Nothing
set.seed(12345)
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
plsR(yCornell,XCornell,10)$InfCrit
plsR(yCornell,XCornell,10,typeVC="standard")$InfCrit
plsR(yCornell,XCornell,6)$AIC
plsR(yCornell,XCornell,6)$AIC.std
plsRglm(yCornell,XCornell,3)$uscores
plsRglm(yCornell,XCornell,3)$pp
plsRglm(yCornell,XCornell,3)$Coeffs
plsRglm(yCornell,XCornell,10)$InfCrit
plsRglm(yCornell,XCornell,10,modele="pls-glm-gaussian")$InfCrit
bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,keepcoeffs=TRUE)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb,MClassed=TRUE)
bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=10,keepcoeffs=TRUE)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb,MClassed=TRUE)
bbb <- PLS_glm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls")
kfolds2CVinfos_glm(bbb)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=12,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=12,keepfolds=FALSE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=6,NK=2,random=FALSE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=6,NK=2,random=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,keepcoeffs=TRUE,keepfolds=TRUE)
PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,keepcoeffs=TRUE,keepfolds=FALSE)
bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv(dataY=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),nt=6,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm(yCornell,XCornell,6,typeVC="standard")$InfCrit
data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
modpls <- PLS_lm(yaze_compl,Xaze_compl,10,MClassed=TRUE)
modpls$AIC
modpls$AIC.std
modpls$MissClassed
modpls$Probs
modpls$Probs.trc
modpls$Probs-modpls$Probs.trc
modpls$InfCrit
PLS_lm(yaze_compl,Xaze_compl,10)$InfCrit
PLS_lm(yaze_compl,Xaze_compl,10,typeVC="standard")$InfCrit
PLS_lm(yaze_compl,Xaze_compl,10,typeVC="standard",MClassed=TRUE)$InfCrit
rm(list=c("Xaze_compl","yaze_compl","modpls"))
dimX <- 24
Astar <- 2
simul_data_UniYX(dimX,Astar)
dataAstar2 <- t(replicate(250,simul_data_UniYX(dimX,Astar)))
ydataAstar2 <- dataAstar2[,1]
XdataAstar2 <- dataAstar2[,2:(dimX+1)]
ysimbin1 <- dicho(ydataAstar2)
Xsimbin1 <- dicho(XdataAstar2)
PLS_lm(ysimbin1,Xsimbin1,10,MClassed=TRUE)$Probs
PLS_lm(ysimbin1,Xsimbin1,10,MClassed=TRUE)$Probs.trc
PLS_lm(ysimbin1,Xsimbin1,10,MClassed=TRUE)$MissClassed
PLS_lm(ysimbin1,Xsimbin1,10,typeVC="standard",MClassed=TRUE)$InfCrit
PLS_lm(ysimbin1,XdataAstar2,10,typeVC="standard",MClassed=TRUE)$InfCrit
PLS_lm(ydataAstar2,XdataAstar2,10,typeVC="standard")$InfCrit
rm(list=c("dimX","Astar","dataAstar2","ysimbin1","Xsimbin1","ydataAstar2","XdataAstar2"))
dimX <- 6
Astar <- 4
dataAstar4 <- t(replicate(250,simul_data_UniYX(dimX,Astar)))
ydataAstar4 <- dataAstar4[,1]
XdataAstar4 <- dataAstar4[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar4,XdataAstar4,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
str(modpls)
rm(list=c("dimX","Astar","dataAstar4","modpls","ydataAstar4","XdataAstar4"))
dimX <- 24
Astar <- 2
dataAstar2 <- t(replicate(250,simul_data_UniYX(dimX,Astar)))
