Partial least squares regression glm models with k-fold cross validation
cv.plsRglm.RdThis function implements k-fold cross-validation on complete or incomplete datasets for partial least squares regression generalized linear models
Usage
cv.plsRglm(object, ...)
# Default S3 method
cv.plsRglmmodel(object,dataX,nt=2,limQ2set=.0975,
modele="pls", family=NULL, 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, method,
verbose=TRUE,...)
# S3 method for class 'formula'
cv.plsRglmmodel(object,data=NULL,nt=2,limQ2set=.0975,
modele="pls", family=NULL, 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,
start=NULL,etastart,mustart,offset,method,control= list(),contrasts=NULL,
verbose=TRUE,...)
PLS_glm_kfoldcv(dataY, dataX, nt = 2, limQ2set = 0.0975, modele = "pls",
family = NULL, 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, method,
verbose=TRUE)
PLS_glm_kfoldcv_formula(formula,data=NULL,nt=2,limQ2set=.0975,modele="pls",
family=NULL, 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,start=NULL,etastart,
mustart,offset,method,control= list(),contrasts=NULL, verbose=TRUE)Arguments
- object
response (training) dataset or an object of class "
formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.- dataY
response (training) dataset
- dataX
predictor(s) (training) dataset
- formula
an object of class "
formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.- data
an optional data frame, list or environment (or object coercible by
as.data.frameto a data frame) containing the variables in the model. If not found indata, the variables are taken fromenvironment(formula), typically the environment from whichplsRglmis called.- nt
number of components to be extracted
- limQ2set
limit value for the Q2
- modele
name of the PLS glm model to be fitted (
"pls","pls-glm-Gamma","pls-glm-gaussian","pls-glm-inverse.gaussian","pls-glm-logistic","pls-glm-poisson","pls-glm-polr"). Use"modele=pls-glm-family"to enable thefamilyoption.- family
a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See
familyfor details of family functions.) To use the family option, please setmodele="pls-glm-family". User defined families can also be defined. See details.- K
number of groups. Defaults to 5.
- NK
number of times the group division is made
- grouplist
to specify the members of the
Kgroups- random
should the
Kgroups be made randomly. Defaults toTRUE- 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.
- 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 (unavailable)
- 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 \(10^{-12}\)- weights
an optional vector of 'prior weights' to be used in the fitting process. Should be
NULLor a numeric vector.- subset
an optional vector specifying a subset of observations to be used in the fitting process.
- start
starting values for the parameters in the linear predictor.
- etastart
starting values for the linear predictor.
- mustart
starting values for the vector of means.
- offset
this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be
NULLor a numeric vector of length equal to the number of cases. One or moreoffsetterms can be included in the formula instead or as well, and if more than one is specified their sum is used. Seemodel.offset.- method
- for fitting glms with glm (
"pls-glm-Gamma","pls-glm-gaussian","pls-glm-inverse.gaussian","pls-glm-logistic","pls-glm-poisson","modele=pls-glm-family") the method to be used in fitting the model. The default method
"glm.fit"uses iteratively reweighted least squares (IWLS). User-supplied fitting functions can be supplied either as a function or a character string naming a function, with a function which takes the same arguments asglm.fit. If "model.frame", the model frame is returned.pls-glm-polrlogistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
- for fitting glms with glm (
- control
a list of parameters for controlling the fitting process. For
glm.fitthis is passed toglm.control.- contrasts
an optional list. See the
contrasts.argofmodel.matrix.default.- verbose
should info messages be displayed ?
- ...
arguments to pass to
cv.plsRglmmodel.defaultor tocv.plsRglmmodel.formula
Details
Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX).
There are seven different predefined models with predefined link functions available :
"pls"ordinary pls models
"pls-glm-Gamma"glm gaussian with inverse link pls models
"pls-glm-gaussian"glm gaussian with identity link pls models
"pls-glm-inverse-gamma"glm binomial with square inverse link pls models
"pls-glm-logistic"glm binomial with logit link pls models
"pls-glm-poisson"glm poisson with log link pls models
"pls-glm-polr"glm polr with logit link pls models
Using the "family=" option and setting "modele=pls-glm-family" allows changing the family and link function the same way as for the glm function. As a consequence user-specified families can also be used.
- The
gaussianfamily accepts the links (as names)
identity,logandinverse.- The
binomialfamily accepts the links
logit,probit,cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively)logandcloglog(complementary log-log).- The
Gammafamily accepts the links
inverse,identityandlog.- The
poissonfamily accepts the links
log,identity, andsqrt.- The
inverse.gaussianfamily accepts the links
1/mu^2,inverse,identityandlog.- The
quasifamily accepts the links
logit,probit,cloglog,identity,inverse,log,1/mu^2andsqrt.- The function
power can be used to create a power link function.
- ...
arguments to pass to
cv.plsRglmmodel.defaultor tocv.plsRglmmodel.formula
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 re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this pass a terms object as the formula.
Non-NULL 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 unit-weight observations.
Value
An object of class "cv.plsRglmmodel".
- results_kfolds
list of
NK. Each element of the list sums up the results for a group division:- list
of
Kmatrices of size aboutnrow(dataX)/K * ntwith the predicted values for a growing number of components- ...
...
- list
of
Kmatrices of size aboutnrow(dataX)/K * ntwith the predicted values for a growing number of components
- folds
list of
NK. Each element of the list sums up the informations for a group division:- list
of
Kvectors of length aboutnrow(dataX)with the numbers of the rows ofdataXthat were used as a training set- ...
...
- list
of
Kvectors of length aboutnrow(dataX)with the numbers of the rows ofdataXthat were used as a training set
- dataY_kfolds
list of
NK. Each element of the list sums up the results for a group division:- list
of
Kmatrices of size aboutnrow(dataX)/K * 1with the observed values of the response- ...
...
- list
of
Kmatrices of size aboutnrow(dataX)/K * 1with the observed values of the response
- call
the call of the function
References
Nicolas Meyer, Myriam Maumy-Bertrand 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 1-18.
Author
Frederic Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
See also
Summary method summary.cv.plsRglmmodel. kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and summary to extract and transform results from k-fold cross validation.
Examples
data(Cornell)
bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10)
#>
#> Model: pls
#>
#> NK: 1
#> Number of groups : 5
#> 1
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 2
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 3
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#>
#> 4
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 5
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
(sum1<-summary(bbb))
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8625059 0.0975 0.8625059 64.31930 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.8328694 0.0975 -0.2155470 43.44667 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.5549232 0.0975 -1.6630483 29.47091 4.418081 0.9905556
#> Nb_Comp_4 33.76477 -2.9860985 0.0975 -7.9559789 39.56824 4.309235 0.9907882
#> Nb_Comp_5 33.34373 -33.2216598 0.0975 -7.5852520 36.99587 3.521924 0.9924713
#> Nb_Comp_6 35.25533 NA 0.0975 NA NA 3.496074 0.9925265
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
cvtable(sum1)
#>
#> CV Q2 criterion:
#> 0 1
#> 0 1
#>
#> CV Press criterion:
#> 1 2 3
#> 0 0 1
bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=3,
modele="pls-glm-family",family=gaussian(),K=12,verbose=FALSE)
(sum2<-summary(bbb2))
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8809146 0.0975 0.8809146 55.70774
#> Nb_Comp_2 31.46903 33.40866 0.9182731 0.0975 0.3137113 24.52966
#> Nb_Comp_3 31.54404 33.96857 0.6570253 0.0975 -3.1965930 20.84377
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
cvtable(sum2)
#>
#> CV Q2Chi2 criterion:
#> 0 1 2
#> 0 0 1
#>
#> CV PreChi2 criterion:
#> 1 2 3
#> 0 0 1
# \donttest{
#random=TRUE is the default to randomly create folds for repeated CV
bbb3 <- cv.plsRglm(Y~.,data=Cornell,nt=3,
modele="pls-glm-family",family=gaussian(),K=6,NK=10, verbose=FALSE)
(sum3<-summary(bbb3))
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8909831 0.0975 0.8909831 50.99774
#> Nb_Comp_2 31.46903 33.40866 0.9186718 0.0975 0.2539856 26.66441
#> Nb_Comp_3 31.54404 33.96857 0.7075171 0.0975 -2.5963292 17.86236
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[2]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8941767 0.0975 0.8941767 49.50377
#> Nb_Comp_2 31.46903 33.40866 0.8696415 0.0975 -0.2318510 44.02942
#> Nb_Comp_3 31.54404 33.96857 0.2809944 0.0975 -4.5156021 27.39506
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[3]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8920096 0.0975 0.8920096 50.51753
#> Nb_Comp_2 31.46903 33.40866 0.9224725 0.0975 0.2820887 25.65994
#> Nb_Comp_3 31.54404 33.96857 0.7961141 0.0975 -1.6298534 13.06204
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[4]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8650559 0.0975 0.8650559 63.12640
#> Nb_Comp_2 31.46903 33.40866 0.9351616 0.0975 0.5195166 17.17367
#> Nb_Comp_3 31.54404 33.96857 0.8063984 0.0975 -1.9859086 14.83050
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[5]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8767872 0.0975 0.8767872 57.63854
#> Nb_Comp_2 31.46903 33.40866 0.8506422 0.0975 -0.2121934 43.32681
#> Nb_Comp_3 31.54404 33.96857 0.5192922 0.0975 -2.2184994 15.98574
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[6]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8943815 0.0975 0.8943815 49.40800
#> Nb_Comp_2 31.46903 33.40866 0.8694290 0.0975 -0.2362506 44.18667
#> Nb_Comp_3 31.54404 33.96857 0.2786060 0.0975 -4.5249187 27.44134
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[7]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8786200 0.0975 0.8786200 56.78115
#> Nb_Comp_2 31.46903 33.40866 0.8966674 0.0975 0.1486847 30.42813
#> Nb_Comp_3 31.54404 33.96857 0.7057117 0.0975 -1.8479707 14.14539
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[8]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.89262850 0.0975 0.8926285 50.22803
#> Nb_Comp_2 31.46903 33.40866 0.82478362 0.0975 -0.6318705 58.32711
#> Nb_Comp_3 31.54404 33.96857 0.05193796 0.0975 -4.4108070 26.87456
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[9]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.8935639 0.0975 0.8935639 49.79045
#> Nb_Comp_2 31.46903 33.40866 0.9164468 0.0975 0.2149922 28.05813
#> Nb_Comp_3 31.54404 33.96857 0.6378345 0.0975 -3.3345511 21.52898
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> [[10]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 53.15173 54.60645 0.83094829 0.0975 0.8309483 79.08183
#> Nb_Comp_2 31.46903 33.40866 0.86100399 0.0975 0.1777900 29.38783
#> Nb_Comp_3 31.54404 33.96857 0.04531766 0.0975 -5.8684157 34.11426
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 35.742486 35.742486 0.9235940
#> Nb_Comp_2 4.966831 4.966831 0.9893825
#> Nb_Comp_3 4.230693 4.230693 0.9909561
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
plot(cvtable(sum3))
#>
#> CV Q2Chi2 criterion:
#> 0 1 2
#> 0 4 6
#>
#> CV PreChi2 criterion:
#> 1 2 3
#> 0 1 9
data(aze_compl)
bbb <- cv.plsRglm(y~.,data=aze_compl,nt=10,K=10,modele="pls",keepcoeffs=TRUE, verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.17952318 -0.18358069 0.5161098 -0.1970097 0.3432269 0.14406722
#> [2,] 0.35486763 -0.13637388 0.3957869 -0.2173580 0.3219187 0.03170179
#> [3,] 0.33521485 -0.11313171 0.3619222 -0.2489085 0.2235826 0.14783733
#> [4,] 0.36137537 -0.13489880 0.4751676 -0.1456797 0.3143053 0.07825339
#> [5,] 0.39892318 -0.14974277 0.4912091 -0.1333353 0.2465573 0.04141418
#> [6,] 0.35575963 -0.15979357 0.4360585 -0.1439848 0.1418600 0.09004068
#> [7,] 0.36192756 -0.16936160 0.3541551 -0.1643634 0.2103803 0.17025162
#> [8,] 0.37423079 -0.11674480 0.4160774 -0.1919962 0.2852640 0.13706961
#> [9,] -0.02132443 -0.10957065 0.5659451 -0.2705819 0.3429421 0.11972351
#> [10,] 0.22012069 -0.06492909 0.5845200 -0.1663546 0.3168039 0.04866365
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 0.005055119 -0.038703832 -0.2571432 0.146789151 -0.168382704
#> [2,] -0.019036273 -0.003253334 -0.2559151 0.118744491 -0.075399509
#> [3,] -0.121191704 0.107641274 -0.1411623 0.093327808 -0.059216708
#> [4,] -0.152479468 0.023338255 -0.1098179 0.008658965 -0.141339811
#> [5,] -0.111047521 -0.023397267 -0.2485575 -0.065852638 -0.038422674
#> [6,] -0.015909361 0.007412513 -0.1291427 0.063113505 -0.057988769
#> [7,] -0.093195865 0.042269770 -0.1805481 -0.003189911 -0.125462227
#> [8,] -0.116861565 -0.002197013 -0.2515660 0.032865891 0.017641030
#> [9,] -0.012303380 -0.046829189 -0.2522609 0.041513610 -0.007180318
#> [10,] 0.060093993 0.065599396 -0.2409495 0.037350847 -0.204718238
#> [,12] [,13] [,14] [,15] [,16] [,17]
#> [1,] 0.1249068075 -0.17751959 0.10265542 0.007837119 0.10071757 0.118916503
#> [2,] -0.0022470110 -0.12354564 0.12454837 0.039703445 0.01694103 0.022199126
#> [3,] 0.0752765026 -0.04104846 0.07988420 0.066697127 0.07220045 0.009193862
#> [4,] 0.0243577425 -0.08327565 0.14772026 0.089837980 0.12111513 0.004168097
#> [5,] 0.0706978339 -0.16541495 0.15677722 0.183976063 -0.05341081 0.060792750
#> [6,] 0.0200666191 -0.06576887 0.06466309 0.117137444 0.03326864 -0.029718013
#> [7,] 0.0754839409 -0.10527587 0.12421903 0.184423100 0.04300596 -0.105705980
#> [8,] 0.0055059576 -0.09703297 0.14873490 0.080873461 0.08790355 -0.050843806
#> [9,] 0.1244068338 -0.24806077 0.10833856 0.118967892 0.06089489 0.002948510
#> [10,] -0.0002526879 -0.18986208 0.08533294 0.187802532 0.08557121 0.053980146
#> [,18] [,19] [,20] [,21] [,22] [,23]
#> [1,] 0.2350007 -0.042572440 0.11002000 -0.17439453 0.11034362 0.27575858
#> [2,] 0.2924087 0.010195821 0.06649023 -0.11157999 0.08148841 0.20909839
#> [3,] 0.1810613 0.049828840 -0.01354591 -0.22894350 0.13780002 0.19368056
#> [4,] 0.2280875 -0.007786965 -0.01794921 -0.10961352 0.13152759 0.05164439
#> [5,] 0.1814771 0.044180255 0.11897818 -0.09146756 -0.02083331 0.26713912
#> [6,] 0.2114864 -0.073119375 0.07654015 -0.18757912 0.07562632 0.16732477
#> [7,] 0.2884797 0.061275351 0.08129355 -0.06677417 0.07334575 0.11644107
#> [8,] 0.2495068 0.048902989 0.10683997 -0.06861327 0.06705986 0.15148672
#> [9,] 0.3647231 -0.021627249 0.05201960 -0.05902643 0.08953918 0.18270856
#> [10,] 0.1907015 0.064084386 0.18049238 -0.10081597 -0.07573383 0.27895839
#> [,24] [,25] [,26] [,27] [,28] [,29]
#> [1,] -0.21923953 -0.2446405 -0.3733561 0.19518539 0.2252522 -0.18334833
#> [2,] -0.19784877 -0.1763453 -0.3101657 0.20005392 0.1613447 -0.04828271
#> [3,] -0.10504568 -0.1764676 -0.2070636 0.09461131 0.2380637 -0.07371022
#> [4,] -0.15396328 -0.2432382 -0.2015450 0.21816483 0.1523498 -0.12841479
#> [5,] -0.14435273 -0.1305746 -0.3134875 0.12506679 0.1454776 -0.10287116
#> [6,] -0.08353783 -0.1654616 -0.2942352 0.23180525 0.2992130 -0.07559580
#> [7,] -0.07799205 -0.1208744 -0.3483218 0.17504792 0.1808582 -0.08645249
#> [8,] -0.14895954 -0.1674702 -0.3162844 0.23426714 0.2464161 -0.18714513
#> [9,] -0.19616815 -0.0310772 -0.2753396 0.20505455 0.1873829 -0.11823695
#> [10,] -0.15493743 -0.3530348 -0.2561731 0.15461451 0.1007065 -0.02512739
#> [,30] [,31] [,32] [,33] [,34]
#> [1,] 0.065690072 0.088159481 -0.0322657267 -0.2784938 0.042173811
#> [2,] 0.009509667 0.182200256 -0.0863661964 -0.3992084 0.043228795
#> [3,] -0.031892160 0.148228132 -0.0687252063 -0.4172826 -0.081560209
#> [4,] 0.023731318 0.126998699 -0.0359740247 -0.4201255 0.037335195
#> [5,] 0.061829943 -0.028519777 0.0004144259 -0.3579925 0.040781211
#> [6,] -0.051164051 0.115084495 0.0446461823 -0.5016755 -0.003751708
#> [7,] -0.019958880 0.177658366 -0.0183375618 -0.4407841 -0.091256937
#> [8,] 0.021704477 0.111346123 -0.0931150117 -0.4516393 -0.010121379
#> [9,] 0.054898697 0.206397884 -0.1818664097 -0.3522866 -0.029160853
#> [10,] 0.032878376 0.006803997 0.0331389178 -0.3266800 -0.038402853
bbb2 <- cv.plsRglm(y~.,data=aze_compl,nt=10,K=10,modele="pls-glm-family",
family=binomial(probit),keepcoeffs=TRUE, verbose=FALSE)
bbb2 <- cv.plsRglm(y~.,data=aze_compl,nt=10,K=10,
modele="pls-glm-logistic",keepcoeffs=TRUE, verbose=FALSE)
summary(bbb,MClassed=TRUE)
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC MissClassed CV_MissClassed Q2cum_Y LimQ2_Y Q2_Y
#> Nb_Comp_0 154.6179 49 NA NA NA NA
#> Nb_Comp_1 126.4083 27 49 -0.1768169 0.0975 -0.1768169
#> Nb_Comp_2 119.3375 25 48 -0.8158860 0.0975 -0.5430488
#> Nb_Comp_3 114.2313 27 49 -2.4018547 0.0975 -0.8733856
#> Nb_Comp_4 112.3463 23 50 -6.5321615 0.0975 -1.2141338
#> Nb_Comp_5 113.2362 22 50 -16.2613341 0.0975 -1.2916840
#> Nb_Comp_6 114.7620 21 50 -39.7178594 0.0975 -1.3589057
#> Nb_Comp_7 116.5264 20 50 -95.8630906 0.0975 -1.3788846
#> Nb_Comp_8 118.4601 20 51 -231.0774120 0.0975 -1.3959323
#> Nb_Comp_9 120.4452 19 51 -556.5611537 0.0975 -1.4024792
#> Nb_Comp_10 122.4395 19 51 -1338.2107374 0.0975 -1.4019083
#> PRESS_Y RSS_Y R2_Y AIC.std DoF.dof sigmahat.dof AIC.dof
#> Nb_Comp_0 NA 25.91346 NA 298.1344 1.00000 0.5015845 0.2540061
#> Nb_Comp_1 30.49540 19.38086 0.2520929 269.9248 22.55372 0.4848429 0.2883114
#> Nb_Comp_2 29.90562 17.76209 0.3145613 262.8540 27.31542 0.4781670 0.2908950
#> Nb_Comp_3 33.27524 16.58896 0.3598323 257.7478 30.52370 0.4719550 0.2902572
#> Nb_Comp_4 36.73018 15.98071 0.3833049 255.8628 34.00000 0.4744263 0.3008285
#> Nb_Comp_5 36.62273 15.81104 0.3898523 256.7527 34.00000 0.4719012 0.2976347
#> Nb_Comp_6 37.29675 15.73910 0.3926285 258.2785 34.00000 0.4708264 0.2962804
#> Nb_Comp_7 37.44150 15.70350 0.3940024 260.0429 33.71066 0.4693382 0.2937976
#> Nb_Comp_8 37.62451 15.69348 0.3943888 261.9766 34.00000 0.4701436 0.2954217
#> Nb_Comp_9 37.70326 15.69123 0.3944758 263.9617 33.87284 0.4696894 0.2945815
#> Nb_Comp_10 37.68889 15.69037 0.3945088 265.9560 34.00000 0.4700970 0.2953632
#> BIC.dof GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive
#> Nb_Comp_0 0.2604032 -67.17645 1 0.5015845 0.2540061 0.2604032
#> Nb_Comp_1 0.4231184 -53.56607 2 0.4358996 0.1936625 0.2033251
#> Nb_Comp_2 0.4496983 -52.42272 3 0.4193593 0.1809352 0.1943501
#> Nb_Comp_3 0.4631316 -51.93343 4 0.4072955 0.1722700 0.1891422
#> Nb_Comp_4 0.4954133 -50.37079 5 0.4017727 0.1691819 0.1897041
#> Nb_Comp_5 0.4901536 -50.65724 6 0.4016679 0.1706451 0.1952588
#> Nb_Comp_6 0.4879234 -50.78005 7 0.4028135 0.1731800 0.2020601
#> Nb_Comp_7 0.4826103 -51.05525 8 0.4044479 0.1761610 0.2094352
#> Nb_Comp_8 0.4865092 -50.85833 9 0.4064413 0.1794902 0.2172936
#> Nb_Comp_9 0.4845867 -50.95616 10 0.4085682 0.1829787 0.2254232
#> Nb_Comp_10 0.4864128 -50.86368 11 0.4107477 0.1865584 0.2337468
#> GMDL.naive
#> Nb_Comp_0 -67.17645
#> Nb_Comp_1 -79.67755
