Nothing
inst/SensMapUI/server.R
In SensMap: Sensory and Consumer Data Mapping
library(FactoMineR)
library(factoextra)
library(MASS)
library(fields)
library(plotly)
library(mgcv)
library(MCMCpack)
library(glmulti)
library(plyr)
library(lattice)
library(ggplot2)
library(reshape2)
library(shiny)
library(googleVis)
library(multcomp)
library(sjPlot)
library(scales)
library(doBy)
library(gridExtra)
library(agricolae)
library(devtools)
library(magrittr)
library(ggbiplot)
library(ggdendro)
library(dendextend)
library(grid)
#devtools::install_github("vqv/ggbiplot")
#install.packages("ggdendro")
#install.packages("pastecs")
library(pastecs)
library(shinydashboard);
source('global.R',local = TRUE)
shinyServer(function(input,output,session)
{
#### download data
rawInputData0<- reactive({
rawData = input$rawInputFile
headerTag = input$headerUI;
sepTag = input$sepUI;
quoteTag = input$quoteUI;
decTag = input$decUI;
if(!is.null(rawData)) {
data = read.csv(rawData$datapath,header=headerTag,dec=decTag ,sep=sepTag,quote=quoteTag);
} else {
return(NULL);
}
});
output$product <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("product","Select product:",items);
})
output$assessor <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("assessor","Select assessor:",items,multiple = T)
})
output$replication <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("replication","Select replication:",items,multiple = T)
})
col=reactive({
dat=rawInputData0()
s=0
n=which(colnames(dat) %in% c(paste(input$product),paste(input$replication),paste(input$assessor)))
for(i in 1:ncol(dat)){
if (i %in% n)
s[i]="factor"
else s[i]=class(dat[,i])
}
return(s)
})
rawInputData<- reactive({
rawData = input$rawInputFile
headerTag = input$headerUI;
sepTag = input$sepUI;
quoteTag = input$quoteUI;
decTag = input$decUI;
if(!is.null(rawData)) {
data = read.csv(rawData$datapath,header=headerTag,sep=sepTag,quote=quoteTag,dec=decTag,colClasses = col());
} else {
return(NULL);
}
});
consInputData = reactive({
rd = input$consInputFile
ht = input$headUI;
st = input$sepaUI;
qt = input$quotUI;
decc = input$deccUI;
if(!is.null(rd)) {
data = read.csv(rd$datapath,header=ht,sep=st,dec=decc,quote=qt,row.names = 1);
} else {
return(NULL);
}
});
output$stat_sum <- renderDataTable({
data = rawInputData()
df.data = stat.desc(data)
format(df.data,digits=2)
})
output$pre.data <- renderDataTable({
data = rawInputData()
df.data = data.frame(data)
df.data
})
output$str <- renderPrint({
dat <- rawInputData()
str(dat)
})
moy=reactive({
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),id=paste(input$origine),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
base
})
moy1=reactive({
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base
})
moy2=reactive({
data=rawInputData()
if(!is.null(data)){
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
if(!is.null(physicalData())){
base=cbind.data.frame(base,physicalData())
base}
else{base}}
else{base=data.frame(physicalData())
base}
})
mo=reactive({
data=rawInputData()
if(!is.null(data)){
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
if(!is.null(physicalData())){
base=cbind.data.frame(base,physicalData())
av=format(base,digits = 3,decimal.mark = ".")}
else{av=format(base,digits = 3,decimal.mark = ".")}}
else{base=data.frame(physicalData())
av=format(base,digits = 3,decimal.mark = ".")}
})
moy3=reactive({
data=rawInputData()
nums <- sapply(data, is.factor)
items=names(nums[nums])
df=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T,id= items )
df=data.frame(df)
})
output$av<- renderDataTable({
mo()
})
output$downloadav <- downloadHandler(
filename = paste0("average_", Sys.Date(),".csv"),
content = function(file) {
write.csv(mo(),file,row.names=F)
}
)
output$av.table<- renderDataTable({
moy2()
})
moy4=reactive({
if(!is.null(input$assessor)){
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,"+",input$assessor))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=base[,-3]}
else if (is.null(input$assessor)) return(NULL)
})
output$av.table2<- renderDataTable({
moy4()
})
output$downloadavt <- downloadHandler(
filename = paste0("averagejuge_", Sys.Date(),".csv"),
content = function(file) {
write.csv(moy4(),file,row.names=T)
}
)
cercle <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+
scale_color_gradient2(low="grey", mid="blue",
high="red", midpoint=0.6, space = "Lab")+theme_minimal()
})
output$cercle <- renderPlot({
cercle()
})
output$downloadPlot1 <- downloadHandler(
filename = function() {
paste("cercle", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cercle())
dev.off(which=dev.cur())}
)
scree <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_eig(res, addlabels=TRUE, hjust = -0.3,
linecolor ="red") +
theme_minimal()
})
output$scree <- renderPlot({
scree()
})
output$downloadPlot2 <- downloadHandler(
filename = function() {
paste("scree", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(scree())
dev.off(which=dev.cur())}
)
stable1 <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_screeplot(res, linecolor = "red",cex=0.7)
res$eig
})
output$stable <- renderTable({
stable1()
})
output$downloadstab <- downloadHandler(
filename = paste0("Eigen_", Sys.Date(),".csv"),
content = function(file) {
write.csv(stable1(),file,row.names=T)
}
)
ind <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_pca_ind(res)
})
output$ind <- renderPlot({
ind()
})
output$downloadPlot3 <- downloadHandler(
filename = function() {
paste("ind", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(ind())
dev.off(which=dev.cur())}
)
bi <- reactive({
X=moy2()
res.pca = PCA(X,ncp = 3, graph = FALSE, scale.unit=1)
library(devtools)
library(ggbiplot)
pca <- prcomp(X, scale. = TRUE)
g <- ggbiplot(pca)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
biplot=fviz_pca_biplot(res.pca,col.ind = "blue", col.var = "black", title="") + theme_bw()+ggtitle( "Biplot of variables and individuals on first PCA factor map")
biplot + theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
})
output$bi <- renderPlot({
bi()
})
output$downloadPlot4 <- downloadHandler(
filename = function() {
paste("bi", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(bi())
dev.off(which=dev.cur())}
)
output$cons.data <- renderDataTable({
data = consInputData()
df.data = data.frame(data)
df.data
})
cerclec <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+theme_minimal()
})
output$cerclec <- renderPlot({
cerclec()
})
output$downloadPlot5 <- downloadHandler(
filename = function() {
paste("cerclec", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cerclec())
dev.off(which=dev.cur())}
)
output$cerclec1 <- renderPlot({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+
scale_color_gradient2(low="grey", mid="navy",
high="red", midpoint=96, space = "Lab")
})
screec <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_eig(res, addlabels=TRUE, hjust = -0.3,
linecolor ="red") +
theme_minimal()
})
output$screec <- renderPlot({
screec()
})
output$downloadPlot6 <- downloadHandler(
filename = function() {
paste("screec", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(screec())
dev.off(which=dev.cur())}
)
stablec1 <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_screeplot(res, linecolor = "red",cex=0.7)
res$eig
})
output$stablec <- renderTable({
stablec1()
})
output$downloadstab1 <- downloadHandler(
filename = paste0("Eigen_", Sys.Date(),".csv"),
content = function(file) {
write.csv(stablec1(),file,row.names=T)
}
)
indc <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_ind(res)
})
output$indc <- renderPlot({
indc()
})
output$downloadPlot7 <- downloadHandler(
filename = function() {
paste("indc", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(indc())
dev.off(which=dev.cur())}
)
bic <- reactive({
X=consInputData()
res.pca = PCA(X,ncp = 3, graph = FALSE, scale.unit=1)
library(devtools)
library(ggbiplot)
pca <- prcomp(X, scale. = TRUE)
g <- ggbiplot(pca)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
biplot=fviz_pca_biplot(res.pca,col.ind = "blue", col.var = "black", title="") + theme_bw()+ggtitle( "Biplot of variables and individuals on first PCA factor map")
biplot + theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
})
output$bic <- renderPlot({
bic()
})
output$downloadPlot8 <- downloadHandler(
filename = function() {
paste("bic", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(bic())
dev.off(which=dev.cur())}
)
output$summary.data <- renderTable({
data = rawInputData()
summary(data)
})
output$variablee= renderUI({
df = rawInputData();
if (is.null(df)) return(NULL)
nums <- sapply(df, is.numeric)
items=names(nums[nums])
names(items)=items
selectInput("variablee","Select variable from:",items)
})
output$Min=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[1,c]],
"Minimum",
color = "red",
icon = icon("thumbs-o-down")
)
})
output$first=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[2,c]],
"1st Quartile",
color = "maroon",
icon = icon("list-ol")
)
})
output$Median=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[3,c]],
"Median",
color = "olive",
icon = icon("align-center")
)
})
output$Mean=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[4,c]],
"Mean",
color = "teal",
icon = icon("align-center")
)
})
output$third=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[5,c]],
"3rd Quartile",
color = "orange",
icon = icon("list-ol")
)
})
output$Max=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[6,c]],
"Maximum",
color = "purple",
icon = icon("thumbs-o-up")
)
})
