:46:14", NA, NA),
test=c(1,2,3)
)
query <- data.table::as.data.table(query)
query <- query[,.(region_id=1:5),by=c(colnames(query))]
query[, treatment := rep(1:3,length.out=.N)]
out <- link_ethoscope_metadata(query, dir)
expect_equal(nrow(out), 3*5)
})
test_that("link_ethoscope_metadata with path", {
dir <- scopr_example_dir()
test_file <- paste(dir, "ethoscope_results/029/E_029/2016-01-25_21-14-55/2016-01-25_21-14-55_029.db",sep="/")
query <- data.frame(path =test_file,
test=c(1,2,3),
region_id=1:3)
out <- link_ethoscope_metadata(query, dir)
expect_equal(nrow(out), 3)
expect_equal(unique(sapply(out$file_info, function(x) x$path)), test_file)
expect_equal(sort(out$region_id), 1:3)
})
test_that("link_ethoscope_metadata detect duplicated rows", {
dir <- paste0(scopr_example_dir(), "/ethoscope_results/")
query <- data.frame(machine_name = c("E_014", "E_014","E_029"),
date = c("2016-01-25", "2016-02-17","2016-01-25"),
time = c("21:46:14", NA, NA),
test=c(1,2,3)
)
query <- data.table::as.data.table(query)
query <- query[,.(region_id=1:3),by=c(colnames(query))]
query[machine_name=="E_029" & region_id==3, region_id := 2]
expect_warning(out <- scopr:::link_ethoscope_metadata(query, dir), "Duplicated row")
expect_equal(nrow(out), 3*3-1)
}) |
norm3 <-
function(X,n,m,p,mode){
Y=as.matrix(X)
if (mode==1){
Y=t(nrm2(t(Y)))
Y=((m*p)^.5)*Y
}
if (mode==2){
Y=permnew(Y,n,m,p)
Y=t(nrm2(t(Y)))
Y=permnew(Y,m,p,n)
Y=permnew(Y,p,n,m)
Y=((n*p)^.5)*Y
}
if (mode==3){
Y=permnew(Y,n,m,p)
Y=permnew(Y,m,p,n)
Y=t(nrm2(t(Y)))
Y=permnew(Y,p,n,m)
Y=((n*m)^.5)*Y
}
return(Y)
} |
moler <- function(n) {
if (length(n) != 1 || n != round(n))
stop("Argument 'n' must be an integer.")
if (n <= 0) return(c())
A <- matrix(0, nrow = n, ncol = n)
for (i in 1:n) {
A[i, 1:i] <- (1:i) - 2
}
A <- A + t(A)
diag(A) <- 1:n
A
} |
skewness.test <-
function(x)
{
x.n <- length(x)
x.mean <- mean(x)
x.demeaned <- x - x.mean
numer <- I(x.n^2)*sum(x.demeaned^3)^2
denom <- 6*sum(x.demeaned^2)^3
statistic <- numer/denom
out <- list()
out$statistic <- statistic
out$p.value <- pchisq(statistic, 1, lower.tail=FALSE)
return(out)
} |
library(hamcrest)
library(stats)
set.seed(1)
test.rchisq.1 <- function() assertThat({set.seed(1);rchisq(n = 0x1p+0, df = c(0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2))}, identicalTo(0x1.94bd88c398c42p-3, tol = 0.000100))
test.rchisq.2 <- function() assertThat({set.seed(1);rchisq(n = 1:5, df = c(0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2))}, identicalTo(c(0x1.94bd88c398c42p-3, 0x1.46cd823ef4d7ep+0, 0x1.5b132294fbe7fp-1, 0x1.068100b13bcdap+2, 0x1.5eb0939be9dd7p-7), tol = 0.000100))