#> Nb_Comp_2 -81.93501
#> Nb_Comp_3 -83.31503
#> Nb_Comp_4 -83.23369
#> Nb_Comp_5 -81.93513
#> Nb_Comp_6 -80.42345
#> Nb_Comp_7 -78.87607
#> Nb_Comp_8 -77.31942
#> Nb_Comp_9 -75.80069
#> Nb_Comp_10 -74.33325
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
summary(bbb2,MClassed=TRUE)
#> ____************************************************____
#>
#> Family: binomial
#> Link function: logit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2
#> Nb_Comp_0 145.8283 148.4727 49 NA NA NA
#> Nb_Comp_1 118.1398 123.4285 28 44 -1.137905e+00 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 45 -7.911290e+00 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 49 -2.196646e+02 0.0975
#> Nb_Comp_4 103.8382 117.0601 21 48 -9.534571e+03 0.0975
#> Nb_Comp_5 104.7338 120.6001 21 50 -4.735207e+05 0.0975
#> Nb_Comp_6 105.6770 124.1878 21 48 -3.105521e+07 0.0975
#> Nb_Comp_7 107.2828 128.4380 20 48 -2.872864e+09 0.0975
#> Nb_Comp_8 109.0172 132.8167 22 50 -3.179467e+11 0.0975
#> Nb_Comp_9 110.9354 137.3793 21 49 -3.493236e+13 0.0975
#> Nb_Comp_10 112.9021 141.9904 20 48 -3.755538e+15 0.0975
#> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 NA NA 104.00000 25.91346 NA
#> Nb_Comp_1 -1.137905 222.3421 100.53823 19.32272 0.2543365
#> Nb_Comp_2 -3.168234 419.0669 99.17955 17.33735 0.3309519
#> Nb_Comp_3 -23.762360 2455.9199 123.37836 15.58198 0.3986915
#> Nb_Comp_4 -42.212970 5331.5453 114.77551 15.14046 0.4157299
#> Nb_Comp_5 -48.658454 5699.5744 105.35382 15.08411 0.4179043
#> Nb_Comp_6 -64.583493 6909.4714 98.87767 14.93200 0.4237744
#> Nb_Comp_7 -91.508279 9147.0030 97.04072 14.87506 0.4259715
#> Nb_Comp_8 -109.672382 10739.7273 98.90110 14.84925 0.4269676
#> Nb_Comp_9 -108.868593 10866.1246 100.35563 14.84317 0.4272022
#> Nb_Comp_10 -106.508863 10789.1196 102.85214 14.79133 0.4292027
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] -1.965799 -1.0199331 3.648911 -1.827265 2.662801 0.8363928 -0.20691823
#> [2,] -1.830537 -0.7563970 2.471137 -1.328305 1.373733 0.2233750 -0.01177607
#> [3,] -2.407847 -2.0298046 3.945164 -1.373957 1.150147 0.5031490 -0.28988306
#> [4,] -3.203007 -2.8372141 5.033710 -2.342386 2.849491 0.5291630 -0.35109681
#> [5,] -3.921451 -2.3715250 4.589552 -2.449563 3.207610 1.0504067 -0.28974803
#> [6,] -3.282263 -1.0293596 3.976786 -1.858869 2.238536 0.1722852 -0.05050652
#> [7,] -2.812126 -0.6713289 5.375383 -1.346515 3.262103 -0.3730782 0.54959518
#> [8,] -1.927544 -1.1442212 3.731474 -3.196959 3.488297 0.9931449 -0.44058243
#> [9,] -1.531716 -0.8715758 3.034173 -1.404271 1.773505 0.6354226 0.11996263
#> [10,] -2.414664 -0.3558774 3.794280 -1.785590 1.969097 1.6414859 -0.25026263
#> [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] -0.49557050 -2.2071901 0.3144860 -0.54202797 0.89228175 -1.3974488
#> [2,] 0.08175729 -0.8963582 0.3744940 -0.82162989 0.95615239 -0.9206204
#> [3,] 0.43607504 -0.3824681 0.3625755 -1.45131465 0.84913838 -0.3492717
#> [4,] -0.58858681 -1.2308754 1.7161346 -1.43443870 0.69995889 -1.4772216
#> [5,] -0.35619065 -1.8369971 -0.5525098 -0.89540254 1.90236001 -1.0874626
#> [6,] -0.37069014 -1.1498791 -0.0109854 -0.54891131 0.37492146 -0.5548299
#> [7,] -0.30273554 -2.3051430 0.6134508 -1.03987768 0.29071503 -1.0123951
#> [8,] 0.72584141 -1.4468813 0.6799241 -1.48397492 0.74533508 -0.3907250
#> [9,] -0.92881692 -1.4740544 0.2470470 -0.78119954 0.46362094 -1.2201283
#> [10,] -0.50050095 -1.6644052 0.9803552 -0.05002341 -0.02921065 -1.8217372
#> [,14] [,15] [,16] [,17] [,18] [,19]
#> [1,] 0.4358246 0.6140765 0.4353961 1.03774234 1.831834 -0.196193746
#> [2,] 0.4477398 0.9288757 0.3006236 0.13455052 1.987615 -0.100381114
#> [3,] 0.3238327 1.3386612 -0.1676638 1.05782999 1.098517 0.615658509
#> [4,] 1.8742007 0.5617816 3.9698098 1.75700382 1.849360 -1.337655891
#> [5,] -0.1440952 1.5457708 -0.2065585 0.42532822 3.712537 0.334844801
#> [6,] 0.6290360 0.2229422 0.6668117 0.67706931 1.594161 0.001342479
#> [7,] 0.2792567 1.0277483 0.6998584 0.71725754 1.622390 0.400745609
#> [8,] 0.4989434 0.3625643 0.6576075 0.05803996 2.996122 -0.013229745
#> [9,] 0.8207341 0.8339903 0.5055199 0.60297127 1.527743 -0.430524781
#> [10,] 2.6738156 0.8785213 0.2864730 0.12896572 1.034729 0.113203504
#> [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 0.972378415 -0.4871654 0.5628326 1.8361471 -1.28128703 -1.988099
#> [2,] 0.627611896 -0.6617926 0.7704099 1.5821050 -1.33485590 -1.477002
#> [3,] 1.174386144 -1.2668516 0.2016689 0.8706718 -0.07905719 -2.052309
#> [4,] 1.595173575 -2.4129503 1.3251517 -0.5706750 -0.93031259 -2.544465
#> [5,] 1.116156919 -1.5859034 0.1183023 1.8599384 -1.50675883 -1.298512
#> [6,] 0.303664715 -0.3820341 0.1644571 1.4632345 -1.39241213 -1.340381
#> [7,] 0.183816922 -1.4149841 -0.4554074 2.0560514 -1.35446911 -1.867863
#> [8,] -0.098670036 -1.1387885 0.4048529 1.1567518 -2.55717510 -1.480850
#> [9,] 0.739530460 -1.0162033 0.1812029 1.7540139 -0.88453664 -1.599275
#> [10,] 0.007075075 -0.9902770 0.6096433 1.2025498 -1.56825066 -1.090705
#> [,26] [,27] [,28] [,29] [,30] [,31] [,32]
#> [1,] -2.337064 1.388788 1.5932181 -0.5114985 0.7249617 1.0289371 -0.4060729
#> [2,] -1.787068 1.183226 1.3537645 -0.3917539 0.5019871 0.7369473 -0.4116624
#> [3,] -2.629235 2.833334 2.1034713 -0.2423233 -0.5243678 1.6754232 -0.3921395
#> [4,] -1.430462 0.695463 1.5404238 -0.4989249 0.4161941 1.3803824 -0.3206621
#> [5,] -2.792315 2.118639 1.5949681 -0.7218437 0.3504715 1.8499016 0.4807796
#> [6,] -1.558588 1.458914 1.4431533 -0.5113983 0.5674723 1.9399099 -0.4903038
#> [7,] -2.215763 1.773044 0.9484737 -0.6892981 0.6376707 1.1413413 0.2866099
#> [8,] -3.023586 1.985707 2.0889874 0.4568915 0.8408701 1.5481679 -0.9643611
#> [9,] -1.775669 1.827119 1.3916357 -0.3777829 0.2414968 0.9526591 -0.2185785
#> [10,] -2.488184 1.647320 2.1733116 -1.3909588 0.7063196 1.5892143 -0.4530108
#> [,33] [,34]
#> [1,] -2.785066 -0.30193814
#> [2,] -2.818961 0.16363750
#> [3,] -3.901225 -0.03908195
#> [4,] -3.593725 -0.14883634
#> [5,] -3.359821 -0.30516790
#> [6,] -2.883337 0.30333766
#> [7,] -3.519127 0.05782243
#> [8,] -4.382122 0.53328853
#> [9,] -2.264007 -0.09067303
#> [10,] -3.599580 0.15623576
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 9.138129 9.940345 10.656152 11.792951 23.539081 22.980702 24.630842
#> [8] 27.656301 28.953563 28.656831
#>
#> [[1]][[2]]
#> [1] 26.77943 22.18569 24.18229 21.06303 20.68927 17.84010 17.56275 17.49662
#> [9] 17.17441 16.08457
#>
#> [[1]][[3]]
#> [1] 35.93140 72.26203 117.09286 213.96365 270.39345 396.41635 399.80898
#> [8] 451.63438 456.65919 404.48063
#>
#> [[1]][[4]]
#> [1] 36.45611 80.40065 307.30286 575.34925 1090.85333 1385.55853
#> [7] 1814.76025 1972.87463 2142.48155 2243.37735
#>
#> [[1]][[5]]
#> [1] 20.71883 87.51129 567.42311 903.21928 806.57769 1228.45450
#> [7] 2701.43600 3171.17408 3046.45690 2499.28901
#>
#> [[1]][[6]]
#> [1] 14.646120 7.531857 6.426014 9.173460 13.174570 19.394117 17.950400
#> [8] 15.465632 15.597041 15.517834
#>
#> [[1]][[7]]
#> [1] 34.23608 92.06347 1343.75103 3500.00729 3369.49587 3663.63618
#> [7] 3875.15003 4678.94455 4743.05595 5166.47579
#>
#> [[1]][[8]]
#> [1] 11.68854 12.97723 19.08495 27.39190 33.33952 95.69299 201.37583
#> [8] 294.72375 293.31858 292.47884
#>
#> [[1]][[9]]
#> [1] 13.727902 13.720644 9.302802 10.677076 11.456733 12.552194 11.100323
#> [8] 9.024091 8.672000 8.607707
#>
#> [[1]][[10]]
#> [1] 19.01959 20.47371 50.69779 58.90737 60.05489 66.94569 83.22761
#> [8] 100.73331 113.75541 114.15105
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 222.3421 419.0669 2455.9199 5331.5453 5699.5744 6909.4714
#> [7] 9147.0030 10739.7273 10866.1246 10789.1196
#>
summary(bbb2)
#> ____************************************************____
#>
#> Family: binomial
#> Link function: logit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 145.8283 148.4727 NA NA NA NA
#> Nb_Comp_1 118.1398 123.4285 -1.137905e+00 0.0975 -1.137905 222.3421
#> Nb_Comp_2 109.9553 117.8885 -7.911290e+00 0.0975 -3.168234 419.0669
#> Nb_Comp_3 105.1591 115.7366 -2.196646e+02 0.0975 -23.762360 2455.9199
#> Nb_Comp_4 103.8382 117.0601 -9.534571e+03 0.0975 -42.212970 5331.5453
#> Nb_Comp_5 104.7338 120.6001 -4.735207e+05 0.0975 -48.658454 5699.5744
#> Nb_Comp_6 105.6770 124.1878 -3.105521e+07 0.0975 -64.583493 6909.4714
#> Nb_Comp_7 107.2828 128.4380 -2.872864e+09 0.0975 -91.508279 9147.0030
#> Nb_Comp_8 109.0172 132.8167 -3.179467e+11 0.0975 -109.672382 10739.7273
#> Nb_Comp_9 110.9354 137.3793 -3.493236e+13 0.0975 -108.868593 10866.1246
#> Nb_Comp_10 112.9021 141.9904 -3.755538e+15 0.0975 -106.508863 10789.1196
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 104.00000 25.91346 NA
#> Nb_Comp_1 100.53823 19.32272 0.2543365
#> Nb_Comp_2 99.17955 17.33735 0.3309519
#> Nb_Comp_3 123.37836 15.58198 0.3986915
#> Nb_Comp_4 114.77551 15.14046 0.4157299
#> Nb_Comp_5 105.35382 15.08411 0.4179043
#> Nb_Comp_6 98.87767 14.93200 0.4237744
#> Nb_Comp_7 97.04072 14.87506 0.4259715
#> Nb_Comp_8 98.90110 14.84925 0.4269676
#> Nb_Comp_9 100.35563 14.84317 0.4272022
#> Nb_Comp_10 102.85214 14.79133 0.4292027
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
rm(list=c("bbb","bbb2"))
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(round(x11)~.,data=pine,nt=10,modele="pls-glm-family",
family=poisson(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
bbb <- cv.plsRglm(round(x11)~.,data=pine,nt=10,
modele="pls-glm-poisson",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 12.23228 -0.005536853 -0.06035313 0.21075559 -1.509566 0.3891260
#> [2,] 14.32616 -0.005472639 -0.07659818 0.25653307 -1.496063 0.2455091
#> [3,] 12.40342 -0.004631656 -0.10646369 0.16293855 -2.485515 0.4488413
#> [4,] 10.97834 -0.004237452 -0.05073310 0.17068724 -1.595761 0.3037007
#> [5,] 11.99869 -0.004589766 -0.07217495 0.19467679 -1.301550 0.2616567
#> [6,] 11.21735 -0.004246082 -0.07199980 0.15406592 -1.438080 0.2934104
#> [7,] 16.85425 -0.006536052 -0.09856612 0.34244991 -2.109424 0.4062731
#> [8,] 10.92865 -0.005912708 -0.03022862 -0.02194749 -1.316067 0.2801402
#> [9,] 11.28964 -0.004643210 -0.07253036 0.15351588 -1.612797 0.3242276
#> [10,] 14.59362 -0.005455122 -0.05837141 0.25584500 -1.779435 0.3188999
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] -2.2221164 0.154839413 -0.0148678 -0.9159189 -0.043456849
#> [2,] -2.9221081 0.036086621 0.3780611 -1.5914109 -0.201445332
#> [3,] -2.0592645 0.663714218 0.5454282 -1.1510459 -0.627219682
#> [4,] -1.6471132 0.142996046 0.1627417 -1.3784248 -0.006574006
#> [5,] -1.8966704 -0.224268493 0.1396611 -1.3784609 0.164126510
#> [6,] -1.6236813 0.002402134 0.1512606 -0.9878680 -0.307230536
#> [7,] -4.3402151 0.359188340 0.3560064 -1.1404673 -0.372509115
#> [8,] 0.4838324 -0.522774677 0.1006362 -1.0965444 -0.011750707
#> [9,] -1.5812611 0.141304409 0.1891793 -1.0272417 -0.132808486
#> [10,] -2.9669311 0.479145437 0.2613261 -1.1668214 -0.921124035
boxplot(kfolds2coeff(bbb)[,1])
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.142498 1.441775 1.980300 2.598358 2.669610 3.049673 3.012338 2.985366
#> [9] 2.880423 2.889056
#>
#> [[1]][[2]]
#> [1] 2.591149 2.778573 2.027467 2.142058 2.098839 2.008267 2.413857 2.517996
#> [9] 2.590328 2.609335
#>
#> [[1]][[3]]
#> [1] 0.7392703 2.0734013 2.1311453 1.8991696 1.0721471 3.4101252 5.4459136
#> [8] 6.3284543 6.2193184 5.9980376
#>
#> [[1]][[4]]
#> [1] 1.6837200 0.7466781 1.1656998 1.2825498 1.3679960 1.1822751 1.2835441
#> [8] 1.2558035 1.2396572 1.2334661
#>
#> [[1]][[5]]
#> [1] 1.0715319 1.5693955 1.1456871 1.3559922 0.9495254 0.7828828 0.7282813
#> [8] 0.6074119 0.6128180 0.6063229
#>
#> [[1]][[6]]
#> [1] 10.9521509 5.9784679 2.9987869 2.2389311 1.7708144 1.8583071
#> [7] 1.2022792 0.8668432 0.8383545 0.8111822
#>
#> [[1]][[7]]
#> [1] 5.245962 11.124279 11.218884 12.827187 12.727385 19.304297 23.763276
#> [8] 26.981549 28.312951 28.275528
#>
#> [[1]][[8]]
#> [1] 7.056454 11.983755 9.739079 10.606415 10.719232 7.373670 9.422256
#> [8] 10.136700 10.645665 10.566862
#>
#> [[1]][[9]]
#> [1] 1.2404751 0.7736069 0.7740438 0.8537660 0.5927995 0.6294607 0.6640181
#> [8] 0.6412462 0.5899091 0.5879259
#>
#> [[1]][[10]]
#> [1] 1.1265687 1.8549784 1.5645359 1.8487013 1.0962112 0.8740828 0.7308147
#> [8] 1.1825454 1.1297039 1.1396595
#>
#>
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 32.84978 40.32491 34.74563 37.65313 35.06456 40.47304 48.66658 53.50391
#> [9] 55.05913 54.71737
#>
summary(bbb)
#> ____************************************************____
#>
#> Family: poisson
#> Link function: log
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 76.61170 78.10821 NA NA NA NA
#> Nb_Comp_1 65.70029 68.69331 2.667317e-02 0.0975 0.02667317 32.84978
#> Nb_Comp_2 62.49440 66.98392 -6.450590e-01 0.0975 -0.69014040 40.32491
#> Nb_Comp_3 62.47987 68.46590 -2.303982e+00 0.0975 -1.00842742 34.74563
#> Nb_Comp_4 64.21704 71.69958 -7.021296e+00 0.0975 -1.42776669 37.65313
#> Nb_Comp_5 65.81654 74.79559 -1.745640e+01 0.0975 -1.30092438 35.06456
#> Nb_Comp_6 66.48888 76.96443 -4.794182e+01 0.0975 -1.65175367 40.47304
#> Nb_Comp_7 68.40234 80.37440 -1.332157e+02 0.0975 -1.74235150 48.66658
#> Nb_Comp_8 70.39399 83.86256 -3.969620e+02 0.0975 -1.96509348 53.50391
#> Nb_Comp_9 72.37642 87.34149 -1.204328e+03 0.0975 -2.02875247 55.05913
#> Nb_Comp_10 74.37612 90.83770 -3.593284e+03 0.0975 -1.98199539 54.71737
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 33.75000 24.545455 NA
#> Nb_Comp_1 23.85891 12.599337 0.4866937
#> Nb_Comp_2 17.29992 9.056074 0.6310488
#> Nb_Comp_3 15.50937 8.232069 0.6646194
#> Nb_Comp_4 15.23934 8.125808 0.6689485
#> Nb_Comp_5 15.26275 7.862134 0.6796909
#> Nb_Comp_6 17.74629 6.203270 0.7472742
#> Nb_Comp_7 18.04460 5.879880 0.7604493
#> Nb_Comp_8 18.17881 5.827065 0.7626011
#> Nb_Comp_9 18.34925 5.837300 0.7621841
#> Nb_Comp_10 18.39332 5.832437 0.7623822
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm(ypine,Xpine,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.61896 0.38248575 0.0975 0.38248575 12.844390 11.074659
#> Nb_Comp_2 58.47638 0.34836456 0.0975 -0.05525570 11.686597 8.919303
#> Nb_Comp_3 56.55421 0.23688359 0.0975 -0.17107874 10.445206 7.919786
#> Nb_Comp_4 54.35053 0.06999681 0.0975 -0.21869112 9.651773 6.972542
#> Nb_Comp_5 55.99834 -0.07691053 0.0975 -0.15796434 8.073955 6.898523
#> Nb_Comp_6 57.69592 -0.19968885 0.0975 -0.11400977 7.685022 6.835594
#> Nb_Comp_7 59.37953 -0.27722139 0.0975 -0.06462721 7.277359 6.770369
#> Nb_Comp_8 61.21213 -0.30602578 0.0975 -0.02255238 6.923057 6.736112
#> Nb_Comp_9 63.18426 -0.39920228 0.0975 -0.07134354 7.216690 6.730426
#> Nb_Comp_10 65.15982 -0.43743644 0.0975 -0.02732569 6.914340 6.725443
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4675684 0.4675684 17.03781 19.76046 0.38248575 0.0975
#> Nb_Comp_2 0.5711905 0.5711905 13.72190 17.97925 -0.05525570 0.0975
#> Nb_Comp_3 0.6192438 0.6192438 12.18420 16.06943 -0.17107874 0.0975
#> Nb_Comp_4 0.6647841 0.6647841 10.72691 14.84877 -0.21869112 0.0975
#> Nb_Comp_5 0.6683426 0.6683426 10.61304 12.42138 -0.15796434 0.0975
#> Nb_Comp_6 0.6713681 0.6713681 10.51622 11.82303 -0.11400977 0.0975
#> Nb_Comp_7 0.6745039 0.6745039 10.41588 11.19586 -0.06462721 0.0975
#> Nb_Comp_8 0.6761508 0.6761508 10.36317 10.65078 -0.02255238 0.0975
#> Nb_Comp_9 0.6764242 0.6764242 10.35443 11.10252 -0.07134354 0.0975
#> Nb_Comp_10 0.6766638 0.6766638 10.34676 10.63737 -0.02732569 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof
#> Nb_Comp_0 NA 96.63448 1.000000 0.8062287 0.6697018 0.6991787
#> Nb_Comp_1 0.38248575 77.83455 3.176360 0.5994089 0.4047616 0.4565153
#> Nb_Comp_2 0.34836456 72.69198 7.133559 0.5761829 0.4138120 0.5212090
#> Nb_Comp_3 0.23688359 70.76981 8.778329 0.5603634 0.4070516 0.5320535
#> Nb_Comp_4 0.06999681 68.56612 8.427874 0.5221703 0.3505594 0.4547689
#> Nb_Comp_5 -0.07691053 70.21393 9.308247 0.5285695 0.3666578 0.4845912
#> Nb_Comp_6 -0.19968885 71.91152 9.291931 0.5259794 0.3629363 0.4795121
#> Nb_Comp_7 -0.27722139 73.59512 9.756305 0.5284535 0.3702885 0.4938445
#> Nb_Comp_8 -0.30602578 75.42772 10.363948 0.5338475 0.3831339 0.5170783
#> Nb_Comp_9 -0.39920228 77.39986 10.732148 0.5378277 0.3920957 0.5328747
#> Nb_Comp_10 -0.43743644 79.37542 11.000000 0.5407500 0.3987417 0.5446065
#> GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 -3.605128 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 -9.875081 2 0.5977015 0.3788984 0.4112998 -11.451340
#> Nb_Comp_2 -6.985517 3 0.5452615 0.3243383 0.3647862 -12.822703
#> Nb_Comp_3 -6.260610 4 0.5225859 0.3061986 0.3557368 -12.756838
#> Nb_Comp_4 -8.152986 5 0.4990184 0.2867496 0.3432131 -12.811575
#> Nb_Comp_5 -7.111583 6 0.5054709 0.3019556 0.3714754 -11.329638
#> Nb_Comp_6 -7.233043 7 0.5127450 0.3186757 0.4021333 -9.918688
#> Nb_Comp_7 -6.742195 8 0.5203986 0.3364668 0.4347156 -8.592770
#> Nb_Comp_8 -6.038372 9 0.5297842 0.3572181 0.4717708 -7.287834
#> Nb_Comp_9 -5.600235 10 0.5409503 0.3813021 0.5140048 -6.008747
#> Nb_Comp_10 -5.288422 11 0.5529032 0.4076026 0.5600977 -4.799453
data(pineNAX21)
bbb2 <- cv.plsRglm(round(x11)~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=poisson(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
bbb2 <- cv.plsRglm(round(x11)~.,data=pineNAX21,nt=10,
modele="pls-glm-poisson",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 11.51811 -0.005952386 -0.04705644 0.01579405 -1.519639 0.3274029
#> [2,] 13.48966 -0.004981385 -0.06984414 0.19631381 -1.560965 0.3040376
#> [3,] 12.63692 -0.004543425 -0.05466002 0.15527180 -1.371838 0.2951140
#> [4,] 12.81233 -0.005251442 -0.05779519 0.19878499 -1.341721 0.3182705
#> [5,] 13.30235 -0.004022254 -0.09036926 0.20118035 -2.168910 0.4199286
#> [6,] 15.62810 -0.006164254 -0.08473175 0.22820188 -1.902573 0.3330847
#> [7,] 13.61742 -0.005004306 -0.06550345 0.19769215 -1.506993 0.2906218
#> [8,] 13.82062 -0.005346061 -0.06939079 0.25433075 -1.570217 0.2798791
#> [9,] 19.74373 -0.007589103 -0.10421749 0.37990696 -2.120497 0.4017688
#> [10,] 14.84834 -0.004928453 -0.04972723 0.24531299 -1.503833 0.2712783
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] -0.2014834 0.10523208 0.06110290 -0.4074067 -0.12976644
#> [2,] -2.2805101 0.04360632 0.24608496 -1.0725974 -0.29707532
#> [3,] -1.2419615 -0.34704147 0.02554851 -1.3843230 0.11912524
#> [4,] -1.9245434 0.01273538 0.08933275 -1.5051381 0.26169495
#> [5,] -2.2112053 0.18672331 0.30611669 -1.3219579 -0.32977787
#> [6,] -2.6356030 0.42081192 0.41712124 -1.3295615 -0.48652744
#> [7,] -2.0755348 -0.06300028 0.22280792 -1.3888067 -0.16197199
#> [8,] -2.7872614 0.05704597 0.28563651 -1.5290880 -0.09594567
#> [9,] -5.0683254 0.81985345 0.45414536 -1.0032677 -0.83199603
#> [10,] -2.6971167 0.03780900 0.20135091 -1.4205660 -0.61724236
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 7.213648 8.752262 5.572425 4.818952 3.028990 6.217531 4.122426 6.855565
#> [9] 6.499983
#>
#> [[1]][[2]]
#> [1] 2.0849314 1.4360473 0.8946700 0.9929068 0.7545875 0.6423280 0.2974474
#> [8] 0.3367926 0.3605493
#>
#> [[1]][[3]]
#> [1] 1.631623 2.180784 2.543712 2.620713 2.486795 1.941632 1.545911 1.377616
#> [9] 1.291857
#>
#> [[1]][[4]]
#> [1] 1.463748 2.075750 3.379147 3.695561 3.477909 3.489421 3.374752 3.056892
#> [9] 3.052002
#>
#> [[1]][[5]]
#> [1] 0.4531292 0.5589994 1.1387310 1.2324005 1.2403710 4.5318536 5.6961986
#> [8] 5.5266582 5.3318349
#>
#> [[1]][[6]]
#> [1] 0.6869926 2.9909089 1.7537878 1.7477216 2.0374322 1.9382188 2.4194387
#> [8] 2.4847905 2.6806979
#>
#> [[1]][[7]]
#> [1] 9.0634896 3.8153296 0.8093908 0.3964339 0.6056960 0.2921411 0.2268577
#> [8] 0.1989748 0.1924728
#>
#> [[1]][[8]]
#> [1] 2.002490 4.357975 7.375590 16.235660 75.062697 2544.879428