output$var_qual <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("var_qual", "Select a variable :",items)
})
output$pp<- renderPrint({
df=rawInputData()
c=grep(input$var_qual, colnames(df))
data=df[,c]
a=sjt.frq(data)
return(HTML(a$knitr))
})
output$var_qual<- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("var_qual", "Select a variable :",items)
})
output$pp_qual<- renderPrint({
df=rawInputData()
c=grep(input$var_qual, colnames(df))
data=df[,c]
a=sjt.frq(data)
return(HTML(a$knitr))
})
output$summary.data1 <- renderTable({
data = consInputData()
summary(data)
})
output$variablee1= renderUI({
df = consInputData();
if (is.null(df)) return(NULL)
nums <- sapply(df, is.numeric)
items=names(nums[nums])
names(items)=items
selectInput("variablee1","Select variable from:",items)
})
output$Min1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[1,c]],
"Minimum",
color = "red",
icon = icon("thumbs-o-down")
)
})
output$first1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[2,c]],
"1st Quartile",
color = "maroon",
icon = icon("list-ol")
)
})
output$Median1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[3,c]],
"Median",
color = "olive",
icon = icon("align-center")
)
})
output$Mean1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[4,c]],
"Mean",
color = "teal",
icon = icon("align-center")
)
})
output$third1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[5,c]],
"3rd Quartile",
color = "orange",
icon = icon("list-ol")
)
})
output$Max1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[6,c]],
"Maximum",
color = "purple",
icon = icon("thumbs-o-up")
)
})
###############################################
#simple datatable of the data
output$rawDataView = renderDataTable({
newData = rawInputData();
if(is.null(newData))
return();
newData;
});
output$labelSelectUI = renderUI({
data = rawInputData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
output$labelconsUI = renderUI({
data = consInputData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
physicalData = reactive({
rd_phy = input$physicalInputFile
ht_phy = input$headUI_phy;
st_phy = input$sepaUI_phy;
qt_phy = input$quotUI_phy;
dec_phy = input$decUI_phy;
if(!is.null(rd_phy)) {
data = read.csv(rd_phy$datapath,header=ht_phy,sep=st_phy,quote=qt_phy,dec=dec_phy,row.names = 1);
} else {
return(NULL);
}
});
output$phy.data <- renderDataTable({
data = physicalData()
df.data = data.frame(data)
df.data
})
output$labelphyUI = renderUI({
data = physicalData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
output$jitter <- renderUI({
if (identical(rawInputData()$input$product, '') || identical(rawInputData()$input$product,data.frame())) return(NULL)
checkboxInput('jitter', 'Jitter')
})
output$gdesc <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("gdesc","Select descriptor variable:",items,multiple = T)
})
output$gfactor <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("gfactor", "Select a factor :",items)
})
plotAreac <- reactive({
data = rawInputData()
if(input$jitter){
ggplot(data=data, aes(x=data[,input$gfactor],y=data[,input$gdesc],fill=data[,input$gfactor])) +
geom_boxplot() +geom_jitter()+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) + xlab("") + ylab("") + labs(fill = "")
}
else if(!input$jitter){
ggplot(data=data, aes(x=data[,input$gfactor],y=data[,input$gdesc],fill=data[,input$gfactor])) +
geom_boxplot() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + xlab("") + ylab("") + labs(fill = "")
}
})
output$plotA <- renderPlot({
plotAreac()
})
output$dependent <- renderUI({
df <- rawInputData()
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependent","Select descriptor variable from:",items)
});
output$independents <- renderUI({
df <- rawInputData()
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
items =items*levels(items)
selectInput("independents","Select variable(s) from:",items,multiple=TRUE)
});
runRegression <- reactive({
lm(as.formula(paste(input$dependent," ~ ",paste(input$independents,collapse="+"))),data=rawInputData())
})
output$regTab <- renderPrint({
if(!is.null(input$independents)){
summary(runRegression())
} else {
print(data.frame(Warning="Please select Model Parameters."))
}
});
output$dependant <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependant","Select response variable : ",items)
});
output$independants <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("independants", "Select explicative variables :",items,multiple = T)
});
runRegression <- reactive({
lm(as.formula(paste(input$dependent," ~ ",paste(input$independents,collapse="+"))),data=rawInputData())
})
output$regTab <- renderPrint({
if(!is.null(input$independents)){
summary(runRegression())
} else {
print(data.frame(Warning="Please select Model Parameters."))
}
})
output$dependant <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependant","Select response variable : ",items)
})
output$independants <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("independants", "Select explicative variables :",items,multiple = T)
})
runAnova <- reactive({
aov(as.formula(paste(input$dependant," ~ ",paste(input$independants,collapse="*"))),data=rawInputData())
})
output$anov <- renderPrint({
summary.aov(runAnova())
})
get_factors <- reactive ({
factorstr <- as.character(formula(paste(input$dependant," ~ ",paste(input$independants,collapse="*"))))[3]
return(sub("\\s","",unlist(strsplit(factorstr,"[*+:]"))))
})
tukeyplot1 <- reactive({
dataset<-rawInputData()
aov.model<-runAnova()
out<-HSD.test(aov.model, get_factors())
# par(cex=1, mar=c(3,8,1,1))
# bar.group(out$groups,horiz=T,col="blue",
# xlim=c(0,max(out$means[,1]*1.2)),las=1)
data=data.frame(out$groups)
ggplot(data,aes(x=trt,y=means,fill=M,colour=M))+
geom_bar(stat = "identity",color="black")+
geom_text(aes(label=paste0(M),
y=means+0.3), size=5)+ ggtitle("tukey HSD")+ xlab("treatment groups")
})
output$tukeyplot <- renderPlot({
tukeyplot1()
})
output$downloadtuk <- downloadHandler(
filename = function() {
paste("tukeyPlot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(tukeyplot1())
dev.off(which=dev.cur())}
)
output$tukey <- renderPrint({
dataset<-rawInputData()
aov.model<-runAnova()
out <-HSD.test(aov.model, get_factors(), " ")
print(out)
})
anovdd <-reactive(
as.data.frame(anova(runAnova()))
)
output$anov.d <-renderDataTable(
as.data.frame(anova(runAnova())), server = TRUE, filter = 'top', escape = FALSE, selection = 'none'
)
output$downloadtable <- downloadHandler(
filename = paste0("ANOVA_", Sys.Date(),".csv"),
content = function(file) {
write.csv(anovdd(),file,row.names=T)
}
)
output$variab <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("variab","Select descriptor variable from:",items,multiple = T)
})
output$Vfactor <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("Vfactor", "Select a factor :",items)
})
plotBreac <- reactive({
df=rawInputData()
attach(df)
h <- list(
title = ""
)
l <- list(
title = "")
plot_ly(data=df,x=df[[input$variab]],color=df[[input$Vfactor]],colors = "Set2",type="box")%>%
layout(xaxis = h, yaxis = l)
})
output$plotB<-renderPlotly ({
plotBreac()
})
##Lines
output$vlines <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vlines","Select descriptor variable from:",items,multiple = T)
})
output$flines <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("flines", "Select a factor :",items)
})
output$plotD<-renderGvis ({
df=rawInputData()
attach(df)
return(gvisLineChart(df,xvar=input$flines,yvar=input$vlines, options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
###Bubbles :
output$vbubble <- renderUI({
data<- rawInputData()
library(doBy)
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vbubble","Select descriptor variable from:",items,multiple = T)
})
output$vbubble1 <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vbubble1","Select descriptor variable from:",items,multiple = T)
})
output$f <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f", "Choose your id var :",items)
})
output$f1 <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f1", "Choose your size var :",items)
})
output$f2 <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f2", "Choose your color var :",items)
})
output$plotEr<-reactive ({
data=rawInputData()
return(gvisBubbleChart(data,idvar=input$f,xvar=input$vbubble,yvar=input$vbubble1,colorvar=input$f2,sizevar=input$f1,options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
output$plotE<-renderGvis ({
data=rawInputData()
return(gvisBubbleChart(data,idvar=input$f,xvar=input$vbubble,yvar=input$vbubble1,colorvar=input$f2,sizevar=input$f1,options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
output$plotF<-renderGvis({
data=rawInputData()
cdata=summaryBy(as.formula(paste(". ~ ",paste(input$product,"+",input$replication))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
transform(cdata, time2 = paste(2010, cdata[,input$replication], sep = "")) -> df1
df1$time2 <- as.Date(df1$time2 ,"%Y-%m-%d")
return(gvisMotionChart(df1,idvar=df1[,input$product],timevar="time2",options=list(height=600,width=600,vAxes="[{title:''}",hAxes="[{title:''}")))
})
downloadPlotType <- reactive({
input$downloadPlotType
})
observe({
plotType <- input$downloadPlotType
plotTypePDF <- plotType == "pdf"
plotUnit <- ifelse(plotTypePDF, "inches", "pixels")
plotUnitDef <- ifelse(plotTypePDF, 9, 480)
updateNumericInput(
session,
inputId = "downloadPlotHeight",
label = sprintf("Height (%s)", plotUnit),
value = plotUnitDef)
updateNumericInput(
session,
inputId = "downloadPlotWidth",
label = sprintf("Width (%s)", plotUnit),
value = plotUnitDef)
})
