Package 'plsRglm'

Title: Partial Least Squares Regression for Generalized Linear Models
Description: Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Authors: Frederic Bertrand [cre, aut] , Myriam Maumy-Bertrand [aut]
Maintainer: Frederic Bertrand <[email protected]>
License: GPL-3
Version: 1.7.0
Built: 2026-05-29 19:32:35 UTC
Source: https://github.com/fbertran/plsrglm

Help Index

Akaike and Bayesian Information Criteria and Generalized minimum description length

Description

This function computes the Akaike and Bayesian Information Criteria and the Generalized minimum description length.

Usage

aic.dof(RSS, n, DoF, sigmahat)

bic.dof(RSS, n, DoF, sigmahat)

gmdl.dof(sigmahat, n, DoF, yhat)

Arguments

RSS

vector of residual sum of squares.
n

number of observations.
DoF

vector of Degrees of Freedom. The length of DoF is the same as the length of RSS.
sigmahat

Estimated model error. The length of sigmahat is the same as the length of RSS.
yhat

vector of squared norm of Yhat. The length of yhat is the same as the length of sigmahat.

Details

The gmdl criterion is defined as

gmdl=n2log(S)+DoF2log(F)+12log(n)gmdl=\frac{n}{2}log(S)+\frac{DoF}{2}log(F)+\frac{1}{2}log(n)

with

S=σ^2S=\hat\sigma^2

Value

vector

numerical values of the requested AIC, BIC or GMDL.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

M. Hansen, B. Yu. (2001). Model Selection and Minimum Descripion Length Principle, Journal of the American Statistical Association, 96, 746-774.
N. Kraemer, M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705.
N. Kraemer, M.L. Braun, Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection, Proceedings of the 24th International Conference on Machine Learning, Omni Press, (2007) 441-448.

See Also

plsR.dof for degrees of freedom computation and infcrit.dof for computing information criteria directly from a previously fitted plsR model.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsR(yCornell,XCornell,4)
dof.object <- plsR.dof(modpls)
aic.dof(modpls$RSS,modpls$nr,dof.object$DoF,dof.object$sigmahat)
bic.dof(modpls$RSS,modpls$nr,dof.object$DoF,dof.object$sigmahat)
gmdl.dof(dof.object$sigmahat,modpls$nr,dof.object$DoF,dof.object$yhat)
naive.object <- plsR.dof(modpls,naive=TRUE)
aic.dof(modpls$RSS,modpls$nr,naive.object$DoF,naive.object$sigmahat)
bic.dof(modpls$RSS,modpls$nr,naive.object$DoF,naive.object$sigmahat)
gmdl.dof(naive.object$sigmahat,modpls$nr,naive.object$DoF,naive.object$yhat)

AIC function for plsR models

Description

This function provides AIC computation for an univariate plsR model.

Usage

AICpls(ncomp, residpls, weights = rep.int(1, length(residpls)))

Arguments

ncomp

Number of components
residpls

Residuals of a fitted univariate plsR model
weights

Weights of observations

Details

AIC function for plsR models with univariate response.

Value

real

AIC value

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Baibing Li, Julian Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

loglikpls for loglikelihood computations for plsR models and AIC for AIC computation for a linear models

Examples

data(pine)
ypine <- pine[,11]
Xpine <- pine[,1:10]
(Pinscaled <- as.data.frame(cbind(scale(ypine),scale(as.matrix(Xpine)))))
colnames(Pinscaled)[1] <- "yy"

lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

modpls <- plsR(ypine,Xpine,10)
modpls$Std.Coeffs
lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

AIC(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))
print(logLik(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))

sum(dnorm(modpls$RepY, modpls$Std.ValsPredictY, sqrt(mean(modpls$residY^2)), log=TRUE))
sum(dnorm(Pinscaled$yy,fitted(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)),
sqrt(mean(residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))^2)), log=TRUE))
loglikpls(modpls$residY)
loglikpls(residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(10,residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(10,modpls$residY)

Microsatellites Dataset

Description

This database was collected on patients carrying a colon adenocarcinoma. It has 104 observations on 33 binary qualitative explanatory variables and one response variable y representing the cancer stage according to the to Astler-Coller classification (Astler and Coller, 1954). This dataset has some missing data due to technical limits. A microsattelite is a non-coding DNA sequence.

Format

A data frame with 104 observations on the following 34 variables.

y

the response: a binary vector (Astler-Coller score).

D2S138

a binary vector that indicates whether this microsatellite is altered or not.

D18S61

a binary vector that indicates whether this microsatellite is altered or not.

D16S422

a binary vector that indicates whether this microsatellite is altered or not.

D17S794

a binary vector that indicates whether this microsatellite is altered or not.

D6S264

a binary vector that indicates whether this microsatellite is altered or not.

D14S65

a binary vector that indicates whether this microsatellite is altered or not.

D18S53

a binary vector that indicates whether this microsatellite is altered or not.

D17S790

a binary vector that indicates whether this microsatellite is altered or not.

D1S225

a binary vector that indicates whether this microsatellite is altered or not.

D3S1282

a binary vector that indicates whether this microsatellite is altered or not.

D9S179

a binary vector that indicates whether this microsatellite is altered or not.

D5S430

a binary vector that indicates whether this microsatellite is altered or not.

D8S283

a binary vector that indicates whether this microsatellite is altered or not.

D11S916

a binary vector that indicates whether this microsatellite is altered or not.

D2S159

a binary vector that indicates whether this microsatellite is altered or not.

D16S408

a binary vector that indicates whether this microsatellite is altered or not.

D5S346

a binary vector that indicates whether this microsatellite is altered or not.

D10S191

a binary vector that indicates whether this microsatellite is altered or not.

D13S173

a binary vector that indicates whether this microsatellite is altered or not.

D6S275

a binary vector that indicates whether this microsatellite is altered or not.

D15S127

a binary vector that indicates whether this microsatellite is altered or not.

D1S305

a binary vector that indicates whether this microsatellite is altered or not.

D4S394

a binary vector that indicates whether this microsatellite is altered or not.

D20S107

a binary vector that indicates whether this microsatellite is altered or not.

D1S197

a binary vector that indicates whether this microsatellite is altered or not.

D1S207

a binary vector that indicates whether this microsatellite is altered or not.

D10S192

a binary vector that indicates whether this microsatellite is altered or not.

D3S1283

a binary vector that indicates whether this microsatellite is altered or not.

D4S414

a binary vector that indicates whether this microsatellite is altered or not.

D8S264

a binary vector that indicates whether this microsatellite is altered or not.

D22S928

a binary vector that indicates whether this microsatellite is altered or not.

TP53

a binary vector that indicates whether this microsatellite is altered or not.

D9S171

a binary vector that indicates whether this microsatellite is altered or not.

Source

Weber et al. (2007). Allelotyping analyzes of synchronous primary and metastasis CIN colon cancers identified different subtypes. Int J Cancer, 120(3), pages 524-32.

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Société Française de Statistique, 151(2), pages 1-18.

Examples

data(aze)
str(aze)

As aze without missing values

Description

This is a single imputation of the aze dataset which was collected on patients carrying a colon adenocarcinoma. It has 104 observations on 33 binary qualitative explanatory variables and one response variable y representing the cancer stage according to the to Astler-Coller classification (Astler and Coller, 1954). A microsattelite is a non-coding DNA sequence.

Format

A data frame with 104 observations on the following 34 variables.

y

the response: a binary vector (Astler-Coller score).

D2S138

a binary vector that indicates whether this microsatellite is altered or not.

D18S61

a binary vector that indicates whether this microsatellite is altered or not.

D16S422

a binary vector that indicates whether this microsatellite is altered or not.

D17S794

a binary vector that indicates whether this microsatellite is altered or not.

D6S264

a binary vector that indicates whether this microsatellite is altered or not.

D14S65

a binary vector that indicates whether this microsatellite is altered or not.

D18S53

a binary vector that indicates whether this microsatellite is altered or not.

D17S790

a binary vector that indicates whether this microsatellite is altered or not.

D1S225

a binary vector that indicates whether this microsatellite is altered or not.

D3S1282

a binary vector that indicates whether this microsatellite is altered or not.

D9S179

a binary vector that indicates whether this microsatellite is altered or not.

D5S430

a binary vector that indicates whether this microsatellite is altered or not.

D8S283

a binary vector that indicates whether this microsatellite is altered or not.

D11S916

a binary vector that indicates whether this microsatellite is altered or not.

D2S159

a binary vector that indicates whether this microsatellite is altered or not.

D16S408

a binary vector that indicates whether this microsatellite is altered or not.

D5S346

a binary vector that indicates whether this microsatellite is altered or not.

D10S191

a binary vector that indicates whether this microsatellite is altered or not.

D13S173

a binary vector that indicates whether this microsatellite is altered or not.

D6S275

a binary vector that indicates whether this microsatellite is altered or not.

D15S127

a binary vector that indicates whether this microsatellite is altered or not.

D1S305

a binary vector that indicates whether this microsatellite is altered or not.

D4S394

a binary vector that indicates whether this microsatellite is altered or not.

D20S107

a binary vector that indicates whether this microsatellite is altered or not.

D1S197

a binary vector that indicates whether this microsatellite is altered or not.

D1S207

a binary vector that indicates whether this microsatellite is altered or not.

D10S192

a binary vector that indicates whether this microsatellite is altered or not.

D3S1283

a binary vector that indicates whether this microsatellite is altered or not.

D4S414

a binary vector that indicates whether this microsatellite is altered or not.

D8S264

a binary vector that indicates whether this microsatellite is altered or not.

D22S928

a binary vector that indicates whether this microsatellite is altered or not.

TP53

a binary vector that indicates whether this microsatellite is altered or not.

D9S171

a binary vector that indicates whether this microsatellite is altered or not.

Source

Weber et al. (2007). Allelotyping analyzes of synchronous primary and metastasis CIN colon cancers identified different subtypes. Int J Cancer, 120(3), pages 524-32.

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparing the linear and the logistic PLS regression with qualitative predictors: application to allelotyping data. Journal de la Société Française de Statistique, 151(2), pages 1-18.

Examples

data(aze_compl)
str(aze_compl)

Non-parametric Bootstrap for PLS models

Description

Provides a wrapper for the bootstrap function boot from the boot R package.
Implements non-parametric bootstraps for PLS Regression models by either (Y,X) or (Y,T) resampling.

Usage

bootpls(
  object,
  typeboot = "plsmodel",
  R = 250,
  statistic = NULL,
  sim = "ordinary",
  stype = "i",
  stabvalue = 1e+06,
  verbose = TRUE,
  ...
)

Arguments

object

An object of class plsRmodel or plsRmultiModel to bootstrap.
typeboot

The type of bootstrap. Either (Y,X) boostrap (typeboot="plsmodel") or (Y,T) bootstrap (typeboot="fmodel_np"). Defaults to (Y,X) resampling.
R

The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
statistic

A function which when applied to data returns a vector containing the statistic(s) of interest. statistic must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the ... argument.
sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".
stype

A character string indicating what the second argument of statistic represents. Possible values of stype are "i" (indices - the default), "f" (frequencies), or "w" (weights).
stabvalue

A value to hard threshold bootstrap estimates computed from atypical resamplings. Especially useful for Generalized Linear Models.
verbose

should info messages be displayed ?
...