test.rchisq.3 <- function() assertThat({set.seed(1);rchisq(n = 0x1.ep+3, df = c(0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2))}, identicalTo(c(0x1.94bd88c398c42p-3, 0x1.46cd823ef4d7ep+0, 0x1.5b132294fbe7fp-1, 0x1.068100b13bcdap+2, 0x1.5eb0939be9dd7p-7, 0x1.e306a9618224cp-4, 0x1.dfe626302fd5fp+1, 0x1.24df5b420433ap+2, 0x1.9e9b6af708fb9p-2, 0x1.11f75bd9ab545p+2, 0x1.6d86c51058b56p+1, 0x1.abd6e04a61b6fp+0, 0x1.a3c1b1643823p-2, 0x1.637523ae26e75p+0, 0x1.3a53a48fe671ep-1), tol = 0.000100))
test.rchisq.4 <- function() assertThat({set.seed(1);rchisq(n = numeric(0), df = c(0x1p+0, 0x1p+1, 0x1.8p+1, 0x1p+2))}, identicalTo(numeric(0)))
test.rchisq.5 <- function() assertThat({set.seed(1);rchisq(n = 0x1.8p+1, df = c(NA, 0x1p+1, 0x1.8p+1, 0x1p+2))}, identicalTo(c(NaN, 0x1.3dbac065e1d86p-2, 0x1.62cd513230be6p+2))) |
try(dev.off(),silent=TRUE)
plot.new()
plot(as.dendrogram(hc)) |
fluidRow(
column(
width = 7,
help_dynamic,
dygraphOutput_175px("dynamic_trace_diagnostic_stepsize_out"),
br(),br(),
plotOutput("stepsize_vs_lp_out", height = "150px")
),
column(width = 5, plotOutput_400px("stepsize_vs_accept_stat_out"))
) |
rayleigh.test <- function(x, mu=NULL) {
x <- na.omit(x)
if (length(x)==0) {
warning("No observations (at least after removing missing values)")
return(NULL)
}
x <- conversion.circular(x, units="radians", zero=0, rotation="counter", modulo="2pi")
attr(x, "circularp") <- attr(x, "class") <- NULL
if (!is.null(mu)) {
mu <- conversion.circular(mu, units="radians", zero=0, rotation="counter", modulo="2pi")
attr(mu, "circularp") <- attr(mu, "class") <- NULL
}
result <- RayleighTestRad(x, mu)
result$call <- match.call()
class(result) <- "rayleigh.test"
return(result)
}
RayleighTestRad <- function(x, mu=NULL) {
n <- length(x)
if (is.null(mu)) {
ss <- sum(sin(x))
cc <- sum(cos(x))
rbar <- (sqrt(ss^2 + cc^2))/n
z <- (n * rbar^2)
p.value <- exp( - z)
if (n < 50)
temp <- 1 + (2 * z - z^2)/(4 * n) - (24 * z - 132 * z^2 + 76 * z^3 - 9 * z^4)/(288 * n^2)
else
temp <- 1
p.value <- min(max(p.value * temp,0),1)
result <- list(statistic = rbar, p.value = p.value, mu=NA)
} else {
r0.bar <- (sum(cos(x - mu)))/n
z0 <- sqrt(2 * n) * r0.bar
pz <- pnorm(z0)
fz <- dnorm(z0)
p.value <- 1 - pz + fz * ((3 * z0 - z0^3)/(16 * n) + (15 * z0 + 305 * z0^3 - 125 * z0^5 + 9 * z0^7)/(4608 * n^2))
p.value <- min(max(p.value,0),1)
result <- list(statistic = r0.bar, p.value = p.value, mu=mu)
}
return(result)
}
print.rayleigh.test <- function(x, digits=4, ...) {
rbar <- x$statistic
p.value <- x$p.value
mu <- x$mu
cat("\n", " Rayleigh Test of Uniformity \n")
if (is.na(mu)) {
cat(" General Unimodal Alternative \n\n")
} else {
cat(" Alternative with Specified Mean Direction: ", mu, "\n\n")
}
cat("Test Statistic: ", round(rbar, digits=digits), "\n")
cat("P-value: ", round(p.value, digits=digits), "\n\n")
invisible(x)
} |
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