# Get the download dimensions.
downloadPlotHeight <- reactive({
input$downloadPlotHeight
})
downloadPlotWidth <- reactive({
input$downloadPlotWidth
})
# Get the download file name.
downloadPlotFileName <- reactive({
input$downloadPlotFileName
})
# Include a downloadable file of the plot in the output list.
output$downloadPlot <- downloadHandler(
filename = function() {
paste(downloadPlotFileName(), downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(plotAreac())
dev.off(which=dev.cur())}
)
output$interval=renderUI({
Y=consInputData()
sliderInput("interval", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40),width = '60%')
})
output$cart<-renderPlot({cart() })
cart=eventReactive(input$actioncarto,{
Y=consInputData()
X=moy2()
hedo_senso=cbind.data.frame(Y[,input$interval[1]:input$interval[2]],X)
res.inter=PCA(hedo_senso ,scale.unit=T,quanti.sup = (ncol(Y[,input$interval[1]:input$interval[2]])+1):ncol(hedo_senso),graph=FALSE)
c1=fviz_pca_ind(res.inter, axes = c(1, 2), geom = c("point","text"),title="Plot of individuals" )
c3=fviz_pca_var(res.inter,invisible = "quanti.sup",geom=c( "arrow", "text"),title="Homogeniety of consumers")
c2=fviz_pca_var(res.inter,geom = c("arrow","text"),invisible ="var",title="Projection of sensory variables")
p=grid.arrange(c1,c2,c3,padding = unit(1, "line"),heights = 700)
p
})
output$downloadPlotinternal <- downloadHandler(
filename = function() {
paste("plot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = 15, height = downloadPlotHeight())
plot(cart())
dev.off(which=dev.cur())}
)
################# External preference mapping
output$interval1=renderUI({
Y=consInputData()
sliderInput("interval1", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$modelch=renderUI({
selectInput("modelch", "Choose type of model :",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model"))
})
output$Dimension_var<-renderUI({
selectInput("Dimension_var","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var<-renderUI({
selectInput("Prediction_var","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model", "Bayesian model","Others"))
})
output$par_var<-renderUI({
if(input$Prediction_var != "Bayesian model"){
selectInput("par_var","If rejection of predictions outside liking scores:",choices = c("NO","YES"))}
else{selectInput("par_var","Choose Rejection of predictions outside of [0:10]:",choices = c("NO"))}
})
output$mod1<-renderUI({
if(input$Prediction_var == "Others"){
selectInput("mod1","Choose type of model:",choices = c("Polynomial regression","Generalized Additive Model" , "Generalized Linear Model", "Bayesian Model"))}
else return(NULL)
})
output$formula_lm1=renderUI({
if(input$Prediction_var == "Others"){
if(input$mod1 == "Polynomial regression"){
textInput("formula_lm1","Input formula for polynomial model :")}
else if(input$mod1 == "Generalized Additive Model"){
textInput("formula_lm1","Input formula for GAM :")}
else if(input$mod1 == "Generalized Linear Model"){
textInput("formula_lm1","Input formula for GLM :")}
else if(input$mod1 == "Bayesian Model"){
textInput("formula_lm1","Input formula for Bayesian model :")}}
})
output$interval2=renderUI({
Y=consInputData()
sliderInput("interval2", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$epmplot=renderPlot({cart13()})
cart13=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{
drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
}
})
output$epmplot2=renderPlot({cart132()})
cart132=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
##################
#################
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
})
output$epmplot3=renderPlotly({cart133()})
cart133=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )
}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
##################
#################
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T)}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
})
####### Results of models selection
output$Dimension_var1<-renderUI({
selectInput("Dimension_var1","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var1<-renderUI({
selectInput("Prediction_var1","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model"))
})
output$aic<-renderUI({
selectInput("aic","Choose parameter of comparison:",choices = c("AIC","R2", "Fstat","nb-NA"))
})
output$model_selection=renderPlot({cart14()})
cart14=eventReactive(input$actioncarto14,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
library(ggplot2)
bas=data.frame(x3,x2)
g2<-ggplot(bas,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g2<-g2+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g2
}
else if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps)
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas1=data.frame(x3,x2)
g21<-ggplot(bas1,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g21<-g21+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g21
}
else if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas2=data.frame(x3,x2)
g23<-ggplot(bas2,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g23<-g23+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g23
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas3=data.frame(x3,x2)
g22<-ggplot(bas3,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g22<-g22+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g22
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
## If R2 and MFA
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("Generalized Additive Model",ncol(Y)),rep("Generalized Linear Model",ncol(Y)))
bas4=data.frame(x3,x2)
g22<-ggplot(bas4,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g23<-g23+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g23
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
## If R2 and CA
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas4=data.frame(x3,x2)
g24<-ggplot(bas4,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g24<-g24+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g24
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) f.glm[[i]]=0}
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas5=data.frame(x3,x2)
g25<-ggplot(bas5,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g25<-g25+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g25+ylim(min=0, max=100)
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) {f.glm[[i]]=0} }
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas6=data.frame(x3,x2)
g26<-ggplot(bas6,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g26<-g26+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g26+ylim(min=0, max=100)
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
## If fstat and CA
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) {f.glm[[i]]=0} }
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas7=data.frame(x3,x2)
g27<-ggplot(bas7,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g27<-g27+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g27+ylim(min=0, max=100)
}
###########
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from PCA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
##################
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2)) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from MFA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
############
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
map=map.with.ca(X,S,Y) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from CA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
})
output$downloadPlot_aic <- downloadHandler(
filename = function() {
paste("PLOT", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cart14())
dev.off(which=dev.cur())}
)
###### EPM smoothing
output$Dimension_var3<-renderUI({
selectInput("Dimension_var3","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var3<-renderUI({
selectInput("Prediction_var3","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model", "Bayesian model","Others"))
})
output$mod2<-renderUI({
if(input$Prediction_var3 == "Others"){
selectInput("mod2","Choose type of model:",choices = c("Polynomial model","GAM" , "GLM", "Bayesian model"))}
else return(NULL)
})
output$formula_lm2=renderUI({
if(input$Prediction_var3 == "Others"){
if(input$mod2 == "Polynomial model"){
textInput("formula_lm2","Input formula for polynomial model :")}
else if(input$mod2 == "GAM"){
textInput("formula_lm2","Input formula for GAM :")}
else if(input$mod2 == "GLM"){
textInput("formula_lm2","Input formula for GLM :")}
else if(input$mod2 == "Bayesian model"){
textInput("formula_lm2","Input formula for Bayesian model :")}}
})
output$par_var3<-renderUI({
if(input$Prediction_var3 != "Bayesian model"){
selectInput("par_var3","If rejection of predictions outside of [0:10]:",choices = c("NO","YES"))}
else{selectInput("par_var3","If rejection of predictions outside of [0:10]:",choices = c("NO"))}
})
output$interval3=renderUI({
Y=consInputData()
sliderInput("interval3", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$smooth=renderPlot({cart15()})
cart15=eventReactive(input$actioncarto15,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar1=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar1)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar2=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar2)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar3=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar3)}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar4=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar4)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar5=par(mfrow = c(1,2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar5)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar6=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar6)
}
###############
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar7=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar7)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar8=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar8)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar9=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar9)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar10=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar10)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar11=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar11)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar12=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar12)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar13=par(mfrow = c(1, 2))
a=drawmap (Y,X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y,S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar13)
}
################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar14=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar14)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar15=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar15)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar16=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar16)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar17=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar17)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar18=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar18)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar19=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar19)
}