Other named arguments for statistic which are passed unchanged each time it is called. Any such arguments to statistic should follow the arguments which statistic is required to have for the simulation. Beware of partial matching to arguments of boot listed above.

Details

More details on bootstrap techniques are available in the help of the boot function.

Value

An object of class "boot". See the Value part of the help of the function boot.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

A. Lazraq, R. Cleroux, and J.-P. Gauchi. (2003). Selecting both latent and explanatory variables in the PLS1 regression model. Chemometrics and Intelligent Laboratory Systems, 66(2):117-126.
P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.
A. C. Davison and D. V. Hinkley. (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge.

See Also

boot

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS ordinary bootstrap
set.seed(250)
modpls <- plsR(yCornell,XCornell,3)

#(Y,X) resampling
Cornell.bootYX <- bootpls(modpls, R=250, verbose=FALSE)

#(Y,T) resampling
Cornell.bootYT <- bootpls(modpls, typeboot="fmodel_np", R=250, verbose=FALSE)

# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.bootYX,indices=2:8)
# Confidence intervals plotting
confints.bootpls(Cornell.bootYX,indices=2:8)
plots.confints.bootpls(confints.bootpls(Cornell.bootYX,indices=2:8))
# Graph similar to the one of Bastien et al. in CSDA 2005
boxplot(as.vector(Cornell.bootYX$t[,-1])~factor(rep(1:7,rep(250,7))), 
main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.bootYX$t0[-1],col="red",pch=19)



library(boot)
boot.ci(Cornell.bootYX, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), index=2)
plot(Cornell.bootYX,index=2)
jack.after.boot(Cornell.bootYX, index=2, useJ=TRUE, nt=3)
plot(Cornell.bootYX,index=2,jack=TRUE)

car::dataEllipse(Cornell.bootYX$t[,2], Cornell.bootYX$t[,3], cex=.3, 
levels=c(.5, .95, .99), robust=TRUE)
rm(Cornell.bootYX)


# PLS balanced bootstrap

set.seed(225)
Cornell.bootYX <- bootpls(modpls, sim="balanced", R=250, verbose=FALSE)
boot.array(Cornell.bootYX, indices=TRUE)

# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.bootYX,indices=2:8)
# Confidence intervals plotting
confints.bootpls(Cornell.bootYX,indices=2:8)
plots.confints.bootpls(confints.bootpls(Cornell.bootYX,indices=2:8))
# Graph similar to the one of Bastien et al. in CSDA 2005
boxplot(as.vector(Cornell.bootYX$t[,-1])~factor(rep(1:7,rep(250,7))), 
main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.bootYX$t0[-1],col="red",pch=19)


library(boot)
boot.ci(Cornell.bootYX, conf = c(0.90,0.95), type = c("norm","basic","perc","bca"), 
index=2, verbose=FALSE)
plot(Cornell.bootYX,index=2)
jack.after.boot(Cornell.bootYX, index=2, useJ=TRUE, nt=3)
plot(Cornell.bootYX,index=2,jack=TRUE)

rm(Cornell.bootYX)

# PLS permutation bootstrap

set.seed(500)
Cornell.bootYX <- bootpls(modpls, sim="permutation", R=1000, verbose=FALSE)
boot.array(Cornell.bootYX, indices=TRUE)


# Graph of bootstrap distributions
boxplot(as.vector(Cornell.bootYX$t[,-1])~factor(rep(1:7,rep(1000,7))),
main="Bootstrap distributions of standardised bj (j = 1, ..., 7).")
points(c(1:7),Cornell.bootYX$t0[-1],col="red",pch=19)
# Using the boxplots.bootpls function
boxplots.bootpls(Cornell.bootYX,indices=2:8)


library(boot)
plot(Cornell.bootYX,index=2)

qqnorm(Cornell.bootYX$t[,2],ylim=c(-1,1))
abline(h=Cornell.bootYX$t0[2],lty=2)
(sum(abs(Cornell.bootYX$t[,2])>=abs(Cornell.bootYX$t0[2]))+1)/(length(Cornell.bootYX$t[,2])+1)

rm(Cornell.bootYX)

Non-parametric Bootstrap for PLS generalized linear models

Description

Provides a wrapper for the bootstrap function boot from the boot R package.
Implements non-parametric bootstraps for PLS Generalized Linear Regression models by either (Y,X) or (Y,T) resampling.

Usage

bootplsglm(
  object,
  typeboot = "fmodel_np",
  R = 250,
  statistic = NULL,
  sim = "ordinary",
  stype = "i",
  stabvalue = 1e+06,
  verbose = TRUE,
  ...
)

Arguments

object

An object of class plsRglmmodel to bootstrap
typeboot

The type of bootstrap. Either (Y,X) boostrap (typeboot="plsmodel") or (Y,T) bootstrap (typeboot="fmodel_np"). Defaults to (Y,T) resampling.
R

The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
statistic

A function which when applied to data returns a vector containing the statistic(s) of interest. statistic must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the ... argument.
sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".
stype

A character string indicating what the second argument of statistic represents. Possible values of stype are "i" (indices - the default), "f" (frequencies), or "w" (weights).
stabvalue

A value to hard threshold bootstrap estimates computed from atypical resamplings. Especially useful for Generalized Linear Models.
verbose

should info messages be displayed ?
...

Other named arguments for statistic which are passed unchanged each time it is called. Any such arguments to statistic should follow the arguments which statistic is required to have for the simulation. Beware of partial matching to arguments of boot listed above.

Details

More details on bootstrap techniques are available in the help of the boot function.

Value

An object of class "boot". See the Value part of the help of the function boot.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

A. Lazraq, R. Cleroux, and J.-P. Gauchi. (2003). Selecting both latent and explanatory variables in the PLS1 regression model. Chemometrics and Intelligent Laboratory Systems, 66(2):117-126.
P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.
A. C. Davison and D. V. Hinkley. (1997). Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge.

See Also

boot

Examples

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

dataset <- cbind(y=yaze_compl,Xaze_compl)
modplsglm <- plsRglm(y~.,data=dataset,3,modele="pls-glm-logistic")

library(boot)
# Bastien (Y,T) PLS bootstrap
aze_compl.bootYT <- bootplsglm(modplsglm, R=250, verbose=FALSE)
boxplots.bootpls(aze_compl.bootYT)
confints.bootpls(aze_compl.bootYT)
plots.confints.bootpls(confints.bootpls(aze_compl.bootYT))


# (Y,X) PLS bootstrap
aze_compl.bootYX <- bootplsglm(modplsglm, R=250, verbose=FALSE, 
typeboot = "plsmodel")
boxplots.bootpls(aze_compl.bootYX)
confints.bootpls(aze_compl.bootYX)
plots.confints.bootpls(confints.bootpls(aze_compl.bootYX))

# (Y,X) PLS bootstrap raw coefficients
aze_compl.bootYX.raw <- bootplsglm(modplsglm, R=250, verbose=FALSE, 
typeboot = "plsmodel", statistic=coefs.plsRglm.raw)
boxplots.bootpls(aze_compl.bootYX.raw)
confints.bootpls(aze_compl.bootYX.raw)
plots.confints.bootpls(confints.bootpls(aze_compl.bootYX.raw))

plot(aze_compl.bootYT,index=2)
jack.after.boot(aze_compl.bootYT, index=2, useJ=TRUE, nt=3)
plot(aze_compl.bootYT, index=2,jack=TRUE)
aze_compl.tilt.boot <- tilt.bootplsglm(modplsglm, statistic=coefs.plsRglm, 
R=c(499, 100, 100), alpha=c(0.025, 0.975), sim="ordinary", stype="i", index=1)

# PLS bootstrap balanced
aze_compl.bootYT <- bootplsglm(modplsglm, sim="balanced", R=250, verbose=FALSE)
boxplots.bootpls(aze_compl.bootYT)
confints.bootpls(aze_compl.bootYT)
plots.confints.bootpls(confints.bootpls(aze_compl.bootYT))


plot(aze_compl.bootYT)
jack.after.boot(aze_compl.bootYT, index=1, useJ=TRUE, nt=3)
plot(aze_compl.bootYT,jack=TRUE)
aze_compl.tilt.boot <- tilt.bootplsglm(modplsglm, statistic=coefs.plsR,
R=c(499, 100, 100), alpha=c(0.025, 0.975), sim="balanced", stype="i", index=1)


# PLS permutation bootstrap

aze_compl.bootYT <- bootplsglm(modplsglm, sim="permutation", R=250, verbose=FALSE)
boxplots.bootpls(aze_compl.bootYT)
plot(aze_compl.bootYT)


#Original aze dataset with missing values
data(aze)
Xaze<-aze[,2:34]
yaze<-aze$y

library(boot)
modplsglm2 <- plsRglm(yaze,Xaze,3,modele="pls-glm-logistic")
aze.bootYT <- bootplsglm(modplsglm2, R=250, verbose=FALSE)
boxplots.bootpls(aze.bootYT)
confints.bootpls(aze.bootYT)
plots.confints.bootpls(confints.bootpls(aze.bootYT))




#Ordinal logistic regression
data(bordeaux)
Xbordeaux<-bordeaux[,1:4]
ybordeaux<-factor(bordeaux$Quality,ordered=TRUE)
dataset <- cbind(y=ybordeaux,Xbordeaux)
options(contrasts = c("contr.treatment", "contr.poly"))
modplsglm3 <- plsRglm(ybordeaux,Xbordeaux,1,modele="pls-glm-polr")
bordeaux.bootYT<- bootplsglm(modplsglm3, sim="permutation", R=250, verbose=FALSE)
boxplots.bootpls(bordeaux.bootYT)
boxplots.bootpls(bordeaux.bootYT,ranget0=TRUE)

bordeaux.bootYT2<- bootplsglm(modplsglm3, sim="permutation", R=250, 
strata=unclass(ybordeaux), verbose=FALSE)
boxplots.bootpls(bordeaux.bootYT2,ranget0=TRUE)


if(require(chemometrics)){
data(hyptis)
hyptis
yhyptis <- factor(hyptis$Group,ordered=TRUE)
Xhyptis <- as.data.frame(hyptis[,c(1:6)])
dataset <- cbind(y=yhyptis,Xhyptis)
options(contrasts = c("contr.treatment", "contr.poly"))
modplsglm4 <- plsRglm(yhyptis,Xhyptis,3,modele="pls-glm-polr")
hyptis.bootYT3<- bootplsglm(modplsglm4, sim="permutation", R=250, verbose=FALSE)
rownames(hyptis.bootYT3$t0)<-c("Sabi\nnene","Pin\nene",
"Cine\nole","Terpi\nnene","Fenc\nhone","Terpi\nnolene")
boxplots.bootpls(hyptis.bootYT3)
boxplots.bootpls(hyptis.bootYT3,xaxisticks=FALSE)
boxplots.bootpls(hyptis.bootYT3,ranget0=TRUE)
boxplots.bootpls(hyptis.bootYT3,ranget0=TRUE,xaxisticks=FALSE)
}

Quality of wine dataset

Description

Quality of Bordeaux wines (Quality) and four potentially predictive variables (Temperature, Sunshine, Heat and Rain).