ydataAstar2 <- dataAstar2[,1]
XdataAstar2 <- dataAstar2[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar2,XdataAstar2,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
rm(list=c("dimX","Astar","dataAstar2","modpls","ydataAstar2","XdataAstar2"))
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
set.seed(1385)
Cornell.tilt.boot <- tilt.bootpls(plsR(yCornell,XCornell,3), statistic=coefs.plsR, R=c(499, 100, 100), alpha=c(0.025, 0.975), sim="ordinary", stype="i", index=1)
Cornell.tilt.boot
str(Cornell.tilt.boot)
boxplots.bootpls(Cornell.tilt.boot,indices=2:7)
rm(Cornell.tilt.boot)
# Comparing the results with the plspm package and SIMCA results in Tenenhaus's book
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_lm(yCornell,XCornell,3)$uscores
PLS_lm(yCornell,XCornell,3)$pp
PLS_lm(yCornell,XCornell,3)$Coeffs
PLS_lm(yCornell,XCornell,4,typeVC="standard")$press.ind
PLS_lm(yCornell,XCornell,4,typeVC="standard")$press.tot
PLS_lm(yCornell,XCornell,4,typeVC="standard")$InfCrit
PLS_lm_wvc(dataY=yCornell,dataX=XCornell,nt=3,dataPredictY=XCornell[1,])
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),])
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],keepcoeffs=TRUE)
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
PLS_lm(log(ypine),Xpine,4)$Std.Coeffs
PLS_lm(log(ypine),Xpine,4)$Coeffs
PLS_lm(log(ypine),Xpine,1)$Std.Coeffs
PLS_lm(log(ypine),Xpine,1)$Coeffs
PLS_lm(log(ypine),Xpine,10,typeVC="standard")$InfCrit
data(pine_full)
Xpine_full<-pine_full[,1:10]
ypine_full<-pine_full[,11]
PLS_lm(log(ypine_full),Xpine_full,1)$Std.Coeffs
PLS_lm(log(ypine_full),Xpine_full,1)$Coeffs
cor(cbind(Xpine,log(ypine)))
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
PLS_lm(log(ypine),XpineNAX21,4)$Std.Coeffs
PLS_lm(log(ypine),XpineNAX21,4)$YChapeau[1,]
PLS_lm(log(ypine),Xpine,4)$YChapeau[1,]
PLS_lm(log(ypine),XpineNAX21,4)$CoeffC
PLS_lm(log(ypine),XpineNAX21,2,dataPredictY=XpineNAX21[1,])$ValsPredictY
PLS_lm(log(ypine),Xpine,10,typeVC="none")$InfCrit
PLS_lm(log(ypine),Xpine,10,typeVC="standard")$InfCrit
PLS_lm(log(ypine),Xpine,10,typeVC="adaptative")$InfCrit
PLS_lm(log(ypine),Xpine,10,typeVC="missingdata")$InfCrit
PLS_lm(log(ypine),XpineNAX21,10,typeVC="none")$InfCrit
PLS_lm(log(ypine),XpineNAX21,10,typeVC="standard")$InfCrit
PLS_lm(log(ypine),XpineNAX21,10,typeVC="adaptative")$InfCrit
PLS_lm(log(ypine),XpineNAX21,10,typeVC="missingdata")$InfCrit
PLS_lm(log(ypine),XpineNAX21,4,EstimXNA=TRUE)$XChapeau
PLS_lm(log(ypine),XpineNAX21,4,EstimXNA=TRUE)$XChapeauNA
# The results from plspm were uncorrect
if ("plspm" %in% installed.packages()){
library(plspm)
plsreg1(x=XCornell,y=as.vector(yCornell),nc=3)$coeffs
plsreg1(x=XCornell,y=as.vector(yCornell),nc=4,cv=TRUE)
plsreg1(x=XCornell,y=as.vector(yCornell),nc=4,cv=TRUE)$Q2
plsreg1(x=Xpine,y=log(as.vector(ypine)),nc=4)$std.coef
plsreg1(x=Xpine,y=log(as.vector(ypine)),nc=4)$coeffs
plsreg1(x=Xpine,y=log(as.vector(ypine)),nc=10)$Q2
plsreg1(x=Xpine,y=log(as.vector(ypine)),nc=4,cv=TRUE)$Q2
plsreg1(x=Xpine_full,y=log(as.vector(ypine_full)),nc=4)$coeffs
plsreg1(x=XpineNAX21,y=as.vector(log(ypine)),nc=4,cv=TRUE)
