diff --git a/server.R b/server.R
new file mode 100644
index 0000000000000000000000000000000000000000..7c1a1f4da6ebe4d44340950a254c05b100cfc329
--- /dev/null
+++ b/server.R
@@ -0,0 +1,981 @@
+Sys.setlocale("LC_ALL", "fr_FR.UTF-8")#to be sure that accents in text will be allowed in plots
+
+
+library(shiny)
+library(plotrix)
+
+
+
+shinyServer(function(input, output){
+
+ rv<-reactiveValues() # Create a reactiveValues object, to let us use settable reactive values
+
+ rv$last.takesample.value<-0
+ rv$samples.z<-list()
+
+ rv$lastAction<-'none'# To start out, lastAction == NULL, meaning nothing clicked yet
+
+ rv$lastDist<-" "
+
+ rv$lastp<-0.5
+ rv$lastminrud<-1
+ rv$lastmaxrud<-6
+ rv$lastdf<-5
+ rv$lastdf1<-5
+ rv$lastdf2<-20
+ rv$lastm1<-8
+ rv$lastm2<-4
+ rv$lastsd1<-1.5
+ rv$lastsd2<-1.1
+
+ rv$lastN<-0
+
+ # An observe block for each button, to record that the action happened
+ observe({
+ if (input$takesample != 0) {
+ rv$lastAction <- 'takesample'
+ }
+ })
+ observe({
+ if (input$reset != 0) {
+ rv$lastAction <- 'reset'
+ rv$last.takesample.value<-0
+ rv$samples.z<-list()
+ }
+ })
+
+
+ getSamples<-reactive({
+ if(input$takesample > rv$last.takesample.value && rv$lastAction == "takesample"){
+ return(isolate({#Now do the expensive stuff
+ samples<-list()
+ for (i in 1:input$ns){
+ if (input$dist == "DN") {samples[[i]]<-rnorm(input$n)}
+ if (input$dist == "DBin") {samples[[i]]<-rbinom(n = 1, size = input$n, prob = input$p)}
+ #if (input$dist == "DLN"){samples[[i]]<-rlnorm(input$n)}
+ if (input$dist == "DUD") {x <- min(input$RUD) : max(input$RUD)
+ samples[[i]]<-sample(x, input$n, replace = TRUE)}
+ if (input$dist == "DU") {samples[[i]]<-runif (input$n)}
+ if (input$dist == "DE") {samples[[i]]<-rexp(input$n)}
+ if (input$dist == "DC") {samples[[i]]<-rchisq(input$n, df = input$df)}
+ if (input$dist == "DF") {samples[[i]]<-rf(input$n,df1 = input$df1,df2 = input$df2)}
+ if (input$dist == "DB") {samples[[i]]<-c(rnorm(input$n/2,input$m1, input$sd1), rnorm(input$n/2, input$m2, input$sd2))}
+ }
+ return(samples)
+ }))
+ } else {
+ return(NULL)
+ }})
+
+
+ getPlotHeight <- function() {
+ if(input$display=="default") {
+ unit.height<-320 #cannot be auto because height is already "auto" in ui and double auto = conflict
+ }
+ if(input$display=="1024") {
+ unit.height<-280
+ }
+ if(input$display=="800") {
+ unit.height<-250
+ }
+ return(2*unit.height)
+ }
+
+ getPlotWidth <- function() {
+ if(input$display=="default") {
+ full.plot.width<-1310-400#"auto"
+ }
+ if(input$display=="1024") {
+ full.plot.width<-900-200
+ }
+ if(input$display=="800") {
+ full.plot.width<-700-200
+ }
+ if(input$visM && input$display!="default"){
+ full.plot.width<-full.plot.width+400
+ }
+ return(full.plot.width)
+ }
+
+
+ getInputValues<-reactive({
+ return(input)#collect all inputs
+ })
+
+ getComputedValues<-reactive({
+ samples<-list()
+ samples<-getSamples()
+ rv$samples.z<-c(rv$samples.z,samples)
+
+
+ v<-getInputValues() # get all values of input list
+ cv<-list()#created empty computed values list
+ cv$samples.x<-list()
+ cv$n.samples<-length(rv$samples.z)
+ cv$vx <- v$sx^2
+
+ ## Computation of sample related values ##
+ if(cv$n.samples>0){
+ for(i in 1:cv$n.samples){
+ if (v$dist == "DN") {cv$samples.x[[i]]<-round((rv$samples.z[[i]]*v$sx)+v$mx,2)}
+ if (v$dist == "DBin") {cv$samples.x[[i]]<-round(rv$samples.z[[i]],2)}
+ #if (v$dist == "DLN"){cv$samples.x[[i]]<-round((rv$samples.z[[i]]*v$lsx)+v$lmx,2)}
+ if (v$dist == "DUD") {cv$samples.x[[i]]<-rv$samples.z[[i]]}
+ if (v$dist == "DU") {cv$samples.x[[i]]<-round(rv$samples.z[[i]]*v$b,2)}
+ if (v$dist == "DE") {cv$samples.x[[i]]<-round(rv$samples.z[[i]]*(1/v$Lambda),2)}
+ if (v$dist == "DC") {cv$samples.x[[i]]<-round(rv$samples.z[[i]], 2)}
+ if (v$dist == "DF") {cv$samples.x[[i]]<-round(rv$samples.z[[i]], 2)}
+ if (v$dist == "DB") {cv$samples.x[[i]]<-round(rv$samples.z[[i]], 2)}
+ }
+
+ ## Automatic reset en cas de modification des paramètres
+ if (rv$lastDist!=v$dist) {
+ rv$lastAction <- 'changeDist'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastN!=v$n) {
+ rv$lastAction <- 'changeN'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastp!=v$p) {
+ rv$lastAction <- 'changep'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastminrud!=min(v$RUD)) {
+ rv$lastAction <- 'changeminrud'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastmaxrud!=max(v$RUD)) {
+ rv$lastAction <- 'changemaxrud'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastdf!=v$df) {