#######################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar20=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar20)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar21=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar21)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar22=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar22)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar23=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar23)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar24=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar24)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar25=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar25)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar26=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar26)
}
#################################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar27=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar27)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar28=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar28)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar29=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar29)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar30=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar30)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar31=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar31)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar32=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar32)
}
#################################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar33=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar33)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar34=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar34)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar35=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar35)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar36=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar36)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar37=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar37)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar38=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar38)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar39=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar39)
}
#######################
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar40=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar40)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar41=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar41)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="GAM" )
{impar42=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar42)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar43=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar43)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar44=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar44)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar45=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar46)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar47=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar47)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar48=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar48)
}
#########################
#######################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar49=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar49)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar50=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar50)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Additive Model" )
{impar51=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar51)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar52=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar52)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar53=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar53)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar54=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar54)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar55=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar55)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar56=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar56)
}
#########################
#######################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar57=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar57)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar58=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar58)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Additive Model" )
{impar59=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar59)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar60=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar60)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar61=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar61)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar62=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar62)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar63=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar63)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar64=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar64)
}
})
##### comparison of maps stability
output$reduction<-renderUI({
selectInput("reduction","Choose dimension reduction method:",choices = c("Principal Component Analysis",
"Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_com<-renderUI({
selectInput("Prediction_com","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model"))
})
output$formula_lm=renderUI({
textInput("formula_lm","Input formula for Polynomial and GLM models :")
})
output$formula_gam=renderUI({
textInput("formula_gam","Input formula for GAM :")
})
output$numm<-renderUI({
numericInput("numm","Introduce number of sampling n (at least 2):", 2,
min = 2, max = 100)
})
output$comparaison=renderPlot({cart16()})
cart16=eventReactive(input$actioncarto16,{
Y=consInputData()
X=moy2()
S=moy4()
# nbpoints=50
if(input$reduction=="Principal Component Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=1,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
else if(input$reduction=="Multiple Factor Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=2,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
else if(input$reduction=="Canonical Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=3,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
})
output$downloadPlot_comp <- downloadHandler(
filename = function() {
paste("comp", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cart16())
dev.off(which=dev.cur())}
)
output$dend<-renderPlot({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
plot.HCPC(res.hcpc,choice="tree",new.plot=F)
})
clus1=reactive({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
fviz_cluster(res.hcpc) + theme_minimal()+ ggtitle("Clustering Plot")
})
output$clus<-renderPlot({
clus1()
})
output$downloadclus <- downloadHandler(
filename = function() {
paste("Plot", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(clus1())
dev.off(which=dev.cur())}
)
dend11<-reactive({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
b=res.hcpc$call$t$tree
c=as.dendrogram(b)
nb=res.hcpc$call$t$nb.clust
d=c %>%
set("branches_k_color", k = nb) %>% set("branches_lwd", 0.7) %>%
set("labels_cex", 0.6) %>% set("labels_colors", k = nb) %>%
set("leaves_pch", 10) %>% set("leaves_cex", 0.5)%>%raise.dendrogram(0.015)%>%remove_nodes_nodePar
ggd1 <- as.ggdend(d)
ggplot(ggd1,labels=F)+ggtitle("Dendrogram")+scale_y_continuous(0,1.2)
plot.HCPC(res.hcpc,choice="tree",new.plot=F)
})
output$dend1<-renderPlot({
dend11()
})
output$downloaddend <- downloadHandler(
filename = function() {
paste("Plot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(dend11())
dev.off(which=dev.cur())}
)
output$dend12<-renderPrint({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
res.hcpc$desc.var
})
})
Try the SensMap package in your browser
Any scripts or data that you put into this service are public.
SensMap documentation built on July 5, 2022, 1:09 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(FactoMineR)
library(factoextra)
library(MASS)
library(fields)
library(plotly)
library(mgcv)
library(MCMCpack)
library(glmulti)
library(plyr)
library(lattice)
library(ggplot2)
library(reshape2)
library(shiny)
library(googleVis)
library(multcomp)
library(sjPlot)
library(scales)
library(doBy)
library(gridExtra)
library(agricolae)
library(devtools)
library(magrittr)
library(ggbiplot)
library(ggdendro)
library(dendextend)
library(grid)
#devtools::install_github("vqv/ggbiplot")
#install.packages("ggdendro")
#install.packages("pastecs")
library(pastecs)
library(shinydashboard);
source('global.R',local = TRUE)
shinyServer(function(input,output,session)
{
#### download data
rawInputData0<- reactive({
rawData = input$rawInputFile
headerTag = input$headerUI;
sepTag = input$sepUI;
quoteTag = input$quoteUI;
decTag = input$decUI;
if(!is.null(rawData)) {
data = read.csv(rawData$datapath,header=headerTag,dec=decTag ,sep=sepTag,quote=quoteTag);
} else {
return(NULL);
}
});
output$product <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("product","Select product:",items);
})
output$assessor <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("assessor","Select assessor:",items,multiple = T)
})
output$replication <- renderUI({
data = rawInputData0();
if (is.null(data)) return(NULL)
items=names(data)
names(items)=items
selectInput("replication","Select replication:",items,multiple = T)
})
col=reactive({
dat=rawInputData0()
s=0
n=which(colnames(dat) %in% c(paste(input$product),paste(input$replication),paste(input$assessor)))
for(i in 1:ncol(dat)){
if (i %in% n)
s[i]="factor"
else s[i]=class(dat[,i])
}
return(s)
})
rawInputData<- reactive({
rawData = input$rawInputFile
headerTag = input$headerUI;
sepTag = input$sepUI;
quoteTag = input$quoteUI;
decTag = input$decUI;
if(!is.null(rawData)) {
data = read.csv(rawData$datapath,header=headerTag,sep=sepTag,quote=quoteTag,dec=decTag,colClasses = col());
} else {
return(NULL);
}
});
consInputData = reactive({
rd = input$consInputFile
ht = input$headUI;
st = input$sepaUI;
qt = input$quotUI;
decc = input$deccUI;
if(!is.null(rd)) {
data = read.csv(rd$datapath,header=ht,sep=st,dec=decc,quote=qt,row.names = 1);
} else {
return(NULL);
}
});
output$stat_sum <- renderDataTable({
data = rawInputData()
df.data = stat.desc(data)
format(df.data,digits=2)
})
output$pre.data <- renderDataTable({
data = rawInputData()
df.data = data.frame(data)
df.data
})
output$str <- renderPrint({
dat <- rawInputData()
str(dat)
})
moy=reactive({
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),id=paste(input$origine),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
base
})
moy1=reactive({
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base
})
moy2=reactive({
data=rawInputData()
if(!is.null(data)){
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
if(!is.null(physicalData())){
base=cbind.data.frame(base,physicalData())
base}
else{base}}
else{base=data.frame(physicalData())
base}
})
mo=reactive({
data=rawInputData()
if(!is.null(data)){
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=data.frame(base,row.names =1)
if(!is.null(physicalData())){