Format

A data frame with 34 observations on the following 5 variables.

Temperature

a numeric vector

Sunshine

a numeric vector

Heat

a numeric vector

Rain

a numeric vector

Quality

an ordered factor with levels 1 < 2 < 3

Source

P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.

References

M. Tenenhaus. (2005). La regression logistique PLS. In J.-J. Droesbeke, M. Lejeune, and G. Saporta, editors, Modeles statistiques pour donnees qualitatives. Editions Technip, Paris.

Examples

data(bordeaux)
str(bordeaux)

Quality of wine dataset

Description

Quality of Bordeaux wines (Quality) and four potentially predictive variables (Temperature, Sunshine, Heat and Rain).

Format

A data frame with 34 observations on the following 5 variables.

Temperature

a numeric vector

Sunshine

a numeric vector

Heat

a numeric vector

Rain

a numeric vector

Quality

an ordered factor with levels 1 < 2 < 3

Details

The value of x1 for the first observation was removed from the matrix of predictors on purpose.

The bordeauxNA is a dataset with a missing value for testing purpose.

Source

P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.

References

M. Tenenhaus. (2005). La regression logistique PLS. In J.-J. Droesbeke, M. Lejeune, and G. Saporta, editors, Modeles statistiques pour donnees qualitatives. Editions Technip, Paris.

Examples

data(bordeauxNA)
str(bordeauxNA)

Boxplot bootstrap distributions

Description

Boxplots for bootstrap distributions.

Usage

boxplots.bootpls(
  bootobject,
  indices = NULL,
  prednames = TRUE,
  articlestyle = TRUE,
  xaxisticks = TRUE,
  ranget0 = FALSE,
  las = par("las"),
  mar,
  mgp,
  ...
)

Arguments

bootobject

a object of class "boot"
indices

vector of indices of the variables to plot. Defaults to NULL: all the predictors will be used.
prednames

do the original names of the predictors shall be plotted ? Defaults to TRUE: the names are plotted.
articlestyle

do the extra blank zones of the margin shall be removed from the plot ? Defaults to TRUE: the margins are removed.
xaxisticks

do ticks for the x axis shall be plotted ? Defaults to TRUE: the ticks are plotted.
ranget0

does the vertival range of the plot shall be computed to include the initial estimates of the coefficients ? Defaults to FALSE: the vertical range is calculated only using the bootstrapped values of the statistics. Especially using for permutation bootstrap.
las

numeric in 0,1,2,3; the style of axis labels. 0: always parallel to the axis [default], 1: always horizontal, 2: always perpendicular to the axis, 3: always vertical.
mar

A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(5, 4, 4, 2) + 0.1.
mgp

The margin line (in mex units) for the axis title, axis labels and axis line. Note that mgp[1] affects title whereas mgp[2:3] affect axis. The default is c(3, 1, 0).
...

further options to pass to the boxplot function.

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

bootpls

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS ordinary bootstrap
set.seed(250)
modpls <- plsR(yCornell,XCornell,3)
Cornell.bootYX <- bootpls(modpls, R=250)

# Graph similar to the one of Bastien et al. in CSDA 2005
boxplots.bootpls(Cornell.bootYX,indices=2:8)


data(aze_compl)
modplsglm<-plsRglm(y~.,data=aze_compl,3,modele="pls-glm-logistic")
aze_compl.boot3 <- bootplsglm(modplsglm, R=250, verbose=FALSE)
boxplots.bootpls(aze_compl.boot3)
boxplots.bootpls(aze_compl.boot3,las=3,mar=c(5,2,1,1))
boxplots.bootpls(aze_compl.boot3,indices=c(2,4,6),prednames=FALSE)

Grouped biplot for PLS scores and loadings

Description

This helper plots the individuals and predictors from a fitted plsR or plsRglm model while coloring the individuals according to a grouping variable.

Usage

classbiplot(
  object,
  group = NULL,
  comps = 1:2,
  col,
  colvar = "gray30",
  pch = 19,
  cex = rep(par("cex"), 2),
  xlabs = NULL,
  ylabs = NULL,
  point.labels = FALSE,
  show.legend = TRUE,
  legendpos = "topright",
  var.axes = TRUE,
  expand = 1,
  xlim = NULL,
  ylim = NULL,
  arrow.len = 0.1,
  main = NULL,
  sub = NULL,
  xlab = NULL,
  ylab = NULL,
  ...
)

Arguments

object

an object containing score and loading matrices in object$tt and object$pp, typically returned by plsR or plsRglm.
group

optional grouping vector for the individuals. When supplied, observations are colored according to the levels of group.
comps

integer vector of length 2 giving the components to display.
col

colors for the individuals. If group is provided, col can have length 1, the number of groups, or the number of individuals.
colvar

color used for variable labels, arrows and axes.
pch

plotting character for the individuals.
cex

character expansion. Length 1 is recycled to length 2: the first value is used for the individuals and the second one for the variables.
xlabs

optional labels for the individuals.
ylabs

optional labels for the variables.
point.labels

shall the individuals be displayed using text labels instead of points? Defaults to FALSE.
show.legend

shall a legend be added when group is provided? Defaults to TRUE.
legendpos

position of the legend as in legend, defaults to "topright".
var.axes

shall arrows be drawn for the variables? Defaults to TRUE.
expand

expansion factor for the variables layer, as in biplot. Defaults to 1.
xlim, ylim

limits for the scores panel. When both are missing they are chosen symmetrically as in biplot.
arrow.len

length of the arrows for the variables.
main, sub, xlab, ylab

usual graphical parameters passed to plot.
...

further graphical parameters passed to plot for the scores panel.

Value

Invisibly returns a list with the scores, loadings, colors, grouping factor and scaling ratio used in the plot.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

plsR, plsRglm, biplot

Examples

data(Cornell)
modpls <- plsR(Y ~ ., data = Cornell, nt = 2)
grp <- factor(Cornell$Y > median(Cornell$Y), labels = c("Low", "High"))
classbiplot(modpls, group = grp, col = c("firebrick3", "steelblue3"))

coef method for plsR models

Description

This function provides a coef method for the class "plsRglmmodel"

Usage

## S3 method for class 'plsRglmmodel'
coef(object, type = c("scaled", "original"), ...)

Arguments

object

an object of the class "plsRglmmodel"
type

if scaled, the coefficients of the predictors are given for the scaled predictors, if original the coefficients are to be used with the predictors on their original scale.
...

not used

Value

An object of class coef.plsRglmmodel.

CoeffC

Coefficients of the components.
Std.Coeffs

Coefficients of the scaled predictors in the regression function.
Coeffs

Coefficients of the untransformed predictors (on their original scale).

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

coef

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsRglm(yCornell,XCornell,3,modele="pls-glm-family",family=gaussian())
class(modpls)
coef(modpls)
coef(modpls,type="scaled")
rm(list=c("XCornell","yCornell","modpls"))

coef method for plsR models

Description

This function provides a coef method for the class "plsRmodel"

Usage

## S3 method for class 'plsRmodel'
coef(object, type = c("scaled", "original"), ...)

Arguments

object

an object of the class "plsRmodel"
type

if scaled, the coefficients of the predictors are given for the scaled predictors, if original the coefficients are to be used with the predictors on their original scale.
...

not used

Value

An object of class coef.plsRmodel.

CoeffC

Coefficients of the components.
Std.Coeffs

Coefficients of the scaled predictors.
Coeffs

Coefficients of the untransformed predictors (on their original scale).

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

coef

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsRglm(yCornell,XCornell,3,modele="pls")
class(modpls)
coef(modpls)
coef(modpls,type="scaled")
rm(list=c("XCornell","yCornell","modpls"))

Coefficients for bootstrap computations of PLSR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsR(dataset, ind, nt, modele, maxcoefvalues, ifbootfail, verbose)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsR
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
# statistic=coefs.plsR is the default for (Y,X) resampling of PLSR models.
set.seed(250)
modpls <- plsR(yCornell,XCornell,1)
Cornell.bootYX <- bootpls(modpls, R=250, statistic=coefs.plsR, verbose=FALSE)

Raw coefficients for bootstrap computations of PLSR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsR.raw(dataset, ind, nt, modele, maxcoefvalues, ifbootfail, verbose)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsR
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
set.seed(250)
modpls <- coefs.plsR.raw(Cornell[,-8],1:nrow(Cornell),nt=3,
maxcoefvalues=1e5,ifbootfail=rep(0,3),verbose=FALSE)

Coefficients for bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsRglm(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  fit_backend = "stats",
  maxcoefvalues,
  ifbootfail,
  verbose
)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" or "fastglm".
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm.

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
# statistic=coefs.plsRglm is the default for (Y,X) bootstrap of a PLSGLR models.
set.seed(250)
modplsglm <- plsRglm(Y~.,data=Cornell,1,modele="pls-glm-family",family=gaussian)
Cornell.bootYX <- bootplsglm(modplsglm, R=250, typeboot="plsmodel", 
statistic=coefs.plsRglm, verbose=FALSE)

Raw coefficients for bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsRglm.raw(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  fit_backend = "stats",
  maxcoefvalues,
  ifbootfail,
  verbose
)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" or "fastglm".
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm.

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
set.seed(250)
modplsglm <- coefs.plsRglm.raw(Cornell[,-8],1:nrow(Cornell),nt=3,
modele="pls-glm-family",family=gaussian,maxcoefvalues=1e5,
ifbootfail=rep(0,3),verbose=FALSE)

Coefficients for bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsRglmnp(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = NULL,
  maxcoefvalues,
  wwetoile,
  ifbootfail
)

Arguments

dataRepYtt

components' coordinates to bootstrap
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
wwetoile

values of the Wstar matrix in the original fit
ifbootfail

value to return if the estimation fails on a bootstrap sample

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Note

~~some notes~~

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
# statistic=coefs.plsRglm is the default for (Y,X) bootstrap of a PLSGLR models.
set.seed(250)
modplsglm <- plsRglm(Y~.,data=Cornell,1,modele="pls-glm-family",family=gaussian)
Cornell.bootYT <- bootplsglm(modplsglm, R=250, statistic=coefs.plsRglmnp, verbose=FALSE)

Coefficients for bootstrap computations of PLSR models

Description

A function passed to boot to perform bootstrap.

Usage

coefs.plsRnp(dataRepYtt, ind, nt, modele, maxcoefvalues, wwetoile, ifbootfail)

Arguments

dataRepYtt

components' coordinates to bootstrap
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
wwetoile

values of the Wstar matrix in the original fit
ifbootfail

value to return if the estimation fails on a bootstrap sample

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
# statistic=coefs.plsR is the default for (Y,X) resampling of PLSR models.
set.seed(250)
modpls <- plsR(yCornell,XCornell,1)
Cornell.bootYT <- bootpls(modpls, R=250, typeboot="fmodel_np",
statistic=coefs.plsRnp, verbose=FALSE)

Bootstrap confidence intervals

Description

This function is a wrapper for boot.ci to derive bootstrap-based confidence intervals from a "boot" object.