+ rv$lastAction <- 'changedf'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastdf1!=v$df1) {
+ rv$lastAction <- 'changedf1'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastdf2!=v$df2) {
+ rv$lastAction <- 'changedf2'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastm1!=v$m1) {
+ rv$lastAction <- 'changem1'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastm2!=v$m2) {
+ rv$lastAction <- 'changem2'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastsd1!=v$sd1) {
+ rv$lastAction <- 'changesd1'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+ }
+
+ if (rv$lastsd2!=v$sd2) {
+ rv$lastAction <- 'changesd2'
+ rv$last.takesample.value<-0
+ rv$samples.z <- list()
+ cv$samples.x <- list()
+
+ }
+
+ ##Pour passer d'une liste à une matrice
+ if(v$dist == "DBin"){
+ cv$samples.x.mat <- matrix(nrow = cv$n.samples, ncol = 1)
+ cv$samples.p.mat <- matrix(nrow = cv$n.samples, ncol = 1)
+ for(i in 1:cv$n.samples){
+ cv$samples.x.mat[i,1] <- cv$samples.x[[i]]
+ cv$samples.p.mat[i,1] <-cv$samples.x[[i]]/v$n
+ }
+ }
+ else{
+ cv$samples.x.mat <- matrix(nrow = cv$n.samples, ncol = v$n)
+ for(i in 1:cv$n.samples){
+ cv$samples.x.mat[i,] <- cv$samples.x[[i]]
+ }
+ }
+
+ ## Computation of descriptives
+ cv$samples.x.m.vec<-c() # vector of mean values, each line a sample
+ cv$samples.x.sd.vec<-c()
+ cv$samples.x.m.vec<-round(apply(cv$samples.x.mat,1,mean),2)#means of samples
+ cv$samples.x.sd.vec<-round(apply(cv$samples.x.mat,1,sd),2)#sds of samples
+ if(v$dist == "DBin"){
+ cv$samples.p.m.vec<-round(apply(cv$samples.p.mat,1,mean),2)#means of samples
+ cv$samples.p.sd.vec<-round(apply(cv$samples.p.mat,1,sd),2)#sds of samples
+ }
+
+ ## Define subset to plot
+ cv$samples.x.n.toshow<-0
+ cv$samples.x.from<-1
+ if(cv$n.samples>5){
+ cv$samples.x.from<-cv$n.samples-5+1
+ }
+ cv$samples.x.to<-cv$n.samples
+
+
+ if(v$dist =="DBin"){
+ cv$samples.x.mat.toshow<-matrix(cv$samples.x.mat[cv$samples.x.from:cv$samples.x.to,],ncol=1)
+ }
+ else{
+ cv$samples.x.mat.toshow<-matrix(cv$samples.x.mat[cv$samples.x.from:cv$samples.x.to,],ncol=v$n)
+ }
+
+ cv$samples.x.m.vec.toshow<-cv$samples.x.m.vec[cv$samples.x.from:cv$samples.x.to]
+ cv$samples.x.sd.vec.toshow<-cv$samples.x.sd.vec[cv$samples.x.from:cv$samples.x.to]
+ cv$samples.x.i.vec.toshow<-c(cv$samples.x.from:cv$samples.x.to)
+ cv$samples.x.n.toshow<-length(cv$samples.x.mat.toshow[,1])
+
+
+ cv$samples.x.m.m <- round(mean(cv$samples.x.m.vec),4)
+ cv$samples.x.v.m <- round(var(cv$samples.x.m.vec),4)
+
+ if(v$dist == "DBin"){
+ cv$samples.p.m.m <- round(mean(cv$samples.p.m.vec),4)
+ cv$samples.p.v.m <- round(var(cv$samples.p.m.vec),4)
+
+ cv$vx<-v$sx^2
+ cv$lvx<-v$lsx^2
+
+ }
+
+ ## Valeurs qui serviront à définir les limites des axes
+ hf<-hist(cv$samples.x.mat, freq= TRUE, breaks = 50)
+ freqcl<- hf$counts
+ cv$maxfreqcl<-max(freqcl)
+ n.obs.tot<-length(cv$samples.x.mat)
+ probcl <- freqcl/n.obs.tot
+ cv$maxprobcl<-max(probcl)
+
+
+ #if(cv$n.samples <100){breaks =10}
+ #else{breaks <- sqrt(cv$n.samples)}
+
+ #hm<-hist(cv$samples.x.m.vec, freq = TRUE, breaks = breaks)
+ #cv$freqmcl <- unlist(hm[2])
+
+ #densm<-density(cv$samples.x.m.vec)
+ #cv$highdensm <- unlist(densm[2])
+ }
+
+ ## Last takesample value
+ rv$last.takesample.value<-v$takesample
+ rv$lastDist<-v$dist
+ rv$lastp<-v$p
+ rv$lastminrud<-min(v$RUD)
+ rv$lastmaxrud<-max(v$RUD)
+ rv$lastdf<-v$df
+ rv$lastdf1<-v$df1
+ rv$lastdf2<-v$df2
+ rv$lastm1<-v$m1
+ rv$lastm2<-v$m2
+ rv$lastsd1<-v$sd1
+ rv$lastsd2<-v$sd2
+
+ rv$lastN<-v$n
+
+
+ return(cv)
+
+ })
+
+
+ output$doublePlot <- renderPlot({
+
+ v <- getInputValues ()
+ cv <- getComputedValues ()
+
+ par(mfcol = c(2,2))
+ m<-matrix(c(1,2,3,4),2,2,byrow=TRUE)
+ layout(m, width=c(4,3)) #
+
+ ## Set graphic parameters
+ if(v$display=="default") {
+ cex.main.title<-2
+ cex.title<-1.5
+ cex.samples<-1.5
+ cex.axis<-1.1
+ cex.label<-1.2
+ }
+ if(v$display=="1024") {
+ cex.main.title<-1.75
+ cex.title<-1.2
+ cex.samples<-1.2
+ cex.axis<-1
+ cex.label<-1
+ }
+ if(v$display=="800") {
+ cex.main.title<-1.5
+ cex.title<-1
+ cex.samples<-1
+ cex.axis<-0.8
+ cex.label<-0.8
+ }
+
+
+ #Définition des limites de l'axe des abscisses pour le plot
+
+ if(v$dist=="DN"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdn)
+ x.lim.sup<-max(v$rangeXdn)}
+ if(v$dist=="DBin"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdbin)
+ x.lim.sup<-max(v$rangeXdbin)}
+ #if(v$dist=="DLN"&& v$range =="SameRange"){x.lim.inf<-min(v$rangeXdln)
+ # x.lim.sup<-max(v$rangeXdln)}
+ if(v$dist=="DUD"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdud)