base=cbind.data.frame(base,physicalData())
av=format(base,digits = 3,decimal.mark = ".")}
else{av=format(base,digits = 3,decimal.mark = ".")}}
else{base=data.frame(physicalData())
av=format(base,digits = 3,decimal.mark = ".")}
})
moy3=reactive({
data=rawInputData()
nums <- sapply(data, is.factor)
items=names(nums[nums])
df=summaryBy(as.formula(paste(". ~ ",paste(input$product,collapse="+"))),data=data,FUN=c(mean),na.rm=T,keep.names=T,id= items )
df=data.frame(df)
})
output$av<- renderDataTable({
mo()
})
output$downloadav <- downloadHandler(
filename = paste0("average_", Sys.Date(),".csv"),
content = function(file) {
write.csv(mo(),file,row.names=F)
}
)
output$av.table<- renderDataTable({
moy2()
})
moy4=reactive({
if(!is.null(input$assessor)){
data=rawInputData()
base=summaryBy(as.formula(paste(". ~ ",paste(input$product,"+",input$assessor))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
base=base[,-3]}
else if (is.null(input$assessor)) return(NULL)
})
output$av.table2<- renderDataTable({
moy4()
})
output$downloadavt <- downloadHandler(
filename = paste0("averagejuge_", Sys.Date(),".csv"),
content = function(file) {
write.csv(moy4(),file,row.names=T)
}
)
cercle <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+
scale_color_gradient2(low="grey", mid="blue",
high="red", midpoint=0.6, space = "Lab")+theme_minimal()
})
output$cercle <- renderPlot({
cercle()
})
output$downloadPlot1 <- downloadHandler(
filename = function() {
paste("cercle", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cercle())
dev.off(which=dev.cur())}
)
scree <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_eig(res, addlabels=TRUE, hjust = -0.3,
linecolor ="red") +
theme_minimal()
})
output$scree <- renderPlot({
scree()
})
output$downloadPlot2 <- downloadHandler(
filename = function() {
paste("scree", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(scree())
dev.off(which=dev.cur())}
)
stable1 <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_screeplot(res, linecolor = "red",cex=0.7)
res$eig
})
output$stable <- renderTable({
stable1()
})
output$downloadstab <- downloadHandler(
filename = paste0("Eigen_", Sys.Date(),".csv"),
content = function(file) {
write.csv(stable1(),file,row.names=T)
}
)
ind <- reactive({
base=moy2()
res <-PCA(base,ncp=2)
fviz_pca_ind(res)
})
output$ind <- renderPlot({
ind()
})
output$downloadPlot3 <- downloadHandler(
filename = function() {
paste("ind", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(ind())
dev.off(which=dev.cur())}
)
bi <- reactive({
X=moy2()
res.pca = PCA(X,ncp = 3, graph = FALSE, scale.unit=1)
library(devtools)
library(ggbiplot)
pca <- prcomp(X, scale. = TRUE)
g <- ggbiplot(pca)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
biplot=fviz_pca_biplot(res.pca,col.ind = "blue", col.var = "black", title="") + theme_bw()+ggtitle( "Biplot of variables and individuals on first PCA factor map")
biplot + theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
})
output$bi <- renderPlot({
bi()
})
output$downloadPlot4 <- downloadHandler(
filename = function() {
paste("bi", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(bi())
dev.off(which=dev.cur())}
)
output$cons.data <- renderDataTable({
data = consInputData()
df.data = data.frame(data)
df.data
})
cerclec <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+theme_minimal()
})
output$cerclec <- renderPlot({
cerclec()
})
output$downloadPlot5 <- downloadHandler(
filename = function() {
paste("cerclec", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cerclec())
dev.off(which=dev.cur())}
)
output$cerclec1 <- renderPlot({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_var(res)+
scale_color_gradient2(low="grey", mid="navy",
high="red", midpoint=96, space = "Lab")
})
screec <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_eig(res, addlabels=TRUE, hjust = -0.3,
linecolor ="red") +
theme_minimal()
})
output$screec <- renderPlot({
screec()
})
output$downloadPlot6 <- downloadHandler(
filename = function() {
paste("screec", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(screec())
dev.off(which=dev.cur())}
)
stablec1 <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_screeplot(res, linecolor = "red",cex=0.7)
res$eig
})
output$stablec <- renderTable({
stablec1()
})
output$downloadstab1 <- downloadHandler(
filename = paste0("Eigen_", Sys.Date(),".csv"),
content = function(file) {
write.csv(stablec1(),file,row.names=T)
}
)
indc <- reactive({
base=consInputData()
res <-PCA(base,ncp=2)
fviz_pca_ind(res)
})
output$indc <- renderPlot({
indc()
})
output$downloadPlot7 <- downloadHandler(
filename = function() {
paste("indc", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(indc())
dev.off(which=dev.cur())}
)
bic <- reactive({
X=consInputData()
res.pca = PCA(X,ncp = 3, graph = FALSE, scale.unit=1)
library(devtools)
library(ggbiplot)
pca <- prcomp(X, scale. = TRUE)
g <- ggbiplot(pca)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal',
legend.position = 'top')
print(g)
biplot=fviz_pca_biplot(res.pca,col.ind = "blue", col.var = "black", title="") + theme_bw()+ggtitle( "Biplot of variables and individuals on first PCA factor map")
biplot + theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
})
output$bic <- renderPlot({
bic()
})
output$downloadPlot8 <- downloadHandler(
filename = function() {
paste("bic", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(bic())
dev.off(which=dev.cur())}
)
output$summary.data <- renderTable({
data = rawInputData()
summary(data)
})
output$variablee= renderUI({
df = rawInputData();
if (is.null(df)) return(NULL)
nums <- sapply(df, is.numeric)
items=names(nums[nums])
names(items)=items
selectInput("variablee","Select variable from:",items)
})
output$Min=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[1,c]],
"Minimum",
color = "red",
icon = icon("thumbs-o-down")
)
})
output$first=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[2,c]],
"1st Quartile",
color = "maroon",
icon = icon("list-ol")
)
})
output$Median=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[3,c]],
"Median",
color = "olive",
icon = icon("align-center")
)
})
output$Mean=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[4,c]],
"Mean",
color = "teal",
icon = icon("align-center")
)
})
output$third=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[5,c]],
"3rd Quartile",
color = "orange",
icon = icon("list-ol")
)
})
output$Max=renderValueBox({
df=rawInputData()
a=summary(df)
c=grep(input$variablee, colnames(df))
c=c[1]
valueBox(a[[6,c]],
"Maximum",
color = "purple",
icon = icon("thumbs-o-up")
)
})
output$var_qual <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("var_qual", "Select a variable :",items)
})
output$pp<- renderPrint({
df=rawInputData()
c=grep(input$var_qual, colnames(df))
data=df[,c]
a=sjt.frq(data)
return(HTML(a$knitr))
})
output$var_qual<- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("var_qual", "Select a variable :",items)
})
output$pp_qual<- renderPrint({
df=rawInputData()
c=grep(input$var_qual, colnames(df))
data=df[,c]
a=sjt.frq(data)
return(HTML(a$knitr))
})
output$summary.data1 <- renderTable({
data = consInputData()
summary(data)
})
output$variablee1= renderUI({
df = consInputData();
if (is.null(df)) return(NULL)
nums <- sapply(df, is.numeric)
items=names(nums[nums])
names(items)=items
selectInput("variablee1","Select variable from:",items)
})
output$Min1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[1,c]],
"Minimum",
color = "red",
icon = icon("thumbs-o-down")
)
})
output$first1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[2,c]],
"1st Quartile",
color = "maroon",
icon = icon("list-ol")
)
})
output$Median1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[3,c]],
"Median",
color = "olive",
icon = icon("align-center")
)
})
output$Mean1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[4,c]],
"Mean",
color = "teal",
icon = icon("align-center")
)
})
output$third1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[5,c]],
"3rd Quartile",
color = "orange",
icon = icon("list-ol")
)
})
output$Max1=renderValueBox({
df=consInputData()
a=summary(df)
c=grep(input$variablee1, colnames(df))
c=c[1]
valueBox(a[[6,c]],
"Maximum",
color = "purple",
icon = icon("thumbs-o-up")
)
})
###############################################
#simple datatable of the data
output$rawDataView = renderDataTable({
newData = rawInputData();
if(is.null(newData))
return();
newData;
});
output$labelSelectUI = renderUI({
data = rawInputData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
output$labelconsUI = renderUI({
data = consInputData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
physicalData = reactive({
rd_phy = input$physicalInputFile
ht_phy = input$headUI_phy;
st_phy = input$sepaUI_phy;
qt_phy = input$quotUI_phy;
dec_phy = input$decUI_phy;
if(!is.null(rd_phy)) {
data = read.csv(rd_phy$datapath,header=ht_phy,sep=st_phy,quote=qt_phy,dec=dec_phy,row.names = 1);
} else {
return(NULL);
}
});
output$phy.data <- renderDataTable({
data = physicalData()
df.data = data.frame(data)
df.data
})
output$labelphyUI = renderUI({
data = physicalData();
return(selectInput("modelLabelUI","List of variables",colnames(data),colnames(data)[1]));
});
output$jitter <- renderUI({
if (identical(rawInputData()$input$product, '') || identical(rawInputData()$input$product,data.frame())) return(NULL)
checkboxInput('jitter', 'Jitter')
})
output$gdesc <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("gdesc","Select descriptor variable:",items,multiple = T)
})
output$gfactor <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("gfactor", "Select a factor :",items)
})
plotAreac <- reactive({
data = rawInputData()
if(input$jitter){
ggplot(data=data, aes(x=data[,input$gfactor],y=data[,input$gdesc],fill=data[,input$gfactor])) +
geom_boxplot() +geom_jitter()+ theme(axis.text.x = element_text(angle = 45, hjust = 1)) + xlab("") + ylab("") + labs(fill = "")
}
else if(!input$jitter){
ggplot(data=data, aes(x=data[,input$gfactor],y=data[,input$gdesc],fill=data[,input$gfactor])) +
geom_boxplot() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + xlab("") + ylab("") + labs(fill = "")
}
})
output$plotA <- renderPlot({
plotAreac()
})
output$dependent <- renderUI({
df <- rawInputData()
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependent","Select descriptor variable from:",items)
});
output$independents <- renderUI({
df <- rawInputData()
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
items =items*levels(items)
selectInput("independents","Select variable(s) from:",items,multiple=TRUE)
});
runRegression <- reactive({
lm(as.formula(paste(input$dependent," ~ ",paste(input$independents,collapse="+"))),data=rawInputData())
})
output$regTab <- renderPrint({
if(!is.null(input$independents)){
summary(runRegression())
} else {
print(data.frame(Warning="Please select Model Parameters."))
}
});
output$dependant <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependant","Select response variable : ",items)
});
output$independants <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("independants", "Select explicative variables :",items,multiple = T)
});
runRegression <- reactive({
lm(as.formula(paste(input$dependent," ~ ",paste(input$independents,collapse="+"))),data=rawInputData())
})
output$regTab <- renderPrint({
if(!is.null(input$independents)){
summary(runRegression())
} else {
print(data.frame(Warning="Please select Model Parameters."))