#> [7] 1892.216705 373.635078 641.649673
#>
#> [[1]][[9]]
#> [1] 5.703873 9.951464 9.445938 9.009311 9.552172 16.060253 21.770003
#> [8] 27.592524 31.304674
#>
#> [[1]][[10]]
#> [1] 2.8335216 1.7839647 0.8467397 0.9960074 0.8965102 0.8109255 0.9058031
#> [8] 0.9511547 1.0797027
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 33.13745 37.90349 33.76013 41.74567 99.14316 2580.80373 1932.57554
#> [8] 422.01605 693.44345
#>
summary(bbb2)
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: poisson
#> Link function: log
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y
#> Nb_Comp_0 76.61170 78.10821 NA NA NA
#> Nb_Comp_1 65.74449 68.73751 1.814974e-02 0.0975 0.01814974
#> Nb_Comp_2 62.35674 66.84626 -5.577194e-01 0.0975 -0.58651419
#> Nb_Comp_3 62.39804 68.38407 -2.037757e+00 0.0975 -0.95013098
#> Nb_Comp_4 64.08113 71.56366 -7.172691e+00 0.0975 -1.69037050
#> Nb_Comp_5 65.63784 74.61689 -5.191654e+01 0.0975 -5.47479973
#> Nb_Comp_6 67.18468 77.66024 -8.803205e+03 0.0975 -165.37908247
#> Nb_Comp_7 68.61004 80.58210 -1.043500e+06 0.0975 -117.52302582
#> Nb_Comp_8 70.54487 84.01344 -2.513541e+07 0.0975 -23.08757731
#> Nb_Comp_9 72.37296 87.33803 -9.815474e+08 0.0975 -38.05037858
#> PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 NA 33.75000 24.545455 NA
#> Nb_Comp_1 33.13745 23.89105 12.654950 0.4844280
#> Nb_Comp_2 37.90349 17.31172 8.871122 0.6385839
#> Nb_Comp_3 33.76013 15.51670 8.203709 0.6657748
#> Nb_Comp_4 41.74567 15.31216 7.959332 0.6757309
#> Nb_Comp_5 99.14316 15.51159 7.724832 0.6852846
#> Nb_Comp_6 2580.80373 16.30549 6.814620 0.7223673
#> Nb_Comp_7 1932.57554 17.52007 6.284737 0.7439552
#> Nb_Comp_8 422.01605 17.75766 6.160827 0.7490034
#> Nb_Comp_9 693.44345 18.30206 5.831059 0.7624383
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
data(XpineNAX21)
PLS_lm(ypine,XpineNAX21,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> Only naive DoF can be used with missing data
#> ____There are some NAs in X but not in Y____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.69250 0.35639805 0.0975 0.35639805 13.387018 11.099368
#> Nb_Comp_2 58.35228 0.28395028 0.0975 -0.11256611 12.348781 8.885823
#> Nb_Comp_3 56.36553 0.07664889 0.0975 -0.28950699 11.458331 7.874634
#> Nb_Comp_4 54.02416 -0.70355579 0.0975 -0.84497074 14.528469 6.903925
#> Nb_Comp_5 55.80450 -0.94905654 0.0975 -0.14411078 7.898855 6.858120
#> Nb_Comp_6 57.45753 -1.27568315 0.0975 -0.16758190 8.007417 6.786392
#> Nb_Comp_7 58.73951 -1.63309014 0.0975 -0.15705481 7.852227 6.640327
#> Nb_Comp_8 60.61227 -1.67907859 0.0975 -0.01746558 6.756304 6.614773
#> Nb_Comp_9 62.25948 -2.15165796 0.0975 -0.17639623 7.781594 6.544432
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4663804 0.4663804 17.07583 20.59526 0.35639805 0.0975
#> Nb_Comp_2 0.5728001 0.5728001 13.67040 18.99799 -0.11256611 0.0975
#> Nb_Comp_3 0.6214146 0.6214146 12.11473 17.62807 -0.28950699 0.0975
#> Nb_Comp_4 0.6680830 0.6680830 10.62135 22.35133 -0.84497074 0.0975
#> Nb_Comp_5 0.6702851 0.6702851 10.55088 12.15200 -0.14411078 0.0975
#> Nb_Comp_6 0.6737336 0.6737336 10.44053 12.31901 -0.16758190 0.0975
#> Nb_Comp_7 0.6807558 0.6807558 10.21581 12.08026 -0.15705481 0.0975
#> Nb_Comp_8 0.6819844 0.6819844 10.17650 10.39424 -0.01746558 0.0975
#> Nb_Comp_9 0.6853661 0.6853661 10.06828 11.97160 -0.17639623 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 NA 96.63448 NA NA NA NA NA
#> Nb_Comp_1 0.35639805 77.90810 NA NA NA NA NA
#> Nb_Comp_2 0.28395028 72.56787 NA NA NA NA NA
#> Nb_Comp_3 0.07664889 70.58113 NA NA NA NA NA
#> Nb_Comp_4 -0.70355579 68.23976 NA NA NA NA NA
#> Nb_Comp_5 -0.94905654 70.02009 NA NA NA NA NA
#> Nb_Comp_6 -1.27568315 71.67313 NA NA NA NA NA
#> Nb_Comp_7 -1.63309014 72.95511 NA NA NA NA NA
#> Nb_Comp_8 -1.67907859 74.82787 NA NA NA NA NA
#> Nb_Comp_9 -2.15165796 76.47507 NA NA NA NA NA
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 2 0.5983679 0.3797438 0.4122175 -11.413749
#> Nb_Comp_2 3 0.5442372 0.3231208 0.3634169 -12.847656
#> Nb_Comp_3 4 0.5210941 0.3044529 0.3537087 -12.776843
#> Nb_Comp_4 5 0.4965569 0.2839276 0.3398355 -12.891035
#> Nb_Comp_5 6 0.5039885 0.3001871 0.3692997 -11.349498
#> Nb_Comp_6 7 0.5108963 0.3163819 0.3992388 -9.922119
#> Nb_Comp_7 8 0.5153766 0.3300041 0.4263658 -8.696873
#> Nb_Comp_8 9 0.5249910 0.3507834 0.4632727 -7.337679
#> Nb_Comp_9 10 0.5334234 0.3707649 0.4998004 -6.033403
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2"))
#> Warning: object 'XpineNAX21' not found
data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-family",
family=Gamma,K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: NaNs produced
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-Gamma",
K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -12.538587 0.006081631 0.051485268 -0.17200406 1.9564449 -0.4121492
#> [2,] -12.361937 0.009237988 0.039134946 -0.32515811 2.0576931 -0.3290479
#> [3,] -9.057436 0.004677511 0.007988804 -0.07922100 0.9813196 -0.2186069
#> [4,] -10.586646 0.005689529 0.032376860 -0.09035462 1.9699193 -0.3928204
#> [5,] -10.927667 0.004506554 0.030789213 -0.21565214 1.3977266 -0.2934423
#> [6,] -11.501775 0.005522298 0.051836980 -0.13536601 1.5245202 -0.3109914
#> [7,] -16.494083 0.005806689 0.095088328 -0.32343382 3.6142443 -0.6056931
#> [8,] -9.210819 0.007641321 -0.008129801 0.13439933 1.6843999 -0.3819648
#> [9,] -13.596107 0.007307016 0.062945208 -0.25963003 1.7436597 -0.3738348
#> [10,] -11.656738 0.005533928 0.040602824 -0.16403631 1.7953572 -0.3669673
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 2.3545656 -0.4281201 -0.197686771 0.7935866 0.7913407
#> [2,] 3.3769636 -4.2565134 -0.553253701 2.5194587 0.8576111
#> [3,] 0.9108687 0.2556929 0.004448834 1.0393063 0.2458766
#> [4,] 1.3923929 -0.7220402 -0.193888178 0.4000389 1.0641619
#> [5,] 2.6197499 -0.1320216 -0.081918997 1.0256996 0.5635740
#> [6,] 1.3504613 -0.1576628 -0.130366130 1.2050842 0.4686387
#> [7,] 4.3052478 -0.6259748 -0.740700803 1.5778828 0.8433978
#> [8,] -1.4808742 -0.1824521 -0.090878056 0.7290671 0.4302643
#> [9,] 4.2000986 -0.9848552 -0.134796298 -0.5740824 0.8225901
#> [10,] 2.2218202 -0.3694189 -0.189664822 0.8733890 0.7222851
boxplot(kfolds2coeff(bbb)[,1])
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 2.540288 4.844394 4.351359 4.702662 4.571178 4.326014 4.525768 4.806861
#> [9] 4.914484 4.930287
#>
#> [[1]][[2]]
#> [1] 4.668016 8.396943 12.484710 14.500103 15.711237 25.102374 35.372325
#> [8] 53.624929 52.047930 50.024451
#>
#> [[1]][[3]]
#> [1] 2.133621 2.053730 1.892977 1.637721 1.669219 1.536641 1.413236 1.369726
#> [9] 1.328652 1.328094
#>
#> [[1]][[4]]
#> [1] 1.262670 1.288643 1.209826 1.329300 1.176611 1.123009 1.196177 1.403819
#> [9] 1.364180 1.366575
#>
#> [[1]][[5]]
#> [1] 1.524722 1.302587 1.294947 1.456490 1.580015 1.357941 1.381343 1.381842
#> [9] 1.390103 1.390346
#>
#> [[1]][[6]]
#> [1] 1.400649 2.825090 3.668589 3.790930 4.147190 2.384792 2.692231 1.891024
#> [9] 1.361738 1.346266
#>
#> [[1]][[7]]
#> [1] 2.177392 1.747842 5.055109 4.816448 13.184320 13.122975 18.657676
#> [8] 18.310017 21.463970 20.825055
#>
#> [[1]][[8]]
#> [1] 2.303359 5.066905 5.164362 3.046195 3.183245 7.164604 6.825827 6.591949
#> [9] 8.059339 8.090565
#>
#> [[1]][[9]]
#> [1] 2.2108761 1.9416990 0.9562836 0.7962834 0.7057318 1.4156486 1.0268659
#> [8] 1.8523938 5.2462466 4.7963261
#>
#> [[1]][[10]]
#> [1] 3.15634606 3.47171099 0.87925308 0.13423429 0.62522908 0.03142367
#> [7] 0.02830618 0.04542298 0.07141477 0.07561845
#>
#>
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 23.37794 32.93954 36.95741 36.21037 46.55398 57.56542 73.11976 91.27798
#> [9] 97.24806 94.17358
#>
summary(bbb)
#> ____************************************************____
#>
#> Family: Gamma
#> Link function: inverse
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 56.60919 59.60220 NA NA NA NA
#> Nb_Comp_1 39.01090 43.50042 2.603800e-01 0.0975 0.2603800 23.37794
#> Nb_Comp_2 37.30801 43.29404 -4.070876e-01 0.0975 -0.9024467 32.93954
#> Nb_Comp_3 36.87524 44.35777 -2.057095e+00 0.0975 -1.1726405 36.95741
#> Nb_Comp_4 36.55795 45.53700 -5.991094e+00 0.0975 -1.2868420 36.21037
#> Nb_Comp_5 37.13611 47.61167 -2.306070e+01 0.0975 -2.4416214 46.55398
#> Nb_Comp_6 38.27656 50.24862 -1.007710e+02 0.0975 -3.2297587 57.56542
#> Nb_Comp_7 39.39377 52.86234 -5.339127e+02 0.0975 -4.2560451 73.11976
#> Nb_Comp_8 40.96122 55.92630 -3.266271e+03 0.0975 -5.1080446 91.27798
#> Nb_Comp_9 42.90816 59.36974 -2.082680e+04 0.0975 -5.3746790 97.24806
#> Nb_Comp_10 44.90815 62.86625 -1.294169e+05 0.0975 -5.2137104 94.17358
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 31.60805 20.800152 NA
#> Nb_Comp_1 17.31431 11.804594 0.4324756
#> Nb_Comp_2 17.01037 6.357437 0.6943562
#> Nb_Comp_3 15.83422 5.699662 0.7259798
#> Nb_Comp_4 13.52676 7.679741 0.6307844
#> Nb_Comp_5 13.60962 6.099077 0.7067773
#> Nb_Comp_6 13.91155 5.205052 0.7497590
#> Nb_Comp_7 14.94390 4.650377 0.7764258
#> Nb_Comp_8 15.25537 4.321314 0.7922461
#> Nb_Comp_9 15.15577 4.307757 0.7928978
#> Nb_Comp_10 15.15490 4.307391 0.7929154
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm(ypine,Xpine,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.61896 0.38248575 0.0975 0.38248575 12.844390 11.074659
#> Nb_Comp_2 58.47638 0.34836456 0.0975 -0.05525570 11.686597 8.919303
#> Nb_Comp_3 56.55421 0.23688359 0.0975 -0.17107874 10.445206 7.919786
#> Nb_Comp_4 54.35053 0.06999681 0.0975 -0.21869112 9.651773 6.972542
#> Nb_Comp_5 55.99834 -0.07691053 0.0975 -0.15796434 8.073955 6.898523
#> Nb_Comp_6 57.69592 -0.19968885 0.0975 -0.11400977 7.685022 6.835594
#> Nb_Comp_7 59.37953 -0.27722139 0.0975 -0.06462721 7.277359 6.770369
#> Nb_Comp_8 61.21213 -0.30602578 0.0975 -0.02255238 6.923057 6.736112
#> Nb_Comp_9 63.18426 -0.39920228 0.0975 -0.07134354 7.216690 6.730426
#> Nb_Comp_10 65.15982 -0.43743644 0.0975 -0.02732569 6.914340 6.725443
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4675684 0.4675684 17.03781 19.76046 0.38248575 0.0975
#> Nb_Comp_2 0.5711905 0.5711905 13.72190 17.97925 -0.05525570 0.0975
#> Nb_Comp_3 0.6192438 0.6192438 12.18420 16.06943 -0.17107874 0.0975
#> Nb_Comp_4 0.6647841 0.6647841 10.72691 14.84877 -0.21869112 0.0975
#> Nb_Comp_5 0.6683426 0.6683426 10.61304 12.42138 -0.15796434 0.0975
#> Nb_Comp_6 0.6713681 0.6713681 10.51622 11.82303 -0.11400977 0.0975
#> Nb_Comp_7 0.6745039 0.6745039 10.41588 11.19586 -0.06462721 0.0975
#> Nb_Comp_8 0.6761508 0.6761508 10.36317 10.65078 -0.02255238 0.0975
#> Nb_Comp_9 0.6764242 0.6764242 10.35443 11.10252 -0.07134354 0.0975
#> Nb_Comp_10 0.6766638 0.6766638 10.34676 10.63737 -0.02732569 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof
#> Nb_Comp_0 NA 96.63448 1.000000 0.8062287 0.6697018 0.6991787
#> Nb_Comp_1 0.38248575 77.83455 3.176360 0.5994089 0.4047616 0.4565153
#> Nb_Comp_2 0.34836456 72.69198 7.133559 0.5761829 0.4138120 0.5212090
#> Nb_Comp_3 0.23688359 70.76981 8.778329 0.5603634 0.4070516 0.5320535
#> Nb_Comp_4 0.06999681 68.56612 8.427874 0.5221703 0.3505594 0.4547689
#> Nb_Comp_5 -0.07691053 70.21393 9.308247 0.5285695 0.3666578 0.4845912
#> Nb_Comp_6 -0.19968885 71.91152 9.291931 0.5259794 0.3629363 0.4795121
#> Nb_Comp_7 -0.27722139 73.59512 9.756305 0.5284535 0.3702885 0.4938445
#> Nb_Comp_8 -0.30602578 75.42772 10.363948 0.5338475 0.3831339 0.5170783
#> Nb_Comp_9 -0.39920228 77.39986 10.732148 0.5378277 0.3920957 0.5328747
#> Nb_Comp_10 -0.43743644 79.37542 11.000000 0.5407500 0.3987417 0.5446065
#> GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 -3.605128 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 -9.875081 2 0.5977015 0.3788984 0.4112998 -11.451340
#> Nb_Comp_2 -6.985517 3 0.5452615 0.3243383 0.3647862 -12.822703
#> Nb_Comp_3 -6.260610 4 0.5225859 0.3061986 0.3557368 -12.756838
#> Nb_Comp_4 -8.152986 5 0.4990184 0.2867496 0.3432131 -12.811575
#> Nb_Comp_5 -7.111583 6 0.5054709 0.3019556 0.3714754 -11.329638
#> Nb_Comp_6 -7.233043 7 0.5127450 0.3186757 0.4021333 -9.918688
#> Nb_Comp_7 -6.742195 8 0.5203986 0.3364668 0.4347156 -8.592770
#> Nb_Comp_8 -6.038372 9 0.5297842 0.3572181 0.4717708 -7.287834
#> Nb_Comp_9 -5.600235 10 0.5409503 0.3813021 0.5140048 -6.008747
#> Nb_Comp_10 -5.288422 11 0.5529032 0.4076026 0.5600977 -4.799453
data(pineNAX21)
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=Gamma(),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-Gamma",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -15.25147 0.006490479 0.04204947 -0.19442714 1.781305 -0.2952820
#> [2,] -15.16986 0.005474605 0.03527656 -0.27648100 1.257921 -0.2420263
#> [3,] -13.97006 0.010594537 -0.02927609 0.30604636 2.061606 -0.3365954
#> [4,] -12.03743 0.005373534 0.01951001 -0.08011735 1.311064 -0.2972826
#> [5,] -13.61994 0.005054427 0.01112295 -0.14897854 1.789084 -0.3465339
#> [6,] -14.86458 0.006538230 0.05144328 -0.19171348 1.762460 -0.3669618
#> [7,] -13.85270 0.006060799 0.06967588 -0.02328877 1.870853 -0.4052438
#> [8,] -14.36499 0.005602741 0.03767463 -0.16984964 1.754658 -0.3354075
#> [9,] -14.67958 0.006164958 0.05589650 -0.17300465 1.964310 -0.4224304
#> [10,] -15.30241 0.004702082 0.06265784 -0.28441615 2.354652 -0.5135518
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 2.5339085 -0.869322794 -0.50052940 1.5016832 1.1335442
#> [2,] 3.6679546 0.949554915 -0.27881465 1.4859881 0.7737842
#> [3,] -2.4661214 -0.929662624 -0.49572148 0.7713872 0.9698994
#> [4,] 1.1018507 -0.007227048 -0.02295972 0.5747632 0.6669312
#> [5,] 1.8359211 -0.272589375 -0.10305410 0.9830253 0.8989810
#> [6,] 2.8883524 -0.608454563 -0.18145293 0.2719423 0.7192753
#> [7,] -0.4468633 0.251324140 -0.03449214 1.2065942 0.0875273
#> [8,] 2.0699983 0.023096103 -0.21486194 1.1778879 0.3679135
#> [9,] 2.4902882 -0.580864643 -0.14887012 0.2397186 1.0540600
#> [10,] 3.5201888 -0.311832092 -0.18456971 1.1482720 0.6852035
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.463179 1.531142 1.608290 1.829257 1.162081 1.191037 1.238357 1.296691
#> [9] 1.843372
#>
#> [[1]][[2]]
#> [1] 2.477326 3.570153 5.299694 13.232160 6.846131 42.482041 2.192721
#> [8] 19.699755 533.685869
#>
#> [[1]][[3]]
#> [1] 12.43755 21.55714 21.77690 18.34100 23.44719 31.95209 35.89362 37.58604
#> [9] 38.03269
#>
#> [[1]][[4]]
#> [1] 1.642436 1.522085 1.331390 1.276175 1.251882 1.196762 1.164422 1.168649
#> [9] 1.142255
#>
#> [[1]][[5]]
#> [1] 1.1726192 0.8839238 0.7895764 0.7055315 0.9398147 0.7111289 0.7615644
#> [8] 1.2020841 1.1560004
#>
#> [[1]][[6]]
#> [1] 2.9105254 3.1114201 1.9082122 1.2488792 0.7675007 0.1535418 0.2796247
#> [8] 0.3177351 1.1315258
#>
#> [[1]][[7]]
#> [1] 0.693484 2.084585 4.506156 5.289879 5.498737 3.627927 5.417774 5.270269
#> [9] 4.780969
#>
#> [[1]][[8]]
#> [1] 0.6566677 0.3051880 2.1918274 1.0515080 1.1735817 2.0113381 1.0441987
#> [8] 0.7190080 0.6158175
#>
#> [[1]][[9]]
#> [1] 1.953828 2.719823 2.828047 3.078667 3.120304 3.325169 3.628322 4.480910
#> [9] 4.546886
#>
#> [[1]][[10]]
#> [1] 1.504998 1.513776 1.333460 2.381695 5.714160 6.536569 8.279555 9.419846
#> [9] 9.466334
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 26.91261 38.79924 43.57356 48.43475 49.92138 93.18760 59.90016
#> [8] 81.16099 596.40172
#>
summary(bbb2)
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: Gamma
#> Link function: inverse
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 56.60919 59.60220 NA NA NA NA
#> Nb_Comp_1 39.08940 43.57892 1.485520e-01 0.0975 0.148552 26.91261
#> Nb_Comp_2 37.36154 43.34757 -9.085873e-01 0.0975 -1.241578 38.79924
#> Nb_Comp_3 36.81173 44.29427 -3.862364e+00 0.0975 -1.547625 43.57356
#> Nb_Comp_4 36.53654 45.51559 -1.391895e+01 0.0975 -2.068251 48.43475
#> Nb_Comp_5 37.24312 47.71867 -5.420887e+01 0.0975 -2.700587 49.92138
#> Nb_Comp_6 38.18649 50.15855 -3.781519e+02 0.0975 -5.867590 93.18760
#> Nb_Comp_7 39.35575 52.82432 -1.618578e+03 0.0975 -3.271581 59.90016
#> Nb_Comp_8 40.86209 55.82716 -8.727793e+03 0.0975 -4.389547 81.16099
#> Nb_Comp_9 42.80511 59.26669 -3.406856e+05 0.0975 -38.030202 596.40172
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 31.60805 20.800152 NA
#> Nb_Comp_1 17.30890 12.031518 0.4215659
#> Nb_Comp_2 17.10360 6.183372 0.7027247
#> Nb_Comp_3 15.78579 5.756462 0.7232490
#> Nb_Comp_4 13.49013 7.630460 0.6331536
#> Nb_Comp_5 13.56918 6.303455 0.6969515
#> Nb_Comp_6 14.02295 5.274716 0.7464097
#> Nb_Comp_7 15.05896 4.867806 0.7659726
#> Nb_Comp_8 15.28052 4.317488 0.7924300
#> Nb_Comp_9 15.19429 4.298593 0.7933384
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
PLS_lm(ypine,XpineNAX21,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> Only naive DoF can be used with missing data
#> ____There are some NAs in X but not in Y____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.69250 0.35639805 0.0975 0.35639805 13.387018 11.099368
#> Nb_Comp_2 58.35228 0.28395028 0.0975 -0.11256611 12.348781 8.885823
#> Nb_Comp_3 56.36553 0.07664889 0.0975 -0.28950699 11.458331 7.874634
#> Nb_Comp_4 54.02416 -0.70355579 0.0975 -0.84497074 14.528469 6.903925
#> Nb_Comp_5 55.80450 -0.94905654 0.0975 -0.14411078 7.898855 6.858120
#> Nb_Comp_6 57.45753 -1.27568315 0.0975 -0.16758190 8.007417 6.786392
#> Nb_Comp_7 58.73951 -1.63309014 0.0975 -0.15705481 7.852227 6.640327
#> Nb_Comp_8 60.61227 -1.67907859 0.0975 -0.01746558 6.756304 6.614773
#> Nb_Comp_9 62.25948 -2.15165796 0.0975 -0.17639623 7.781594 6.544432
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4663804 0.4663804 17.07583 20.59526 0.35639805 0.0975
#> Nb_Comp_2 0.5728001 0.5728001 13.67040 18.99799 -0.11256611 0.0975
#> Nb_Comp_3 0.6214146 0.6214146 12.11473 17.62807 -0.28950699 0.0975
#> Nb_Comp_4 0.6680830 0.6680830 10.62135 22.35133 -0.84497074 0.0975
#> Nb_Comp_5 0.6702851 0.6702851 10.55088 12.15200 -0.14411078 0.0975
#> Nb_Comp_6 0.6737336 0.6737336 10.44053 12.31901 -0.16758190 0.0975
#> Nb_Comp_7 0.6807558 0.6807558 10.21581 12.08026 -0.15705481 0.0975
#> Nb_Comp_8 0.6819844 0.6819844 10.17650 10.39424 -0.01746558 0.0975
#> Nb_Comp_9 0.6853661 0.6853661 10.06828 11.97160 -0.17639623 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 NA 96.63448 NA NA NA NA NA
#> Nb_Comp_1 0.35639805 77.90810 NA NA NA NA NA
#> Nb_Comp_2 0.28395028 72.56787 NA NA NA NA NA
#> Nb_Comp_3 0.07664889 70.58113 NA NA NA NA NA
#> Nb_Comp_4 -0.70355579 68.23976 NA NA NA NA NA
#> Nb_Comp_5 -0.94905654 70.02009 NA NA NA NA NA
#> Nb_Comp_6 -1.27568315 71.67313 NA NA NA NA NA
#> Nb_Comp_7 -1.63309014 72.95511 NA NA NA NA NA
#> Nb_Comp_8 -1.67907859 74.82787 NA NA NA NA NA
#> Nb_Comp_9 -2.15165796 76.47507 NA NA NA NA NA
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 2 0.5983679 0.3797438 0.4122175 -11.413749
#> Nb_Comp_2 3 0.5442372 0.3231208 0.3634169 -12.847656
#> Nb_Comp_3 4 0.5210941 0.3044529 0.3537087 -12.776843
#> Nb_Comp_4 5 0.4965569 0.2839276 0.3398355 -12.891035
#> Nb_Comp_5 6 0.5039885 0.3001871 0.3692997 -11.349498
#> Nb_Comp_6 7 0.5108963 0.3163819 0.3992388 -9.922119
#> Nb_Comp_7 8 0.5153766 0.3300041 0.4263658 -8.696873
#> Nb_Comp_8 9 0.5249910 0.3507834 0.4632727 -7.337679
#> Nb_Comp_9 10 0.5334234 0.3707649 0.4998004 -6.033403
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2"))
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,modele="pls",verbose=FALSE)
summary(bbb)
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8375127 0.0975 0.8375127 76.01101 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.7843768 0.0975 -0.3270158 47.43084 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.3361792 0.0975 -2.0786151 34.06982 4.418081 0.9905556
#> Nb_Comp_4 33.76477 -5.7521467 0.0975 -9.1716408 44.93914 4.309235 0.9907882
#> Nb_Comp_5 33.34373 -64.2459132 0.0975 -8.6629881 41.64009 3.521924 0.9924713