+ x.lim.sup<-max(v$rangeXdud)}
+ if(v$dist=="DU"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdu)
+ x.lim.sup<-max(v$rangeXdu)}
+ if(v$dist=="DE"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXde)
+ x.lim.sup<-max(v$rangeXde)}
+ if(v$dist=="DC"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdc)
+ x.lim.sup<-max(v$rangeXdc)}
+ if(v$dist=="DF"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdf)
+ x.lim.sup<-max(v$rangeXdf)}
+ if(v$dist=="DB"&& v$range =="SameRange") {x.lim.inf<-min(v$rangeXdb)
+ x.lim.sup<-max(v$rangeXdb)}
+
+ if(v$dist=="DN"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdn)
+ Obs.lim.sup<-max(v$rangeObsdn)
+ Xbar.lim.inf<-min(v$rangeXbardn)
+ Xbar.lim.sup<-max(v$rangeXbardn)}
+ if(v$dist=="DBin"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdbin)
+ Obs.lim.sup<-max(v$rangeObsdbin)
+ Xbar.lim.inf<-min(v$rangeXbardbin)
+ Xbar.lim.sup<-max(v$rangeXbardbin)}
+ #if(v$dist=="DLN"&& v$range =="DifRange"){Obs.lim.inf<-min(v$rangeObsdln)
+ # Obs.lim.sup<-max(v$rangeObsdln)
+ # Xbar.lim.inf<-min(v$rangeXbardln)
+ # Xbar.lim.sup<-max(v$rangeXbardln)}
+ if(v$dist=="DUD"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdud)
+ Obs.lim.sup<-max(v$rangeObsdud)
+ Xbar.lim.inf<-min(v$rangeXbardud)
+ Xbar.lim.sup<-max(v$rangeXbardud)}
+ if(v$dist=="DU"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdu)
+ Obs.lim.sup<-max(v$rangeObsdu)
+ Xbar.lim.inf<-min(v$rangeXbardu)
+ Xbar.lim.sup<-max(v$rangeXbardu)}
+ if(v$dist=="DE"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsde)
+ Obs.lim.sup<-max(v$rangeObsde)
+ Xbar.lim.inf<-min(v$rangeXbarde)
+ Xbar.lim.sup<-max(v$rangeXbarde)}
+ if(v$dist=="DC"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdc)
+ Obs.lim.sup<-max(v$rangeObsdc)
+ Xbar.lim.inf<-min(v$rangeXbardc)
+ Xbar.lim.sup<-max(v$rangeXbardc)}
+ if(v$dist=="DF"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdf)
+ Obs.lim.sup<-max(v$rangeObsdf)
+ Xbar.lim.inf<-min(v$rangeXbardf)
+ Xbar.lim.sup<-max(v$rangeXbardf)}
+ if(v$dist=="DB"&& v$range =="DifRange") {Obs.lim.inf<-min(v$rangeObsdb)
+ Obs.lim.sup<-max(v$rangeObsdb)
+ Xbar.lim.inf<-min(v$rangeXbardb)
+ Xbar.lim.sup<-max(v$rangeXbardb)}
+
+
+ #Définition des X conditionnellement à la distribution
+ if(v$dist=="DN"){X=seq(-10,40, length=1000)}
+ if(v$dist=="DBin"){X=0:v$n}
+ #if(v$dist=="DLN"){X=seq(-10,40, length=1000)}
+ if(v$dist=="DUD"){X= min(v$RUD):max(v$RUD)}
+ if(v$dist=="DU"){X=seq(-5,25, length=1000)}
+ if(v$dist=="DE"){X=seq(-5,20, length=1000)}
+ if(v$dist=="DC"){X=seq(-5,60, length=1000)}
+ if(v$dist=="DF"){X=seq(-5,10, length=1000)}
+
+ #Définition de la densité théorique
+ getY <-reactive({
+ if (v$dist=="DN")
+ return(dnorm(X, mean=v$mx, sd=v$sx))
+ if (v$dist=="DBin")
+ return(dbinom(X, size=v$n, prob=v$p))
+ #if (v$dist=="DLN")
+ # return(dlnorm(X,meanlog=v$lmx, sdlog =v$lsx))
+ if (v$dist=="DU")
+ return(dunif (X, min=0, max=v$b))
+ if (v$dist=="DE")
+ return (dexp(X, rate=v$Lambda))
+ if (v$dist=="DC")
+ return (dchisq(X, df=v$df))
+ if (v$dist=="DF")
+ return(df(X,df1=v$df1, df2=v$df2))
+ if (v$dist=="DB")
+ return(density(c(rnorm(1000000/2,v$m1, v$sd1), rnorm(1000000/2, v$m2, v$sd2))))
+ })
+
+ #------------------- Output 1 : ------------------------------
+ #Afficher les observations pour 5 échantillons prélevés
+ #Afficher la distribution théorique d'origine (optionnel
+ #-------------------------------------------------------------
+
+ if (v$dist == "DB"){
+ dens<-getY()
+ d<-unlist(dens[2])
+ y.delta<-max(d)
+ }
+ else{
+ if (v$dist == "DUD"){
+ p <- rep(1/length(X), length(X))
+ y.delta<-p[1]+p[1]/length(X)
+ }
+ else{
+ y.delta <- max(getY())
+ }
+ }
+
+ cv$samples.y.mat.toshow<-c()
+ if(cv$n.samples>0 && cv$samples.x.n.toshow>0){
+ if(v$dist == "DBin"){
+ cv$samples.y.mat.toshow<-matrix(rep(y.delta/(5+1)*c(1:cv$samples.x.n.toshow),length(cv$samples.x.mat.toshow[,1])),nrow=length(cv$samples.x.mat.toshow[,1]), ncol = 1)
+ }
+ else{
+ cv$samples.y.mat.toshow<-matrix(rep(y.delta/(5+1)*c(1:cv$samples.x.n.toshow),length(cv$samples.x.mat.toshow[,1])),nrow=length(cv$samples.x.mat.toshow[,1]), ncol = v$n)
+ }
+ }
+
+ ## Définition de pour l'axe des X
+ if(v$range =="SameRange"){
+ lim.inf<-x.lim.inf
+ lim.sup<-x.lim.sup
+ }
+ if(v$range =="DifRange"){
+ lim.inf<-Obs.lim.inf
+ lim.sup<-Obs.lim.sup
+ }
+ range<-lim.sup-lim.inf
+
+
+ ## Définition du nb de graduations pour l'axe des X
+ if(v$dist == "DBin"){
+ nbgrad<-10
+ }
+ else{
+ if(v$dist == "DUD"){
+ nbgrad<-range
+ }
+ else{
+ if(range>10){nbgrad <- range}