Usage

confints.bootpls(bootobject, indices = NULL, typeBCa = TRUE)

Arguments

bootobject

an object of class "boot"
indices

the indices of the predictor for which CIs should be calculated. Defaults to NULL: all the predictors will be used.
typeBCa

shall BCa bootstrap based CI derived ? Defaults to TRUE. This is a safety option since sometimes computing BCa bootstrap based CI fails whereas the other types of CI can still be derived.

Value

Matrix with the limits of bootstrap based CI for all (defaults) or only the selected predictors (indices option). The limits are given in that order: Normal Lower then Upper Limit, Basic Lower then Upper Limit, Percentile Lower then Upper Limit, BCa Lower then Upper Limit.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls and bootplsglm.

Examples

data(Cornell)

#Lazraq-Cleroux PLS (Y,X) bootstrap
set.seed(250)
modpls <- plsR(Y~.,data=Cornell,3)
Cornell.bootYX <- bootpls(modpls, R=250, verbose=FALSE)
confints.bootpls(Cornell.bootYX,2:8)
confints.bootpls(Cornell.bootYX,2:8,typeBCa=FALSE)

Correlation matrix for simulating plsR datasets

Description

A correlation matrix to simulate datasets

Format

A data frame with 17 observations on the following 17 variables.

y

a numeric vector

x11

a numeric vector

x12

a numeric vector

x13

a numeric vector

x21

a numeric vector

x22

a numeric vector

x31

a numeric vector

x32

a numeric vector

x33

a numeric vector

x34

a numeric vector

x41

a numeric vector

x42

a numeric vector

x51

a numeric vector

x61

a numeric vector

x62

a numeric vector

x63

a numeric vector

x64

a numeric vector

Source

Handmade.

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

Examples

data(CorMat)
str(CorMat)

Cornell dataset

Description

The famous Cornell dataset. A mixture experiment on X1, X2, X3, X4, X5, X6 and X7 to analyse octane degree (Y) in gazoline.

Format

A data frame with 12 observations on the following 8 variables.

X1

a numeric vector

X2

a numeric vector

X3

a numeric vector

X4

a numeric vector

X5

a numeric vector

X6

a numeric vector

X7

a numeric vector

Y

response value: a numeric vector

Source

M. Tenenhaus. (1998). La regression PLS, Theorie et pratique. Editions Technip, Paris.

References

N. Kettaneh-Wold. Analysis of mixture data with partial least squares. (1992). Chemometrics and Intelligent Laboratory Systems, 14(1):57-69.

Examples

data(Cornell)
str(Cornell)

Partial least squares regression models with k-fold cross-validation

Description

This function implements k-fold cross-validation on complete or incomplete datasets for partial least squares regression models

Usage

cv.plsR(object, ...)
## Default S3 method:
cv.plsRmodel(object,dataX,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights, verbose=TRUE,...)
## S3 method for class 'formula'
cv.plsRmodel(object,data=NULL,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights,subset,contrasts=NULL, verbose=TRUE,...)
PLS_lm_kfoldcv(dataY, dataX, nt = 2, limQ2set = 0.0975, modele = "pls", 
K = 5, NK = 1, grouplist = NULL, random = TRUE, scaleX = TRUE, 
scaleY = NULL, keepcoeffs = FALSE, keepfolds = FALSE, keepdataY = TRUE, 
keepMclassed=FALSE, tol_Xi = 10^(-12), weights, verbose=TRUE)
PLS_lm_kfoldcv_formula(formula,data=NULL,nt=2,limQ2set=.0975,modele="pls", 
K=5, NK=1, grouplist=NULL, random=TRUE, scaleX=TRUE, 
scaleY=NULL, keepcoeffs=FALSE, keepfolds=FALSE, keepdataY=TRUE, 
keepMclassed=FALSE, tol_Xi=10^(-12), weights,subset,contrasts=NULL,verbose=TRUE)

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.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which plsRglm is called.
nt

number of components to be extracted
limQ2set

limit value for the Q2
modele

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

number of groups. Defaults to 5.
NK

number of times the group division is made
grouplist

to specify the members of the K groups
random

should the K groups be made randomly. Defaults to TRUE
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
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
subset

an optional vector specifying a subset of observations to be used in the fitting process.
contrasts

an optional list. See the contrasts.arg of model.matrix.default.
verbose

should info messages be displayed ?
...

arguments to pass to cv.plsRmodel.default or to cv.plsRmodel.formula

Details

Predicts 1 group with the K-1 other groups. Leave one out cross validation is thus obtained for K==nrow(dataX).

A typical predictor has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with any duplicates removed.

A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

The terms in the formula will be 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.plsRmodel".

results_kfolds

list of NK. Each element of the list sums up the results for a group division:

list

of K matrices of size about nrow(dataX)/K * nt with the predicted values for a growing number of components

...

...

list

of K matrices of size about nrow(dataX)/K * nt with the predicted values for a growing number of components
folds

list of NK. Each element of the list sums up the results for a group division:

list

of K vectors of length about nrow(dataX) with the numbers of the rows of dataX that were used as a training set

...

...

list

of K vectors of length about nrow(dataX) with the numbers of the rows of dataX that 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 K matrices of size about nrow(dataX)/K * 1 with the observed values of the response

...

...

list

of K matrices of size about nrow(dataX)/K * 1 with the observed values of the response
call

the call of the function

Note

Work for complete and incomplete datasets.

Author(s)

Frederic Bertrand
[email protected]
https://fbertran.github.io/homepage/

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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

Summary method summary.cv.plsRmodel. kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and kfolds2CVinfos_lm to extract and transform results from k-fold cross-validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

#Leave one out CV (K=nrow(Cornell)) one time (NK=1)
bbb <- cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=nrow(Cornell),NK=1)
bbb2 <- cv.plsR(Y~.,data=Cornell,nt=6,K=12,NK=1,verbose=FALSE)
(sum1<-summary(bbb2))

#6-fold CV (K=6) two times (NK=2)
#use random=TRUE to randomly create folds for repeated CV
bbb3 <- cv.plsR(object=yCornell,dataX=XCornell,nt=6,K=6,NK=2)
bbb4 <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=2,verbose=FALSE)
(sum3<-summary(bbb4))

cvtable(sum1)
cvtable(sum3)
rm(list=c("XCornell","yCornell","bbb","bbb2","bbb3","bbb4"))

Partial least squares regression glm models with k-fold cross validation

Description

This 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,
fit_backend="stats",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,
fit_backend="stats",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,
fit_backend="stats",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, fit_backend="stats",
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.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which plsRglm is 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 the family option.
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 family for details of family functions.) To use the family option, please set modele="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 K groups
random

should the K groups be made randomly. Defaults to TRUE
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 det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
fit_backend

backend used for repeated non-ordinal score-space GLM fits during cross-validation. Use "stats" for the compatibility path or "fastglm" to opt into the accelerated complete-data backend. Unsupported cases fall back to "stats" with a warning.
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 NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.
method

For non-ordinal GLM modes this argument is kept for backward compatibility; use fit_backend to choose the score-space fitting backend. For pls-glm-polr, use logistic, probit, complementary log-log or cauchit.
control

a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.
contrasts

an optional list. See the contrasts.arg of model.matrix.default.
verbose

should info messages be displayed ?
...

arguments to pass to cv.plsRglmmodel.default or to cv.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 gaussian family

accepts the links (as names) identity, log and inverse.

The binomial family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The Gamma family

accepts the links inverse, identity and log.

The poisson family

accepts the links log, identity, and sqrt.

The inverse.gaussian family

accepts the links 1/mu^2, inverse, identity and log.

The quasi family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function power

can be used to create a power link function.

...

arguments to pass to cv.plsRglmmodel.default or to cv.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 K matrices of size about nrow(dataX)/K * nt with the predicted values for a growing number of components

...

...

list

of K matrices of size about nrow(dataX)/K * nt with 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 K vectors of length about nrow(dataX) with the numbers of the rows of dataX that were used as a training set

...

...

list

of K vectors of length about nrow(dataX) with the numbers of the rows of dataX that 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 K matrices of size about nrow(dataX)/K * 1 with the observed values of the response

...

...

list

of K matrices of size about nrow(dataX)/K * 1 with the observed values of the response
fit_backend

backend used for repeated non-ordinal score-space GLM fits during cross-validation
call

the call of the function

Note

Work for complete and incomplete datasets.

Author(s)

Frederic Bertrand
[email protected]
https://fbertran.github.io/homepage/

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.

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)
(sum1<-summary(bbb))
cvtable(sum1)

bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=3,
modele="pls-glm-family",family=gaussian(),K=12,verbose=FALSE)
(sum2<-summary(bbb2))
cvtable(sum2)


#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))
plot(cvtable(sum3))

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)
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)
summary(bbb2,MClassed=TRUE)
kfolds2coeff(bbb2)

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
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)
boxplot(kfolds2coeff(bbb)[,1])

kfolds2Chisqind(bbb)
kfolds2Chisq(bbb)
summary(bbb)
PLS_lm(ypine,Xpine,10,typeVC="standard")$InfCrit

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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)

data(XpineNAX21)
PLS_lm(ypine,XpineNAX21,10,typeVC="standard")$InfCrit
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2"))



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)
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)
boxplot(kfolds2coeff(bbb)[,1])

kfolds2Chisqind(bbb)
kfolds2Chisq(bbb)
summary(bbb)
PLS_lm(ypine,Xpine,10,typeVC="standard")$InfCrit

data(pineNAX21)
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=Gamma(),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
PLS_lm(ypine,XpineNAX21,10,typeVC="standard")$InfCrit
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)

cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=12,verbose=FALSE)
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian,K=12,verbose=FALSE)
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",family=inverse.gaussian(),
K=6,NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(link = "1/mu^2"),K=6,NK=2,verbose=FALSE)$results_kfolds

bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=10,
modele="pls-glm-inverse.gaussian",keepcoeffs=TRUE,verbose=FALSE)

#For Jackknife computations
kfolds2coeff(bbb2)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm(yCornell,XCornell,10,typeVC="standard")$InfCrit
rm(list=c("XCornell","yCornell","bbb","bbb2"))

data(Cornell)
bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,modele="pls")
summary(bbb)

cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),K=12)


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

#Different ways of model specifications
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),K=6,
NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian,
K=6,NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(),
K=6,NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=gaussian(link=log),
K=6,NK=2,verbose=FALSE)$results_kfolds

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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm_formula(Y~.,data=Cornell,10,typeVC="standard")$InfCrit
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)
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)
boxplot(kfolds2coeff(bbb)[,1])

kfolds2Chisqind(bbb)
kfolds2Chisq(bbb)
summary(bbb)
PLS_lm_formula(x11~.,data=pine,nt=10,typeVC="standard")$InfCrit

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)
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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm_formula(x11~.,data=pineNAX21,nt=10,typeVC="standard")$InfCrit
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)
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)
summary(bbb2,MClassed=TRUE)
kfolds2coeff(bbb2)

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
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)
boxplot(kfolds2coeff(bbb)[,1])

kfolds2Chisqind(bbb)
kfolds2Chisq(bbb)
summary(bbb)
PLS_lm_formula(x11~.,data=pine,10,typeVC="standard")$InfCrit