#> Nb_Comp_6 35.25533 NA 0.0975 NA NA 3.496074 0.9925265
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=12,verbose=FALSE)
#> Number of repeated crossvalidations:
#> [1] 1
#> Number of folds for each crossvalidation:
#> [1] 12
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian,K=12,verbose=FALSE)
#> Number of repeated crossvalidations:
#> [1] 1
#> Number of folds for each crossvalidation:
#> [1] 12
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 2 95.08010 96.44940 97.09980
#> 1 93.23005 94.54028 95.85236
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 11 82.41674 82.38616 81.93566
#> 12 87.54766 87.52369 89.40288
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 10 82.18756 82.96181 82.81149
#> 8 81.97836 82.48513 82.34459
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 7 81.79662 81.77255 81.88023
#> 9 82.08282 82.45580 82.57525
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 3 96.52367 97.77654 97.81463
#> 6 94.13493 92.01372 92.13219
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 4 95.96203 92.91269 90.22726
#> 5 88.58623 88.32200 84.41198
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 5 87.61323 89.0497 85.81831
#> 3 95.69562 97.8202 98.24510
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 6 94.15649 92.49562 92.41182
#> 11 82.51704 82.52189 82.31869
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 2 95.08010 96.44940 97.09980
#> 1 93.23005 94.54028 95.85236
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 8 82.18031 82.59243 82.48103
#> 12 87.40306 87.79647 89.48632
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 7 81.66306 81.74072 81.79826
#> 10 82.35888 83.04357 83.02591
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 4 95.36489 92.51571 91.52622
#> 9 82.06647 82.55843 82.55524
#>
#>
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",family=inverse.gaussian(),
K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 2 96.97257 97.56815 97.41466
#> 6 93.60394 92.25570 92.59526
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 11 82.41674 82.38616 81.93566
#> 12 87.54766 87.52369 89.40288
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 5 88.12776 88.54310 85.32603
#> 10 82.67247 82.97135 83.00083
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 3 95.54239 98.04545 98.06654
#> 8 82.24235 82.35979 82.49361
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 1 93.67092 95.01013 96.02979
#> 9 82.11977 82.40273 82.50324
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 4 95.44676 92.43331 91.48325
#> 7 81.79505 81.83779 81.76982
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 10 82.52637 83.25532 83.27621
#> 11 82.09018 82.10220 82.32048
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 5 88.06746 88.53727 85.25048
#> 8 82.44673 82.50563 82.40858
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 12 86.83575 86.59791 88.12756
#> 1 93.48074 94.34564 96.46141
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 4 95.36489 92.51571 91.52622
#> 9 82.06647 82.55843 82.55524
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 7 81.85234 81.83027 81.82584
#> 6 93.64727 92.15930 92.37209
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 3 94.00695 96.56079 97.64113
#> 2 94.75823 96.91475 98.01025
#>
#>
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 9 81.89236 82.51683 82.52717
#> 10 82.30686 83.12912 83.03868
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 4 95.56230 93.12092 91.09742
#> 12 87.64961 87.08013 89.59203
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 2 95.08010 96.44940 97.09980
#> 1 93.23005 94.54028 95.85236
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 6 93.64727 92.15930 92.37209
#> 7 81.85234 81.83027 81.82584
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 5 88.06746 88.53727 85.25048
#> 8 82.44673 82.50563 82.40858
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 3 95.03541 97.95611 97.96361
#> 11 82.63660 81.45836 82.07905
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 5 87.65801 88.80272 85.50149
#> 2 96.42504 97.55338 98.02034
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 4 95.56230 93.12092 91.09742
#> 12 87.64961 87.08013 89.59203
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 3 95.36186 97.94995 98.04350
#> 7 82.03388 81.64447 81.78355
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 8 82.34082 82.65220 82.70685
#> 11 82.09986 81.91098 82.20593
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 9 81.89236 82.51683 82.52717
#> 10 82.30686 83.12912 83.03868
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 1 93.41724 95.97174 95.91487
#> 6 91.29124 90.70632 90.64439
#>
#>
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(link = "1/mu^2"),K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 3 95.36186 97.94995 98.04350
#> 7 82.03388 81.64447 81.78355
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 8 82.33405 82.57720 82.55853
#> 1 93.67751 95.07576 96.06678
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 5 88.12776 88.54310 85.32603
#> 10 82.67247 82.97135 83.00083
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 12 87.64961 87.08013 89.59203
#> 4 95.56230 93.12092 91.09742
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 9 82.12535 82.56460 82.53338
#> 6 93.45057 92.26382 92.21097
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 2 95.74052 97.89405 98.00186
#> 11 82.56310 81.59883 82.15787
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 2 96.13073 97.86873 97.96498
#> 8 82.24293 82.38287 82.48015
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 6 93.25013 92.42267 92.24201
#> 10 82.56492 82.99060 82.94155
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 12 87.46933 87.90360 89.32176
#> 9 82.06973 82.52492 82.68256
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 3 96.61339 98.57737 98.3464
#> 4 95.61777 92.38878 92.0582
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 5 88.04376 88.64422 85.34050
#> 7 82.05772 81.84324 81.76576
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 11 82.31956 82.01545 82.35014
#> 1 93.72589 94.82253 95.92674
#>
#>
bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=10,
modele="pls-glm-inverse.gaussian",keepcoeffs=TRUE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.0002136666 -3.152061e-05 -8.004098e-05 -5.358504e-05 -4.875042e-05
#> [2,] 0.0024298065 -3.505496e-03 -2.306928e-03 3.172305e-04 -2.115984e-03
#> [3,] 0.0001395896 2.587855e-04 -6.570595e-06 -3.435778e-04 1.261472e-05
#> [4,] -0.0019335956 2.585441e-03 2.063289e-03 1.273572e-03 2.075435e-03
#> [5,] -0.0001726175 7.866832e-04 3.087020e-04 -4.618726e-04 3.028237e-04
#> [,6] [,7] [,8]
#> [1,] -6.981066e-05 -1.163207e-04 -1.863569e-04
#> [2,] -2.324571e-03 -2.277295e-03 -3.688473e-03
#> [3,] -2.704572e-05 -3.926685e-05 -4.286597e-05
#> [4,] 2.065248e-03 2.026394e-03 2.118389e-03
#> [5,] 2.972386e-04 2.591324e-04 4.486940e-04
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 5.663753e-06 3.892966e-06 7.464327e-06 6.280145e-06 6.778551e-06
#>
#> [[1]][[2]]
#> [1] 1.158086e-05 3.364512e-07 1.070409e-07 7.188387e-07 2.196760e-05
#> [6] 1.109926e-04
#>
#> [[1]][[3]]
#> [1] 3.173657e-05 1.576891e-05 8.341915e-06 6.792655e-06 6.613310e-06
#> [6] 6.593724e-06
#>
#> [[1]][[4]]
#> [1] 5.899971e-06 1.970303e-06 1.500514e-06 3.123051e-06 1.437575e-06
#> [6] 2.530327e-06
#>
#> [[1]][[5]]
#> [1] 4.053814e-06 5.416716e-06 3.479330e-06 5.235664e-06 4.606876e-06
#> [6] 5.506172e-06
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 5.893496e-05 2.738535e-05 2.089313e-05 2.215035e-05 4.140391e-05
#>
summary(bbb2)
#> ____************************************************____
#>
#> Family: inverse.gaussian
#> Link function: 1/mu^2
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 81.67928 82.64909 NA NA NA NA
#> Nb_Comp_1 49.90521 51.35993 0.9124267 0.0975 0.9124267 5.893496e-05
#> Nb_Comp_2 31.06918 33.00881 0.9394032 0.0975 0.3080453 2.738535e-05
#> Nb_Comp_3 28.40632 30.83085 0.8193787 0.0975 -1.9807078 2.089313e-05
#> Nb_Comp_4 27.08522 29.99466 0.1537615 0.0975 -3.6851524 2.215035e-05
#> Nb_Comp_5 28.46056 31.85490 -8.7751672 0.0975 -10.5513153 4.140391e-05
#> Nb_Comp_6 29.68366 33.56292 NA 0.0975 NA NA
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 6.729783e-04 467.796667 NA
#> Nb_Comp_1 3.957680e-05 32.478677 0.9305710
#> Nb_Comp_2 7.009452e-06 6.020269 0.9871306
#> Nb_Comp_3 4.727777e-06 3.795855 0.9918857
#> Nb_Comp_4 3.584346e-06 2.699884 0.9942285
#> Nb_Comp_5 3.408069e-06 2.598572 0.9944451
#> Nb_Comp_6 3.195402e-06 2.492371 0.9946721
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm(yCornell,XCornell,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8966556 0.0975 0.89665563 48.344150 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.9175426 0.0975 0.20210989 28.518576 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.9399676 0.0975 0.27195907 8.056942 4.418081 0.9905556
#> Nb_Comp_4 33.76477 0.9197009 0.0975 -0.33759604 5.909608 4.309235 0.9907882
#> Nb_Comp_5 33.34373 0.9281373 0.0975 0.10506161 3.856500 3.521924 0.9924713
#> Nb_Comp_6 35.25533 0.9232562 0.0975 -0.06792167 3.761138 3.496074 0.9925265
#> R2_residY RSS_residY PRESS_residY Q2_residY LimQ2 Q2cum_residY
#> Nb_Comp_0 NA 11.00000000 NA NA NA NA
#> Nb_Comp_1 0.9235940 0.84046633 1.13678803 0.89665563 0.0975 0.8966556
#> Nb_Comp_2 0.9763431 0.26022559 0.67059977 0.20210989 0.0975 0.9175426
#> Nb_Comp_3 0.9905556 0.10388893 0.18945488 0.27195907 0.0975 0.9399676
#> Nb_Comp_4 0.9907882 0.10132947 0.13896142 -0.33759604 0.0975 0.9197009
#> Nb_Comp_5 0.9924713 0.08281624 0.09068364 0.10506161 0.0975 0.9281373
#> Nb_Comp_6 0.9925265 0.08220840 0.08844125 -0.06792167 0.0975 0.9232562
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
rm(list=c("XCornell","yCornell","bbb","bbb2"))
# }
data(Cornell)
bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,modele="pls")
#>
#> Model: pls
#>
#> NK: 1
#> Number of groups : 5
#> 1
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 2
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 3
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 5 components could thus be extracted
#> ****________________________________________________****
#>
#> 4
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
#> 5
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ****________________________________________________****
#>
summary(bbb)
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8825364 0.0975 0.8825364 54.94910 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.8269042 0.0975 -0.4736115 52.67054 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.5775041 0.0975 -1.4408218 27.01161 4.418081 0.9905556
#> Nb_Comp_4 33.76477 -0.6610330 0.0975 -2.9314767 17.36958 4.309235 0.9907882
#> Nb_Comp_5 33.34373 -4.2608140 0.0975 -2.1671942 13.64819 3.521924 0.9924713
#> Nb_Comp_6 35.25533 NA 0.0975 NA NA 3.496074 0.9925265
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),K=12)
#>
#> Family: gaussian
#> Link function: identity
#>
#> NK: 1
#> Leave One Out
#> Number of groups : 12
#> 1
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 2
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 3
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 4
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 5
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 6
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 7
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 8
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 9
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 10
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 11
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> 12
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ****________________________________________________****
#>
#> Number of repeated crossvalidations:
#> [1] 1
#> Number of folds for each crossvalidation:
#> [1] 12
# \donttest{
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),K=6,
NK=2,random=TRUE,keepfolds=TRUE,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 8 82.26250 82.68784 82.57390
#> 1 93.77726 95.43707 96.22502
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 12 87.99852 88.21195 90.33982
#> 7 81.62441 81.45137 81.59378
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 5 88.69762 88.72588 84.52895
#> 9 82.14663 82.42626 82.57381
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 2 94.64037 96.78604 97.68458
#> 3 94.04046 96.54685 97.38485
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 10 82.55134 83.14227 83.15714
#> 6 93.61280 93.23121 92.59065
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 4 95.67527 93.25885 92.29906
#> 11 82.27810 82.82857 82.12475
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 1 93.77726 95.43707 96.22502
#> 8 82.26250 82.68784 82.57390
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 9 81.71032 82.63498 82.60915
#> 10 82.25439 83.47314 83.41923
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 2 95.70841 97.71098 97.74896
#> 7 81.76041 81.36644 81.45882
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 11 82.50024 81.21191 82.58315
#> 5 88.65842 88.57044 84.05944
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 12 88.28185 87.27538 89.61124
#> 4 95.61065 93.37520 91.52727
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 6 94.33615 92.86486 92.25602
#> 3 96.32394 97.51708 97.76579
#>
#>
#Different ways of model specifications
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),K=6,
NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 3 96.32394 97.51708 97.76579
#> 6 94.33615 92.86486 92.25602
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 7 81.76041 81.36644 81.45882
#> 2 95.70841 97.71098 97.74896
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 1 94.22332 95.36373 95.85522
#> 4 94.54209 93.49347 92.39735
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 5 88.70327 88.65979 84.42108
#> 8 82.42406 82.59556 82.40613
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 10 82.42399 83.46719 83.47687
#> 12 88.03750 88.00537 89.86291
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 11 82.17510 82.18875 82.36369
#> 9 82.14254 82.73114 82.84385
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 9 81.66333 82.45539 82.42717
#> 8 81.93358 82.62278 82.54069
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 3 95.63098 97.65603 97.96495
#> 5 88.20917 89.24828 84.89718
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 4 95.37792 93.28680 91.85874
#> 10 82.45503 83.28528 83.40513
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 1 93.69278 96.29864 96.20283
#> 6 91.81562 91.42217 91.03072
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 11 82.51652 81.57490 82.10380
#> 2 95.49781 97.77827 97.75467
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 7 81.62441 81.45137 81.59378
#> 12 87.99852 88.21195 90.33982
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian,
K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 6 93.82267 93.12621 92.64847
#> 2 96.55892 97.35037 97.35321
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 12 87.63217 89.62193 89.26717
#> 3 95.16282 98.45056 98.26072
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 8 82.22220 82.78130 82.65782
#> 11 81.99705 82.07142 81.97785
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 5 88.69762 88.72588 84.52895
#> 9 82.14663 82.42626 82.57381
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 7 81.35111 81.44917 81.42097
#> 10 82.27244 83.31538 83.23261
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 4 94.54209 93.49347 92.39735
#> 1 94.22332 95.36373 95.85522
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 8 82.19502 82.44263 82.39279
#> 6 93.70842 93.19639 92.60544
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 11 82.36708 82.46841 81.46889
#> 12 88.07277 87.76781 89.97618
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 9 81.95438 82.40908 82.47999
#> 1 93.77529 95.37353 96.19607
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 2 95.70841 97.71098 97.74896
#> 7 81.76041 81.36644 81.45882
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 4 96.08759 93.15114 90.18511
#> 5 89.33387 88.45998 83.14531
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 10 82.38856 83.32360 83.40165
#> 3 95.44801 98.06096 98.01431
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),
K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 7 81.82483 81.49752 81.34635
#> 5 88.63291 88.75749 84.45984
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 12 87.98256 88.00708 89.95495
#> 8 82.13009 82.67518 82.62926
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 2 96.55892 97.35037 97.35321
#> 6 93.82267 93.12621 92.64847
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 9 82.14254 82.73114 82.84385
#> 11 82.17510 82.18875 82.36369
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 10 82.45503 83.28528 83.40513
#> 4 95.37792 93.28680 91.85874
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 3 94.52271 97.05014 97.42782
#> 1 93.47990 95.48701 96.41990
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 10 82.42248 83.29460 83.33617
#> 2 95.80543 97.75045 97.79476
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 7 81.53113 81.55462 81.46664
#> 6 93.95423 92.98641 92.45456
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 3 95.63098 97.65603 97.96495
#> 5 88.20917 89.24828 84.89718
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 11 81.99705 82.07142 81.97785
#> 8 82.22220 82.78130 82.65782
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 12 88.03359 88.12688 90.22298
#> 9 81.94172 82.56690 82.72971
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 4 94.54209 93.49347 92.39735
#> 1 94.22332 95.36373 95.85522
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(link=log),
K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 3 95.65204 97.76671 98.02903
#> 5 87.97354 89.18415 85.16903
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 4 95.00945 95.18881 93.46179
#> 6 93.02499 94.69311 93.97618
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 7 81.83718 81.47340 81.60346
#> 2 95.83184 97.76267 97.84660
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 9 81.95851 82.55679 82.74230
#> 12 87.80288 88.01796 90.32582
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 11 82.22853 82.01571 82.35287
#> 1 93.81539 95.00873 95.97317
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 8 81.87769 82.59788 82.45967
#> 10 82.12875 83.20064 83.06435
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 3 95.65204 97.76671 98.02903
#> 5 87.97354 89.18415 85.16903
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 10 82.52382 83.26628 83.19475
#> 1 93.76363 95.29913 96.21594
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 6 93.59266 92.73169 92.08803
#> 12 87.88944 87.81086 89.95350
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 4 95.43796 92.88376 91.78010
#> 9 81.91009 82.54160 82.56662
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 8 82.22865 82.72594 82.71324
#> 11 81.99015 81.96542 82.16627
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 7 81.83718 81.47340 81.60346
#> 2 95.83184 97.76267 97.84660
#>
#>
bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=10,
modele="pls-glm-gaussian",keepcoeffs=TRUE,verbose=FALSE)
bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",
family=gaussian(link=log),K=6,keepcoeffs=TRUE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 4.470273 -0.06317216 -0.015130085 -0.10739267 -0.05657930 0.030866914