+ if(range>5 & range <=10){nbgrad <- range*2}
+ if(range<=5){nbgrad <- range*4}
+ }}
+
+ ## Test about range of 'x'
+
+ if(cv$n.samples>0){
+ if(max(cv$samples.x.mat) > lim.sup || min(cv$samples.x.mat) < lim.inf) {error <-1}
+ else{error <-0}
+ }
+
+
+##############PLOT########################
+par(mai=c(0.5,0.8,0.5,0.5))
+### CAS N°1 : Si aucune donnée :
+ if(is.null(cv$samples.x.mat)){
+
+ ### CAS N°1.1 : Si l'option "afficher la distribution théorique est cochée :
+ if(v$showreality){
+ #Si distribution Binomiale
+ if (v$dist=="DBin"){
+ Y <- getY()
+ plot(Y, type = "h",bty="n", xaxs="i",yaxs="i",xlab ="x",cex.lab=cex.label,cex.axis=cex.axis, ylab = expression(P(x)), xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad), lwd=2, col = "red", main = "")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ mtext(bquote(paste(X*"~"*Bin(n*","*p)," ",X*"~"*Bin(.(v$n)*","*.(v$p)),sep='')), side=3,line=-1,adj=0.05, cex=cex.label)
+ }
+ else{
+ #Si distribution Uniforme Discrète
+ if (v$dist=="DUD"){
+ plot(X, p, col = "red", type = "h",bty="n", xaxs="i",yaxs="i",,cex.lab=cex.label,cex.axis=cex.axis, xlab =" ", ylab=" ",lwd = 2, xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad),ylim = c(0, y.delta), main = "")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ mtext(bquote(paste(X*"~"*U*"{"*.(min(v$RUD))*",...,"*.(max(v$RUD))*"}",sep='')), side=3,line=1,adj=-0.1, cex=cex.label)
+ points(X,p, col = "red", lwd = 2, pch = 19)
+ lines(X, p, lty = 3)
+ }
+ #Pour les autres distributions
+ else{
+ plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=cex.label,cex.axis=cex.axis,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="",ylab=" ",xaxp=c(lim.inf,lim.sup,nbgrad),main="")
+ axis(2,las=2,yaxp=c(0,signif(y.delta,1),5),cex.axis=cex.axis)
+ if (v$dist == "DB"){
+ dens <- getY()
+ lines(dens)
+ }
+ else {
+ Y<-getY()
+ points(X,Y, type="l")
+ }
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+
+ if(v$dist=="DN"){mtext(bquote(paste(X*"~"*N(mu*","*sigma^2) ," ", X*"~"*N(.(v$mx)*","*.(cv$vx)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DU"){mtext(bquote(paste(X*"~"*U(theta[1]*","*theta[2]) ," ", X*"~"*U(.0*","*.(v$b)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DE"){mtext(bquote(paste(X*"~"*E(lambda) ," ", X*"~"*E(.(v$Lambda)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DC"){mtext(bquote(paste(X*"~"*chi^2, (nu)," ", X*"~"*chi^2,(.(v$df)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DF"){mtext(bquote(paste(X*"~"*F[nu[1]*","*nu[2]] ," ", X*"~"*F[.(v$df1)*","*.(v$df2)],sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+
+ }}
+ }
+
+ ###CAS N°1.2 : Si l'option "afficher la distribution théorique" n'est pas cochée :
+ else{
+ #par(mai=c(0.5,0.8,0.5,0.5))
+ plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=cex.label,cex.axis=cex.axis,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="",ylab=" ",xaxp=c(lim.inf,lim.sup,nbgrad),main="")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ }
+ }
+
+##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
+ else{
+ ### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
+ if(error==1){
+ plot(1:10,1:10, col = "white", xlab="",ylab="",xaxt="n",yaxt="n",bty="n",type='l')
+ text(5,8, labels = bquote("Certaines valeurs dépassent les limites défines en abscisse."), cex = cex.label, col = "red")
+ text(5,7, labels = bquote("Modifiez le choix de l'étendue au moyen du slider adéquat."), cex = cex.label, col = "red")
+ }
+ ### CAS N°2.2 : Si pas d'erreur :
+ if(error==0){
+ ### CAS N°2.2.1 : Si l'option "afficher la distribution théorique" est cochée :
+ if(v$showreality){
+ ##Si la distribution est binomiale :
+ if (v$dist=="DBin"){
+ Y <- getY()
+ plot(Y, type = "h",bty="n", xaxs="i",yaxs="i",xlab ="x", ylab = expression(P(x)),cex.lab=cex.label,cex.axis=cex.axis, xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad), lwd=2, col = "red", main = "")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ mtext(bquote(paste(X*"~"*Bin(n*","*p)," ",X*"~"*Bin(.(v$n)*","*.(v$p)),sep='')), side=3,line=-1,adj=0.05, cex=cex.label)
+ for(i in 1:cv$samples.x.n.toshow){
+ points(cv$samples.x.mat.toshow[i,],cv$samples.y.mat.toshow[i,],cex=cex.samples*0.8)
+ text(cv$samples.x.m.vec.toshow[i],cv$samples.y.mat.toshow[i,1],labels=bquote(x[.(cv$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col="blue")
+ }
+ }
+
+ else{
+ ##Si la distribution est uniforme discète :
+ if (v$dist=="DUD"){