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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm_formula(x11~.,data=pineNAX21,10,typeVC="standard")$InfCrit
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)
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)
boxplot(kfolds2coeff(bbb)[,1])

kfolds2Chisqind(bbb)
kfolds2Chisq(bbb)
summary(bbb)
PLS_lm_formula(x11~.,data=pine,10,typeVC="standard")$InfCrit

data(pineNAX21)
bbb2 <- cv.plsRglm(x11~.,data=pineNAX21,nt=10,
modele="pls-glm-family",family=Gamma(),K=10,keepcoeffs=TRUE,keepfolds=FALSE,verbose=FALSE)
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)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm_formula(x11~.,data=pineNAX21,10,typeVC="standard")$InfCrit
rm(list=c("bbb","bbb2"))



data(Cornell)
summary(cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,modele="pls",verbose=FALSE))

cv.plsRglm(Y~.,data=Cornell,nt=3,
modele="pls-glm-inverse.gaussian",K=12,verbose=FALSE)
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",family=inverse.gaussian,K=12,verbose=FALSE)
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(),K=6,NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-inverse.gaussian",K=6,
NK=2,verbose=FALSE)$results_kfolds
cv.plsRglm(Y~.,data=Cornell,nt=3,modele="pls-glm-family",
family=inverse.gaussian(link = "1/mu^2"),K=6,NK=2,verbose=FALSE)$results_kfolds

bbb2 <- cv.plsRglm(Y~.,data=Cornell,nt=10,
modele="pls-glm-inverse.gaussian",keepcoeffs=TRUE,verbose=FALSE)

#For Jackknife computations
kfolds2coeff(bbb2)
boxplot(kfolds2coeff(bbb2)[,1])

kfolds2Chisqind(bbb2)
kfolds2Chisq(bbb2)
summary(bbb2)
PLS_lm_formula(Y~.,data=Cornell,10,typeVC="standard")$InfCrit
rm(list=c("bbb","bbb2"))


data(bordeaux)
summary(cv.plsRglm(Quality~.,data=bordeaux,10,
modele="pls-glm-polr",K=7))

data(bordeauxNA)
summary(cv.plsRglm(Quality~.,data=bordeauxNA,
10,modele="pls-glm-polr",K=10,verbose=FALSE))

summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="logistic",verbose=FALSE))
summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="probit",verbose=FALSE))
summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="cloglog",verbose=FALSE))
suppressWarnings(summary(cv.plsRglm(Quality~.,data=bordeaux,nt=2,K=7,
modele="pls-glm-polr",method="cauchit",verbose=FALSE)))

Experimental k-fold cross-validation for multivariate-response PLS2

Description

cv.plsRmulti() performs repeated k-fold cross-validation for the experimental complete-case linear plsRmulti workflow.

Usage

cv.plsRmulti(object, ...)

## Default S3 method:
cv.plsRmultiModel(
  object,
  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,
  EstimXNA = FALSE,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
cv.plsRmultiModel(
  object,
  data = NULL,
  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,
  EstimXNA = FALSE,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights = NULL,
  subset = NULL,
  contrasts = NULL,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object

For the default method, a numeric multivariate response matrix or data frame with at least two columns. For the formula method, a formula of the form cbind(y1, y2, ...) ~ ..
...

Not used. Extra arguments are rejected in this experimental release.
dataX

Numeric predictor matrix or data frame.
nt

Number of components to extract in each fold fit.
limQ2set

Threshold used by cvtable for the aggregated Q2 criterion.
modele

Only "pls" is supported.
family

Not supported in this experimental release.
K

Number of groups for each partition.
NK

Number of repeated partitions.
grouplist

Optional user-supplied partitions.
random

Should the folds be generated randomly?
scaleX

Should predictors be scaled?
scaleY

Should responses be scaled? Defaults to TRUE.
keepcoeffs

Should standardized coefficient vectors be stored for each fold fit?
keepfolds

Should training indices be stored for each fold fit?
keepdataY

Kept for interface compatibility. Observed fold responses are stored so that summaries can be computed.
keepMclassed

Not supported in this experimental release.
EstimXNA

Not supported in this experimental release.
pvals.expli

Not supported in this experimental release.
alpha.pvals.expli

Not supported in this experimental release.
MClassed

Not supported in this experimental release.
tol_Xi

Tolerance used for degeneracy checks during component extraction.
weights

Not supported in this experimental release.
sparse

Not supported in this experimental release.
sparseStop

Not supported in this experimental release.
naive

Not supported in this experimental release.
verbose

Should informational messages be displayed?
data

An optional data frame for the formula method.
subset

An optional subset for the formula method.
contrasts

Optional contrasts for the formula method.

Details

Only the linear multivariate-response PLS2 mode is supported here. Missing values, weights, sparse extraction options, classification diagnostics, and GLM families remain out of scope for this experimental API.

Value

An object of class "cv.plsRmultiModel" with repeated fold predictions, observed fold responses, optional coefficient vectors and fold indices, and the reference full-data "plsRmultiModel" fit used for aggregated summary metrics.

See Also

plsRmulti, summary.cv.plsRmultiModel, cvtable, bootpls

Examples

set.seed(123)
X <- matrix(rnorm(60 * 4), ncol = 4)
Y <- cbind(
  y1 = X[, 1] - 0.5 * X[, 2] + rnorm(60, sd = 0.1),
  y2 = 0.3 * X[, 2] + X[, 3] + rnorm(60, sd = 0.1)
)

cv_fit <- cv.plsRmulti(Y, X, nt = 2, K = 3, NK = 1, verbose = FALSE)
summary(cv_fit, verbose = FALSE)

Table method for summary of cross validated PLSR and PLSGLR models

Description

The function cvtable is wrapper of cvtable.plsR and cvtable.plsRglm that provides a table summary for the classes "summary.cv.plsRmodel" and "summary.cv.plsRglmmodel"

Usage

cvtable(x, verbose = TRUE, ...)

Arguments

x

an object of the class "summary.cv.plsRmodel"
verbose

should results be displayed ?
...

further arguments to be passed to or from methods.

Value

listList of Information Criteria computed for each fold.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=5)
res.cv.modpls <- cvtable(summary(cv.modpls))
plot(res.cv.modpls) #defaults to type="CVQ2"
rm(list=c("cv.modpls","res.cv.modpls"))


data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=25,verbose=FALSE)
res.cv.modpls <- cvtable(summary(cv.modpls))
plot(res.cv.modpls) #defaults to type="CVQ2"
rm(list=c("cv.modpls","res.cv.modpls"))
	
data(Cornell)
cv.modpls <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=100,verbose=FALSE)
res.cv.modpls <- cvtable(summary(cv.modpls))
plot(res.cv.modpls) #defaults to type="CVQ2"
rm(list=c("cv.modpls","res.cv.modpls"))

data(Cornell)
cv.modplsglm <- cv.plsRglm(Y~.,data=Cornell,nt=6,K=6,
modele="pls-glm-gaussian",NK=100,verbose=FALSE)
res.cv.modplsglm <- cvtable(summary(cv.modplsglm))
plot(res.cv.modplsglm) #defaults to type="CVQ2Chi2"
rm(list=c("res.cv.modplsglm"))

Dichotomization

Description

This function takes a real value and converts it to 1 if it is positive and else to 0.

Usage

dicho(val)

Arguments

val

A real value

Value

0 or 1.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

ifelse

Examples

dimX <- 6
Astar <- 4
(dataAstar4 <- t(replicate(10,simul_data_YX(dimX,Astar))))

dicho(dataAstar4)

rm(list=c("dimX","Astar"))

Fowlkes dataset

Description

A classic dataset from Fowlkes.

Format

A data frame with 9949 observations on the following 13 variables.

Y

binary response

MA

a numeric vector

MW

a numeric vector

NE

a numeric vector

NW

a numeric vector

PA

a numeric vector

SO

a numeric vector

SW

a numeric vector

color

a numeric vector

age1

a numeric vector

age2

a numeric vector

age3

a numeric vector

sexe

a numeric vector

Examples

data(fowlkes)
str(fowlkes)

Information criteria

Description

This function computes information criteria for existing plsR model using Degrees of Freedom estimation.

Usage

infcrit.dof(modplsR, naive = FALSE)

Arguments

modplsR

A plsR model i.e. an object returned by one of the functions plsR, plsRmodel.default, plsRmodel.formula, PLS_lm or PLS_lm_formula.
naive

A boolean.

Details

If naive=FALSE returns AIC, BIC and gmdl values for estimated and naive degrees of freedom. If naive=TRUE returns NULL.

Value

matrix

AIC, BIC and gmdl values or NULL.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

M. Hansen, B. Yu. (2001). Model Selection and Minimum Descripion Length Principle, Journal of the American Statistical Association, 96, 746-774.
N. Kraemer, M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705.
N. Kraemer, M. Sugiyama, M.L. Braun. (2009). Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 272-279.

See Also

plsR.dof for degrees of freedom computation and infcrit.dof for computing information criteria directly from a previously fitted plsR model.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsR(yCornell,XCornell,4)
infcrit.dof(modpls)

Computes Predicted Chisquare for k-fold cross-validated partial least squares regression models.

Description

This function computes Predicted Chisquare for k-fold cross validated partial least squares regression models.

Usage

kfolds2Chisq(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression glm model

Value

list

Total Predicted Chisquare vs number of components for the first group partition
list()

...
list

Total Predicted Chisquare vs number of components for the last group partition

Note

Use cv.plsRglm to create k-fold cross validated partial least squares regression glm models.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisqind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=16,verbose=FALSE)
bbb2 <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=5,verbose=FALSE)
kfolds2Chisq(bbb)
kfolds2Chisq(bbb2)
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(object=ypine,dataX=Xpine,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbb2 <- cv.plsRglm(object=ypine,dataX=Xpine,nt=10,modele="pls-glm-gaussian",K=10,verbose=FALSE)
kfolds2Chisq(bbb)
kfolds2Chisq(bbb2)
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Press(bbbNA)
kfolds2Chisq(bbbNA)
bbbNA2 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbbNA3 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian",K=10,
verbose=FALSE)
kfolds2Chisq(bbbNA2)
kfolds2Chisq(bbbNA3)
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2","bbbNA3"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family="binomial",verbose=FALSE))
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
kfolds2Chisq(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-logistic",
K=10,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))

Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

Description

This function computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.