#> [2,] 4.468766 -0.08427095 -0.019204062 -0.13596596 -0.05734367 0.031006389
#> [3,] 4.465960 -0.06389364 -0.019541280 -0.10394949 -0.05167567 0.060119790
#> [4,] 4.477324 -0.09243744 -0.027226841 -0.15458083 -0.05247624 0.022959125
#> [5,] 4.452397 -0.04645103 -0.004221374 -0.07332015 -0.04885178 -0.001590875
#> [6,] 4.496170 -0.08978082 -0.046226381 -0.14905411 -0.05666651 0.079637184
#> [,7] [,8]
#> [1,] 0.1612710 -0.2068408
#> [2,] 0.1628564 -0.1304898
#> [3,] 0.1729426 -0.2247461
#> [4,] 0.1554308 -0.2314175
#> [5,] 0.1889002 -0.2092133
#> [6,] 0.1230279 -0.3766808
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 0.6460407 0.5745404 1.3085258
#>
#> [[1]][[2]]
#> [1] 4.374429 1.490981 0.657909
#>
#> [[1]][[3]]
#> [1] 13.027216 3.804195 2.570854
#>
#> [[1]][[4]]
#> [1] 2.6416340 0.2521208 0.4284244
#>
#> [[1]][[5]]
#> [1] 3.304672 4.409153 3.740272
#>
#> [[1]][[6]]
#> [1] 25.751584 6.278168 6.934872
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 49.74558 16.80916 15.64086
#>
summary(bbb2)
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: log
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.01205 82.98186 NA NA NA NA
#> Nb_Comp_1 52.67938 54.13410 0.8936598 0.0975 0.8936598 49.74558
#> Nb_Comp_2 32.16524 34.10487 0.9479820 0.0975 0.5108343 16.80916
#> Nb_Comp_3 30.58789 33.01242 0.8454256 0.0975 -1.9715587 15.64086
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 467.796667 467.796667 NA
#> Nb_Comp_1 34.362913 34.362913 0.9265431
#> Nb_Comp_2 5.263520 5.263520 0.9887483
#> Nb_Comp_3 3.906676 3.906676 0.9916488
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(Y~.,data=Cornell,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8966556 0.0975 0.89665563 48.344150 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.9175426 0.0975 0.20210989 28.518576 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.9399676 0.0975 0.27195907 8.056942 4.418081 0.9905556
#> Nb_Comp_4 33.76477 0.9197009 0.0975 -0.33759604 5.909608 4.309235 0.9907882
#> Nb_Comp_5 33.34373 0.9281373 0.0975 0.10506161 3.856500 3.521924 0.9924713
#> Nb_Comp_6 35.25533 0.9232562 0.0975 -0.06792167 3.761138 3.496074 0.9925265
#> R2_residY RSS_residY PRESS_residY Q2_residY LimQ2 Q2cum_residY
#> Nb_Comp_0 NA 11.00000000 NA NA NA NA
#> Nb_Comp_1 0.9235940 0.84046633 1.13678803 0.89665563 0.0975 0.8966556
#> Nb_Comp_2 0.9763431 0.26022559 0.67059977 0.20210989 0.0975 0.9175426
#> Nb_Comp_3 0.9905556 0.10388893 0.18945488 0.27195907 0.0975 0.9399676
#> Nb_Comp_4 0.9907882 0.10132947 0.13896142 -0.33759604 0.0975 0.9197009
#> Nb_Comp_5 0.9924713 0.08281624 0.09068364 0.10506161 0.0975 0.9281373
#> Nb_Comp_6 0.9925265 0.08220840 0.08844125 -0.06792167 0.0975 0.9232562
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
rm(list=c("bbb","bbb2"))
data(pine)
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-family",
family=gaussian(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-family",family=gaussian(),
K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 8.156030 -0.002843084 -0.03644892 0.027297442 -0.4943102 0.10194191
#> [2,] 8.588526 -0.002897746 -0.03843733 0.018948973 -0.5076750 0.10318074
#> [3,] 7.346555 -0.002639189 -0.03197018 0.029176977 -0.3788608 0.09274711
#> [4,] 7.644263 -0.002720788 -0.03020072 0.025195551 -0.5070876 0.11257349
#> [5,] 9.468184 -0.003056342 -0.03244721 0.080968844 -0.5349550 0.08134066
#> [6,] 8.476948 -0.003052482 -0.03844314 0.043200815 -0.3823907 0.09840346
#> [7,] 7.485734 -0.002118536 -0.03848000 0.006338868 -0.1314073 0.03168094
#> [8,] 8.415013 -0.003637688 -0.03532410 0.026646064 -0.6087039 0.17020306
#> [9,] 9.000407 -0.003254954 -0.03482172 0.027392108 -0.5920017 0.12489329
#> [10,] 8.641954 -0.002960673 -0.03038941 0.024750762 -0.4061744 0.09193541
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] -0.02168238 -0.32787943 0.0522727243 -0.7800157 -0.268530683
#> [2,] 0.31824223 -0.41832606 0.0139150953 -0.8928500 -0.330604891
#> [3,] 0.09974150 0.02083909 0.0009329017 -0.8186071 -0.516438300
#> [4,] -0.01614965 -0.22930917 0.0376489360 -0.7629602 -0.294052133
#> [5,] -0.74630708 -0.22164995 0.1547819588 -1.1247555 -0.416971506
#> [6,] -0.21142520 -0.22984493 0.0096712788 -0.7951799 -0.316391684
#> [7,] 0.51108868 -0.64801419 -0.0627153804 -1.0417407 0.000622326
#> [8,] -0.06239897 0.12598389 -0.0484719959 -0.5177580 -0.379773648
#> [9,] 0.33490906 -0.45844428 0.0711930649 -1.2312607 -0.222402358
#> [10,] 0.18660012 -0.36608742 0.0016756723 -0.9511047 -0.435404655
boxplot(kfolds2coeff(bbb)[,1])
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 0.6822920 0.9898822 0.4567531 0.2848432 0.2855490 0.2581613 0.2622199
#> [8] 0.2682081 0.2664009 0.2665069
#>
#> [[1]][[2]]
#> [1] 0.3290331 0.6943732 0.8920147 0.6517735 0.7773682 0.8291191 0.7856140
#> [8] 0.7803094 0.7793632 0.7830987
#>
#> [[1]][[3]]
#> [1] 5.134608 4.352800 4.110543 4.101116 4.142351 3.957491 3.937956 4.015741
#> [9] 4.009503 4.010102
#>
#> [[1]][[4]]
#> [1] 1.5892089 0.8884867 0.3169854 0.3283711 0.3081848 0.4885121 0.4698701
#> [8] 0.4410752 0.4635755 0.4632303
#>
#> [[1]][[5]]
#> [1] 0.9480681 0.5598881 0.3043579 0.2901408 0.5615256 0.7317172 1.0167362
#> [8] 0.9335151 0.8741441 0.8688230
#>
#> [[1]][[6]]
#> [1] 0.8326586 1.3736050 0.9055842 0.9137274 0.6946235 0.6002510 0.6703785
#> [8] 0.6943911 0.7019240 0.6976481
#>
#> [[1]][[7]]
#> [1] 2.391120 1.841140 1.594341 1.835096 1.618454 1.638564 1.609431 1.594453
#> [9] 1.594428 1.594464
#>
#> [[1]][[8]]
#> [1] 0.9870604 1.7092037 1.6641294 1.9964626 2.2877309 2.7372381 2.7936109
#> [8] 2.8345897 2.8228265 2.8242678
#>
#> [[1]][[9]]
#> [1] 0.9878324 0.5034288 0.4767363 0.7297164 1.0435982 0.9677027 1.0976810
#> [8] 1.0734911 1.0457530 1.0454549
#>
#> [[1]][[10]]
#> [1] 0.7573681 0.8173985 0.5431485 0.5457155 0.5348865 0.5413112 0.4724061
#> [8] 0.3789233 0.4008979 0.4007754
#>
#>
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 14.63925 13.73021 11.26459 11.67696 12.25427 12.75007 13.11590 13.01470
#> [9] 12.95882 12.95437
#>
summary(bbb)
#> ____************************************************____
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.41888 85.41190 NA NA NA NA
#> Nb_Comp_1 63.61896 68.10848 0.2961950 0.0975 0.2961950 14.63925
#> Nb_Comp_2 54.15489 60.14092 0.1274325 0.0975 -0.2397860 13.73021
#> Nb_Comp_3 53.47303 60.95556 -0.2561931 0.0975 -0.4396515 11.26459
#> Nb_Comp_4 54.83398 63.81302 -1.0333990 0.0975 -0.6186993 11.67696
#> Nb_Comp_5 56.32757 66.80312 -2.5217342 0.0975 -0.7319445 12.25427
#> Nb_Comp_6 57.45220 69.42426 -5.4443638 0.0975 -0.8298836 12.75007
#> Nb_Comp_7 59.31417 72.78274 -11.4568863 0.0975 -0.9329893 13.11590
#> Nb_Comp_8 61.20356 76.16863 -22.9933784 0.0975 -0.9261136 13.01470
#> Nb_Comp_9 63.16270 79.62429 -45.1700349 0.0975 -0.9242824 12.95882
#> Nb_Comp_10 65.15982 83.11791 -87.9237460 0.0975 -0.9260056 12.95437
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 20.800152 20.800152 NA
#> Nb_Comp_1 11.074659 11.074659 0.4675684
#> Nb_Comp_2 7.824528 7.824528 0.6238235
#> Nb_Comp_3 7.213793 7.213793 0.6531855
#> Nb_Comp_4 7.075441 7.075441 0.6598370
#> Nb_Comp_5 6.967693 6.967693 0.6650172
#> Nb_Comp_6 6.785296 6.785296 0.6737862
#> Nb_Comp_7 6.756973 6.756973 0.6751479
#> Nb_Comp_8 6.734363 6.734363 0.6762349
#> Nb_Comp_9 6.726030 6.726030 0.6766355
#> Nb_Comp_10 6.725443 6.725443 0.6766638
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pine,nt=10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.61896 0.38248575 0.0975 0.38248575 12.844390 11.074659
#> Nb_Comp_2 58.47638 0.34836456 0.0975 -0.05525570 11.686597 8.919303
#> Nb_Comp_3 56.55421 0.23688359 0.0975 -0.17107874 10.445206 7.919786
#> Nb_Comp_4 54.35053 0.06999681 0.0975 -0.21869112 9.651773 6.972542
#> Nb_Comp_5 55.99834 -0.07691053 0.0975 -0.15796434 8.073955 6.898523
#> Nb_Comp_6 57.69592 -0.19968885 0.0975 -0.11400977 7.685022 6.835594
#> Nb_Comp_7 59.37953 -0.27722139 0.0975 -0.06462721 7.277359 6.770369
#> Nb_Comp_8 61.21213 -0.30602578 0.0975 -0.02255238 6.923057 6.736112
#> Nb_Comp_9 63.18426 -0.39920228 0.0975 -0.07134354 7.216690 6.730426
#> Nb_Comp_10 65.15982 -0.43743644 0.0975 -0.02732569 6.914340 6.725443
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4675684 0.4675684 17.03781 19.76046 0.38248575 0.0975
#> Nb_Comp_2 0.5711905 0.5711905 13.72190 17.97925 -0.05525570 0.0975
#> Nb_Comp_3 0.6192438 0.6192438 12.18420 16.06943 -0.17107874 0.0975
#> Nb_Comp_4 0.6647841 0.6647841 10.72691 14.84877 -0.21869112 0.0975
#> Nb_Comp_5 0.6683426 0.6683426 10.61304 12.42138 -0.15796434 0.0975
#> Nb_Comp_6 0.6713681 0.6713681 10.51622 11.82303 -0.11400977 0.0975
#> Nb_Comp_7 0.6745039 0.6745039 10.41588 11.19586 -0.06462721 0.0975
#> Nb_Comp_8 0.6761508 0.6761508 10.36317 10.65078 -0.02255238 0.0975
#> Nb_Comp_9 0.6764242 0.6764242 10.35443 11.10252 -0.07134354 0.0975
#> Nb_Comp_10 0.6766638 0.6766638 10.34676 10.63737 -0.02732569 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof
#> Nb_Comp_0 NA 96.63448 1.000000 0.8062287 0.6697018 0.6991787
#> Nb_Comp_1 0.38248575 77.83455 3.176360 0.5994089 0.4047616 0.4565153
#> Nb_Comp_2 0.34836456 72.69198 7.133559 0.5761829 0.4138120 0.5212090
#> Nb_Comp_3 0.23688359 70.76981 8.778329 0.5603634 0.4070516 0.5320535
#> Nb_Comp_4 0.06999681 68.56612 8.427874 0.5221703 0.3505594 0.4547689
#> Nb_Comp_5 -0.07691053 70.21393 9.308247 0.5285695 0.3666578 0.4845912
#> Nb_Comp_6 -0.19968885 71.91152 9.291931 0.5259794 0.3629363 0.4795121
#> Nb_Comp_7 -0.27722139 73.59512 9.756305 0.5284535 0.3702885 0.4938445
#> Nb_Comp_8 -0.30602578 75.42772 10.363948 0.5338475 0.3831339 0.5170783
#> Nb_Comp_9 -0.39920228 77.39986 10.732148 0.5378277 0.3920957 0.5328747
#> Nb_Comp_10 -0.43743644 79.37542 11.000000 0.5407500 0.3987417 0.5446065
#> GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 -3.605128 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 -9.875081 2 0.5977015 0.3788984 0.4112998 -11.451340
#> Nb_Comp_2 -6.985517 3 0.5452615 0.3243383 0.3647862 -12.822703
#> Nb_Comp_3 -6.260610 4 0.5225859 0.3061986 0.3557368 -12.756838
#> Nb_Comp_4 -8.152986 5 0.4990184 0.2867496 0.3432131 -12.811575
#> Nb_Comp_5 -7.111583 6 0.5054709 0.3019556 0.3714754 -11.329638
#> Nb_Comp_6 -7.233043 7 0.5127450 0.3186757 0.4021333 -9.918688
#> Nb_Comp_7 -6.742195 8 0.5203986 0.3364668 0.4347156 -8.592770
#> Nb_Comp_8 -6.038372 9 0.5297842 0.3572181 0.4717708 -7.287834
#> Nb_Comp_9 -5.600235 10 0.5409503 0.3813021 0.5140048 -6.008747
#> Nb_Comp_10 -5.288422 11 0.5529032 0.4076026 0.5600977 -4.799453
data(pineNAX21)
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=gaussian(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: algorithm did not converge
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-gaussian",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 9.417696 -0.003085519 -0.03292365 0.081998660 -0.8184090 0.19049980
#> [2,] 7.145708 -0.002834763 -0.02678488 0.007015912 -0.3495267 0.08573336
#> [3,] 8.817251 -0.003483655 -0.03351176 0.091086187 -0.4417299 0.11033386
#> [4,] 8.622256 -0.003640537 -0.02347172 0.066090708 -0.5491435 0.14689903
#> [5,] 7.747370 -0.003093857 -0.03153373 0.053811988 -0.6004494 0.12626423
#> [6,] 7.317040 -0.002601577 -0.03656803 0.031504671 -0.6394931 0.12915114
#> [7,] 7.219156 -0.002823633 -0.03491071 0.016672955 -0.4603733 0.11156140
#> [8,] 9.296004 -0.003476210 -0.03318613 0.095264146 -0.6903655 0.13611562
#> [9,] 7.748634 -0.003134650 -0.03015868 0.052055792 -0.5073365 0.11210967
#> [10,] 7.052572 -0.002695906 -0.03075540 0.019841606 -0.5949681 0.12257379
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] -0.76515582 -1.113753776 0.042258992 -0.2870644 -0.6195097
#> [2,] 0.35323924 -0.453700941 -0.001620362 -0.9331617 -0.2876929
#> [3,] -0.94698645 -0.130691076 0.015035900 -0.6366736 -0.5886837
#> [4,] -0.49671881 -0.191433470 -0.018161853 -0.6552005 -0.5806575
#> [5,] -0.29506311 0.241657428 0.025942148 -0.7431118 -0.7081757
#> [6,] -0.04158871 -0.130602216 0.076694670 -0.9206700 -0.4439884
#> [7,] 0.17786780 -0.006821097 -0.037157408 -0.5816967 -0.2203753
#> [8,] -0.90468636 0.249695617 0.068564185 -0.7575834 -0.9584379
#> [9,] -0.27249346 0.042666152 0.026255077 -0.7869057 -0.5688790
#> [10,] -0.02787941 -0.295338724 0.051259453 -0.6910382 -0.2890005
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 3.402945 3.656572 3.273628 3.450831 3.486171 3.235487 3.218240 3.080754
#> [9] 3.936084
#>
#> [[1]][[2]]
#> [1] 1.0828422 0.9777051 0.5551255 0.6121587 0.5185004 0.6057535 0.3657192
#> [8] 0.3336632 0.3226815
#>
#> [[1]][[3]]
#> [1] 0.8152388 0.7651711 0.4826267 0.5750911 0.6830450 0.9995395 1.0633777
#> [8] 0.8130788 0.9121713
#>
#> [[1]][[4]]
#> [1] 2.855770 2.230451 2.292592 2.502052 2.367000 2.268647 2.237789 2.188098
#> [9] 1.913121
#>
#> [[1]][[5]]
#> [1] 0.5917999 0.2743389 0.5452149 0.4854152 0.4633855 0.4810781 0.4958757
#> [8] 0.5344595 0.4076186
#>
#> [[1]][[6]]
#> [1] 0.2165681 0.2511958 0.4173566 0.4109749 0.4086318 0.6283137 0.6012819
#> [8] 0.5056380 0.4856855
#>
#> [[1]][[7]]
#> [1] 1.7034215 1.6926689 0.5584487 0.9850864 3.3320737 8.2268787
#> [7] 7.5009589 3.2474497 551.7200782
#>
#> [[1]][[8]]
#> [1] 0.4455789 0.9113333 0.8791301 1.0514390 1.0140258 1.0458390 1.0655180
#> [8] 1.1615826 1.3890024
#>
#> [[1]][[9]]
#> [1] 0.7294811 0.3893972 0.3488275 0.2704331 0.2958156 0.2396339 0.2289992
#> [8] 0.2062736 0.1403598
#>
#> [[1]][[10]]
#> [1] 2.1323545 1.6146945 0.8255678 0.6813533 0.6899731 0.7938227 1.0181191
#> [8] 1.0462273 1.0227324
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 13.97600 12.76353 10.17852 11.02483 13.25862 18.52499 17.79588
#> [8] 13.11723 562.24953
#>
summary(bbb2)
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: gaussian
#> Link function: identity
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 82.41888 85.41190 NA NA NA NA
#> Nb_Comp_1 63.90814 68.39766 3.280818e-01 0.0975 0.3280818 13.97600
#> Nb_Comp_2 54.06295 60.04898 2.323716e-01 0.0975 -0.1424432 12.76353
#> Nb_Comp_3 53.77276 61.25530 -1.353191e-03 0.0975 -0.3044765 10.17852
#> Nb_Comp_4 55.18223 64.16127 -5.165300e-01 0.0975 -0.5144806 11.02483
#> Nb_Comp_5 56.53963 67.01518 -1.811983e+00 0.0975 -0.8542221 13.25862
#> Nb_Comp_6 57.73540 69.70746 -6.428327e+00 0.0975 -1.6416682 18.52499
#> Nb_Comp_7 59.46634 72.93491 -1.831589e+01 0.0975 -1.6003014 17.79588
#> Nb_Comp_8 60.79943 75.76451 -3.632518e+01 0.0975 -0.9323561 13.11723
#> Nb_Comp_9 62.14147 78.60305 -3.153663e+03 0.0975 -83.5183678 562.24953
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 20.800152 20.800152 NA
#> Nb_Comp_1 11.172133 11.172133 0.4628821
#> Nb_Comp_2 7.802760 7.802760 0.6248700
#> Nb_Comp_3 7.279614 7.279614 0.6500211
#> Nb_Comp_4 7.150504 7.150504 0.6562283
#> Nb_Comp_5 7.012612 7.012612 0.6628577
#> Nb_Comp_6 6.843775 6.843775 0.6709747
#> Nb_Comp_7 6.788203 6.788203 0.6736465
#> Nb_Comp_8 6.652395 6.652395 0.6801757
#> Nb_Comp_9 6.521071 6.521071 0.6864893
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pineNAX21,nt=10,typeVC="standard")$InfCrit
#> ____************************************************____
#> Only naive DoF can be used with missing data
#> ____There are some NAs in X but not in Y____
#> ____TypeVC____ standard ____
#> ____TypeVC____ standard ____unknown____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.69250 0.35639805 0.0975 0.35639805 13.387018 11.099368
#> Nb_Comp_2 58.35228 0.28395028 0.0975 -0.11256611 12.348781 8.885823
#> Nb_Comp_3 56.36553 0.07664889 0.0975 -0.28950699 11.458331 7.874634
#> Nb_Comp_4 54.02416 -0.70355579 0.0975 -0.84497074 14.528469 6.903925
#> Nb_Comp_5 55.80450 -0.94905654 0.0975 -0.14411078 7.898855 6.858120
#> Nb_Comp_6 57.45753 -1.27568315 0.0975 -0.16758190 8.007417 6.786392
#> Nb_Comp_7 58.73951 -1.63309014 0.0975 -0.15705481 7.852227 6.640327
#> Nb_Comp_8 60.61227 -1.67907859 0.0975 -0.01746558 6.756304 6.614773
#> Nb_Comp_9 62.25948 -2.15165796 0.0975 -0.17639623 7.781594 6.544432
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4663804 0.4663804 17.07583 20.59526 0.35639805 0.0975
#> Nb_Comp_2 0.5728001 0.5728001 13.67040 18.99799 -0.11256611 0.0975
#> Nb_Comp_3 0.6214146 0.6214146 12.11473 17.62807 -0.28950699 0.0975
#> Nb_Comp_4 0.6680830 0.6680830 10.62135 22.35133 -0.84497074 0.0975
#> Nb_Comp_5 0.6702851 0.6702851 10.55088 12.15200 -0.14411078 0.0975
#> Nb_Comp_6 0.6737336 0.6737336 10.44053 12.31901 -0.16758190 0.0975
#> Nb_Comp_7 0.6807558 0.6807558 10.21581 12.08026 -0.15705481 0.0975
#> Nb_Comp_8 0.6819844 0.6819844 10.17650 10.39424 -0.01746558 0.0975
#> Nb_Comp_9 0.6853661 0.6853661 10.06828 11.97160 -0.17639623 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 NA 96.63448 NA NA NA NA NA
#> Nb_Comp_1 0.35639805 77.90810 NA NA NA NA NA
#> Nb_Comp_2 0.28395028 72.56787 NA NA NA NA NA
#> Nb_Comp_3 0.07664889 70.58113 NA NA NA NA NA
#> Nb_Comp_4 -0.70355579 68.23976 NA NA NA NA NA
#> Nb_Comp_5 -0.94905654 70.02009 NA NA NA NA NA
#> Nb_Comp_6 -1.27568315 71.67313 NA NA NA NA NA
#> Nb_Comp_7 -1.63309014 72.95511 NA NA NA NA NA
#> Nb_Comp_8 -1.67907859 74.82787 NA NA NA NA NA
#> Nb_Comp_9 -2.15165796 76.47507 NA NA NA NA NA
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 2 0.5983679 0.3797438 0.4122175 -11.413749
#> Nb_Comp_2 3 0.5442372 0.3231208 0.3634169 -12.847656
#> Nb_Comp_3 4 0.5210941 0.3044529 0.3537087 -12.776843
#> Nb_Comp_4 5 0.4965569 0.2839276 0.3398355 -12.891035
#> Nb_Comp_5 6 0.5039885 0.3001871 0.3692997 -11.349498
#> Nb_Comp_6 7 0.5108963 0.3163819 0.3992388 -9.922119
#> Nb_Comp_7 8 0.5153766 0.3300041 0.4263658 -8.696873
#> Nb_Comp_8 9 0.5249910 0.3507834 0.4632727 -7.337679
#> Nb_Comp_9 10 0.5334234 0.3707649 0.4998004 -6.033403
rm(list=c("bbb","bbb2"))
data(aze_compl)
bbb <- cv.plsRglm(y~.,data=aze_compl,nt=10,K=10,modele="pls",
keepcoeffs=TRUE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.2904228 -0.16546999 0.4613569 -0.2286029 0.3856101 0.19997604
#> [2,] 0.2788494 -0.15379671 0.2872360 -0.1616180 0.2799155 0.06554366
#> [3,] 0.2611695 -0.10970491 0.4432985 -0.1519190 0.2286516 0.16025696
#> [4,] 0.3107607 -0.09353284 0.4504198 -0.1732064 0.1558267 0.14893733
#> [5,] 0.2353885 -0.20528895 0.4614184 -0.2310755 0.3168370 0.06767269
#> [6,] 0.3838840 -0.14015351 0.4260461 -0.2183473 0.2572394 0.12343355
#> [7,] 0.4397244 -0.05753894 0.4190984 -0.1646686 0.2418824 0.08820954
#> [8,] 0.3215070 -0.15750170 0.4834184 -0.1708883 0.3484636 0.07815465
#> [9,] 0.4178985 -0.16920876 0.4723971 -0.1464944 0.1380466 0.09771230
#> [10,] 0.1603134 -0.10438813 0.5847150 -0.2461680 0.3367957 0.05361732
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] -0.03147007 0.005447243 -0.25466893 0.075540157 -0.12212338 0.07503150
#> [2,] -0.04483009 0.006006823 -0.12822608 0.059277262 -0.05628416 0.01172033
#> [3,] -0.08010147 -0.004494762 -0.17458325 0.079604238 -0.03021918 -0.01326882
#> [4,] -0.07870415 0.046118297 -0.16437668 0.047937140 -0.11303248 0.05961749
#> [5,] -0.02190554 -0.035631497 -0.25287862 0.032991764 -0.12731337 0.07825341
#> [6,] -0.12150132 0.041957218 -0.27680064 -0.007849660 -0.02937310 0.09737774
#> [7,] -0.09555267 -0.004121272 -0.16888557 0.008354877 -0.10718182 0.07161800
#> [8,] 0.03342254 -0.076191370 -0.35907808 0.045943810 -0.13552329 0.08341805
#> [9,] -0.06646697 0.037302167 -0.04875343 0.006454279 -0.15806923 0.09880511
#> [10,] 0.02576205 0.057609599 -0.30717154 0.111079203 -0.11998307 0.06447860
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] -0.23605039 0.09132327 0.11871387 0.05238841 0.079533632 0.2310862
#> [2,] -0.08778194 0.20025677 0.14271415 0.15825821 -0.005158815 0.1463330