+ plot(X, p, col = "red", type = "h",bty="n", xaxs="i",yaxs="i", ylab=" ",cex.lab=cex.label,cex.axis=cex.axis,lwd = 2, xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad),ylim = c(0, y.delta), main = "")
+ mtext(bquote(paste("Distribution théorique")), side=3,line=1,adj=0.5, cex=cex.label)
+ mtext(bquote(paste(X*"~"*U*"{"*.(min(v$RUD))*",...,"*.(max(v$RUD))*"}",sep='')), side=3,line=1,adj=-0.1, cex=cex.label)
+ points(X,p, col = "red", lwd = 2, pch = 19)
+ lines(X, p, lty = 3)
+ for(i in 1:cv$samples.x.n.toshow){
+ points(jitter(cv$samples.x.mat.toshow[i,],0.5),jitter(cv$samples.y.mat.toshow[i,],0.5),cex=cex.samples*0.8)
+ text(cv$samples.x.m.vec.toshow[i],cv$samples.y.mat.toshow[i,1],labels=bquote(bar(x)[.(cv$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col="blue")
+ }
+ }
+
+ ##Pour toutes les autres distributions :
+ else{
+ plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=cex.label,cex.axis=cex.axis,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="",ylab=" ",xaxp=c(lim.inf,lim.sup,nbgrad),main="")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ for(i in 1:cv$samples.x.n.toshow){
+ points(cv$samples.x.mat.toshow[i,],cv$samples.y.mat.toshow[i,],cex=cex.samples*0.8)
+ text(cv$samples.x.m.vec.toshow[i],cv$samples.y.mat.toshow[i,1],labels=bquote(bar(x)[.(cv$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col="blue")
+ }
+ axis(2,las=2,yaxp=c(0,signif(y.delta,1),5),cex.axis=cex.axis)
+
+ if (v$dist == "DB"){
+ dens <- getY()
+ lines(dens)
+ }
+ else {
+ Y<-getY()
+ points(X,Y, type="l")
+ }
+
+ if(v$dist=="DN"){mtext(bquote(paste(X*"~"*N(mu*","*sigma^2) ," ", X*"~"*N(.(v$mx)*","*.(v$sx)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DU"){mtext(bquote(paste(X*"~"*U(theta[1]*","*theta[2]) ," ", X*"~"*U(.0*","*.(v$b)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DE"){mtext(bquote(paste(X*"~"*E(lambda) ," ", X*"~"*E(.(v$Lambda)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DC"){mtext(bquote(paste(X*"~"*chi^2, (nu)," ", X*"~"*chi^2,(.(v$df)),sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ if(v$dist=="DF"){mtext(bquote(paste(X*"~"*F[nu[1]*","*nu[2]] ," ", X*"~"*F[.(v$df1)*","*.(v$df2)],sep='')), side=3,line=1,adj=-0.1, cex=cex.label)}
+ }
+ }
+ }
+
+ ###CAS N°2.2.2 : Si la case "Afficher la distribution théorique n'est pas cochée":
+ else{
+ ##Si la distribution est binomiale :
+ if (v$dist=="DBin"){
+ plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=cex.label,cex.axis=cex.axis,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="",ylab=" ",xaxp=c(lim.inf,lim.sup,nbgrad), main="") #,
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ for(i in 1:cv$samples.x.n.toshow){
+ points(cv$samples.x.mat.toshow[i,],cv$samples.y.mat.toshow[i,],cex=cex.samples*0.8)
+ text(cv$samples.x.m.vec.toshow[i],cv$samples.y.mat.toshow[i,1],labels=bquote(x[.(cv$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col="blue")
+ }
+ }
+ else{
+ plot(c(0),c(-5),lty=1,lwd=1,col="black",yaxt="n",bty="n",las=1,xaxs="i",yaxs="i",cex.lab=cex.label,cex.axis=cex.axis,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="",ylab=" ",xaxp=c(lim.inf,lim.sup,nbgrad), main="")
+# mtext(bquote(paste("Echantillons prélevés :")), side=3,line=1,adj=0.5, cex=cex.label)
+ for(i in 1:cv$samples.x.n.toshow){
+ if(v$dist=="DUD"){points(jitter(cv$samples.x.mat.toshow[i,],0.5),jitter(cv$samples.y.mat.toshow[i,],0.5),cex=cex.samples*0.8)}
+ else{points(cv$samples.x.mat.toshow[i,],cv$samples.y.mat.toshow[i,],cex=cex.samples*0.8)}
+ text(cv$samples.x.m.vec.toshow[i],cv$samples.y.mat.toshow[i,1],labels=bquote(bar(x)[.(cv$samples.x.i.vec.toshow[i])]),cex=cex.samples*1.2,col="blue")
+ }
+ }}}
+ }
+
+
+
+
+
+ #------------------- Output 2 : --------------------------------------
+ #Afficher les stats descriptives des échantillons prélevés (optionnel)
+ #---------------------------------------------------------------------
+
+### CAS n°1 : Si aucune donnée :
+ if(is.null(cv$samples.x.mat)){
+ par(mai=c(0.5,0,0.5,0))
+ plot(c(0,1),c(0,0),col="white",xaxt="n",yaxt="n",xlab="",ylab="",ylim=c(0,y.delta),bty="n",las=1)
+ mtext(bquote(paste("Descriptives : ", N == .(0), sep="")),side=3,line=1,adj=0, at=0.00, cex=cex.label)
+ }
+
+### CAS n°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
+ else{
+ par(mai=c(0.5,0,0.5,0))
+ plot(c(0,1),c(0,0),col="white",xaxt="n",yaxt="n",xlab="",ylab="",ylim=c(0,y.delta),bty="n",las=1)
+
+### CAS N°2.1 : Si l'option "afficher les stat descr" est cochée :
+ if(v$empPl){
+ mtext(bquote(paste("Descriptives : ", N == .(cv$n.samples), sep="")),side=3,line=1,adj=0, at=0.00, cex=cex.label)