}
})
output$dependant <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("dependant","Select response variable : ",items)
})
output$independants <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("independants", "Select explicative variables :",items,multiple = T)
})
runAnova <- reactive({
aov(as.formula(paste(input$dependant," ~ ",paste(input$independants,collapse="*"))),data=rawInputData())
})
output$anov <- renderPrint({
summary.aov(runAnova())
})
get_factors <- reactive ({
factorstr <- as.character(formula(paste(input$dependant," ~ ",paste(input$independants,collapse="*"))))[3]
return(sub("\\s","",unlist(strsplit(factorstr,"[*+:]"))))
})
tukeyplot1 <- reactive({
dataset<-rawInputData()
aov.model<-runAnova()
out<-HSD.test(aov.model, get_factors())
# par(cex=1, mar=c(3,8,1,1))
# bar.group(out$groups,horiz=T,col="blue",
# xlim=c(0,max(out$means[,1]*1.2)),las=1)
data=data.frame(out$groups)
ggplot(data,aes(x=trt,y=means,fill=M,colour=M))+
geom_bar(stat = "identity",color="black")+
geom_text(aes(label=paste0(M),
y=means+0.3), size=5)+ ggtitle("tukey HSD")+ xlab("treatment groups")
})
output$tukeyplot <- renderPlot({
tukeyplot1()
})
output$downloadtuk <- downloadHandler(
filename = function() {
paste("tukeyPlot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(tukeyplot1())
dev.off(which=dev.cur())}
)
output$tukey <- renderPrint({
dataset<-rawInputData()
aov.model<-runAnova()
out <-HSD.test(aov.model, get_factors(), " ")
print(out)
})
anovdd <-reactive(
as.data.frame(anova(runAnova()))
)
output$anov.d <-renderDataTable(
as.data.frame(anova(runAnova())), server = TRUE, filter = 'top', escape = FALSE, selection = 'none'
)
output$downloadtable <- downloadHandler(
filename = paste0("ANOVA_", Sys.Date(),".csv"),
content = function(file) {
write.csv(anovdd(),file,row.names=T)
}
)
output$variab <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("variab","Select descriptor variable from:",items,multiple = T)
})
output$Vfactor <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("Vfactor", "Select a factor :",items)
})
plotBreac <- reactive({
df=rawInputData()
attach(df)
h <- list(
title = ""
)
l <- list(
title = "")
plot_ly(data=df,x=df[[input$variab]],color=df[[input$Vfactor]],colors = "Set2",type="box")%>%
layout(xaxis = h, yaxis = l)
})
output$plotB<-renderPlotly ({
plotBreac()
})
##Lines
output$vlines <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vlines","Select descriptor variable from:",items,multiple = T)
})
output$flines <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("flines", "Select a factor :",items)
})
output$plotD<-renderGvis ({
df=rawInputData()
attach(df)
return(gvisLineChart(df,xvar=input$flines,yvar=input$vlines, options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
###Bubbles :
output$vbubble <- renderUI({
data<- rawInputData()
library(doBy)
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vbubble","Select descriptor variable from:",items,multiple = T)
})
output$vbubble1 <- renderUI({
df=moy1()
df=data.frame(df,row.names = 1)
if (is.null(df)) return(NULL)
items=names(df)
names(items)=items
selectInput("vbubble1","Select descriptor variable from:",items,multiple = T)
})
output$f <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f", "Choose your id var :",items)
})
output$f1 <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f1", "Choose your size var :",items)
})
output$f2 <- renderUI({
df<- rawInputData()
if (is.null(df)) return(NULL)
#Let's only show factor columns
nums <- sapply(df, is.factor)
items=names(nums[nums])
names(items)=items
selectInput("f2", "Choose your color var :",items)
})
output$plotEr<-reactive ({
data=rawInputData()
return(gvisBubbleChart(data,idvar=input$f,xvar=input$vbubble,yvar=input$vbubble1,colorvar=input$f2,sizevar=input$f1,options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
output$plotE<-renderGvis ({
data=rawInputData()
return(gvisBubbleChart(data,idvar=input$f,xvar=input$vbubble,yvar=input$vbubble1,colorvar=input$f2,sizevar=input$f1,options=list(hAxis="{title:''}",vAxis="{title:''}",width=700,height=600)))
})
output$plotF<-renderGvis({
data=rawInputData()
cdata=summaryBy(as.formula(paste(". ~ ",paste(input$product,"+",input$replication))),data=data,FUN=c(mean),na.rm=T,keep.names=T)
transform(cdata, time2 = paste(2010, cdata[,input$replication], sep = "")) -> df1
df1$time2 <- as.Date(df1$time2 ,"%Y-%m-%d")
return(gvisMotionChart(df1,idvar=df1[,input$product],timevar="time2",options=list(height=600,width=600,vAxes="[{title:''}",hAxes="[{title:''}")))
})
downloadPlotType <- reactive({
input$downloadPlotType
})
observe({
plotType <- input$downloadPlotType
plotTypePDF <- plotType == "pdf"
plotUnit <- ifelse(plotTypePDF, "inches", "pixels")
plotUnitDef <- ifelse(plotTypePDF, 9, 480)
updateNumericInput(
session,
inputId = "downloadPlotHeight",
label = sprintf("Height (%s)", plotUnit),
value = plotUnitDef)
updateNumericInput(
session,
inputId = "downloadPlotWidth",
label = sprintf("Width (%s)", plotUnit),
value = plotUnitDef)
})
# Get the download dimensions.
downloadPlotHeight <- reactive({
input$downloadPlotHeight
})
downloadPlotWidth <- reactive({
input$downloadPlotWidth
})
# Get the download file name.
downloadPlotFileName <- reactive({
input$downloadPlotFileName
})
# Include a downloadable file of the plot in the output list.
output$downloadPlot <- downloadHandler(
filename = function() {
paste(downloadPlotFileName(), downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(plotAreac())
dev.off(which=dev.cur())}
)
output$interval=renderUI({
Y=consInputData()
sliderInput("interval", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40),width = '60%')
})
output$cart<-renderPlot({cart() })
cart=eventReactive(input$actioncarto,{
Y=consInputData()
X=moy2()
hedo_senso=cbind.data.frame(Y[,input$interval[1]:input$interval[2]],X)
res.inter=PCA(hedo_senso ,scale.unit=T,quanti.sup = (ncol(Y[,input$interval[1]:input$interval[2]])+1):ncol(hedo_senso),graph=FALSE)
c1=fviz_pca_ind(res.inter, axes = c(1, 2), geom = c("point","text"),title="Plot of individuals" )
c3=fviz_pca_var(res.inter,invisible = "quanti.sup",geom=c( "arrow", "text"),title="Homogeniety of consumers")
c2=fviz_pca_var(res.inter,geom = c("arrow","text"),invisible ="var",title="Projection of sensory variables")
p=grid.arrange(c1,c2,c3,padding = unit(1, "line"),heights = 700)
p
})
output$downloadPlotinternal <- downloadHandler(
filename = function() {
paste("plot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = 15, height = downloadPlotHeight())
plot(cart())
dev.off(which=dev.cur())}
)
################# External preference mapping
output$interval1=renderUI({
Y=consInputData()
sliderInput("interval1", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$modelch=renderUI({
selectInput("modelch", "Choose type of model :",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model"))
})
output$Dimension_var<-renderUI({
selectInput("Dimension_var","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var<-renderUI({
selectInput("Prediction_var","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model", "Bayesian model","Others"))
})
output$par_var<-renderUI({
if(input$Prediction_var != "Bayesian model"){
selectInput("par_var","If rejection of predictions outside liking scores:",choices = c("NO","YES"))}
else{selectInput("par_var","Choose Rejection of predictions outside of [0:10]:",choices = c("NO"))}
})
output$mod1<-renderUI({
if(input$Prediction_var == "Others"){
selectInput("mod1","Choose type of model:",choices = c("Polynomial regression","Generalized Additive Model" , "Generalized Linear Model", "Bayesian Model"))}
else return(NULL)
})
output$formula_lm1=renderUI({
if(input$Prediction_var == "Others"){
if(input$mod1 == "Polynomial regression"){
textInput("formula_lm1","Input formula for polynomial model :")}
else if(input$mod1 == "Generalized Additive Model"){
textInput("formula_lm1","Input formula for GAM :")}
else if(input$mod1 == "Generalized Linear Model"){
textInput("formula_lm1","Input formula for GLM :")}
else if(input$mod1 == "Bayesian Model"){
textInput("formula_lm1","Input formula for Bayesian model :")}}
})
output$interval2=renderUI({
Y=consInputData()
sliderInput("interval2", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$epmplot=renderPlot({cart13()})
cart13=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{
drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
}
})
output$epmplot2=renderPlot({cart132()})
cart132=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
##################
#################
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =TRUE, graph.map =FALSE,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =T, graph.map =F,
graph.map.3D =FALSE )}
})
output$epmplot3=renderPlotly({cart133()})
cart133=eventReactive(input$actioncarto13,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )
}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
###############
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="YES")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#################################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Vector model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Circular model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Elliptic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Quadratic model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Additive Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Generalized Linear Model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Bayesian model" && input$par_var=="NO")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
##################
#################
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T)}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =FALSE,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Principal Component Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
#######################
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Multiple Factor Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
######################
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Polynomial regression")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Additive Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Generalized Linear Model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="YES"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
else if(input$Dimension_var=="Canonical Analysis" && input$Prediction_var=="Others" && input$par_var=="NO"
&&input$mod1=="Bayesian model")
{drawmap (Y[,input$interval2[1]:input$interval2[2]],X,S,axis=c(1,2),formula=input$formula_lm1,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =F, graph.map =F,
graph.map.3D =T )}
})
####### Results of models selection
output$Dimension_var1<-renderUI({
selectInput("Dimension_var1","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var1<-renderUI({
selectInput("Prediction_var1","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model"))
})
output$aic<-renderUI({
selectInput("aic","Choose parameter of comparison:",choices = c("AIC","R2", "Fstat","nb-NA"))
})
output$model_selection=renderPlot({cart14()})
cart14=eventReactive(input$actioncarto14,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
library(ggplot2)
bas=data.frame(x3,x2)
g2<-ggplot(bas,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g2<-g2+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g2
}
else if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps)
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas1=data.frame(x3,x2)
g21<-ggplot(bas1,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g21<-g21+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g21
}
else if(input$aic=="AIC" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
aic.lm=extract.lm(x.lm,what=c("aic"))
aic.vect=extract.lm(x.vect,what=c("aic"))
aic.circ=extract.lm(x.circ,what=c("aic"))
aic.ellip=extract.lm(x.ellip,what=c("aic"))
aic.gam=extract.gam(x.gam,what=c("aic"))
aic.glm=extract.glm(x.glm,what=c("aic"))## ???
