plsRglm (version 0.3.3)

plsR: Partial least squares Regression models with leave one out cross validation

Description

This function implements Partial least squares Regression models with leave one out cross validation for complete or incomplete datasets.

Usage

plsR(dataY, dataX, nt = 2, limQ2set = 0.0975, dataPredictY = dataX, modele = "pls", family = NULL, typeVC = "none", EstimXNA = FALSE, scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE, alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12))

Arguments

dataY

response (training) dataset

dataX

predictor(s) (training) dataset

nt

number of components to be extracted

limQ2set

limit value for the Q2

dataPredictY

predictor(s) (testing) dataset

modele

name of the PLS model to be fitted, only ("pls" available for this fonction.

family

for the present moment the family argumlent is ignored and set thanks to the value of modele.

typeVC

type of leave one out crossed validation. Several procedures are available and may be forced. [object Object],[object Object],[object Object],[object Object]

EstimXNA

only for modele="pls". Set whether the missing X values have to be estimated.

scaleX

scale the predictor(s) : must be set to TRUE for modele="pls" and should be for glms pls.

scaleY

scale the response : Yes/No. Ignored since non always possible for glm responses.

pvals.expli

should individual p-values be reported to tune model selection ?

alpha.pvals.expli

level of significance for predictors when pvals.expli=TRUE

MClassed

number of missclassed cases, should only be used for binary responses

tol_Xi

minimal value for Norm2(Xi) and $\mathrm{det}(pp' \times pp)$ if there is any missing value in the dataX. It defaults to $10^{-12}$

Value

• nr
  Description of 'comp1'
• nc
  Description of 'comp2'
• nt
  Description of 'comp2'
• ww
  Description of 'comp2'
• wwnorm
  Description of 'comp2'
• wwetoile
  Description of 'comp2'
• tt
  Description of 'comp2'
• pp
  Description of 'comp2'
• CoeffC
  Description of 'comp2'
• uscores
  Description of 'comp2'
• YChapeau
  Description of 'comp2'
• residYChapeau
  Description of 'comp2'
• RepY
  Description of 'comp2'
• na.miss.Y
  Description of 'comp2'
• YNA
  Description of 'comp2'
• residY
  Description of 'comp2'
• ExpliX
  Description of 'comp2'
• na.miss.X
  Description of 'comp2'
• XXNA
  Description of 'comp2'
• residXX
  Description of 'comp2'
• PredictY
  Description of 'comp2'
• press.ind
  Description of 'comp2'
• press.tot
  Description of 'comp2'
• familly
  Description of 'comp2'
• ttPredictY
  Description of 'comp2'
• typeVC
  Description of 'comp2'
• dataY
  Description of 'comp2'
• computed_nt
  Description of 'comp2'
• CoeffCFull
  Description of 'comp2'
• CoeffConstante
  Description of 'comp2'
• Std.Coeffs
  Description of 'comp2'
• press.ind2
  Description of 'comp2'
• RSSresidY
  Description of 'comp2'
• Coeffs
  Description of 'comp2'
• Yresidus
  Description of 'comp2'
• RSS
  Description of 'comp2'
• residusY
  Description of 'comp2'
• AIC.std
  AIC.std vs number of components (AIC computed for the standardized model
• AIC
  AIC vs nber of components
• optional
  If the response is assumed to be binary: i.e. MClassed=TRUE. [object Object],[object Object],[object Object]
• ttPredictFittedMissingY
  Description of 'comp2'
• optional
  If cross validation was requested: i.e. typeVC="standard", typeVC="missingdata" or typeVC="adaptative". [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
• optional
  If no cross validation was requested : i.e. typeVC="none". [object Object]
• Std.ValsPredictY
  Description of 'comp2'
• ValsPredictY
  Description of 'comp2'
• Std.XChapeau
  Description of 'comp2'
• XXwotNA
  Description of 'comp2'

Details

There are several ways to deal with missing values that leads to different computations of leave one out cross validation criteria.

References

Nicolas Meyer, Myriam Maumy-Bertrand et Fr�d�ric{Fr'ed'eric} Bertrand (2010). Comparaison de la r�gression{r'egression} PLS et de la r�gression{r'egression} logistique PLS : application aux donn�es{donn'ees} d'all�lotypage{d'all'elotypage}. Journal de la Soci�t� Fran�aise de Statistique, 151(2), pages 1-18. http://smf4.emath.fr/Publications/JSFdS/151_2/pdf/sfds_jsfds_151_2_1-18.pdf

See Also

plsRglm

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
plsR(yCornell,XCornell,10)$InfCrit
plsR(yCornell,XCornell,10,typeVC="standard")$CVinfos
plsR(yCornell,XCornell,6)$AIC 
plsR(yCornell,XCornell,6)$AIC.std    
rm(list=c("XCornell","yCornell"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y

modpls <- plsR(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$InfCrit
plsR(yaze_compl,Xaze_compl,10)$InfCrit

plsR(yaze_compl,Xaze_compl,10,typeVC="standard")$CVinfos
plsR(yaze_compl,Xaze_compl,10,typeVC="standard",MClassed=TRUE)$CVinfos
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)
plsR(ysimbin1,Xsimbin1,10,MClassed=TRUE)$Probs
plsR(ysimbin1,Xsimbin1,10,MClassed=TRUE)$Probs.trc
plsR(ysimbin1,Xsimbin1,10,MClassed=TRUE)$MissClassed
plsR(ysimbin1,Xsimbin1,10,typeVC="standard",MClassed=TRUE)$CVinfos
plsR(ysimbin1,XdataAstar2,10,typeVC="standard",MClassed=TRUE)$CVinfos
plsR(ydataAstar2,XdataAstar2,10,typeVC="standard")$CVinfos
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 <- plsR(ydataAstar4,XdataAstar4,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
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 <- plsR(ydataAstar2,XdataAstar2,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
rm(list=c("dimX","Astar","dataAstar2","modpls","ydataAstar2","XdataAstar2"))


dimX <- 24
Astar <- 3
dataAstar3 <- t(replicate(200,simul_data_UniYX(dimX,Astar)))
ydataAstar3 <- dataAstar3[,1]
XdataAstar3 <- dataAstar3[,2:(dimX+1)]
modpls <- plsR(ydataAstar3,XdataAstar3,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
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 <- plsR(ydataAstar4,XdataAstar4,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
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 <- plsR(ydataAstar5,XdataAstar5,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
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 <- plsR(ydataAstar3,XdataAstar6,10,typeVC="standard")
modpls$computed_nt
modpls$CVinfos
rm(list=c("dimX","Astar","dataAstar6","modpls","ydataAstar6","XdataAstar6"))