+
+ if(cv$samples.x.n.toshow>0){
+ if(v$dist =="DBin"){
+ for(i in 1:cv$samples.x.n.toshow){
+ text(0,cv$samples.y.mat.toshow[i,1],labels=bquote(paste(x[.(cv$samples.x.i.vec.toshow[i])] == .(sprintf("%.2f",cv$samples.x.mat.toshow[i])),sep="")),col="blue",pos=4, cex=cex.samples)
+ text(0.3,cv$samples.y.mat.toshow[i,1],labels=bquote(paste(p[.(cv$samples.x.i.vec.toshow[i])] == .(sprintf("%.2f",cv$samples.x.mat.toshow[i]/v$n)),sep="")),pos=4,cex=cex.samples)
+ }
+ }
+ else{
+ for(i in 1:cv$samples.x.n.toshow){
+ text(0,cv$samples.y.mat.toshow[i,1],labels=bquote(paste(bar(x)[.(cv$samples.x.i.vec.toshow[i])] == .(sprintf("%.2f",cv$samples.x.m.vec.toshow[i])),sep="")),col="blue",pos=4, cex=cex.samples)
+ text(0.3,cv$samples.y.mat.toshow[i,1],labels=bquote(paste(s[.(cv$samples.x.i.vec.toshow[i])] == .(sprintf("%.2f",cv$samples.x.sd.vec.toshow[i])),sep="")),pos=4,cex=cex.samples)
+ }
+ }
+ if(cv$n.samples>1 && v$dist!="DBin"){
+ mtext(bquote(paste("E(",bar(X),")" == .(cv$samples.x.m.m), sep="")),side=1,line=1,adj=0, at=0.01, cex=cex.label)
+ mtext(bquote(paste("V(",bar(X),")" == .(cv$samples.x.v.m), sep="")),side=1,line=1,adj=0, at=0.31, cex=cex.label)
+ }
+ if(cv$n.samples>1 && v$dist=="DBin"){
+ mtext(bquote(paste("E(",X,")" == .(cv$samples.x.m.m), sep="")),side=1,line=-1,adj=0, at=0.01, cex=cex.label)
+ mtext(bquote(paste("V(",X,")" == .(cv$samples.x.v.m), sep="")),side=1,line= 1,adj=0, at=0.01, cex=cex.label)
+ mtext(bquote(paste("E(",p,")" == .(cv$samples.p.m.m), sep="")),side=1,line=-1,adj=0, at=0.31, cex=cex.label)
+ mtext(bquote(paste("V(",p,")" == .(cv$samples.p.v.m), sep="")),side=1,line= 1,adj=0, at=0.31, cex=cex.label)
+
+ }
+
+ }}
+ ### CAS N°2.2 : Si l'option "afficher les stat descr" n'est pas cochée :
+ else {}
+ }
+
+
+
+
+ #------------------- Output 3 : --------------------------------------
+ #Histogramme des données d'échantillonnage
+ #Afficher leur distribution (optionnel)
+ #---------------------------------------------------------------------
+
+ ##Définition des limites pour l'axe des X
+
+ if(v$range =="SameRange"){
+ lim.inf<-x.lim.inf
+ lim.sup<-x.lim.sup
+ }
+ if(v$range =="DifRange"){
+ lim.inf<-Obs.lim.inf
+ lim.sup<-Obs.lim.sup
+ }
+ range<-lim.sup-lim.inf
+
+
+ ## Définition du nb de graduations pour l'axe des X
+ if(v$dist == "DBin"){
+ nbgrad<-10
+ }
+ else{
+ if(v$dist == "DUD"){
+ nbgrad<-range
+ }
+ else{
+ if(range>10){nbgrad <- range}
+ if(range>5 & range <=10){nbgrad <- range*2}
+ if(range<=5){nbgrad <- range*4}
+ }}
+
+ ## Test about range of 'x'
+ if(cv$n.samples>0){
+ if(max(cv$samples.x.mat) > lim.sup || min(cv$samples.x.mat) < lim.inf) {error <-1}
+ else{error <-0}
+ }
+
+
+##############PLOT########################
+par(mai=c(0.5,0.8,0.5,0.5))
+
+### CAS N°1 : Si aucune donnée :
+
+if(is.null(cv$samples.x.mat)){
+ Y <- c()
+ X <-c()
+ plot(X, Y, main="",yaxt="n",bty="n",cex.axis=cex.axis,cex.lab = cex.label,xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta),xlab="", ylab = "",xaxp=c(lim.inf,lim.sup,nbgrad))
+ if(v$dist == "DBin"){mtext(bquote(paste("Distribution du nombre de succès (N tentatives)")), side=3,line=1,adj=0.5, cex=cex.label)}
+ else{
+ if(v$dist == "DUD"){mtext(bquote(paste("Distribution des données d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)}
+ else{mtext(bquote(paste("Histogramme des données d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)}
+ }
+ }
+
+
+##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
+ else{
+ ### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
+ if(error==1){
+ plot(1:10,1:10, col = "white", xlab="",ylab="",xaxt="n",yaxt="n",bty="n",type='l')
+ text(5,8, labels = bquote("Certaines valeurs dépassent les limites défines en abscisse."), cex = cex.label, col = "red")
+ text(5,7, labels = bquote("Modifiez le choix de l'étendue au moyen du slider adéquat."), cex = cex.label, col = "red")
+ }
+ ### CAS N°2.2 : Si pas d'erreur
+ if(error==0){
+
+ #Si la distribution est Binomiale & que l'option "afficher la densité normale" est cochée :
+ if (v$dist=="DBin"&&v$showNdensity) {
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.mat, probability=TRUE,yaxt="n",bty="n",xaxs="i",yaxs="i",xlab="",ylab=HTML("Densité"), xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad), col = 'grey',main = "", breaks = 50, cex.lab = cex.label) #, ylim=c(0,cv$maxfreqcl*1.1)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution du nombre de succès (N tentatives)")), side=3,line=1, adj=0.5, cex=cex.label)
+ if(cv$n.samples>1){