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(aic.lm,aic.vect,aic.circ,aic.ellip,aic.gam,aic.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas2=data.frame(x3,x2)
g23<-ggplot(bas2,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("AIC")+ggtitle( "Comparison of models with AIC ")
g23<-g23+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g23
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas3=data.frame(x3,x2)
g22<-ggplot(bas3,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g22<-g22+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g22
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
## If R2 and MFA
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("Generalized Additive Model",ncol(Y)),rep("Generalized Linear Model",ncol(Y)))
bas4=data.frame(x3,x2)
g22<-ggplot(bas4,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g23<-g23+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g23
}
else if(input$aic=="R2" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
## If R2 and CA
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
r2.lm=extract.lm(x.lm,what=c("rsquare"))
r2.vect=extract.lm(x.vect,what=c("rsquare"))
r2.circ=extract.lm(x.circ,what=c("rsquare"))
r2.ellip=extract.lm(x.ellip,what=c("rsquare"))
r2.gam=extract.gam(x.gam,what=c("rsquare"))
r2.glm=extract.gam(x.glm,what=c("rsquare"))
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(r2.lm,r2.vect,r2.circ,r2.ellip,r2.gam,r2.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
bas4=data.frame(x3,x2)
g24<-ggplot(bas4,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("R2")+ggtitle( "Comparison of models with R-squared ")
g24<-g24+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g24
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) f.glm[[i]]=0}
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas5=data.frame(x3,x2)
g25<-ggplot(bas5,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g25<-g25+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g25+ylim(min=0, max=100)
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2))
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) {f.glm[[i]]=0} }
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas6=data.frame(x3,x2)
g26<-ggplot(bas6,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g26<-g26+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g26+ylim(min=0, max=100)
}
else if(input$aic=="Fstat" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
## If fstat and CA
map=map.with.ca(X,S,Y)
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
attach(maps )
discretspace=discrete.function(map = maps)
x.lm=predict.scores.lm(Y = Y,discretspace = discretspace,map = maps)
x.vect=predict.scores.lm(Y = Y,formula="~F1+F2",discretspace = discretspace,map = maps)
x.circ=predict.scores.lm(Y = Y,formula="~ F1 + F2 + (F1*F1 + F2*F2)",discretspace = discretspace,map = maps)
x.ellip=predict.scores.lm(Y = Y,formula="~I(F1*F1)+I(F2*F2)",discretspace = discretspace,map = maps)
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,map = maps)
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,map = maps)
f.lm=extract.lm(x.lm,what=c("fstastic"))
f.vect=extract.lm(x.vect,what=c("fstastic"))
f.circ=extract.lm(x.circ,what=c("fstastic"))
f.ellip=extract.lm(x.ellip,what=c("fstastic"))
f.gam=extract.gam(x.gam,what=c("fstastic"))
f.glm=extract.glm(x.glm,what=c("fstastic"))
for (i in 1:length(f.glm)){ if (is.null(f.glm[[i]] )) {f.glm[[i]]=0} }
x2=c(f.lm,f.vect,f.circ,f.ellip,f.gam,f.glm)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
x2=unlist(x2)
bas7=data.frame(x3,x2)
g27<-ggplot(bas7,aes(x=x3,y=x2,fill=x3))+geom_boxplot()+xlab("Models")+
ylab("F-statistic")+ ggtitle( "Comparison of models with F-statistic ")
g27<-g27+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90),
plot.title = element_text(size=10, hjust = 0.5,face="bold"))
g27+ylim(min=0, max=100)
}
###########
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Principal Component Analysis"){
map=map.with.pca(X) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from PCA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
##################
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Multiple Factor Analysis"){
map=map.with.mfa(X,Y,axis=c(1,2)) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from MFA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
############
else if(input$aic=="nb-NA" && (input$Prediction_var1=="Vector model" ||input$Prediction_var1=="Circular model"||input$Prediction_var1=="Elliptic model"
||input$Prediction_var1=="Quadratic model"||input$Prediction_var1=="Generalized Additive Model"||input$Prediction_var1=="Generalized Linear Model") &&
input$Dimension_var1== "Canonical Analysis"){
map=map.with.ca(X,S,Y) # pour mfa et ca tu changes cette commande
maps=cbind.data.frame(map$F1,map$F2)
colnames(maps)=c("F1","F2")
discretspace=discrete.function(map = maps)
x.quad=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", map = maps
,pred.na=TRUE)
nb.quad=x.quad$nb.NA
nb.quad=unlist(nb.quad)
nb.quad=as.data.frame(nb.quad)
#vect
x.vect=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2", map = maps
,pred.na=TRUE)
nb.vect=x.vect$nb.NA
nb.vect=unlist(nb.vect)
nb.vect=as.data.frame(nb.vect)
#circ
x.circ=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", map = maps,
pred.na=TRUE)
nb.circ=x.circ$nb.NA
nb.circ=unlist(nb.circ)
nb.circ=as.data.frame(nb.circ)
#ellip
x.ellip=predict.scores.lm(Y = Y,discretspace = discretspace,
formula="~I(F1*F1)+I(F2*F2)", map = maps,
pred.na=TRUE)
nb.ellip=x.ellip$nb.NA
nb.ellip=unlist(nb.ellip)
nb.ellip=as.data.frame(nb.ellip)
# gam
x.gam=predict.scores.gam(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.gam=x.gam$nb.NA
nb.gam=unlist(nb.gam)
nb.gam=as.data.frame(nb.gam)
# glm
x.glm=predict.scores.glmulti(Y = Y,discretspace = discretspace,
map = maps,
pred.na=TRUE)
nb.glm=x.glm$nb.NA
nb.glm=unlist(nb.glm)
nb.glm=as.data.frame(nb.glm)
x1=c(1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y),1:ncol(Y))
x2=c(nb.quad,nb.vect,nb.circ,nb.ellip,nb.gam,nb.glm)
x2=melt(x2)
x3=c(rep("QR",ncol(Y)),rep("Vect",ncol(Y)),rep("Circ",ncol(Y)),rep("Ellip",ncol(Y)),rep("GAM",ncol(Y)),rep("GLM",ncol(Y)))
dt=cbind.data.frame(x1,x2[,1],x3)
colnames(dt)=c("consumer","occur.na","model")
gr2<-ggplot(dt,aes
(x=model,y=occur.na ,fill=model))+geom_boxplot()+xlab("Consumers")+ylab("Occurence of NA")+
ggtitle( "Comparison of nb-NA of prediction models from CA ")
gr2+theme_bw()+ theme(plot.title = element_text(size=16, hjust = 0.5,face="bold"))
}
})
output$downloadPlot_aic <- downloadHandler(
filename = function() {
paste("PLOT", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cart14())
dev.off(which=dev.cur())}
)
###### EPM smoothing
output$Dimension_var3<-renderUI({
selectInput("Dimension_var3","Choose dimension reduction method:",choices = c("Principal Component Analysis", "Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_var3<-renderUI({
selectInput("Prediction_var3","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model", "Bayesian model","Others"))
})
output$mod2<-renderUI({
if(input$Prediction_var3 == "Others"){
selectInput("mod2","Choose type of model:",choices = c("Polynomial model","GAM" , "GLM", "Bayesian model"))}
else return(NULL)
})
output$formula_lm2=renderUI({
if(input$Prediction_var3 == "Others"){
if(input$mod2 == "Polynomial model"){
textInput("formula_lm2","Input formula for polynomial model :")}
else if(input$mod2 == "GAM"){
textInput("formula_lm2","Input formula for GAM :")}
else if(input$mod2 == "GLM"){
textInput("formula_lm2","Input formula for GLM :")}
else if(input$mod2 == "Bayesian model"){
textInput("formula_lm2","Input formula for Bayesian model :")}}
})
output$par_var3<-renderUI({
if(input$Prediction_var3 != "Bayesian model"){
selectInput("par_var3","If rejection of predictions outside of [0:10]:",choices = c("NO","YES"))}
else{selectInput("par_var3","If rejection of predictions outside of [0:10]:",choices = c("NO"))}
})
output$interval3=renderUI({
Y=consInputData()
sliderInput("interval3", "Interval of Consumers",
min = 1, max = ncol(Y), value = c(1,40))
})
output$smooth=renderPlot({cart15()})
cart15=eventReactive(input$actioncarto15,{
Y=consInputData()
X=moy2()
S=moy4()
nbpoints=50
if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar1=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar1)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar2=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar2)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar3=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar3)}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar4=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar4)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar5=par(mfrow = c(1,2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar5)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar6=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar6)
}
###############
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar7=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar7)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar8=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar8)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar9=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar9)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar10=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar10)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar11=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar11)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar12=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar12)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar13=par(mfrow = c(1, 2))
a=drawmap (Y,X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y,S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar13)
}
################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar14=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar14)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar15=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar15)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar16=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar16)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar17=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar17)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar18=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar18)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar19=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar19)
}
#######################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar20=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar20)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar21=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar21)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar22=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar22)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar23=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar23)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar24=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar24)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar25=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar25)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar26=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar26)