#> [3,] -0.13167005 0.18791410 0.13124435 0.01007703 -0.051049524 0.2258172
#> [4,] -0.03986917 0.01164343 0.13930641 0.07874179 0.024434649 0.2053491
#> [5,] -0.08399076 0.06113521 0.12303185 0.01037210 0.025948557 0.2763917
#> [6,] -0.07048540 0.07374076 0.09789945 0.02290605 -0.025232369 0.3149448
#> [7,] -0.11999560 0.18408022 0.04962935 -0.01007692 -0.031605435 0.1593562
#> [8,] -0.16316496 0.12610938 0.04204067 0.19241795 0.057004170 0.2224195
#> [9,] -0.12803369 0.03888839 0.14377609 0.06005691 -0.027012090 0.2602918
#> [10,] -0.22418988 0.07889964 0.11437179 0.05428228 0.060792746 0.3352487
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 0.052229631 0.007385233 -0.25282651 0.056446650 0.33429850 -0.15217124
#> [2,] 0.038417870 0.107278283 -0.07953724 0.075446065 0.13550787 -0.16390057
#> [3,] 0.006927076 0.080982967 -0.10468827 0.108960415 0.18333130 -0.13508928
#> [4,] -0.022328933 0.182848961 -0.17171106 0.047373613 0.06461360 -0.11134306
#> [5,] 0.012402191 0.088233652 -0.07156676 0.136283685 0.17319692 -0.13247308
#> [6,] -0.025334581 0.122883152 -0.05858277 0.008872599 0.26877774 -0.13744291
#> [7,] 0.036579422 -0.035798152 -0.12907028 0.028579269 0.24776651 -0.20559335
#> [8,] -0.089062486 0.088824430 -0.15330373 0.025641891 0.24578349 -0.17179775
#> [9,] 0.053065315 0.052679782 -0.18413276 0.121779310 0.01200132 -0.02829972
#> [10,] 0.068251735 0.063652085 -0.09165695 0.076084106 0.22637119 -0.19472896
#> [,25] [,26] [,27] [,28] [,29] [,30]
#> [1,] -0.1964368 -0.2665771 0.1585707 0.1804507 -0.03106563 0.069155087
#> [2,] -0.1684274 -0.2475508 0.1589951 0.1262438 -0.10324404 -0.001352712
#> [3,] -0.1957547 -0.3164055 0.1924212 0.2549146 -0.09565424 0.034795798
#> [4,] -0.2086419 -0.1905443 0.2017151 0.1801214 -0.17652333 0.082746808
#> [5,] -0.1229588 -0.2931490 0.2342065 0.1693008 -0.13782128 -0.049492555
#> [6,] -0.1806949 -0.3787962 0.1944198 0.1825709 -0.10505455 0.039556420
#> [7,] -0.2023592 -0.2792575 0.1746820 0.2441362 -0.11303775 -0.007783524
#> [8,] -0.1649146 -0.2956775 0.1993148 0.2920899 -0.06420454 -0.010086608
#> [9,] -0.1756940 -0.3132950 0.1772571 0.2278046 -0.10498110 -0.043764396
#> [10,] -0.2225357 -0.3225024 0.1896744 0.1260020 -0.06321541 0.057762191
#> [,31] [,32] [,33] [,34]
#> [1,] 0.04102614 -0.07503820 -0.3786917 -0.028915111
#> [2,] 0.23692286 -0.09989328 -0.3971052 -0.053458363
#> [3,] 0.06245648 -0.03138443 -0.4016336 -0.037755788
#> [4,] 0.10169762 -0.06906329 -0.3787767 -0.032138377
#> [5,] 0.12463061 -0.03563325 -0.2891516 -0.009933863
#> [6,] 0.10843944 -0.05708189 -0.3841296 -0.032853634
#> [7,] 0.17627414 -0.07719379 -0.4426674 0.093447741
#> [8,] 0.06843106 -0.09879500 -0.2923301 0.013059072
#> [9,] 0.22575481 0.07828888 -0.5832959 -0.039093844
#> [10,] 0.11795164 -0.02646349 -0.4134898 -0.029182528
bbb2 <- cv.plsRglm(y~.,data=aze_compl,nt=3,K=10,
modele="pls-glm-family",family=binomial(probit),keepcoeffs=TRUE,verbose=FALSE)
bbb2 <- cv.plsRglm(y~.,data=aze_compl,nt=3,K=10,
modele="pls-glm-logistic",keepcoeffs=TRUE,verbose=FALSE)
summary(bbb,MClassed=TRUE)
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC MissClassed CV_MissClassed Q2cum_Y LimQ2_Y
#> Nb_Comp_0 154.6179 49 NA NA NA
#> Nb_Comp_1 126.4083 27 44 -0.07967991 0.0975
#> Nb_Comp_2 119.3375 25 45 -0.61560068 0.0975
#> Nb_Comp_3 114.2313 27 44 -1.79789662 0.0975
#> Nb_Comp_4 112.3463 23 44 -4.49018690 0.0975
#> Nb_Comp_5 113.2362 22 42 -10.27579259 0.0975
#> Nb_Comp_6 114.7620 21 42 -22.63990722 0.0975
#> Nb_Comp_7 116.5264 20 43 -49.21935486 0.0975
#> Nb_Comp_8 118.4601 20 43 -107.06034605 0.0975
#> Nb_Comp_9 120.4452 19 43 -232.31457561 0.0975
#> Nb_Comp_10 122.4395 19 43 -502.49592428 0.0975
#> Q2_Y PRESS_Y RSS_Y R2_Y AIC.std DoF.dof
#> Nb_Comp_0 NA NA 25.91346 NA 298.1344 1.00000
#> Nb_Comp_1 -0.07967991 27.97824 19.38086 0.2520929 269.9248 22.55372
#> Nb_Comp_2 -0.49637005 29.00094 17.76209 0.3145613 262.8540 27.31542
#> Nb_Comp_3 -0.73179960 30.76038 16.58896 0.3598323 257.7478 30.52370
#> Nb_Comp_4 -0.96225510 32.55177 15.98071 0.3833049 255.8628 34.00000
#> Nb_Comp_5 -1.05380851 32.82131 15.81104 0.3898523 256.7527 34.00000
#> Nb_Comp_6 -1.09651845 33.14814 15.73910 0.3926285 258.2785 34.00000
#> Nb_Comp_7 -1.12434653 33.43530 15.70350 0.3940024 260.0429 33.71066
#> Nb_Comp_8 -1.15176691 33.79026 15.69348 0.3943888 261.9766 34.00000
#> Nb_Comp_9 -1.15911372 33.88401 15.69123 0.3944758 263.9617 33.87284
#> Nb_Comp_10 -1.15801316 33.86188 15.69037 0.3945088 265.9560 34.00000
#> sigmahat.dof AIC.dof BIC.dof GMDL.dof DoF.naive sigmahat.naive
#> Nb_Comp_0 0.5015845 0.2540061 0.2604032 -67.17645 1 0.5015845
#> Nb_Comp_1 0.4848429 0.2883114 0.4231184 -53.56607 2 0.4358996
#> Nb_Comp_2 0.4781670 0.2908950 0.4496983 -52.42272 3 0.4193593
#> Nb_Comp_3 0.4719550 0.2902572 0.4631316 -51.93343 4 0.4072955
#> Nb_Comp_4 0.4744263 0.3008285 0.4954133 -50.37079 5 0.4017727
#> Nb_Comp_5 0.4719012 0.2976347 0.4901536 -50.65724 6 0.4016679
#> Nb_Comp_6 0.4708264 0.2962804 0.4879234 -50.78005 7 0.4028135
#> Nb_Comp_7 0.4693382 0.2937976 0.4826103 -51.05525 8 0.4044479
#> Nb_Comp_8 0.4701436 0.2954217 0.4865092 -50.85833 9 0.4064413
#> Nb_Comp_9 0.4696894 0.2945815 0.4845867 -50.95616 10 0.4085682
#> Nb_Comp_10 0.4700970 0.2953632 0.4864128 -50.86368 11 0.4107477
#> AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 0.2540061 0.2604032 -67.17645
#> Nb_Comp_1 0.1936625 0.2033251 -79.67755
#> Nb_Comp_2 0.1809352 0.1943501 -81.93501
#> Nb_Comp_3 0.1722700 0.1891422 -83.31503
#> Nb_Comp_4 0.1691819 0.1897041 -83.23369
#> Nb_Comp_5 0.1706451 0.1952588 -81.93513
#> Nb_Comp_6 0.1731800 0.2020601 -80.42345
#> Nb_Comp_7 0.1761610 0.2094352 -78.87607
#> Nb_Comp_8 0.1794902 0.2172936 -77.31942
#> Nb_Comp_9 0.1829787 0.2254232 -75.80069
#> Nb_Comp_10 0.1865584 0.2337468 -74.33325
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
summary(bbb2,MClassed=TRUE)
#> ____************************************************____
#>
#> Family: binomial
#> Link function: logit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC MissClassed CV_MissClassed Q2Chisqcum_Y limQ2
#> Nb_Comp_0 145.8283 148.4727 49 NA NA NA
#> Nb_Comp_1 118.1398 123.4285 28 45 -0.7312501 0.0975
#> Nb_Comp_2 109.9553 117.8885 26 45 -5.6396262 0.0975
#> Nb_Comp_3 105.1591 115.7366 22 46 -184.3864110 0.0975
#> Q2Chisq_Y PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 NA NA 104.00000 25.91346 NA
#> Nb_Comp_1 -0.7312501 180.0500 100.53823 19.32272 0.2543365
#> Nb_Comp_2 -2.8351629 385.5805 99.17955 17.33735 0.3309519
#> Nb_Comp_3 -26.9212121 2769.2134 123.37836 15.58198 0.3986915
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -0.5856366 -0.70593213 1.852552 -0.3050775 0.9084177 -0.18877819
#> [2,] -0.8149337 -0.73838107 2.763618 -0.4614720 0.9635515 -0.04805976
#> [3,] -1.2104711 -0.32395091 2.662943 -0.6279237 0.6450661 -0.05595700
#> [4,] -2.1711113 -0.50606432 1.759972 -0.4426020 1.3427648 0.64588373
#> [5,] -1.9935056 0.06530008 1.770743 -0.5783879 1.3377936 -0.17958544
#> [6,] -1.4859983 -0.53089451 2.462009 -0.5974124 0.9383782 0.23660079
#> [7,] -0.3864483 -0.60078416 2.859719 -0.5107874 1.6598961 0.41849554
#> [8,] -1.6510734 0.27315603 3.038843 0.3358956 0.7353031 -0.34258677
#> [9,] -0.1003971 -0.89129549 2.183817 -0.3697520 0.4157663 0.17963575
#> [10,] -0.6625460 -0.64377729 2.526018 -0.6559876 0.9569176 -0.10450252
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 0.008477155 0.25577898 -0.5714143 -0.1721756 -0.7181097 0.29542011
#> [2,] -0.986938549 0.03781580 -0.3094900 0.2855071 -0.5693793 -0.18382052
#> [3,] -0.251958563 -0.51486260 -0.6625394 0.3355462 -0.2945604 0.38942886
#> [4,] -0.510662840 0.36978247 -0.3460350 -0.3202278 -0.8925107 0.53442304
#> [5,] -0.355457619 0.28457491 -0.7914298 0.1330579 -0.9832421 -0.08610594
#> [6,] -0.845401022 0.45731976 -0.6975156 0.1994099 -0.6196171 -0.50806788
#> [7,] -0.965694039 -0.15612775 -0.9140809 -0.6073345 -0.7170569 -0.13182391
#> [8,] -0.618266882 0.68708061 -1.1213766 0.4119966 -0.6572498 -0.27936091
#> [9,] -0.732194986 0.06256693 -0.6381877 0.1896016 -0.8825244 -0.17045254
#> [10,] -0.745248026 0.39187113 -0.7105860 0.6766861 -0.4629398 -0.15548511
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] -0.4677350 1.0126091 1.3258372 0.8628136 0.194131085 0.6326976
#> [2,] -0.3216674 0.4738930 1.6062460 0.1129500 0.300569466 1.4939696
#> [3,] -0.4222692 0.3927179 1.0300889 0.2752369 -0.348980574 1.4146577
#> [4,] -0.6229572 0.9921400 1.4742803 0.3985643 -0.252523176 1.4527357
#> [5,] -0.4068965 0.8343620 1.6595472 0.5022135 0.110644396 0.5872420
#> [6,] -0.5202251 1.1988388 1.1544251 0.5638662 -0.080060451 0.9634235
#> [7,] -0.4290171 1.1734399 0.8459654 0.8235340 -0.124221608 0.7714531
#> [8,] -0.5769116 0.4822520 0.9691335 0.6861565 -0.047622093 1.2112867
#> [9,] -0.1023990 0.6408009 0.9996327 0.1723059 0.072142725 1.1117595
#> [10,] -0.5754584 0.6461705 0.7156629 0.6338462 0.005688769 0.9835615
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 0.04211886 0.4712776 -0.8024290 0.33306839 0.9916961 -0.4896372
#> [2,] -0.31380722 1.0799777 -0.9825220 0.40162252 0.4462640 -0.4953247
#> [3,] 0.23966705 0.7437650 -0.5270733 0.59611897 0.4822368 -0.9027950
#> [4,] 0.27264659 0.2713775 -0.9791310 0.04606772 0.5686849 -0.6341368
#> [5,] -0.17400214 0.5198480 -0.8645098 0.47231855 0.4960366 -0.5216491
#> [6,] -0.05327875 0.8808986 -0.6839469 0.04770620 1.0180354 -1.0647194
#> [7,] 0.34305492 0.7716648 -0.6110081 -0.10219774 0.7983989 -0.8266582
#> [8,] 0.44811485 1.1579532 -0.8321312 -0.25480175 0.7151986 -1.0043173
#> [9,] 0.02639448 0.6706990 -1.2267333 0.49189615 1.5613934 -0.4508710
#> [10,] 0.51225877 0.4497280 -0.8426358 0.33018067 0.4243222 -0.5258497
#> [,25] [,26] [,27] [,28] [,29] [,30]
#> [1,] -1.0528126 -1.093123 0.2301459 1.1119871 -1.2893685 -0.252093424
#> [2,] -0.7105976 -1.244015 0.7649467 0.4726965 -0.6243811 -0.617331695
#> [3,] -0.7793126 -1.427220 0.5230731 0.7290150 -1.3367052 -0.333563870
#> [4,] -0.8968540 -1.552835 0.4248156 1.0669958 -1.2727016 -0.338020891
#> [5,] -1.0773448 -1.315935 0.5379118 0.6151999 -0.6221073 -0.497233136
#> [6,] -0.9794836 -1.410567 0.7320261 1.1101333 -0.9543077 0.031837790
#> [7,] -1.5409095 -1.361478 1.1317922 0.9686751 -1.0120843 0.001022957
#> [8,] -1.6981563 -1.321080 0.6577176 0.6252492 -1.0105108 -0.287975245
#> [9,] -1.9329688 -1.384525 0.7801307 1.3864025 -1.4512861 0.062855637
#> [10,] -1.3367175 -1.550137 0.3331949 0.7286957 -0.9973156 0.002383937
#> [,31] [,32] [,33] [,34]
#> [1,] 0.4374923 0.39035300 -2.005615 -0.56751270
#> [2,] 0.8935647 0.92683013 -3.433172 0.05492884
#> [3,] 0.9212239 0.41337778 -1.953009 0.02387227
#> [4,] 1.9460768 0.47684686 -2.389454 -0.03604205
#> [5,] 1.5763427 0.57492711 -1.512309 -0.09687232
#> [6,] 1.0673302 0.31128852 -2.564469 0.17118097
#> [7,] 0.8383126 -0.16979131 -2.321932 -0.14154021
#> [8,] 1.1259856 0.80665697 -2.443683 -0.24572429
#> [9,] 1.0663707 0.06049022 -2.333540 0.12834523
#> [10,] 1.3432225 0.28651553 -2.245218 -0.05168191
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 15.68058 23.48326 32.28129
#>
#> [[1]][[2]]
#> [1] 21.65825 43.32133 130.13947
#>
#> [[1]][[3]]
#> [1] 12.76393 18.59163 20.19642
#>
#> [[1]][[4]]
#> [1] 29.20445 141.47670 1049.38071
#>
#> [[1]][[5]]
#> [1] 19.37300 22.62269 67.59520
#>
#> [[1]][[6]]
#> [1] 12.04765 11.04876 16.55832
#>
#> [[1]][[7]]
#> [1] 8.342431 13.462267 21.782027
#>
#> [[1]][[8]]
#> [1] 27.47098 72.62313 1371.03149
#>
#> [[1]][[9]]
#> [1] 25.54239 32.29002 49.76008
#>
#> [[1]][[10]]
#> [1] 7.966339 6.660717 10.488378
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 180.0500 385.5805 2769.2134
#>
summary(bbb2)
#> ____************************************************____
#>
#> Family: binomial
#> Link function: logit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 145.8283 148.4727 NA NA NA NA
#> Nb_Comp_1 118.1398 123.4285 -0.7312501 0.0975 -0.7312501 180.0500
#> Nb_Comp_2 109.9553 117.8885 -5.6396262 0.0975 -2.8351629 385.5805
#> Nb_Comp_3 105.1591 115.7366 -184.3864110 0.0975 -26.9212121 2769.2134
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 104.00000 25.91346 NA
#> Nb_Comp_1 100.53823 19.32272 0.2543365
#> Nb_Comp_2 99.17955 17.33735 0.3309519
#> Nb_Comp_3 123.37836 15.58198 0.3986915
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
rm(list=c("bbb","bbb2"))
data(pine)
bbb <- cv.plsRglm(round(x11)~.,data=pine,nt=10,
modele="pls-glm-family",family=poisson(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
bbb <- cv.plsRglm(round(x11)~.,data=pine,nt=10,
modele="pls-glm-poisson",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 12.369713 -0.005162288 -0.06978464 0.17333712 -1.003630 0.2542444
#> [2,] 18.595676 -0.007618793 -0.10171648 0.38178258 -2.058722 0.3901663
#> [3,] 10.439251 -0.003661678 -0.06094272 0.13807230 -1.159559 0.1945328
#> [4,] 11.174660 -0.004349033 -0.05703580 0.16881400 -1.595398 0.3185621
#> [5,] 14.936104 -0.006093933 -0.08391264 0.23973512 -1.888993 0.3310605
#> [6,] 12.720096 -0.004960265 -0.07100791 0.20115847 -1.664469 0.3199172
#> [7,] 9.840377 -0.005871246 -0.04793368 -0.02075348 -1.456662 0.3313351
#> [8,] 13.417143 -0.005196755 -0.07659319 0.22685674 -1.477207 0.2718937
#> [9,] 9.339715 -0.001426923 -0.08756677 0.05029940 -2.686784 0.4757555
#> [10,] 15.500859 -0.008217655 -0.02902573 0.24560682 -1.416538 0.3865396
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] -1.3872315 -0.35245353 -0.117514452 -0.8409484 -0.13511343
#> [2,] -4.5777802 0.97951382 0.380612480 -1.4135057 -0.88985041
#> [3,] -1.1331280 -0.45746498 0.211733634 -1.7934177 0.43987485
#> [4,] -1.6008622 0.05812548 0.125109859 -1.2903566 0.02656087
#> [5,] -2.8304007 0.35310677 0.412611650 -1.2485473 -0.56204672
#> [6,] -2.2556346 0.15811178 0.267798052 -1.2996562 -0.27977888
#> [7,] 0.3502972 0.11215452 -0.004967203 -0.3011419 -0.10658889
#> [8,] -2.6726333 -0.01341568 0.314708765 -1.2911608 -0.21758564
#> [9,] 0.4321676 -0.49098634 0.549019098 -2.8407172 -0.29880651
#> [10,] -1.6104299 -0.37225662 0.062205959 -2.1753942 0.17092750
boxplot(kfolds2coeff(bbb)[,1])
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.442471 3.497844 2.502086 3.154369 2.886545 2.477623 1.469552 1.470941
#> [9] 1.459521 1.499906
#>
#> [[1]][[2]]
#> [1] 4.621335 9.188534 16.661907 20.626577 29.550254 40.300071 41.829950
#> [8] 36.588292 34.654527 34.533510
#>
#> [[1]][[3]]
#> [1] 3.804516 2.939185 2.319704 2.577046 2.516617 2.225765 2.034626 1.959327
#> [9] 1.870617 1.878698
#>
#> [[1]][[4]]
#> [1] 1.0568966 0.7058201 0.7850168 1.0004793 0.9951409 1.0467010 1.0351800
#> [8] 0.9764938 0.9525828 0.9524766
#>
#> [[1]][[5]]
#> [1] 0.8650672 2.9059687 1.5640753 1.7104577 1.7760062 2.1871950 2.4698616
#> [8] 2.5659205 2.7486451 2.7553889
#>
#> [[1]][[6]]
#> [1] 0.8074381 0.8314659 1.5289475 1.2617571 2.0096085 1.6901759 1.1023362
#> [8] 0.3897635 0.3220244 0.3349922
#>
#> [[1]][[7]]
#> [1] 6.570202 6.113771 3.698447 2.392023 2.231749 6.478331 4.839590 7.044384
#> [9] 6.959666 6.696944
#>
#> [[1]][[8]]
#> [1] 1.1780765 1.3030034 0.7083136 0.9203929 1.9469712 1.8634631 0.8699945
#> [8] 0.5797420 0.5698208 0.5709594
#>
#> [[1]][[9]]
#> [1] 2.398462 4.625143 5.114656 4.291948 7.453888 34.281484 34.383919
#> [8] 39.286581 32.056548 32.308211
#>
#> [[1]][[10]]
#> [1] 9.742179 3.683467 2.045690 7.493754 13.338690 12.916636 15.621795
#> [8] 13.471434 12.546697 12.695515
#>
#>
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 32.48664 35.79420 36.92884 45.42880 64.70547 105.46744 105.65680
#> [8] 104.33288 94.14065 94.22660
#>
summary(bbb)
#> ____************************************************____
#>
#> Family: poisson
#> Link function: log
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 76.61170 78.10821 NA NA NA NA
#> Nb_Comp_1 65.70029 68.69331 3.743278e-02 0.0975 0.03743278 32.48664
#> Nb_Comp_2 62.49440 66.98392 -4.440862e-01 0.0975 -0.50024450 35.79420
#> Nb_Comp_3 62.47987 68.46590 -2.082583e+00 0.0975 -1.13462541 36.92884
#> Nb_Comp_4 64.21704 71.69958 -8.029256e+00 0.0975 -1.92912009 45.42880
#> Nb_Comp_5 65.81654 74.79559 -3.733778e+01 0.0975 -3.24595068 64.70547
#> Nb_Comp_6 66.48888 76.96443 -2.639187e+02 0.0975 -5.91012285 105.46744
#> Nb_Comp_7 68.40234 80.37440 -1.576257e+03 0.0975 -4.95373885 105.65680
#> Nb_Comp_8 70.39399 83.86256 -9.118614e+03 0.0975 -4.78194582 104.33288
#> Nb_Comp_9 72.37642 87.34149 -4.722576e+04 0.0975 -4.17859136 94.14065
#> Nb_Comp_10 74.37612 90.83770 -2.425167e+05 0.0975 -4.13517493 94.22660
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 33.75000 24.545455 NA
#> Nb_Comp_1 23.85891 12.599337 0.4866937
#> Nb_Comp_2 17.29992 9.056074 0.6310488
#> Nb_Comp_3 15.50937 8.232069 0.6646194
#> Nb_Comp_4 15.23934 8.125808 0.6689485
#> Nb_Comp_5 15.26275 7.862134 0.6796909
#> Nb_Comp_6 17.74629 6.203270 0.7472742
#> Nb_Comp_7 18.04460 5.879880 0.7604493
#> Nb_Comp_8 18.17881 5.827065 0.7626011
#> Nb_Comp_9 18.34925 5.837300 0.7621841
#> Nb_Comp_10 18.39332 5.832437 0.7623822
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pine,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.61896 0.38248575 0.0975 0.38248575 12.844390 11.074659
#> Nb_Comp_2 58.47638 0.34836456 0.0975 -0.05525570 11.686597 8.919303
#> Nb_Comp_3 56.55421 0.23688359 0.0975 -0.17107874 10.445206 7.919786
#> Nb_Comp_4 54.35053 0.06999681 0.0975 -0.21869112 9.651773 6.972542
#> Nb_Comp_5 55.99834 -0.07691053 0.0975 -0.15796434 8.073955 6.898523
#> Nb_Comp_6 57.69592 -0.19968885 0.0975 -0.11400977 7.685022 6.835594
#> Nb_Comp_7 59.37953 -0.27722139 0.0975 -0.06462721 7.277359 6.770369
#> Nb_Comp_8 61.21213 -0.30602578 0.0975 -0.02255238 6.923057 6.736112
#> Nb_Comp_9 63.18426 -0.39920228 0.0975 -0.07134354 7.216690 6.730426
#> Nb_Comp_10 65.15982 -0.43743644 0.0975 -0.02732569 6.914340 6.725443
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4675684 0.4675684 17.03781 19.76046 0.38248575 0.0975
#> Nb_Comp_2 0.5711905 0.5711905 13.72190 17.97925 -0.05525570 0.0975
#> Nb_Comp_3 0.6192438 0.6192438 12.18420 16.06943 -0.17107874 0.0975
#> Nb_Comp_4 0.6647841 0.6647841 10.72691 14.84877 -0.21869112 0.0975
#> Nb_Comp_5 0.6683426 0.6683426 10.61304 12.42138 -0.15796434 0.0975
#> Nb_Comp_6 0.6713681 0.6713681 10.51622 11.82303 -0.11400977 0.0975
#> Nb_Comp_7 0.6745039 0.6745039 10.41588 11.19586 -0.06462721 0.0975
#> Nb_Comp_8 0.6761508 0.6761508 10.36317 10.65078 -0.02255238 0.0975
#> Nb_Comp_9 0.6764242 0.6764242 10.35443 11.10252 -0.07134354 0.0975
#> Nb_Comp_10 0.6766638 0.6766638 10.34676 10.63737 -0.02732569 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof
#> Nb_Comp_0 NA 96.63448 1.000000 0.8062287 0.6697018 0.6991787
#> Nb_Comp_1 0.38248575 77.83455 3.176360 0.5994089 0.4047616 0.4565153
#> Nb_Comp_2 0.34836456 72.69198 7.133559 0.5761829 0.4138120 0.5212090
#> Nb_Comp_3 0.23688359 70.76981 8.778329 0.5603634 0.4070516 0.5320535
#> Nb_Comp_4 0.06999681 68.56612 8.427874 0.5221703 0.3505594 0.4547689
#> Nb_Comp_5 -0.07691053 70.21393 9.308247 0.5285695 0.3666578 0.4845912
#> Nb_Comp_6 -0.19968885 71.91152 9.291931 0.5259794 0.3629363 0.4795121
#> Nb_Comp_7 -0.27722139 73.59512 9.756305 0.5284535 0.3702885 0.4938445
#> Nb_Comp_8 -0.30602578 75.42772 10.363948 0.5338475 0.3831339 0.5170783
#> Nb_Comp_9 -0.39920228 77.39986 10.732148 0.5378277 0.3920957 0.5328747
#> Nb_Comp_10 -0.43743644 79.37542 11.000000 0.5407500 0.3987417 0.5446065
#> GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 -3.605128 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 -9.875081 2 0.5977015 0.3788984 0.4112998 -11.451340
#> Nb_Comp_2 -6.985517 3 0.5452615 0.3243383 0.3647862 -12.822703
#> Nb_Comp_3 -6.260610 4 0.5225859 0.3061986 0.3557368 -12.756838