+ mtext(bquote(paste(X%~~%N(np*","*np(1-p)),sep='')), side=3,line=-1,adj=0.05, cex=cex.label)
+ mtext(bquote(paste(X%~~%N(.(cv$samples.x.m.m)*","*.(cv$samples.x.v.m)),sep='')), side=3,line=-3,adj=0.05, cex=cex.label)
+ lim_dens_inf <- min (cv$samples.x.mat)-1
+ lim_dens_sup <- max(cv$samples.x.mat)+1
+ xfit<-seq(lim_dens_inf,lim_dens_sup,length=1000)
+ yfit<-dnorm(xfit,mean=mean(cv$samples.x.mat),sd=sd(cv$samples.x.mat))
+ lines(xfit, yfit, col="blue", type = 'l',lwd=2)
+ }}
+
+ #Si la distribution est Binomiale mais que l'option "afficher la densité normale" n'est pas cochée :
+ else {
+ if(v$dist=="DBin"){
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.mat, freq=TRUE,yaxt="n",bty="n",xaxs="i",yaxs="i",xlab="",ylab=HTML("Fréquences"), xlim=c(lim.inf,lim.sup), ylim=c(0,cv$maxfreqcl*1.1),xaxp=c(lim.inf,lim.sup,nbgrad), col = 'grey',main = "", breaks = 50, cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution du nombre de succès (N tentatives)")), side=3,line=1, adj=0.5, cex=cex.label)
+ }
+
+ #Si la distribution est Uniforme discrète et que l'option "afficher la distribution théorique" est cochée:
+ else{
+ if (v$dist=="DUD"){
+ if(v$showreality){
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ b<-barplot(prop.table(table(cv$samples.x.mat)), bty="n", yaxt="n", col = 'grey',main = "",space = 2,xlab="", ylab=HTML("Fréquences relatives"), cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution des données d'échantillonnage")), side=3,line=1, adj=0.5, cex=cex.label)
+ abline(h=p, lty = 3, lwd = 2)
+ }
+ #Si la distribution est Uniforme discrète et que l'option "afficher la distribution théorique" n'est pas cochée:
+ else{
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ b<-barplot(table(cv$samples.x.mat), bty="n", yaxt="n", col = 'grey',main = "",space = 2,xlab="", ylab=HTML("Fréquences"), cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution des données d'échantillonnage")), side=3,line=1, adj=0.5, cex=cex.label)
+ }
+ }
+ #Pour toutes les autres distributions quand l'option "afficher la distribution théorique" est cochée:
+ else{
+ if(v$showreality){
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.mat, probability=TRUE,yaxt="n",bty="n", xaxs="i",yaxs="i",xlab="", ylab=HTML("Densité"),xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad),col = 'grey',main = "",breaks=50, cex.lab=cex.label) #,ylim =c(0, max(c(y.delta, cv$maxprobcl))*1.1)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Histogramme des données d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)
+ #afficher la distribution théorique
+ if (v$dist == "DB"){lines(getY())}
+ else{lines(X, getY(), type = 'l')}
+ }
+ #Pour toutes les autres distributions quand l'option "afficher la distribution théorique" n'est pas cochée:
+ else{
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.mat, freq=TRUE,yaxt="n",bty="n",xaxs="i",yaxs="i",xlab="",ylab=HTML("Fréquences"), xlim=c(lim.inf,lim.sup), ylim=c(0,cv$maxfreqcl*1.1),xaxp=c(lim.inf,lim.sup,nbgrad), col = 'grey',main = "", breaks = 50, cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Histogramme des données d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)
+ }
+ }}
+ }
+}}
+
+ #------------------- Output 4 : --------------------------------------
+ #Histogramme des moyennes d'échantillonnage
+ #Afficher leur distribution (optionnel)
+ #---------------------------------------------------------------------
+
+
+ ##Définition des limites pour l'axe des X
+ if(v$range =="SameRange"){
+ lim.inf<-x.lim.inf
+ lim.sup<-x.lim.sup
+ }
+ if(v$range =="DifRange"){
+ lim.inf<-Xbar.lim.inf
+ lim.sup<-Xbar.lim.sup
+ }
+
+ range <-lim.sup-lim.inf
+
+ ## Définition du nb de graduations pour l'axe des X
+ if(v$dist == "DBin"){
+ nbgrad<-10
+ }
+ else{
+ if(v$dist == "DUD"){
+ nbgrad<-range
+ }
+ else{
+ if(range>10){nbgrad <- range}
+ if(range>5 & range <=10){nbgrad <- range*2}
+ if(range<=5){nbgrad <- range*4}
+ }
+ }
+
+ ##Définition du nb d'intervalles pour l'histogramme
+ if(v$dist =="DE" || v$dist =="DF") {
+ breaks<-seq(lim.inf, lim.sup, 0.01)
+ }
+ else {
+ breaks<-seq(lim.inf, lim.sup, 0.05)
+ }
+
+ ## Test about range of 'x'
+
+ if(v$dist == "DBin"){
+ if(cv$n.samples>0){
+ if(max(cv$samples.p.mat) > lim.sup || min(cv$samples.p.mat) < lim.inf) {error <-1}
+ else{error <-0}
+ }
+ }
+ else{
+ if(cv$n.samples>0){
+ #for (i in 1: length(cv$samples.x.m.vec)){
+ if(max(cv$samples.x.m.vec) > lim.sup || min(cv$samples.x.m.vec)< lim.inf) {error <-1}
+ else{error <-0}
+ #}
+ }
+ }
+