}
#################################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="YES")
{impar27=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar27)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="YES")
{impar28=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar28)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="YES")
{impar29=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar29)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="YES")
{impar30=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar30)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="YES")
{impar31=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar31)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="YES")
{impar32=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar32)
}
#################################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Vector model" && input$par_var3=="NO")
{impar33=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar33)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Circular model" && input$par_var3=="NO")
{impar34=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~ F1 + F2 + (F1*F1 + F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~ F1 + F2 + (F1*F1 + F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar34)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Elliptic model" && input$par_var3=="NO")
{impar35=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar35)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Quadratic model" && input$par_var3=="NO")
{impar36=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar36)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Additive Model" && input$par_var3=="NO")
{impar37=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~s(F1,k=3)+s(F2,k=3)",
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~s(F1,k=3)+s(F2,k=3)", dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar37)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Generalized Linear Model" && input$par_var3=="NO")
{impar38=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar38)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Bayesian model" && input$par_var3=="NO")
{impar39=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula="~I(F1*F1)+I(F2*F2)+F1*F2",
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula="~I(F1*F1)+I(F2*F2)+F1*F2", dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar39)
}
#######################
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar40=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar40)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar41=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar41)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="GAM" )
{impar42=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar42)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar43=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar43)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar44=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar44)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar45=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar46)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar47=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar47)
}
else if(input$Dimension_var3=="Principal Component Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar48=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=1, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=1,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar48)
}
#########################
#######################
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar49=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar49)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar50=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar50)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Additive Model" )
{impar51=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar51)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar52=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar52)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar53=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar53)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar54=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar54)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar55=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar55)
}
else if(input$Dimension_var3=="Multiple Factor Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar56=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=2, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=2,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar56)
}
#########################
#######################
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Polynomial regression" )
{impar57=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar57)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Polynomial regression" )
{impar58=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=1, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=1, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar58)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Additive Model" )
{impar59=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar59)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Additive Model" )
{impar60=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=2, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=2, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar60)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Generalized Linear Model" )
{impar61=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar61)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Generalized Linear Model" )
{impar62=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=3, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=3, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar62)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="NO" &&
input$mod2=="Bayesian model" )
{impar63=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =FALSE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=FALSE,dmap.loess=FALSE)
par(impar63)
}
else if(input$Dimension_var3=="Canonical Analysis" && input$Prediction_var3=="Others" && input$par_var3=="YES" &&
input$mod2=="Bayesian model" )
{impar64=par(mfrow = c(1, 2))
a=drawmap (Y[,input$interval3[1]:input$interval3[2]],X,S,axis=c(1,2),formula=input$formula_lm2,
dimredumethod=3, predmodel=4, nbpoints=50,
pred.na =TRUE, graph.pred =FALSE, graph.map =TRUE,
graph.map.3D =FALSE )
b=denoising.loess.global(X,Y[,input$interval3[1]:input$interval3[2]],S, axis=c(1,2), discretspace=discretspace,
formula=input$formula_lm2, dimredumethod=3,
predmodel=4, graphpred=FALSE, drawmap=TRUE,
pred.na=TRUE,dmap.loess=FALSE)
par(impar64)
}
})
##### comparison of maps stability
output$reduction<-renderUI({
selectInput("reduction","Choose dimension reduction method:",choices = c("Principal Component Analysis",
"Multiple Factor Analysis", "Canonical Analysis"))
})
output$Prediction_com<-renderUI({
selectInput("Prediction_com","Choose prediction model:",choices = c("Vector model", "Circular model","Elliptic model",
"Quadratic model", "Generalized Additive Model" , "Generalized Linear Model"))
})
output$formula_lm=renderUI({
textInput("formula_lm","Input formula for Polynomial and GLM models :")
})
output$formula_gam=renderUI({
textInput("formula_gam","Input formula for GAM :")
})
output$numm<-renderUI({
numericInput("numm","Introduce number of sampling n (at least 2):", 2,
min = 2, max = 100)
})
output$comparaison=renderPlot({cart16()})
cart16=eventReactive(input$actioncarto16,{
Y=consInputData()
X=moy2()
S=moy4()
# nbpoints=50
if(input$reduction=="Principal Component Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=1,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
else if(input$reduction=="Multiple Factor Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=2,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
else if(input$reduction=="Canonical Analysis" && (input$Prediction_com=="Vector model" ||input$Prediction_com=="Circular model"||input$Prediction_com=="Elliptic model"
||input$Prediction_com=="Quadratic model"||input$Prediction_com=="Generalized Additive Model"||input$Prediction_com=="Generalized Linear Model"||input$Prediction_com=="Bayesian model")){
res= Dist_prob(Y,X,S,n=input$numm,axis=c(1,2),formula_lm=input$formula_lm,
formula_gam=input$formula_gam,dimredumethod=3,
nbpoints=50)
res=melt(res)
colnames(res)=c("prob", "var", "value")
gr<-ggplot(res,aes(x=prob,y=value,fill=prob))+geom_boxplot()+xlab("Methods")+
ylab("Difference Sum Squares of Preferences")
gr<-gr+theme_bw()+theme(legend.position = "none",
axis.text.x = element_text(angle = 90))
gr
}
})
output$downloadPlot_comp <- downloadHandler(
filename = function() {
paste("comp", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(cart16())
dev.off(which=dev.cur())}
)
output$dend<-renderPlot({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
plot.HCPC(res.hcpc,choice="tree",new.plot=F)
})
clus1=reactive({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
fviz_cluster(res.hcpc) + theme_minimal()+ ggtitle("Clustering Plot")
})
output$clus<-renderPlot({
clus1()
})
output$downloadclus <- downloadHandler(
filename = function() {
paste("Plot", downloadPlotType(), sep=".")
},
# The argument content below takes filename as a function
# and returns what's printed to it.
content = function(con) {
# Gets the name of the function to use from the
# downloadFileType reactive element. Example:
# returns function pdf() if downloadFileType == "pdf".
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(clus1())
dev.off(which=dev.cur())}
)
dend11<-reactive({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
b=res.hcpc$call$t$tree
c=as.dendrogram(b)
nb=res.hcpc$call$t$nb.clust
d=c %>%
set("branches_k_color", k = nb) %>% set("branches_lwd", 0.7) %>%
set("labels_cex", 0.6) %>% set("labels_colors", k = nb) %>%
set("leaves_pch", 10) %>% set("leaves_cex", 0.5)%>%raise.dendrogram(0.015)%>%remove_nodes_nodePar
ggd1 <- as.ggdend(d)
ggplot(ggd1,labels=F)+ggtitle("Dendrogram")+scale_y_continuous(0,1.2)
plot.HCPC(res.hcpc,choice="tree",new.plot=F)
})
output$dend1<-renderPlot({
dend11()
})
output$downloaddend <- downloadHandler(
filename = function() {
paste("Plot", downloadPlotType(), sep=".")
},
content = function(con) {
plotFunction <- match.fun(downloadPlotType())
plotFunction(con, width = downloadPlotWidth(), height = downloadPlotHeight())
print(dend11())
dev.off(which=dev.cur())}
)
output$dend12<-renderPrint({
df=consInputData()
res.pca=PCA(t(df),graph = F)
res.hcpc=HCPC(res.pca,graph = F)
res.hcpc$desc.var
})
})
Try the SensMap package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.