#> Nb_Comp_4 -8.152986 5 0.4990184 0.2867496 0.3432131 -12.811575
#> Nb_Comp_5 -7.111583 6 0.5054709 0.3019556 0.3714754 -11.329638
#> Nb_Comp_6 -7.233043 7 0.5127450 0.3186757 0.4021333 -9.918688
#> Nb_Comp_7 -6.742195 8 0.5203986 0.3364668 0.4347156 -8.592770
#> Nb_Comp_8 -6.038372 9 0.5297842 0.3572181 0.4717708 -7.287834
#> Nb_Comp_9 -5.600235 10 0.5409503 0.3813021 0.5140048 -6.008747
#> Nb_Comp_10 -5.288422 11 0.5529032 0.4076026 0.5600977 -4.799453
data(pineNAX21)
bbb2 <- cv.plsRglm(round(x11)~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=poisson(log),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
bbb2 <- cv.plsRglm(round(x11)~.,data=pineNAX21,nt=10,
modele="pls-glm-poisson",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 15.65181 -0.007778317 -0.06133387 0.1036472 -2.074764 0.4148445 -1.606337
#> [2,] 13.30561 -0.005364141 -0.07112078 0.2051332 -1.660789 0.3152937 -2.471535
#> [3,] 14.57700 -0.005969412 -0.05590340 0.2797425 -1.307411 0.3266281 -3.285194
#> [4,] 13.74618 -0.005485595 -0.07071813 0.2056446 -1.455331 0.2976372 -2.208170
#> [5,] 14.71844 -0.005191698 -0.06892616 0.2096098 -1.441988 0.2616690 -2.123594
#> [6,] 14.86525 -0.004366072 -0.09181109 0.2197708 -2.340261 0.4152243 -2.461445
#> [7,] 12.78220 -0.004449958 -0.05367658 0.1936654 -1.549712 0.3031097 -2.000991
#> [8,] 13.42163 -0.005025979 -0.07101602 0.2134477 -1.582596 0.3004695 -2.430726
#> [9,] 14.53850 -0.005304882 -0.07734715 0.2055085 -1.671481 0.3139955 -2.073842
#> [10,] 12.98226 -0.004161051 -0.05999490 0.1676080 -1.530884 0.2934076 -1.490332
#> [,8] [,9] [,10] [,11]
#> [1,] 0.64825723 0.2888299 -0.7376983 -0.52127608
#> [2,] 0.61557923 0.2823063 -0.9779591 -0.44916262
#> [3,] 0.33256224 0.0805504 -0.9983641 -0.42883521
#> [4,] 0.31729630 0.1439375 -0.9912127 -0.35008193
#> [5,] -0.40598118 0.2896055 -1.8549197 0.14768310
#> [6,] -0.10506358 0.4162079 -1.5268739 -0.19955829
#> [7,] -0.09727505 0.1616900 -1.3406024 -0.05705524
#> [8,] -0.07500994 0.2802244 -1.3008857 -0.37809504
#> [9,] -0.03135411 0.2668785 -1.5388738 -0.10759112
#> [10,] -0.19670296 0.2650178 -1.8288462 -0.39311440
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 22.30920 29.23253 16.89312 16.54502 14.47897 24.43505 28.58459 38.21246
#> [9] 34.50836
#>
#> [[1]][[2]]
#> [1] 1.1231678 1.3189809 1.3043031 0.9071203 0.6560976 0.5265062 0.6533294
#> [8] 0.6962001 0.7081156
#>
#> [[1]][[3]]
#> [1] 2.321301 2.809304 2.705924 3.273676 3.215357 3.327397 3.522555 3.417765
#> [9] 3.448266
#>
#> [[1]][[4]]
#> [1] 0.8934032 0.9573415 1.1667939 1.0615698 0.8037625 0.4884185 0.4289901
#> [8] 0.5158235 0.5869685
#>
#> [[1]][[5]]
#> [1] 11.8654685 3.8776805 0.8366341 0.7053157 0.7784905 0.6245028 0.4691767
#> [8] 0.4447110 0.4106887
#>
#> [[1]][[6]]
#> [1] 1.040822 2.636376 1.931004 3.001160 2.714714 5.141380 6.656049 6.141840
#> [9] 6.046703
#>
#> [[1]][[7]]
#> [1] 1.4926251 0.8745468 0.8276476 1.1504958 1.0421087 1.0187052 0.9176570
#> [8] 0.9049428 0.9112573
#>
#> [[1]][[8]]
#> [1] 1.013730 2.392011 6.919940 8.271099 8.235061
#> [6] 133.731586 689.611775 40203.426627 38.049486
#>
#> [[1]][[9]]
#> [1] 1.099190 2.058695 2.055539 2.312350 2.354769 2.341801 2.343791 2.444479
#> [9] 2.282510
#>
#> [[1]][[10]]
#> [1] 2.657995 3.817680 2.635557 2.687763 2.749222 3.255578 2.576799 1.898348
#> [9] 1.067236
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 45.81690 49.97515 37.27647 39.91557 37.02856 174.89092
#> [7] 735.76471 40258.10319 88.01959
#>
summary(bbb2)
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: poisson
#> Link function: log
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y
#> Nb_Comp_0 76.61170 78.10821 NA NA NA
#> Nb_Comp_1 65.74449 68.73751 -3.575379e-01 0.0975 -0.3575379
#> Nb_Comp_2 62.35674 66.84626 -1.839690e+00 0.0975 -1.0917941
#> Nb_Comp_3 62.39804 68.38407 -5.114561e+00 0.0975 -1.1532497
#> Nb_Comp_4 64.08113 71.56366 -1.472926e+01 0.0975 -1.5724268
#> Nb_Comp_5 65.63784 74.61689 -3.703721e+01 0.0975 -1.4182454
#> Nb_Comp_6 67.18468 77.66024 -4.278641e+02 0.0975 -10.2748564
#> Nb_Comp_7 68.61004 80.58210 -1.935096e+04 0.0975 -44.1237524
#> Nb_Comp_8 70.54487 84.01344 -4.446747e+07 0.0975 -2296.8277321
#> Nb_Comp_9 72.37296 87.33803 -2.204124e+08 0.0975 -3.9567104
#> PREChi2_Pearson_Y Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 NA 33.75000 24.545455 NA
#> Nb_Comp_1 45.81690 23.89105 12.654950 0.4844280
#> Nb_Comp_2 49.97515 17.31172 8.871122 0.6385839
#> Nb_Comp_3 37.27647 15.51670 8.203709 0.6657748
#> Nb_Comp_4 39.91557 15.31216 7.959332 0.6757309
#> Nb_Comp_5 37.02856 15.51159 7.724832 0.6852846
#> Nb_Comp_6 174.89092 16.30549 6.814620 0.7223673
#> Nb_Comp_7 735.76471 17.52007 6.284737 0.7439552
#> Nb_Comp_8 40258.10319 17.75766 6.160827 0.7490034
#> Nb_Comp_9 88.01959 18.30206 5.831059 0.7624383
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pineNAX21,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> Only naive DoF can be used with missing data
#> ____There are some NAs in X but not in Y____
#> ____TypeVC____ standard ____
#> ____TypeVC____ standard ____unknown____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.69250 0.35639805 0.0975 0.35639805 13.387018 11.099368
#> Nb_Comp_2 58.35228 0.28395028 0.0975 -0.11256611 12.348781 8.885823
#> Nb_Comp_3 56.36553 0.07664889 0.0975 -0.28950699 11.458331 7.874634
#> Nb_Comp_4 54.02416 -0.70355579 0.0975 -0.84497074 14.528469 6.903925
#> Nb_Comp_5 55.80450 -0.94905654 0.0975 -0.14411078 7.898855 6.858120
#> Nb_Comp_6 57.45753 -1.27568315 0.0975 -0.16758190 8.007417 6.786392
#> Nb_Comp_7 58.73951 -1.63309014 0.0975 -0.15705481 7.852227 6.640327
#> Nb_Comp_8 60.61227 -1.67907859 0.0975 -0.01746558 6.756304 6.614773
#> Nb_Comp_9 62.25948 -2.15165796 0.0975 -0.17639623 7.781594 6.544432
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4663804 0.4663804 17.07583 20.59526 0.35639805 0.0975
#> Nb_Comp_2 0.5728001 0.5728001 13.67040 18.99799 -0.11256611 0.0975
#> Nb_Comp_3 0.6214146 0.6214146 12.11473 17.62807 -0.28950699 0.0975
#> Nb_Comp_4 0.6680830 0.6680830 10.62135 22.35133 -0.84497074 0.0975
#> Nb_Comp_5 0.6702851 0.6702851 10.55088 12.15200 -0.14411078 0.0975
#> Nb_Comp_6 0.6737336 0.6737336 10.44053 12.31901 -0.16758190 0.0975
#> Nb_Comp_7 0.6807558 0.6807558 10.21581 12.08026 -0.15705481 0.0975
#> Nb_Comp_8 0.6819844 0.6819844 10.17650 10.39424 -0.01746558 0.0975
#> Nb_Comp_9 0.6853661 0.6853661 10.06828 11.97160 -0.17639623 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 NA 96.63448 NA NA NA NA NA
#> Nb_Comp_1 0.35639805 77.90810 NA NA NA NA NA
#> Nb_Comp_2 0.28395028 72.56787 NA NA NA NA NA
#> Nb_Comp_3 0.07664889 70.58113 NA NA NA NA NA
#> Nb_Comp_4 -0.70355579 68.23976 NA NA NA NA NA
#> Nb_Comp_5 -0.94905654 70.02009 NA NA NA NA NA
#> Nb_Comp_6 -1.27568315 71.67313 NA NA NA NA NA
#> Nb_Comp_7 -1.63309014 72.95511 NA NA NA NA NA
#> Nb_Comp_8 -1.67907859 74.82787 NA NA NA NA NA
#> Nb_Comp_9 -2.15165796 76.47507 NA NA NA NA NA
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 2 0.5983679 0.3797438 0.4122175 -11.413749
#> Nb_Comp_2 3 0.5442372 0.3231208 0.3634169 -12.847656
#> Nb_Comp_3 4 0.5210941 0.3044529 0.3537087 -12.776843
#> Nb_Comp_4 5 0.4965569 0.2839276 0.3398355 -12.891035
#> Nb_Comp_5 6 0.5039885 0.3001871 0.3692997 -11.349498
#> Nb_Comp_6 7 0.5108963 0.3163819 0.3992388 -9.922119
#> Nb_Comp_7 8 0.5153766 0.3300041 0.4263658 -8.696873
#> Nb_Comp_8 9 0.5249910 0.3507834 0.4632727 -7.337679
#> Nb_Comp_9 10 0.5334234 0.3707649 0.4998004 -6.033403
rm(list=c("bbb","bbb2"))
data(pine)
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-family",
family=Gamma,K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: NaNs produced
bbb <- cv.plsRglm(x11~.,data=pine,nt=10,modele="pls-glm-Gamma",
K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -16.792342 0.005236079 0.10926655 -0.21333447 2.301769 -0.4448298
#> [2,] -14.234610 0.006332738 0.04999250 -0.27453618 1.891351 -0.3488068
#> [3,] -5.844789 0.007117475 -0.06469022 0.37920549 1.335298 -0.3061289
#> [4,] -9.932799 0.005322613 0.05942021 -0.07160385 2.053345 -0.4252070
#> [5,] -12.774065 0.006741090 0.03326580 -0.17160692 1.876863 -0.3876373
#> [6,] -13.702999 0.006244324 0.03880042 -0.25984490 1.532138 -0.2949647
#> [7,] -14.210322 0.007292974 0.04353365 -0.22707691 1.832913 -0.3381223
#> [8,] -9.939151 0.005128945 0.03243690 -0.09119951 1.710555 -0.3699950
#> [9,] -10.404425 0.004419537 0.03893195 -0.18407583 1.337004 -0.2877251
#> [10,] -11.697334 0.005560470 0.03743670 -0.17084381 1.694513 -0.3408502
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 2.469793 1.06903574 -0.31839346 1.48350871 0.8169467
#> [2,] 3.828627 -0.67658291 -0.37425278 1.00617016 1.0680206
#> [3,] -3.970980 0.05992600 0.15889027 0.08461725 0.5449955
#> [4,] 0.893972 -0.23958181 -0.19695678 0.60757259 0.3634518
#> [5,] 2.136043 -0.51600999 -0.26135056 1.14341243 0.8057468
#> [6,] 3.153256 -0.46261855 -0.27290448 1.44831281 0.8710504
#> [7,] 3.264836 -0.73899822 -0.36692526 0.96385542 0.8439426
#> [8,] 1.111675 -0.21287594 -0.06494115 0.69672266 0.5415921
#> [9,] 2.212431 0.01529659 -0.06112184 0.96665142 0.3672662
#> [10,] 2.366473 -0.34801334 -0.22585906 0.99273080 0.6957553
boxplot(kfolds2coeff(bbb)[,1])
kfolds2Chisqind(bbb)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.808792 7.389144 37.357823 42.822663 40.651261 32.182244 27.405606
#> [8] 19.943324 21.130738 21.328443
#>
#> [[1]][[2]]
#> [1] 2.504751 2.832532 3.891738 3.326394 3.717222 4.439722 3.872176 4.442990
#> [9] 4.548204 4.543575
#>
#> [[1]][[3]]
#> [1] 7.111664 14.015567 13.460548 9.065039 9.165900 15.536605 21.252966
#> [8] 22.590195 22.753271 22.532481
#>
#> [[1]][[4]]
#> [1] 0.9888071 1.0380953 1.5109132 1.7599616 2.8706034 2.8579693 3.2660505
#> [8] 2.9585912 2.8708790 2.8854578
#>
#> [[1]][[5]]
#> [1] 1.290575 1.517099 2.095550 2.146920 2.067903 2.380557 2.726492 2.898626
#> [9] 2.949727 2.963570
#>
#> [[1]][[6]]
#> [1] 0.8579215 0.9098802 1.1676478 1.5733520 0.8350086 0.7874430 0.7413699
#> [8] 0.8274457 1.2608567 1.2490741
#>
#> [[1]][[7]]
#> [1] 5.368180 5.871824 5.103292 3.566623 2.076320 1.258609 1.163719 1.578068
#> [9] 1.675131 1.848546
#>
#> [[1]][[8]]
#> [1] 0.5809221 0.3984722 0.4063174 0.3905960 0.7137581 0.6295201 0.5258256
#> [8] 0.4425445 0.4416428 0.4417972
#>
#> [[1]][[9]]
#> [1] 1.4802246 1.2855399 1.2226406 1.2563456 1.2601723 1.1563125 0.9961827
#> [8] 0.9637558 0.9623069 0.9632302
#>
#> [[1]][[10]]
#> [1] 0.9228911 1.9663336 1.0890906 1.0790010 1.0428587 0.6505922 0.6577698
#> [8] 0.5757015 0.5376012 0.5715564
#>
#>
kfolds2Chisq(bbb)
#> [[1]]
#> [1] 22.91473 37.22449 67.30556 66.98689 64.40101 61.87957 62.60816 57.22124
#> [9] 59.13036 59.32773
#>
summary(bbb)
#> ____************************************************____
#>
#> Family: Gamma
#> Link function: inverse
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 56.60919 59.60220 NA NA NA NA
#> Nb_Comp_1 39.01090 43.50042 2.750349e-01 0.0975 0.2750349 22.91473
#> Nb_Comp_2 37.30801 43.29404 -5.586218e-01 0.0975 -1.1499267 37.22449
#> Nb_Comp_3 36.87524 44.35777 -5.167057e+00 0.0975 -2.9567375 67.30556
#> Nb_Comp_4 36.55795 45.53700 -2.508982e+01 0.0975 -3.2305135 66.98689
#> Nb_Comp_5 37.13611 47.61167 -1.232138e+02 0.0975 -3.7610086 64.40101
#> Nb_Comp_6 38.27656 50.24862 -5.637695e+02 0.0975 -3.5467515 61.87957
#> Nb_Comp_7 39.39377 52.86234 -2.540713e+03 0.0975 -3.5004430 62.60816
#> Nb_Comp_8 40.96122 55.92630 -9.731400e+03 0.0975 -2.8290712 57.22124
#> Nb_Comp_9 42.90816 59.36974 -3.772214e+04 0.0975 -2.8760368 59.13036
#> Nb_Comp_10 44.90815 62.86625 -1.476674e+05 0.0975 -2.9145302 59.32773
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 31.60805 20.800152 NA
#> Nb_Comp_1 17.31431 11.804594 0.4324756
#> Nb_Comp_2 17.01037 6.357437 0.6943562
#> Nb_Comp_3 15.83422 5.699662 0.7259798
#> Nb_Comp_4 13.52676 7.679741 0.6307844
#> Nb_Comp_5 13.60962 6.099077 0.7067773
#> Nb_Comp_6 13.91155 5.205052 0.7497590
#> Nb_Comp_7 14.94390 4.650377 0.7764258
#> Nb_Comp_8 15.25537 4.321314 0.7922461
#> Nb_Comp_9 15.15577 4.307757 0.7928978
#> Nb_Comp_10 15.15490 4.307391 0.7929154
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pine,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> ____Component____ 10 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.61896 0.38248575 0.0975 0.38248575 12.844390 11.074659
#> Nb_Comp_2 58.47638 0.34836456 0.0975 -0.05525570 11.686597 8.919303
#> Nb_Comp_3 56.55421 0.23688359 0.0975 -0.17107874 10.445206 7.919786
#> Nb_Comp_4 54.35053 0.06999681 0.0975 -0.21869112 9.651773 6.972542
#> Nb_Comp_5 55.99834 -0.07691053 0.0975 -0.15796434 8.073955 6.898523
#> Nb_Comp_6 57.69592 -0.19968885 0.0975 -0.11400977 7.685022 6.835594
#> Nb_Comp_7 59.37953 -0.27722139 0.0975 -0.06462721 7.277359 6.770369
#> Nb_Comp_8 61.21213 -0.30602578 0.0975 -0.02255238 6.923057 6.736112
#> Nb_Comp_9 63.18426 -0.39920228 0.0975 -0.07134354 7.216690 6.730426
#> Nb_Comp_10 65.15982 -0.43743644 0.0975 -0.02732569 6.914340 6.725443
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4675684 0.4675684 17.03781 19.76046 0.38248575 0.0975
#> Nb_Comp_2 0.5711905 0.5711905 13.72190 17.97925 -0.05525570 0.0975
#> Nb_Comp_3 0.6192438 0.6192438 12.18420 16.06943 -0.17107874 0.0975
#> Nb_Comp_4 0.6647841 0.6647841 10.72691 14.84877 -0.21869112 0.0975
#> Nb_Comp_5 0.6683426 0.6683426 10.61304 12.42138 -0.15796434 0.0975
#> Nb_Comp_6 0.6713681 0.6713681 10.51622 11.82303 -0.11400977 0.0975
#> Nb_Comp_7 0.6745039 0.6745039 10.41588 11.19586 -0.06462721 0.0975
#> Nb_Comp_8 0.6761508 0.6761508 10.36317 10.65078 -0.02255238 0.0975
#> Nb_Comp_9 0.6764242 0.6764242 10.35443 11.10252 -0.07134354 0.0975
#> Nb_Comp_10 0.6766638 0.6766638 10.34676 10.63737 -0.02732569 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof
#> Nb_Comp_0 NA 96.63448 1.000000 0.8062287 0.6697018 0.6991787
#> Nb_Comp_1 0.38248575 77.83455 3.176360 0.5994089 0.4047616 0.4565153
#> Nb_Comp_2 0.34836456 72.69198 7.133559 0.5761829 0.4138120 0.5212090
#> Nb_Comp_3 0.23688359 70.76981 8.778329 0.5603634 0.4070516 0.5320535
#> Nb_Comp_4 0.06999681 68.56612 8.427874 0.5221703 0.3505594 0.4547689
#> Nb_Comp_5 -0.07691053 70.21393 9.308247 0.5285695 0.3666578 0.4845912
#> Nb_Comp_6 -0.19968885 71.91152 9.291931 0.5259794 0.3629363 0.4795121
#> Nb_Comp_7 -0.27722139 73.59512 9.756305 0.5284535 0.3702885 0.4938445
#> Nb_Comp_8 -0.30602578 75.42772 10.363948 0.5338475 0.3831339 0.5170783
#> Nb_Comp_9 -0.39920228 77.39986 10.732148 0.5378277 0.3920957 0.5328747
#> Nb_Comp_10 -0.43743644 79.37542 11.000000 0.5407500 0.3987417 0.5446065
#> GMDL.dof DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 -3.605128 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 -9.875081 2 0.5977015 0.3788984 0.4112998 -11.451340
#> Nb_Comp_2 -6.985517 3 0.5452615 0.3243383 0.3647862 -12.822703
#> Nb_Comp_3 -6.260610 4 0.5225859 0.3061986 0.3557368 -12.756838
#> Nb_Comp_4 -8.152986 5 0.4990184 0.2867496 0.3432131 -12.811575
#> Nb_Comp_5 -7.111583 6 0.5054709 0.3019556 0.3714754 -11.329638
#> Nb_Comp_6 -7.233043 7 0.5127450 0.3186757 0.4021333 -9.918688
#> Nb_Comp_7 -6.742195 8 0.5203986 0.3364668 0.4347156 -8.592770
#> Nb_Comp_8 -6.038372 9 0.5297842 0.3572181 0.4717708 -7.287834
#> Nb_Comp_9 -5.600235 10 0.5409503 0.3813021 0.5140048 -6.008747
#> Nb_Comp_10 -5.288422 11 0.5529032 0.4076026 0.5600977 -4.799453
data(pineNAX21)
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=Gamma(),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: NaNs produced
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-Gamma",K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] -21.14485 0.008616130 0.03674745 -0.3581602 1.921926 -0.4081026
#> [2,] -14.59880 0.006080550 0.04715798 -0.2438525 1.489233 -0.2797521
#> [3,] -18.91294 0.006700005 0.01866432 -0.3322595 2.532909 -0.4118893
#> [4,] -12.67608 0.009417885 -0.01566997 0.2352732 1.859993 -0.3336438
#> [5,] -13.34389 0.004652787 0.07361635 -0.1620116 2.834521 -0.5773882
#> [6,] -13.17192 0.005094255 0.04154328 -0.1750301 1.509127 -0.3823807
#> [7,] -14.64076 0.005851807 0.04253858 -0.1765887 1.619940 -0.3502596
#> [8,] -14.05079 0.006416756 0.04498837 -0.1547725 1.853500 -0.3772898
#> [9,] -14.81978 0.005565971 0.03091156 -0.2176143 1.414826 -0.2538928
#> [10,] -16.70714 0.006712930 0.05073523 -0.2151543 2.071076 -0.4216196
#> [,7] [,8] [,9] [,10] [,11]
#> [1,] 5.085774 -1.20930485 -0.11055368 -0.08804458 2.4880806
#> [2,] 3.129944 0.44686098 -0.34791666 1.35572433 0.7578715
#> [3,] 4.666129 -1.54297343 -0.47202093 0.90615274 2.4111899
#> [4,] -2.131302 -0.64364488 -0.40458513 0.81511245 0.7576426
#> [5,] 2.318833 -0.91278561 -0.28582585 0.48299052 1.0030298
#> [6,] 1.722917 0.35048599 0.04318401 1.14731994 0.0239185
#> [7,] 2.522483 -0.06485633 -0.15403482 0.62898504 0.7546944
#> [8,] 2.363459 -0.65334098 -0.21317757 0.36778230 0.7469297
#> [9,] 2.547778 0.02973721 -0.22484351 1.55589269 0.3409361
#> [10,] 2.939051 -0.48799520 -0.26474681 0.85336920 1.1093337
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.5637590 1.9472693 2.4293377 2.4682266 2.6485379 1.8780839 3.0477354
#> [8] 1.9262077 0.9543384
#>
#> [[1]][[2]]
#> [1] 4.025842 3.038711 2.047105 1.733664 13.830160 5.476366 7.459766
#> [8] 162.164516 328.668945
#>
#> [[1]][[3]]
#> [1] 1.353088 1.230745 1.853877 1.652835 2.044960 1.925549 4.037331 5.819196
#> [9] 5.768777
#>
#> [[1]][[4]]
#> [1] 4.367846 8.781205 9.795686 7.331274 7.078643 11.860184 17.346241
#> [8] 17.441595 18.973908
#>
#> [[1]][[5]]
#> [1] 1.5409040 1.2572200 0.8826944 1.4788250 5.1469919 6.9076489 9.1371463
#> [8] 9.3146664 10.0796670
#>
#> [[1]][[6]]
#> [1] 0.995341 1.182147 1.935280 2.606784 3.395031 3.590911 3.551189 3.542292
#> [9] 2.958227
#>
#> [[1]][[7]]
#> [1] 0.9363526 2.0250566 1.3365585 1.3255119 1.2139431 0.7986019 0.7214274
#> [8] 0.7431937 0.7012414
#>
#> [[1]][[8]]
#> [1] 2.3558721 2.4111042 1.2959681 0.8814170 0.9030853 1.1141395 0.8587302
#> [8] 1.2287049 1.3399233
#>
#> [[1]][[9]]
#> [1] 1.744605 1.030181 2.756148 1.911259 1.973422 2.584232 1.308875 1.322855
#> [9] 1.313170
#>
#> [[1]][[10]]
#> [1] 1.391131 2.178484 2.576971 2.933348 2.922503 3.144702 3.639320 4.031659
#> [9] 4.392899
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 20.27474 25.08212 26.90963 24.32314 41.15728 39.28042 51.10776
#> [8] 207.53489 375.15110
#>
summary(bbb2)
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Family: Gamma
#> Link function: inverse
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 56.60919 59.60220 NA NA NA NA
#> Nb_Comp_1 39.08940 43.57892 3.585576e-01 0.0975 0.3585576 20.27474
#> Nb_Comp_2 37.36154 43.34757 7.049318e-02 0.0975 -0.4490885 25.08212
#> Nb_Comp_3 36.81173 44.29427 -4.624220e-01 0.0975 -0.5733311 26.90963
#> Nb_Comp_4 36.53654 45.51559 -1.253337e+00 0.0975 -0.5408256 24.32314
#> Nb_Comp_5 37.24312 47.71867 -5.874748e+00 0.0975 -2.0509186 41.15728
#> Nb_Comp_6 38.18649 50.15855 -1.890119e+01 0.0975 -1.8948253 39.28042
#> Nb_Comp_7 39.35575 52.82432 -7.153151e+01 0.0975 -2.6445807 51.10776
#> Nb_Comp_8 40.86209 55.82716 -9.985920e+02 0.0975 -12.7814859 207.53489
#> Nb_Comp_9 42.80511 59.26669 -2.453992e+04 0.0975 -23.5509407 375.15110
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 31.60805 20.800152 NA
#> Nb_Comp_1 17.30890 12.031518 0.4215659
#> Nb_Comp_2 17.10360 6.183372 0.7027247
#> Nb_Comp_3 15.78579 5.756462 0.7232490
#> Nb_Comp_4 13.49013 7.630460 0.6331536
#> Nb_Comp_5 13.56918 6.303455 0.6969515
#> Nb_Comp_6 14.02295 5.274716 0.7464097
#> Nb_Comp_7 15.05896 4.867806 0.7659726
#> Nb_Comp_8 15.28052 4.317488 0.7924300
#> Nb_Comp_9 15.19429 4.298593 0.7933384