}
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=Xpine,nt=10,K=12,NK=1)
bbb2 <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=Xpine,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbb)
kfolds2CVinfos_lm(bbb2)
PLS_lm(log(ypine),Xpine,10,typeVC="standard")$InfCrit
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=XpineNAX21,nt=10,K=12,NK=1)
bbbNA2 <- PLS_lm_kfoldcv(dataY=log(ypine),dataX=XpineNAX21,nt=10,K=6,NK=1)
kfolds2CVinfos_lm(bbbNA)
kfolds2CVinfos_lm(bbbNA2)
PLS_lm(log(ypine),XpineNAX21,10,typeVC="standard")$InfCrit
data(XpineNAX21)
PLS_lm_wvc(dataY=log(ypine)[-1],dataX=XpineNAX21[-1,],nt=3)
PLS_lm_wvc(dataY=log(ypine)[-1],dataX=XpineNAX21[-1,],nt=3,dataPredictY=XpineNAX21[1,])
PLS_lm_wvc(dataY=log(ypine)[-2],dataX=XpineNAX21[-2,],nt=3,dataPredictY=XpineNAX21[2,])
PLS_lm_wvc(dataY=log(ypine),dataX=XpineNAX21,nt=3)
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2"))
dimX <- 24
Astar <- 3
dataAstar3 <- t(replicate(200,simul_data_UniYX(dimX,Astar)))
ydataAstar3 <- dataAstar3[,1]
XdataAstar3 <- dataAstar3[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar3,XdataAstar3,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
rm(list=c("dimX","Astar","dataAstar3","modpls","ydataAstar3","XdataAstar3"))
dimX <- 24
Astar <- 4
dataAstar4 <- t(replicate(200,simul_data_UniYX(dimX,Astar)))
ydataAstar4 <- dataAstar4[,1]
XdataAstar4 <- dataAstar4[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar4,XdataAstar4,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
rm(list=c("dimX","Astar","dataAstar4","modpls","ydataAstar4","XdataAstar4"))
dimX <- 24
Astar <- 5
dataAstar5 <- t(replicate(200,simul_data_UniYX(dimX,Astar)))
ydataAstar5 <- dataAstar5[,1]
XdataAstar5 <- dataAstar5[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar5,XdataAstar5,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
rm(list=c("dimX","Astar","dataAstar5","modpls","ydataAstar5","XdataAstar5"))
dimX <- 24
Astar <- 6
dataAstar6 <- t(replicate(200,simul_data_UniYX(dimX,Astar)))
ydataAstar6 <- dataAstar6[,1]
XdataAstar6 <- dataAstar6[,2:(dimX+1)]
modpls <- PLS_lm(ydataAstar6,XdataAstar6,10,typeVC="standard")
modpls$computed_nt
modpls$InfCrit
rm(list=c("dimX","Astar","dataAstar6","modpls","ydataAstar6","XdataAstar6"))
# Lazraq-Cleroux PLS ordinary bootstrap
set.seed(250)
Cornell.boot <- bootpls(plsR(yCornell,XCornell,3), sim="ordinary", stype="i", R=250)
boot::boot.array(Cornell.boot, indices=TRUE)
# Graph similar to the one of Bastien et al. in CSDA 2005
boxplot(as.vector(Cornell.boot$t[,-1])~factor(rep(1:7,rep(250,7))), main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.boot$t0[-1],col="red",pch=19)
# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.boot,indices=2:8)
# Confidence intervals plotting
confints.bootpls(Cornell.boot,indices=2:8)
plots.confints.bootpls(confints.bootpls(Cornell.boot,indices=2:8))
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
# Lazraq-Cleroux PLS ordinary bootstrap
set.seed(250)
Cornell.boot <- bootpls(plsR(yCornell,XCornell,3), sim="ordinary", stype="i", R=250)
