cv.plsR  R Documentation 
This function implements kfold crossvalidation on complete or incomplete datasets for partial least squares regression models
cv.plsR(object, ...)
## Default S3 method:
cv.plsRmodel(object,dataX,nt=2,limQ2set=.0975,modele="pls",
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE,
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE,
keepMclassed=FALSE, tol_Xi=10^(12), weights, verbose=TRUE,...)
## S3 method for class 'formula'
cv.plsRmodel(object,data=NULL,nt=2,limQ2set=.0975,modele="pls",
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE,
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE,
keepMclassed=FALSE, tol_Xi=10^(12), weights,subset,contrasts=NULL, verbose=TRUE,...)
PLS_lm_kfoldcv(dataY, dataX, nt = 2, limQ2set = 0.0975, modele = "pls",
K = 5, NK = 1, grouplist = NULL, random = TRUE, scaleX = TRUE,
scaleY = NULL, keepcoeffs = FALSE, keepfolds = FALSE, keepdataY = TRUE,
keepMclassed=FALSE, tol_Xi = 10^(12), weights, verbose=TRUE)
PLS_lm_kfoldcv_formula(formula,data=NULL,nt=2,limQ2set=.0975,modele="pls",
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE,
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE,
keepMclassed=FALSE, tol_Xi=10^(12), weights,subset,contrasts=NULL,verbose=TRUE)
object 
response (training) dataset or an object of class " 
dataY 
response (training) dataset 
dataX 
predictor(s) (training) dataset 
formula 
an object of class " 
data 
an optional data frame, list or environment (or object coercible by 
nt 
number of components to be extracted 
limQ2set 
limit value for the Q2 
modele 
name of the PLS model to be fitted, only ( 
K 
number of groups. Defaults to 5. 
NK 
number of times the group division is made 
grouplist 
to specify the members of the 
random 
should the 
scaleX 
scale the predictor(s) : must be set to TRUE for 
scaleY 
scale the response : Yes/No. Ignored since non always possible for glm responses. 
keepcoeffs 
shall the coefficients for each model be returned 
keepfolds 
shall the groups' composition be returned 
keepdataY 
shall the observed value of the response for each one of the predicted value be returned 
keepMclassed 
shall the number of miss classed be returned 
tol_Xi 
minimal value for Norm2(Xi) and 
weights 
an optional vector of 'prior weights' to be used in the fitting process. Should be 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
contrasts 
an optional list. See the 
verbose 
should info messages be displayed ? 
... 
arguments to pass to 
Predicts 1 group with the K1
other groups. Leave one out cross validation is thus obtained for K==nrow(dataX)
.
A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.
A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
The terms in the formula will be reordered so that main effects come first, followed by the interactions, all secondorder, all thirdorder and so on: to avoid this pass a terms object as the formula.
NonNULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unitweight observations.
An object of class "cv.plsRmodel"
.
results_kfolds 
list of

folds 
list of

dataY_kfolds 
list of

call 
the call of the function 
Work for complete and incomplete datasets.
Frederic Bertrand
frederic.bertrand@utt.fr
https://fbertran.github.io/homepage/
Nicolas Meyer, Myriam MaumyBertrand et Frederic Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Societe Francaise de Statistique, 151(2), pages 118. http://publicationssfds.math.cnrs.fr/index.php/JSFdS/article/view/47
Summary method summary.cv.plsRmodel
. kfolds2coeff
, kfolds2Pressind
, kfolds2Press
, kfolds2Mclassedind
, kfolds2Mclassed
and kfolds2CVinfos_lm
to extract and transform results from kfold crossvalidation.
data(Cornell)
XCornell<Cornell[,1:7]
yCornell<Cornell[,8]
#Leave one out CV (K=nrow(Cornell)) one time (NK=1)
bbb < cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=nrow(Cornell),NK=1)
bbb2 < cv.plsR(Y~.,data=Cornell,nt=6,K=12,NK=1,verbose=FALSE)
(sum1<summary(bbb2))
#6fold CV (K=6) two times (NK=2)
#use random=TRUE to randomly create folds for repeated CV
bbb3 < cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=6,NK=2)
bbb4 < cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=2,verbose=FALSE)
(sum3<summary(bbb4))
cvtable(sum1)
cvtable(sum3)
rm(list=c("XCornell","yCornell","bbb","bbb2","bbb3","bbb4"))
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