Usage

kfolds2Chisqind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression glm model

Value

list

Individual PChisq vs number of components for the first group partition
list()

...
list

Individual PChisq vs number of components for the last group partition

Note

Use cv.plsRglm to create k-fold cross validated partial least squares regression glm models.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind, kfolds2Chisq, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=16,verbose=FALSE)
bbb2 <- cv.plsRglm(object=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",K=5,verbose=FALSE)
kfolds2Chisqind(bbb)
kfolds2Chisqind(bbb2)
rm(list=c("XCornell","yCornell","bbb","bbb2"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb <- cv.plsRglm(object=ypine,dataX=Xpine,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbb2 <- cv.plsRglm(object=ypine,dataX=Xpine,nt=10,modele="pls-glm-gaussian",K=10,verbose=FALSE)
kfolds2Chisqind(bbb)
kfolds2Chisqind(bbb2)
                  
XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
bbbNA <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls",K=10,verbose=FALSE)
kfolds2Pressind(bbbNA)
kfolds2Chisqind(bbbNA)
bbbNA2 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=4,modele="pls-glm-gaussian",verbose=FALSE)
bbbNA3 <- cv.plsRglm(object=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian",
K=10,verbose=FALSE)
kfolds2Chisqind(bbbNA2)
kfolds2Chisqind(bbbNA3)
rm(list=c("Xpine","XpineNAX21","ypine","bbb","bbb2","bbbNA","bbbNA2","bbbNA3"))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-family",
family=binomial(),verbose=FALSE))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=4,modele="pls-glm-logistic",
verbose=FALSE))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,modele="pls-glm-family",
family=binomial(),K=10,verbose=FALSE))
kfolds2Chisqind(cv.plsRglm(object=yaze_compl,dataX=Xaze_compl,nt=10,
modele="pls-glm-logistic",K=10,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))

Extracts coefficients from k-fold cross validated partial least squares regression models

Description

This fonction extracts coefficients from k-fold cross validated partial least squares regression models

Usage

kfolds2coeff(pls_kfolds)

Arguments

pls_kfolds

an object that is a k-fold cross validated partial least squares regression models either lm or glm

Details

This fonctions works for plsR and plsRglm models.

Value

coef.all

matrix with the values of the coefficients for each leave one out step or NULL if another type of cross validation was used.

Note

Only for NK=1 and leave one out CV

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2Pressind, kfolds2Press, kfolds2Mclassedind, kfolds2Mclassed and summary to extract and transform results from k-fold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
bbb <- PLS_lm_kfoldcv(dataY=yCornell,dataX=XCornell,nt=3,K=nrow(XCornell),keepcoeffs=TRUE,
verbose=FALSE)
kfolds2coeff(bbb)
boxplot(kfolds2coeff(bbb)[,2])
rm(list=c("XCornell","yCornell","bbb"))

data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
bbb2 <- cv.plsR(object=ypine,dataX=Xpine,nt=4,K=nrow(Xpine),keepcoeffs=TRUE,verbose=FALSE)
kfolds2coeff(bbb2)
boxplot(kfolds2coeff(bbb2)[,1])
rm(list=c("Xpine","ypine","bbb2"))

Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models

Description

This function extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models for both formula or classic specifications of the model.

Usage

kfolds2CVinfos_glm(pls_kfolds, MClassed = FALSE, verbose = TRUE)

Arguments

pls_kfolds

an object computed using cv.plsRglm
MClassed

should number of miss classed be computed ?
verbose

should infos be displayed ?

Details

The Mclassed option should only set to TRUE if the response is binary.

Value

list

table of fit statistics for first group partition
list()

...
list

table of fit statistics for last group partition

Note

Use summary and cv.plsRglm instead.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(Cornell)
summary(cv.plsRglm(Y~.,data=Cornell,
nt=6,K=12,NK=1,keepfolds=FALSE,keepdataY=TRUE,modele="pls",verbose=FALSE),MClassed=TRUE)


data(aze_compl)
summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,modele="pls",verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=gaussian(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-logistic",
verbose=FALSE),MClassed=TRUE,verbose=FALSE)
summary(cv.plsRglm(y~.,data=aze_compl,nt=10,K=8,
modele="pls-glm-family",
family=binomial(),verbose=FALSE),
MClassed=TRUE,verbose=FALSE)


if(require(chemometrics)){
data(hyptis)
hyptis
yhyptis <- factor(hyptis$Group,ordered=TRUE)
Xhyptis <- as.data.frame(hyptis[,c(1:6)])
options(contrasts = c("contr.treatment", "contr.poly"))
modpls2 <- plsRglm(yhyptis,Xhyptis,6,modele="pls-glm-polr")
modpls2$Coeffsmodel_vals
modpls2$InfCrit
modpls2$Coeffs
modpls2$std.coeffs

table(yhyptis,predict(modpls2$FinalModel,type="class"))

modpls3 <- PLS_glm(yhyptis[-c(1,2,3)],Xhyptis[-c(1,2,3),],3,modele="pls-glm-polr",
dataPredictY=Xhyptis[c(1,2,3),],verbose=FALSE)

summary(cv.plsRglm(factor(Group,ordered=TRUE)~.,data=hyptis[,-c(7,8)],nt=4,K=10,
random=TRUE,modele="pls-glm-polr",keepcoeffs=TRUE,verbose=FALSE),
MClassed=TRUE,verbose=FALSE)
}

Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares models

Description

This function extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares models for both formula or classic specifications of the model.

Usage

kfolds2CVinfos_lm(pls_kfolds, MClassed = FALSE, verbose = TRUE)

Arguments

pls_kfolds

an object computed using PLS_lm_kfoldcv
MClassed

should number of miss classed be computed
verbose

should infos be displayed ?

Details

The Mclassed option should only set to TRUE if the response is binary.

Value

list

table of fit statistics for first group partition
list()

...
list

table of fit statistics for last group partition

Note

Use summary and cv.plsR instead.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(Cornell)
summary(cv.plsR(Y~.,data=Cornell,nt=10,K=6,verbose=FALSE))


data(pine)
summary(cv.plsR(x11~.,data=pine,nt=10,NK=3,verbose=FALSE),verbose=FALSE)
data(pineNAX21)
summary(cv.plsR(x11~.,data=pineNAX21,nt=10,NK=3,
verbose=FALSE),verbose=FALSE)


data(aze_compl)
summary(cv.plsR(y~.,data=aze_compl,nt=10,K=8,NK=3,
verbose=FALSE),MClassed=TRUE,verbose=FALSE)

Number of missclassified individuals for k-fold cross validated partial least squares regression models.

Description

This function indicates the total number of missclassified individuals for k-fold cross validated partial least squares regression models.

Usage

kfolds2Mclassed(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model used on binary data

Value

list

Total number of missclassified individuals vs number of components for the first group partition
list()

...
list

Total number of missclassified individuals vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression models.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind and kfolds2Mclassedind to extract and transforms results from k-fold cross validation.

Examples

data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Mclassed(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=1,verbose=FALSE))
kfolds2Mclassed(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=2,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))

Number of missclassified individuals per group for k-fold cross validated partial least squares regression models.

Description

This function indicates the number of missclassified individuals per group for k-fold cross validated partial least squares regression models.

Usage

kfolds2Mclassedind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model used on binary data

Value

list

Number of missclassified individuals per group vs number of components for the first group partition
list()

...
list

Number of missclassified individuals per group vs number of components for the last group partition

Note

Use cv.plsR or cv.plsRglm to create k-fold cross validated partial least squares regression models or generalized linear ones.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Press, kfolds2Pressind and kfolds2Mclassed to extract and transforms results from k-fold cross-validation.

Examples

data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
kfolds2Mclassedind(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=1,verbose=FALSE))
kfolds2Mclassedind(cv.plsR(object=yaze_compl,dataX=Xaze_compl,nt=10,K=8,NK=2,verbose=FALSE))
rm(list=c("Xaze_compl","yaze_compl"))

Computes PRESS for k-fold cross validated partial least squares regression models.

Description

This function computes PRESS for k-fold cross validated partial least squares regression models.

Usage

kfolds2Press(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model

Value

list

Press vs number of components for the first group partition
list()

...
list

Press vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression models.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Pressind, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=12,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=6,NK=1,verbose=FALSE))
rm(list=c("XCornell","yCornell"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=2,verbose=FALSE))

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=1,verbose=FALSE))
kfolds2Press(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=2,verbose=FALSE))
rm(list=c("Xpine","XpineNAX21","ypine"))

Computes individual PRESS for k-fold cross validated partial least squares regression models.

Description

This function computes individual PRESS for k-fold cross validated partial least squares regression models.

Usage

kfolds2Pressind(pls_kfolds)

Arguments

pls_kfolds

a k-fold cross validated partial least squares regression model

Value

list

Individual Press vs number of components for the first group partition
list()

...
list

Individual Press vs number of components for the last group partition

Note

Use cv.plsR to create k-fold cross validated partial least squares regression models.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

kfolds2coeff, kfolds2Press, kfolds2Mclassedind and kfolds2Mclassed to extract and transforms results from k-fold cross validation.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
kfolds2Pressind(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=12,NK=1))
kfolds2Pressind(cv.plsR(object=yCornell,dataX=data.frame(scale(as.matrix(XCornell))[,]),
nt=6,K=6,NK=1))
rm(list=c("XCornell","yCornell"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
kfolds2Pressind(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=1,verbose=FALSE))
kfolds2Pressind(cv.plsR(object=ypine,dataX=Xpine,nt=10,NK=2,verbose=FALSE))

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
kfolds2Pressind(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=1,verbose=FALSE))
kfolds2Pressind(cv.plsR(object=ypine,dataX=XpineNAX21,nt=10,NK=2,verbose=FALSE))
rm(list=c("Xpine","XpineNAX21","ypine"))

loglikelihood function for plsR models

Description

This function provides loglikelihood computation for an univariate plsR model.

Usage

loglikpls(residpls, weights = rep.int(1, length(residpls)))

Arguments

residpls

Residuals of a fitted univariate plsR model
weights

Weights of observations

Details

Loglikelihood functions for plsR models with univariate response.

Value

real

Loglikelihood value

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Baibing Li, Julian Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

AICpls for AIC computation and logLik for loglikelihood computations for linear models

Examples

data(pine)
ypine <- pine[,11]
Xpine <- pine[,1:10]
(Pinscaled <- as.data.frame(cbind(scale(ypine),scale(as.matrix(Xpine)))))
colnames(Pinscaled)[1] <- "yy"

lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

modpls <- plsR(ypine,Xpine,10)
modpls$Std.Coeffs
lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

AIC(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))
print(logLik(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))

sum(dnorm(modpls$RepY, modpls$Std.ValsPredictY, sqrt(mean(modpls$residY^2)), log=TRUE))
sum(dnorm(Pinscaled$yy,fitted(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)),
sqrt(mean(residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))^2)), log=TRUE))
loglikpls(modpls$residY)
loglikpls(residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(10,residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(10,modpls$residY)

Coefficients for permutation bootstrap computations of PLSR models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsR(dataset, ind, nt, modele, maxcoefvalues, ifbootfail, verbose)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsR
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
# statistic=permcoefs.plsR is the default for (Y,X) permutation resampling of PLSR models.
set.seed(250)
modpls <- plsR(yCornell,XCornell,1)
Cornell.bootYX <- bootpls(modpls, sim="permutation", R=250, statistic=permcoefs.plsR, verbose=FALSE)

Raw coefficients for permutation bootstrap computations of PLSR models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsR.raw(
  dataset,
  ind,
  nt,
  modele,
  maxcoefvalues,
  ifbootfail,
  verbose
)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsR
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
set.seed(250)
modpls <- permcoefs.plsR.raw(Cornell[,-8],1:nrow(Cornell),nt=3,
maxcoefvalues=1e5,ifbootfail=rep(0,3),verbose=FALSE)

Coefficients for permutation bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRglm(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  fit_backend = "stats",
  maxcoefvalues,
  ifbootfail,
  verbose
)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" or "fastglm".
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm.