(temp.ci <- confints.bootpls(Cornell.boot,2:8))
plots.confints.bootpls(temp.ci)
(temp.ci <- confints.bootpls(Cornell.boot,2:8,typeBCa=FALSE))
plots.confints.bootpls(temp.ci)
(temp.ci <- confints.bootpls(Cornell.boot,c(2,4,6)))
plots.confints.bootpls(temp.ci)
plot(Cornell.boot,index=2)
boot::jack.after.boot(Cornell.boot, index=2, useJ=TRUE, nt=3)
plot(Cornell.boot,index=2,jack=TRUE)
car::dataEllipse(Cornell.boot$t[,2], Cornell.boot$t[,3], cex=.3, levels=c(.5, .95, .99), robust=T)
rm(Cornell.boot)
# PLS balanced bootstrap
set.seed(225)
Cornell.boot <- bootpls(plsR(yCornell,XCornell,3), sim="balanced", stype="i", R=250)
boot::boot.array(Cornell.boot, indices=TRUE)
# Graph similar to the one of Bastien et al. in CSDA 2005
boxplot(as.vector(Cornell.boot$t[,-1])~factor(rep(1:7,rep(250,7))), main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.boot$t0[-1],col="red",pch=19)
# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.boot,indices=2:8)
# Confidence intervals plotting
confints.bootpls(Cornell.boot,indices=2:8)
plots.confints.bootpls(confints.bootpls(Cornell.boot,indices=2:8))
library(boot)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=2)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=3)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=4)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=5)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=6)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=7)
boot::boot.ci(Cornell.boot, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=8)
plot(Cornell.boot,index=2)
boot::jack.after.boot(Cornell.boot, index=2, useJ=TRUE, nt=3)
plot(Cornell.boot,index=2,jack=TRUE)
rm(Cornell.boot)
# PLS permutation bootstrap
set.seed(500)
Cornell.boot <- bootpls(plsR(yCornell,XCornell,3), sim="permutation", stype="i", R=1000)
boot::boot.array(Cornell.boot, indices=TRUE)
# Graph of bootstrap distributions
boxplot(as.vector(Cornell.boot$t[,-1])~factor(rep(1:7,rep(1000,7))),main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.boot$t0[-1],col="red",pch=19)
# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.boot,indices=2:8)
library(boot)
plot(Cornell.boot,index=2)
qqnorm(Cornell.boot$t[,2],ylim=c(-1,1))
abline(h=Cornell.boot$t0[2],lty=2)
(sum(abs(Cornell.boot$t[,2])>=abs(Cornell.boot$t0[2]))+1)/(length(Cornell.boot$t[,2])+1)
rm(Cornell.boot)
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
plsRglm(log(ypine),Xpine,1)$Std.Coeffs
plsRglm(log(ypine),Xpine,1)$Coeffs
plsRglm(log(ypine),Xpine,4)$Std.Coeffs
plsRglm(log(ypine),Xpine,4)$Coeffs
plsRglm(log(ypine),Xpine,4)$PredictY[1,]
plsRglm(log(ypine),Xpine,4,dataPredictY=Xpine[1,])$PredictY[1,]
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
str(plsRglm(log(ypine),XpineNAX21,2))
plsRglm(log(ypine),XpineNAX21,4)$Std.Coeffs
plsRglm(log(ypine),XpineNAX21,4)$YChapeau[1,]
plsRglm(log(ypine),Xpine,4)$YChapeau[1,]
plsRglm(log(ypine),XpineNAX21,4)$CoeffC
plsRglm(log(ypine),XpineNAX21,4,EstimXNA=TRUE)$XChapeau
plsRglm(log(ypine),XpineNAX21,4,EstimXNA=TRUE)$XChapeauNA
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.