+ ##############PLOT########################
+ par(mai=c(0.5,0.8,0.5,0.5))
+
+ ### CAS N°1 : Si aucune donnée :
+
+ if(is.null(cv$samples.x.mat)){
+ Y <- c()
+ X <-c()
+ par(mai=c(0.5,0.8,0.5,0), xaxs="i",yaxs="i")
+ plot(X, Y, main="",yaxt="n",bty="n",xlab="", ylab = "", xlim=c(lim.inf,lim.sup),ylim=c(0,y.delta), cex.axis=cex.axis,cex.lab = cex.label, ,xaxp=c(lim.inf,lim.sup,nbgrad))
+ if(v$dist == "DBin"){mtext(bquote(paste("Distribution de la proportion de succès")), side=3,line=1,adj=0.5, cex=cex.label)}
+ else{mtext(bquote(paste("Histogramme des moyennes d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)}
+ }
+
+ ##CAS N°2 : Si 1 ou plusieurs échantillons ont déjà été tirés :
+
+ else{
+
+ ### CAS N°2.1 : Si conflit entre limites des X et observations prélevées : afficher un msg d'erreur
+ if(error==1){
+ plot(1:10,1:10, col = "white", xlab="",ylab="",xaxt="n",yaxt="n",bty="n",type='l')
+ text(5,8, labels = bquote("Certaines valeurs dépassent les limites défines en abscisse."), cex = cex.label*3/4, col = "red")
+ text(5,7, labels = bquote("Modifiez le choix de l'étendue au moyen du slider adéquat."), cex = cex.label*3/4, col = "red")
+ }
+
+ ### CAS N°2.2 : Si pas d'erreur
+ if(error==0){
+
+ #Si la distribution est Binomiale & que l'option "afficher la densité normale" est cochée :
+ if (v$dist=="DBin"){
+ if(v$showNdensity){
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.p.mat, probability=TRUE,yaxt="n",bty="n",xaxs="i",yaxs="i",xlab="",ylab=HTML("Densité"), xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad), col = 'grey',main = "", breaks = 50, cex.lab = cex.label) #, ylim=c(0,cv$maxfreqcl*1.1)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution de la proportion de succès")), side=3,line=1, adj=0.5, cex=cex.label)
+ if(cv$n.samples>1){
+ lim_dens_inf <- min (cv$samples.p.mat)-0.1
+ lim_dens_sup <- max(cv$samples.p.mat)+0.1
+ xfit<-seq(lim_dens_inf,lim_dens_sup,length=1000)
+ yfit<-dnorm(xfit,mean=mean(cv$samples.p.mat),sd=sd(cv$samples.p.mat))
+ lines(xfit, yfit, col="blue", type = 'l',lwd=2)
+ mtext(bquote(paste(bar(X)%~~%N(p*","*p(1-p)/n),sep='')), side=3,line=-1,adj=0.05, cex=cex.label)
+ mtext(bquote(paste(bar(X)%~~%N(.(cv$samples.p.m.m)*","*.(cv$samples.p.v.m)),sep='')), side=3,line=-3,adj=0.05, cex=cex.label)
+ }
+ }
+
+ #Si la distribution est Binomiale mais que l'option "afficher la densité normale" n'est pas cochée :
+ else {
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.mat/v$n, freq=TRUE,yaxt="n",bty="n",xaxs="i",yaxs="i",xlab="",ylab=HTML("Fréquences"), xlim=c(lim.inf,lim.sup), ylim=c(0,cv$maxfreqcl*1.1),xaxp=c(lim.inf,lim.sup,nbgrad), col = 'grey',main = "", breaks = 50, cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Distribution de la proportion de succès")), side=3,line=1, adj=0.5, cex=cex.label)
+ }
+ }
+
+ #Pour toutes les autres distributions quand l'option "afficher la densité normale sur l'histogramme des moyennes" est cochée:
+ else{
+ if(v$showMdensity){
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ hist(cv$samples.x.m.vec, probability=TRUE,yaxt="n",bty="n", xaxs="i",yaxs="i",xlab="", ylab=HTML("Densité"),xlim=c(lim.inf,lim.sup),xaxp=c(lim.inf,lim.sup,nbgrad),col = 'grey',main = "",breaks=breaks, cex.lab=cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Histogramme des moyennes d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)
+ if(cv$n.samples>1){
+ mtext(bquote(paste(bar(X)%~~%N(E(bar(X))*","*V(bar(X))),sep='')), side=3,line=-1,adj=0.05, cex=cex.label)
+ mtext(bquote(paste(bar(X)%~~%N(.(cv$samples.x.m.m )*","*.(cv$samples.x.v.m )),sep='')), side=3,line=-3,adj=0.05, cex=cex.label)
+ lim_inf <- min (cv$samples.x.m.vec)-1
+ lim_sup <- max(cv$samples.x.m.vec)+1
+ xfit<-seq(lim_inf,lim_sup,length=1000)
+ yfit<-dnorm(xfit,mean=mean(cv$samples.x.m.vec),sd=sd(cv$samples.x.m.vec))
+ lines(xfit, yfit, col="blue", type = 'l',lwd=2)
+ }
+ }
+ #Pour toutes les autres distributions quand l'option "afficher la densité normale sur l'histogramme des moyennes" n'est pas cochée:
+ else{
+ par(mai=c(0.5,0.8,0.5,0.5), xaxs="i",yaxs="i")
+ h<-hist(cv$samples.x.m.vec, freq = TRUE, yaxt="n",bty="n", xaxs="i",yaxs="i", xlab="", ylab=HTML("Fréquences"), xlim=c(lim.inf, lim.sup), xaxp=c(lim.inf,lim.sup,nbgrad), col='grey', main="", breaks=breaks, cex.lab = cex.label)
+ axis(2,las=2,cex.axis=cex.axis)
+ mtext(bquote(paste("Histogramme des moyennes d'échantillonnage")), side=3,line=1,adj=0.5, cex=cex.label)
+ }
+
+ }
+ }}
+
+
+ },height = getPlotHeight, width=getPlotWidth)
+
+
+
+})