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
# statistic=coefs.plsRglm is the default for (Y,X) bootstrap of a PLSGLR models.
set.seed(250)
modplsglm <- plsRglm(Y~.,data=Cornell,1,modele="pls-glm-family",family=gaussian)
Cornell.bootYX <- bootplsglm(modplsglm, R=250, typeboot="plsmodel", 
sim="permutation", statistic=permcoefs.plsRglm, verbose=FALSE)

Raw coefficients for permutation bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRglm.raw(
  dataset,
  ind,
  nt,
  modele,
  family = NULL,
  fit_backend = "stats",
  maxcoefvalues,
  ifbootfail,
  verbose
)

Arguments

dataset

dataset to resample
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" or "fastglm".
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
ifbootfail

value to return if the estimation fails on a bootstrap sample
verbose

should info messages be displayed ?

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm.

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
set.seed(250)
modplsglm <- permcoefs.plsRglm.raw(Cornell[,-8],1:nrow(Cornell),nt=3,
modele="pls-glm-family",family=gaussian,maxcoefvalues=1e5,
ifbootfail=rep(0,3),verbose=FALSE)

Coefficients for permutation bootstrap computations of PLSGLR models

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRglmnp(
  dataRepYtt,
  ind,
  nt,
  modele,
  family = NULL,
  maxcoefvalues,
  wwetoile,
  ifbootfail
)

Arguments

dataRepYtt

components' coordinates to bootstrap
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
family

glm family to use, see plsRglm
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
wwetoile

values of the Wstar matrix in the original fit
ifbootfail

value to return if the estimation fails on a bootstrap sample

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Note

~~some notes~~

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootplsglm

Examples

data(Cornell)

# (Y,X) bootstrap of a PLSGLR model
# statistic=coefs.plsRglm is the default for (Y,X) bootstrap of a PLSGLR models.
set.seed(250)
modplsglm <- plsRglm(Y~.,data=Cornell,1,modele="pls-glm-family",family=gaussian)
Cornell.bootYT <- bootplsglm(modplsglm, R=250, statistic=permcoefs.plsRglmnp, verbose=FALSE)

Coefficients computation for permutation bootstrap

Description

A function passed to boot to perform bootstrap.

Usage

permcoefs.plsRnp(
  dataRepYtt,
  ind,
  nt,
  modele,
  maxcoefvalues,
  wwetoile,
  ifbootfail
)

Arguments

dataRepYtt

components' coordinates to bootstrap
ind

indices for resampling
nt

number of components to use
modele

type of modele to use, see plsRglm
maxcoefvalues

maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
wwetoile

values of the Wstar matrix in the original fit
ifbootfail

value to return if the estimation fails on a bootstrap sample

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

See also bootpls

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

# Lazraq-Cleroux PLS (Y,X) bootstrap
# statistic=coefs.plsR is the default for (Y,X) resampling of PLSR models.
set.seed(250)
modpls <- plsR(yCornell,XCornell,1)
Cornell.bootYT <- bootpls(modpls, R=250, typeboot="fmodel_np", sim="permutation",
statistic=permcoefs.plsRnp, verbose=FALSE)

Pine dataset

Description

The caterpillar dataset was extracted from a 1973 study on pine processionary caterpillars. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. The response variable is the logarithmic transform of the average number of nests of caterpillars per tree in an area of 500 square meters (x11). There are k=10 potentially explanatory variables defined on n=33 areas.

Format

A data frame with 33 observations on the following 11 variables.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pines in the area

x4

height (in meters) of the tree sampled at the center of the area

x5

diameter (in meters) of the tree sampled at the center of the area

x6

index of the settlement density

x7

orientation of the area (from 1 if southbound to 2 otherwise)

x8

height (in meters) of the dominant tree

x9

number of vegetation strata

x10

mix settlement index (from 1 if not mixed to 2 if mixed)

x11

logarithmic transform of the average number of nests of caterpillars per tree

Details

These caterpillars got their names from their habit of moving over the ground in incredibly long head-to-tail processions when leaving their nest to create a new colony.

The pine_sup dataset can be used as a test set to assess model prediction error of a model trained on the pine dataset.

Source

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992), “La régression, nouveaux regards sur une ancienne méthode statistique”, INRA, Actualités Scientifiques et Agronomiques, Masson, Paris.

References

J.-M. Marin, C. Robert. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New-York, pages 48-49.

Examples

data(pine)
str(pine)

Complete Pine dataset

Description

This is the complete caterpillar dataset from a 1973 study on pine_full processionary caterpillars. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. The response variable is the logarithmic transform of the average number of nests of caterpillars per tree in an area of 500 square meters (x11). There are k=10 potentially explanatory variables defined on n=55 areas.

Format

A data frame with 55 observations on the following 11 variables.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pine_fulls in the area

x4

height (in meters) of the tree sampled at the center of the area

x5

diameter (in meters) of the tree sampled at the center of the area

x6

index of the settlement density

x7

orientation of the area (from 1 if southbound to 2 otherwise)

x8

height (in meters) of the dominant tree

x9

number of vegetation strata

x10

mix settlement index (from 1 if not mixed to 2 if mixed)

x11

logarithmic transform of the average number of nests of caterpillars per tree

Details

These caterpillars got their names from their habit of moving over the ground in incredibly long head-to-tail processions when leaving their nest to create a new colony.

Source

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992), “La régression, nouveaux regards sur une ancienne méthode statistique”, INRA, Actualités Scientifiques et Agronomiques, Masson, Paris.

References

J.-M. Marin, C. Robert. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New-York, pages 48-49.

Examples

data(pine_full)
str(pine_full)

Complete Pine dataset

Description

This is a supplementary dataset (used as a test set for the pine dataset) that was extracted from a 1973 study on pine_sup processionary caterpillars. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. The response variable is the logarithmic transform of the average number of nests of caterpillars per tree in an area of 500 square meters (x11). There are k=10 potentially explanatory variables defined on n=22 areas.

Format

A data frame with 22 observations on the following 11 variables.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pine_sups in the area

x4

height (in meters) of the tree sampled at the center of the area

x5

diameter (in meters) of the tree sampled at the center of the area

x6

index of the settlement density

x7

orientation of the area (from 1 if southbound to 2 otherwise)

x8

height (in meters) of the dominant tree

x9

number of vegetation strata

x10

mix settlement index (from 1 if not mixed to 2 if mixed)

x11

logarithmic transform of the average number of nests of caterpillars per tree

Details

These caterpillars got their names from their habit of moving over the ground in incredibly long head-to-tail processions when leaving their nest to create a new colony.

The pine_sup dataset can be used as a test set to assess model prediction error of a model trained on the pine dataset.

Source

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992), “La régression, nouveaux regards sur une ancienne méthode statistique”, INRA, Actualités Scientifiques et Agronomiques, Masson, Paris.

References

J.-M. Marin, C. Robert. (2007). Bayesian Core: A Practical Approach to Computational Bayesian Statistics. Springer, New-York, pages 48-49.

Examples

data(pine_sup)
str(pine_sup)

Incomplete dataset from the pine caterpillars example

Description

The caterpillar dataset was extracted from a 1973 study on pine processionary caterpillars. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. There are k=10 potentially explanatory variables defined on n=33 areas.
The value of x2 for the first observation was removed from the matrix of predictors on purpose.

Format

A data frame with 33 observations on the following 11 variables and one missing value.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pines in the area

x4

height (in meters) of the tree sampled at the center of the area

x5

diameter (in meters) of the tree sampled at the center of the area

x6

index of the settlement density

x7

orientation of the area (from 1 if southbound to 2 otherwise)

x8

height (in meters) of the dominant tree

x9

number of vegetation strata

x10

mix settlement index (from 1 if not mixed to 2 if mixed)

x11

logarithmic transform of the average number of nests of caterpillars per tree

Details

These caterpillars got their names from their habit of moving over the ground in incredibly long head-to-tail processions when leaving their nest to create a new colony.
The pineNAX21 is a dataset with a missing value for testing purpose.

Source

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992). “La régression, nouveaux regards sur une ancienne méthode statistique”, INRA, Actualités Scientifiques et Agronomiques, Masson, Paris.

Examples

data(pineNAX21)
str(pineNAX21)

Plot method for table of summary of cross validated plsRglm models

Description

This function provides a table method for the class "summary.cv.plsRglmmodel"

Usage

## S3 method for class 'table.summary.cv.plsRglmmodel'
plot(x, type = c("CVMC", "CVQ2Chi2", "CVPreChi2"), ...)

Arguments

x

an object of the class "table.summary.cv.plsRglmmodel"
type

the type of cross validation criterion to plot.
...

further arguments to be passed to or from methods.

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
bbb <- cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,
modele="pls-glm-family",family=gaussian(), verbose=FALSE)
plot(cvtable(summary(bbb,verbose=FALSE)),type="CVQ2Chi2")
rm(list=c("bbb"))


data(Cornell)
plot(cvtable(summary(cv.plsRglm(Y~.,data=Cornell,nt=10,NK=100,
modele="pls-glm-family",family=gaussian(), verbose=FALSE),
verbose=FALSE)),type="CVQ2Chi2")

Plot method for table of summary of cross validated plsR models

Description

This function provides a table method for the class "summary.cv.plsRmodel"

Usage

## S3 method for class 'table.summary.cv.plsRmodel'
plot(x, type = c("CVMC", "CVQ2", "CVPress"), ...)

Arguments

x

an object of the class "table.summary.cv.plsRmodel"
type

the type of cross validation criterion to plot.
...

further arguments to be passed to or from methods.

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
bbb <- cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=5, verbose=FALSE)
plot(cvtable(summary(bbb)),type="CVQ2")
rm(list=c("bbb"))


data(Cornell)
plot(cvtable(summary(cv.plsR(Y~.,data=Cornell,nt=6,K=6,NK=100, verbose=FALSE))),type="CVQ2")

Plot bootstrap confidence intervals

Description

This function plots the confidence intervals derived using the function confints.bootpls from from a bootpls based object.

Usage

plots.confints.bootpls(
  ic_bootobject,
  indices = NULL,
  legendpos = "topleft",
  prednames = TRUE,
  articlestyle = TRUE,
  xaxisticks = TRUE,
  ltyIC = c(2, 4, 5, 1),
  colIC = c("darkgreen", "blue", "red", "black"),
  typeIC,
  las = par("las"),
  mar,
  mgp,
  ...
)

Arguments

ic_bootobject

an object created with the confints.bootpls function.
indices

vector of indices of the variables to plot. Defaults to NULL: all the predictors will be used.
legendpos

position of the legend as in legend, defaults to "topleft"
prednames

do the original names of the predictors shall be plotted ? Defaults to TRUE: the names are plotted.
articlestyle

do the extra blank zones of the margin shall be removed from the plot ? Defaults to TRUE: the margins are removed.
xaxisticks

do ticks for the x axis shall be plotted ? Defaults to TRUE: the ticks are plotted.
ltyIC

lty as in plot
colIC

col as in plot
typeIC

type of CI to plot. Defaults to typeIC=c("Normal", "Basic", "Percentile", "BCa") if BCa intervals limits were computed and to typeIC=c("Normal", "Basic", "Percentile") otherwise.
las

numeric in 0,1,2,3; the style of axis labels. 0: always parallel to the axis [default], 1: always horizontal, 2: always perpendicular to the axis, 3: always vertical.
mar

A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot. The default is c(5, 4, 4, 2) + 0.1.
mgp

The margin line (in mex units) for the axis title, axis labels and axis line. Note that mgp[1] affects title whereas mgp[2:3] affect axis. The default is c(3, 1, 0).
...

further options to pass to the plot function.