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(x11~.,data=pineNAX21,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> Only naive DoF can be used with missing data
#> ____There are some NAs in X but not in Y____
#> ____TypeVC____ standard ____
#> ____TypeVC____ standard ____unknown____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> ____Component____ 7 ____
#> ____Component____ 8 ____
#> ____Component____ 9 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 9 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y
#> Nb_Comp_0 82.41888 NA NA NA NA 20.800152
#> Nb_Comp_1 63.69250 0.35639805 0.0975 0.35639805 13.387018 11.099368
#> Nb_Comp_2 58.35228 0.28395028 0.0975 -0.11256611 12.348781 8.885823
#> Nb_Comp_3 56.36553 0.07664889 0.0975 -0.28950699 11.458331 7.874634
#> Nb_Comp_4 54.02416 -0.70355579 0.0975 -0.84497074 14.528469 6.903925
#> Nb_Comp_5 55.80450 -0.94905654 0.0975 -0.14411078 7.898855 6.858120
#> Nb_Comp_6 57.45753 -1.27568315 0.0975 -0.16758190 8.007417 6.786392
#> Nb_Comp_7 58.73951 -1.63309014 0.0975 -0.15705481 7.852227 6.640327
#> Nb_Comp_8 60.61227 -1.67907859 0.0975 -0.01746558 6.756304 6.614773
#> Nb_Comp_9 62.25948 -2.15165796 0.0975 -0.17639623 7.781594 6.544432
#> R2_Y R2_residY RSS_residY PRESS_residY Q2_residY LimQ2
#> Nb_Comp_0 NA NA 32.00000 NA NA NA
#> Nb_Comp_1 0.4663804 0.4663804 17.07583 20.59526 0.35639805 0.0975
#> Nb_Comp_2 0.5728001 0.5728001 13.67040 18.99799 -0.11256611 0.0975
#> Nb_Comp_3 0.6214146 0.6214146 12.11473 17.62807 -0.28950699 0.0975
#> Nb_Comp_4 0.6680830 0.6680830 10.62135 22.35133 -0.84497074 0.0975
#> Nb_Comp_5 0.6702851 0.6702851 10.55088 12.15200 -0.14411078 0.0975
#> Nb_Comp_6 0.6737336 0.6737336 10.44053 12.31901 -0.16758190 0.0975
#> Nb_Comp_7 0.6807558 0.6807558 10.21581 12.08026 -0.15705481 0.0975
#> Nb_Comp_8 0.6819844 0.6819844 10.17650 10.39424 -0.01746558 0.0975
#> Nb_Comp_9 0.6853661 0.6853661 10.06828 11.97160 -0.17639623 0.0975
#> Q2cum_residY AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 NA 96.63448 NA NA NA NA NA
#> Nb_Comp_1 0.35639805 77.90810 NA NA NA NA NA
#> Nb_Comp_2 0.28395028 72.56787 NA NA NA NA NA
#> Nb_Comp_3 0.07664889 70.58113 NA NA NA NA NA
#> Nb_Comp_4 -0.70355579 68.23976 NA NA NA NA NA
#> Nb_Comp_5 -0.94905654 70.02009 NA NA NA NA NA
#> Nb_Comp_6 -1.27568315 71.67313 NA NA NA NA NA
#> Nb_Comp_7 -1.63309014 72.95511 NA NA NA NA NA
#> Nb_Comp_8 -1.67907859 74.82787 NA NA NA NA NA
#> Nb_Comp_9 -2.15165796 76.47507 NA NA NA NA NA
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 0.8062287 0.6697018 0.6991787 -3.605128
#> Nb_Comp_1 2 0.5983679 0.3797438 0.4122175 -11.413749
#> Nb_Comp_2 3 0.5442372 0.3231208 0.3634169 -12.847656
#> Nb_Comp_3 4 0.5210941 0.3044529 0.3537087 -12.776843
#> Nb_Comp_4 5 0.4965569 0.2839276 0.3398355 -12.891035
#> Nb_Comp_5 6 0.5039885 0.3001871 0.3692997 -11.349498
#> Nb_Comp_6 7 0.5108963 0.3163819 0.3992388 -9.922119
#> Nb_Comp_7 8 0.5153766 0.3300041 0.4263658 -8.696873
#> Nb_Comp_8 9 0.5249910 0.3507834 0.4632727 -7.337679
#> Nb_Comp_9 10 0.5334234 0.3707649 0.4998004 -6.033403
rm(list=c("bbb","bbb2"))
data(Cornell)
summary(cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,modele="pls",verbose=FALSE))
#> ____************************************************____
#>
#> Model: pls
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8909588 0.0975 0.8909588 51.00912 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.8463898 0.0975 -0.4087353 50.35170 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.5671404 0.0975 -1.8179088 31.18469 4.418081 0.9905556
#> Nb_Comp_4 33.76477 -0.7046676 0.0975 -2.9381536 17.39908 4.309235 0.9907882
#> Nb_Comp_5 33.34373 -5.1246265 0.0975 -2.5928569 15.48247 3.521924 0.9924713
#> Nb_Comp_6 35.25533 NA 0.0975 NA NA 3.496074 0.9925265
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
#>
#> attr(,"class")
#> [1] "summary.cv.plsRmodel"
cv.plsRglm(Y~.,data=Cornell,nt=3,
modele="pls-glm-inverse.gaussian",K=12,verbose=FALSE)
#> Number of repeated crossvalidations:
#> [1] 1
#> Number of folds for each crossvalidation:
#> [1] 12
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=inverse.gaussian,K=12,verbose=FALSE)
#> Number of repeated crossvalidations:
#> [1] 1
#> Number of folds for each crossvalidation:
#> [1] 12
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 8 82.44673 82.50563 82.40858
#> 5 88.06746 88.53727 85.25048
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 11 82.28528 82.07951 82.44527
#> 9 82.32583 82.66131 82.84802
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 2 95.94380 98.36164 98.28510
#> 12 87.18968 88.98872 88.53361
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 4 95.30802 92.66692 91.51834
#> 10 82.47953 83.11466 83.10610
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 3 94.68038 96.79879 97.49218
#> 1 93.43561 94.90620 96.45368
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 6 93.64727 92.15930 92.37209
#> 7 81.85234 81.83027 81.82584
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 8 82.44673 82.50563 82.40858
#> 5 88.06746 88.53727 85.25048
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 7 82.20239 81.91655 82.10715
#> 11 82.43803 81.94278 82.36685
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 1 94.09586 95.21985 95.90520
#> 4 94.47572 93.13652 92.02753
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 10 82.30686 83.12912 83.03868
#> 9 81.89236 82.51683 82.52717
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 12 87.18968 88.98872 88.53361
#> 2 95.94380 98.36164 98.28510
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 6 94.13493 92.01372 92.13219
#> 3 96.52367 97.77654 97.81463
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(),K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 7 81.90468 81.80099 81.86165
#> 1 93.69623 94.94396 95.94943
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 2 97.09841 98.35471 97.69241
#> 4 95.14378 92.16193 91.45998
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 9 82.06973 82.52492 82.68256
#> 12 87.46933 87.90360 89.32176
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 5 88.39491 88.16441 85.28231
#> 6 93.14888 91.74712 92.43230
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 11 82.63660 81.45836 82.07905
#> 3 95.03541 97.95611 97.96361
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 8 81.97836 82.48513 82.34459
#> 10 82.18756 82.96181 82.81149
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 5 88.12776 88.54310 85.32603
#> 10 82.67247 82.97135 83.00083
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 12 87.05810 89.17920 88.66667
#> 3 95.15935 98.49925 98.29314
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 8 82.29409 82.44771 82.38932
#> 6 93.35929 92.34801 92.29057
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 11 82.31956 82.01545 82.35014
#> 1 93.72589 94.82253 95.92674
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 7 81.79662 81.77255 81.88023
#> 9 82.08282 82.45580 82.57525
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 4 95.14378 92.16193 91.45998
#> 2 97.09841 98.35471 97.69241
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 4 94.61069 94.6878 92.64353
#> 6 92.42551 94.1008 93.10934
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 7 81.79662 81.77255 81.88023
#> 9 82.08282 82.45580 82.57525
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 10 82.55172 83.05563 83.04611
#> 1 93.66264 95.12181 96.09819
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 3 95.54239 98.04545 98.06654
#> 8 82.24235 82.35979 82.49361
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 2 95.74052 97.89405 98.00186
#> 11 82.56310 81.59883 82.15787
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 12 86.95192 89.39536 89.96552
#> 5 87.39911 88.86951 86.84356
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 7 82.00848 81.70431 81.82650
#> 2 95.99115 97.79793 97.97055
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 11 82.09018 82.10220 82.32048
#> 10 82.52637 83.25532 83.27621
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 6 93.31880 92.30725 91.80362
#> 12 87.50317 87.66747 89.49735
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 5 88.08780 88.60220 85.35196
#> 9 82.27548 82.42662 82.55342
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 8 82.21740 82.59449 82.51095
#> 4 95.36584 92.58378 91.54210
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 3 94.68038 96.79879 97.49218
#> 1 93.43561 94.90620 96.45368
#>
#>
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(link = "1/mu^2"),K=6,NK=2,verbose=FALSE)$results_kfolds
#> [[1]]
#> [[1]][[1]]
#> [,1] [,2] [,3]
#> 8 82.18031 82.59243 82.48103
#> 12 87.40306 87.79647 89.48632
#>
#> [[1]][[2]]
#> [,1] [,2] [,3]
#> 1 93.69623 94.94396 95.94943
#> 7 81.90468 81.80099 81.86165
#>
#> [[1]][[3]]
#> [,1] [,2] [,3]
#> 9 82.19603 82.39724 82.56306
#> 2 96.05821 97.84569 97.96973
#>
#> [[1]][[4]]
#> [,1] [,2] [,3]
#> 3 95.57967 98.07234 98.07855
#> 10 82.42733 82.91391 83.08196
#>
#> [[1]][[5]]
#> [,1] [,2] [,3]
#> 11 82.34138 82.53805 81.96853
#> 4 95.67965 92.85545 91.69008
#>
#> [[1]][[6]]
#> [,1] [,2] [,3]
#> 6 93.14888 91.74712 92.43230
#> 5 88.39491 88.16441 85.28231
#>
#>
#> [[2]]
#> [[2]][[1]]
#> [,1] [,2] [,3]
#> 2 97.09841 98.35471 97.69241
#> 4 95.14378 92.16193 91.45998
#>
#> [[2]][[2]]
#> [,1] [,2] [,3]
#> 3 95.69562 97.8202 98.24510
#> 5 87.61323 89.0497 85.81831
#>
#> [[2]][[3]]
#> [,1] [,2] [,3]
#> 12 87.40306 87.79647 89.48632
#> 8 82.18031 82.59243 82.48103
#>
#> [[2]][[4]]
#> [,1] [,2] [,3]
#> 9 82.12535 82.56460 82.53338
#> 6 93.45057 92.26382 92.21097
#>
#> [[2]][[5]]
#> [,1] [,2] [,3]
#> 7 82.20239 81.91655 82.10715
#> 11 82.43803 81.94278 82.36685
#>
#> [[2]][[6]]
#> [,1] [,2] [,3]
#> 1 93.66264 95.12181 96.09819
#> 10 82.55172 83.05563 83.04611
#>
#>
bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=10,
modele="pls-glm-inverse.gaussian",keepcoeffs=TRUE,verbose=FALSE)
#For Jackknife computations
kfolds2coeff(bbb2)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1.869017e-04 -4.553089e-04 -5.660398e-05 0.0009027358 2.218935e-05
#> [2,] 8.072612e-02 -7.841236e-02 -8.059362e-02 -0.0844419827 -8.056704e-02
#> [3,] 9.950651e-05 6.063947e-05 3.349715e-05 0.0001030871 5.553751e-05
#> [4,] 1.388570e-04 4.047152e-04 -5.789572e-06 -0.0005984148 1.281348e-05
#> [5,] 4.184020e-04 8.662012e-04 -2.791667e-04 -0.0023317762 -3.214869e-04
#> [,6] [,7] [,8]
#> [1,] -6.507130e-05 -7.137429e-05 -5.643925e-04
#> [2,] -8.060043e-02 -8.064432e-02 -8.033502e-02
#> [3,] 3.123945e-05 -2.142796e-06 -2.646177e-05
#> [4,] -2.663498e-05 -3.872940e-05 -3.725056e-05
#> [5,] -3.190328e-04 -3.460991e-04 1.883699e-04
boxplot(kfolds2coeff(bbb2)[,1])
kfolds2Chisqind(bbb2)
#> [[1]]
#> [[1]][[1]]
#> [1] 1.280882e-05 8.859810e-07 1.508736e-06 1.489118e-06 5.221609e-06
#> [6] 1.434565e-05
#>
#> [[1]][[2]]
#> [1] 8.135250e-06 4.261351e-05 2.765591e-05 7.890466e-06 8.773406e-06
#> [6] 1.853646e-04
#>
#> [[1]][[3]]
#> [1] 2.087888e-06 5.359476e-07 1.750401e-06 1.106243e-06 6.336654e-07
#>
#> [[1]][[4]]
#> [1] 3.091430e-05 1.587497e-05 8.070417e-06 6.269215e-06 6.277834e-06
#> [6] 6.416394e-06
#>
#> [[1]][[5]]
#> [1] 2.258340e-06 2.441722e-06 2.341430e-06 2.221772e-06 2.804888e-06
#> [6] 1.415334e-05
#>
#>
kfolds2Chisq(bbb2)
#> [[1]]
#> [1] 5.620460e-05 6.235213e-05 4.132689e-05 1.897681e-05 2.371140e-05
#>
summary(bbb2)
#> ____************************************************____
#>
#> Family: inverse.gaussian
#> Link function: 1/mu^2
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 81.67928 82.64909 NA NA NA NA
#> Nb_Comp_1 49.90521 51.35993 0.9164838 0.0975 0.9164838 5.620460e-05
#> Nb_Comp_2 31.06918 33.00881 0.8684226 0.0975 -0.5754719 6.235213e-05
#> Nb_Comp_3 28.40632 30.83085 0.2242351 0.0975 -4.8958805 4.132689e-05
#> Nb_Comp_4 27.08522 29.99466 -2.1138412 0.0975 -3.0138981 1.897681e-05
#> Nb_Comp_5 28.46056 31.85490 -19.5988890 0.0975 -5.6152664 2.371140e-05
#> Nb_Comp_6 29.68366 33.56292 NA 0.0975 NA NA
#> Chi2_Pearson_Y RSS_Y R2_Y
#> Nb_Comp_0 6.729783e-04 467.796667 NA
#> Nb_Comp_1 3.957680e-05 32.478677 0.9305710
#> Nb_Comp_2 7.009452e-06 6.020269 0.9871306
#> Nb_Comp_3 4.727777e-06 3.795855 0.9918857
#> Nb_Comp_4 3.584346e-06 2.699884 0.9942285
#> Nb_Comp_5 3.408069e-06 2.598572 0.9944451
#> Nb_Comp_6 3.195402e-06 2.492371 0.9946721
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
PLS_lm_formula(Y~.,data=Cornell,10,typeVC="standard")$InfCrit
#> ____************************************************____
#> ____TypeVC____ standard ____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> ____Component____ 5 ____
#> ____Component____ 6 ____
#> Warning : 1 2 3 4 5 6 7 < 10^{-12}
#> Warning only 6 components could thus be extracted
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#>
#> AIC Q2cum_Y LimQ2_Y Q2_Y PRESS_Y RSS_Y R2_Y
#> Nb_Comp_0 82.01205 NA NA NA NA 467.796667 NA
#> Nb_Comp_1 53.15173 0.8966556 0.0975 0.89665563 48.344150 35.742486 0.9235940
#> Nb_Comp_2 41.08283 0.9175426 0.0975 0.20210989 28.518576 11.066606 0.9763431
#> Nb_Comp_3 32.06411 0.9399676 0.0975 0.27195907 8.056942 4.418081 0.9905556
#> Nb_Comp_4 33.76477 0.9197009 0.0975 -0.33759604 5.909608 4.309235 0.9907882
#> Nb_Comp_5 33.34373 0.9281373 0.0975 0.10506161 3.856500 3.521924 0.9924713
#> Nb_Comp_6 35.25533 0.9232562 0.0975 -0.06792167 3.761138 3.496074 0.9925265
#> R2_residY RSS_residY PRESS_residY Q2_residY LimQ2 Q2cum_residY
#> Nb_Comp_0 NA 11.00000000 NA NA NA NA
#> Nb_Comp_1 0.9235940 0.84046633 1.13678803 0.89665563 0.0975 0.8966556
#> Nb_Comp_2 0.9763431 0.26022559 0.67059977 0.20210989 0.0975 0.9175426
#> Nb_Comp_3 0.9905556 0.10388893 0.18945488 0.27195907 0.0975 0.9399676
#> Nb_Comp_4 0.9907882 0.10132947 0.13896142 -0.33759604 0.0975 0.9197009
#> Nb_Comp_5 0.9924713 0.08281624 0.09068364 0.10506161 0.0975 0.9281373
#> Nb_Comp_6 0.9925265 0.08220840 0.08844125 -0.06792167 0.0975 0.9232562
#> AIC.std DoF.dof sigmahat.dof AIC.dof BIC.dof GMDL.dof
#> Nb_Comp_0 37.010388 1.000000 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 8.150064 2.740749 1.8665281 4.5699686 4.9558156 21.34020
#> Nb_Comp_2 -3.918831 5.085967 1.1825195 2.1075461 2.3949331 27.40202
#> Nb_Comp_3 -12.937550 5.121086 0.7488308 0.8467795 0.9628191 24.40842
#> Nb_Comp_4 -11.236891 5.103312 0.7387162 0.8232505 0.9357846 24.23105
#> Nb_Comp_5 -11.657929 6.006316 0.7096382 0.7976101 0.9198348 28.21184
#> Nb_Comp_6 -9.746328 7.000002 0.7633343 0.9711322 1.1359502 33.18348
#> DoF.naive sigmahat.naive AIC.naive BIC.naive GMDL.naive
#> Nb_Comp_0 1 6.5212706 46.0708838 47.7893514 27.59461
#> Nb_Comp_1 2 1.8905683 4.1699567 4.4588195 18.37545
#> Nb_Comp_2 3 1.1088836 1.5370286 1.6860917 17.71117
#> Nb_Comp_3 4 0.7431421 0.7363469 0.8256118 19.01033
#> Nb_Comp_4 5 0.7846050 0.8721072 0.9964867 24.16510
#> Nb_Comp_5 6 0.7661509 0.8804809 1.0227979 28.64206
#> Nb_Comp_6 7 0.8361907 1.1070902 1.3048716 33.63927
rm(list=c("bbb","bbb2"))
#> Warning: object 'bbb' not found
data(bordeaux)
summary(cv.plsRglm(Quality~.,data=bordeaux,10,
modele="pls-glm-polr",K=7))
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> NK: 1
#> Number of groups : 7
#> 1
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 2
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 3
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 4
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 5
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 6
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> 7
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Predicting X without NA neither in X nor in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ****________________________________________________****
#>
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Component____ 4 ____
#> Warning : 1 2 3 4 < 10^{-12}
#> Warning only 4 components could thus be extracted
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 78.64736 81.70009 NA NA NA NA
#> Nb_Comp_1 36.50286 41.08194 -0.2712984 0.0975 -0.2712984 79.24427
#> Nb_Comp_2 35.58058 41.68602 -7.2493492 0.0975 -5.4889164 60.71369
#> Nb_Comp_3 36.26588 43.89768 -77.8965681 0.0975 -8.5639749 81.95328
#> Nb_Comp_4 38.15799 47.31616 -840.7049976 0.0975 -9.6684615 88.34565
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.333333
#> Nb_Comp_1 9.356521
#> Nb_Comp_2 8.568956
#> Nb_Comp_3 8.281011
#> Nb_Comp_4 8.321689
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
data(bordeauxNA)
summary(cv.plsRglm(Quality~.,data=bordeauxNA,
10,modele="pls-glm-polr",K=10,verbose=FALSE))
#> ____************************************************____
#> Only naive DoF can be used with missing data
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____There are some NAs in X but not in Y____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> Warning : reciprocal condition number of t(cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE])%*%cbind(res$pp,temppp)[XXNA[1,],,drop=FALSE] < 10^{-12}
#> Warning only 3 components could thus be extracted
#> ____Predicting X with NA in X and not in Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 78.64736 81.70009 NA NA NA NA
#> Nb_Comp_1 36.21263 40.79171 -0.952643 0.0975 -0.952643 121.71475
#> Nb_Comp_2 35.29582 41.40126 -19.462740 0.0975 -9.479509 99.07386
#> Nb_Comp_3 35.81623 43.44803 -319.967215 0.0975 -14.685446 129.16455
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.333333
#> Nb_Comp_1 9.454055
#> Nb_Comp_2 8.234674
#> Nb_Comp_3 7.803408
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="logistic",verbose=FALSE))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: logistic
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 78.64736 81.70009 NA NA NA NA
#> Nb_Comp_1 36.50286 41.08194 -0.398239 0.0975 -0.398239 87.15690
#> Nb_Comp_2 35.58058 41.68602 -10.548048 0.0975 -7.258994 77.27545
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.333333
#> Nb_Comp_1 9.356521
#> Nb_Comp_2 8.568956
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="probit",verbose=FALSE))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: probit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 78.64736 81.70009 NA NA NA NA
#> Nb_Comp_1 36.01661 40.59569 -1.784808 0.0975 -1.784808 173.5868
#> Nb_Comp_2 35.13428 41.23972 -32.476519 0.0975 -11.021123 116.8062
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.333496
#> Nb_Comp_1 9.716750
#> Nb_Comp_2 8.549269
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="cloglog",verbose=FALSE))
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: cloglog
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 78.64736 81.70009 NA NA NA NA
#> Nb_Comp_1 36.92722 41.50630 -34871.68 0.0975 -34871.6758 2173779.069
#> Nb_Comp_2 35.54609 41.65153 -6433390.79 0.0975 -183.4823 1904.251
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.334737
#> Nb_Comp_1 10.322134
#> Nb_Comp_2 7.727154
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
suppressWarnings(summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="cauchit",verbose=FALSE)))
#> ____************************************************____
#>
#> Model: pls-glm-polr
#> Method: cauchit
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> NK: 1
#> [[1]]
#> AIC BIC Q2Chisqcum_Y limQ2 Q2Chisq_Y PREChi2_Pearson_Y
#> Nb_Comp_0 79.08163 82.13436 NA NA NA NA
#> Nb_Comp_1 38.11253 42.69161 -0.2568805 0.0975 -0.2568805 78.02523
#> Nb_Comp_2 38.01624 44.12168 -24.1650786 0.0975 -19.0218552 172.04196
#> Chi2_Pearson_Y
#> Nb_Comp_0 62.078483
#> Nb_Comp_1 8.592708
#> Nb_Comp_2 7.421182
#>
#> attr(,"class")
#> [1] "summary.cv.plsRglmmodel"
# }