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

confints.bootpls

Examples

data(Cornell)
modpls <- plsR(Y~.,data=Cornell,3)

# Lazraq-Cleroux PLS (Y,X) bootstrap
set.seed(250)
Cornell.bootYX <- bootpls(modpls, R=250, verbose=FALSE)
temp.ci <- confints.bootpls(Cornell.bootYX,2:8)

plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,prednames=FALSE)
plots.confints.bootpls(temp.ci,prednames=FALSE,articlestyle=FALSE,
main="Bootstrap confidence intervals for the bj")
plots.confints.bootpls(temp.ci,indices=1:3,prednames=FALSE)
plots.confints.bootpls(temp.ci,c(2,4,6),"bottomright")
plots.confints.bootpls(temp.ci,c(2,4,6),articlestyle=FALSE,
main="Bootstrap confidence intervals for some of the bj")

temp.ci <- confints.bootpls(Cornell.bootYX,typeBCa=FALSE)
plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,2:8)
plots.confints.bootpls(temp.ci,prednames=FALSE)


# Bastien CSDA 2005 (Y,T) bootstrap
Cornell.boot <- bootpls(modpls, typeboot="fmodel_np", R=250, verbose=FALSE)
temp.ci <- confints.bootpls(Cornell.boot,2:8)

plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,prednames=FALSE)
plots.confints.bootpls(temp.ci,prednames=FALSE,articlestyle=FALSE,
main="Bootstrap confidence intervals for the bj")
plots.confints.bootpls(temp.ci,indices=1:3,prednames=FALSE)
plots.confints.bootpls(temp.ci,c(2,4,6),"bottomright")
plots.confints.bootpls(temp.ci,c(2,4,6),articlestyle=FALSE,
main="Bootstrap confidence intervals for some of the bj")

temp.ci <- confints.bootpls(Cornell.boot,typeBCa=FALSE)
plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,2:8)
plots.confints.bootpls(temp.ci,prednames=FALSE)



data(aze_compl)
modplsglm <- plsRglm(y~.,data=aze_compl,3,modele="pls-glm-logistic")

# Lazraq-Cleroux PLS (Y,X) bootstrap
# should be run with R=1000 but takes much longer time
aze_compl.bootYX3 <- bootplsglm(modplsglm, typeboot="plsmodel", R=250, verbose=FALSE)
temp.ci <- confints.bootpls(aze_compl.bootYX3)

plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,prednames=FALSE)
plots.confints.bootpls(temp.ci,prednames=FALSE,articlestyle=FALSE,
main="Bootstrap confidence intervals for the bj")
plots.confints.bootpls(temp.ci,indices=1:33,prednames=FALSE)
plots.confints.bootpls(temp.ci,c(2,4,6),"bottomleft")
plots.confints.bootpls(temp.ci,c(2,4,6),articlestyle=FALSE,
main="Bootstrap confidence intervals for some of the bj")
plots.confints.bootpls(temp.ci,indices=1:34,prednames=FALSE)
plots.confints.bootpls(temp.ci,indices=1:33,prednames=FALSE,ltyIC=1,colIC=c(1,2))
 
temp.ci <- confints.bootpls(aze_compl.bootYX3,1:34,typeBCa=FALSE)
plots.confints.bootpls(temp.ci,indices=1:33,prednames=FALSE)


# Bastien CSDA 2005 (Y,T) Bootstrap
# much faster
aze_compl.bootYT3 <- bootplsglm(modplsglm, R=1000, verbose=FALSE)
temp.ci <- confints.bootpls(aze_compl.bootYT3)

plots.confints.bootpls(temp.ci)
plots.confints.bootpls(temp.ci,typeIC="Normal")
plots.confints.bootpls(temp.ci,typeIC=c("Normal","Basic"))
plots.confints.bootpls(temp.ci,typeIC="BCa",legendpos="bottomleft")
plots.confints.bootpls(temp.ci,prednames=FALSE)
plots.confints.bootpls(temp.ci,prednames=FALSE,articlestyle=FALSE,
main="Bootstrap confidence intervals for the bj")
plots.confints.bootpls(temp.ci,indices=1:33,prednames=FALSE)
plots.confints.bootpls(temp.ci,c(2,4,6),"bottomleft")
plots.confints.bootpls(temp.ci,c(2,4,6),articlestyle=FALSE,
main="Bootstrap confidence intervals for some of the bj")
plots.confints.bootpls(temp.ci,prednames=FALSE,ltyIC=c(2,1),colIC=c(1,2))
 
temp.ci <- confints.bootpls(aze_compl.bootYT3,1:33,typeBCa=FALSE)
plots.confints.bootpls(temp.ci,prednames=FALSE)

Light version of PLS_glm for cross validation purposes

Description

Light version of PLS_glm for cross validation purposes either on complete or incomplete datasets.

Usage

PLS_glm_wvc(
  dataY,
  dataX,
  nt = 2,
  dataPredictY = dataX,
  modele = "pls",
  family = NULL,
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepstd.coeffs = FALSE,
  tol_Xi = 10^(-12),
  weights,
  method = "logistic",
  fit_backend = "stats",
  verbose = TRUE
)

Arguments

dataY

response (training) dataset
dataX

predictor(s) (training) dataset
nt

number of components to be extracted
dataPredictY

predictor(s) (testing) dataset
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 the family option.
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 family for details of family functions.) To use the family option, please set modele="pls-glm-family". User defined families can also be defined. See details.
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

whether the coefficients of the linear fit on link scale of unstandardized eXplanatory variables should be returned or not.
keepstd.coeffs

whether the coefficients of the linear fit on link scale of standardized eXplanatory variables should be returned or not.
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
method

logistic, probit, complementary log-log or cauchit (corresponding to a Cauchy latent variable).
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" for the compatibility path or "fastglm" to opt into the accelerated complete-data backend. Unsupported cases fall back to "stats" with a warning.
verbose

should info messages be displayed ?

Details

This function is called by PLS_glm_kfoldcv_formula in order to perform cross-validation either on complete or incomplete datasets.

There are seven different predefined models with predefined link functions available :

list("\"pls\"")

ordinary pls models

list("\"pls-glm-Gamma\"")

glm gaussian with inverse link pls models

list("\"pls-glm-gaussian\"")

glm gaussian with identity link pls models

list("\"pls-glm-inverse-gamma\"")

glm binomial with square inverse link pls models

list("\"pls-glm-logistic\"")

glm binomial with logit link pls models

list("\"pls-glm-poisson\"")

glm poisson with log link pls models

list("\"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

accepts the links (as names) identity, log and inverse.

list("gaussian")

accepts the links (as names) identity, log and inverse.

family

accepts the links (as names) identity, log and inverse.

The

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

list("binomial")

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The

accepts the links inverse, identity and log.

list("Gamma")

accepts the links inverse, identity and log.

family

accepts the links inverse, identity and log.

The

accepts the links log, identity, and sqrt.

list("poisson")

accepts the links log, identity, and sqrt.

family

accepts the links log, identity, and sqrt.

The

accepts the links 1/mu^2, inverse, identity and log.

list("inverse.gaussian")

accepts the links 1/mu^2, inverse, identity and log.

family

accepts the links 1/mu^2, inverse, identity and log.

The

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

list("quasi")

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function

can be used to create a power link function.

list("power")

can be used to create a power link function.

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

valsPredict

nrow(dataPredictY) * nt matrix of the predicted values
list("coeffs")

If the coefficients of the eXplanatory variables were requested:
i.e. keepcoeffs=TRUE.
ncol(dataX) * 1 matrix of the coefficients of the the eXplanatory variables

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

PLS_glm for more detailed results, PLS_glm_kfoldcv for cross-validating models and PLS_lm_wvc for the same function dedicated to plsR models

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,])
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-family",
family=gaussian(),dataPredictY=XCornell[1,], verbose=FALSE)
rm("XCornell","yCornell")


## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_glm_wvc(dataY=ypine,dataX=XpineNAX21,nt=10,modele="pls-glm-gaussian")
rm("XpineNAX21","ypine")

data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
PLS_glm_wvc(ypine,Xpine,10,modele="pls", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-Gamma", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=Gamma(), verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-gaussian", verbose=FALSE)
PLS_glm_wvc(ypine,Xpine,10,modele="pls-glm-family",family=gaussian(log), verbose=FALSE)
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-poisson", verbose=FALSE)
PLS_glm_wvc(round(ypine),Xpine,10,modele="pls-glm-family",family=poisson(log), verbose=FALSE)
rm(list=c("pine","ypine","Xpine"))


data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-inverse.gaussian", verbose=FALSE)
PLS_glm_wvc(yCornell,XCornell,10,modele="pls-glm-family",
family=inverse.gaussian(), verbose=FALSE)
rm(list=c("XCornell","yCornell"))


data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_glm_wvc(dataY=yCornell,dataX=XCornell,nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
PLS_glm_wvc(dataY=yCornell[-1],dataX=XCornell[-1,],nt=3,modele="pls-glm-gaussian",
dataPredictY=XCornell[1,], verbose=FALSE)
rm("XCornell","yCornell")

data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
PLS_glm(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic",typeVC="none", verbose=FALSE)$InfCrit
PLS_glm_wvc(yaze_compl,Xaze_compl,10,modele="pls-glm-logistic", keepcoeffs=TRUE, verbose=FALSE)
rm("Xaze_compl","yaze_compl")

Light version of PLS_lm for cross validation purposes

Description

Light version of PLS_lm for cross validation purposes either on complete or incomplete datasets.

Usage

PLS_lm_wvc(
  dataY,
  dataX,
  nt = 2,
  dataPredictY = dataX,
  modele = "pls",
  scaleX = TRUE,
  scaleY = NULL,
  keepcoeffs = FALSE,
  keepstd.coeffs = FALSE,
  tol_Xi = 10^(-12),
  weights,
  verbose = TRUE
)

Arguments

dataY

response (training) dataset
dataX

predictor(s) (training) dataset
nt

number of components to be extracted
dataPredictY

predictor(s) (testing) dataset
modele

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

whether the coefficients of unstandardized eXplanatory variables should be returned or not.
keepstd.coeffs

whether the coefficients of standardized eXplanatory variables should be returned or not.
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
verbose

should info messages be displayed ?

Details

This function is called by PLS_lm_kfoldcv in order to perform cross-validation either on complete or incomplete datasets.

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

valsPredict

nrow(dataPredictY) * nt matrix of the predicted values
list("coeffs")

If the coefficients of the eXplanatory variables were requested:
i.e. keepcoeffs=TRUE.
ncol(dataX) * 1 matrix of the coefficients of the the eXplanatory variables

Note

Use PLS_lm_kfoldcv for a wrapper in view of cross-validation.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

PLS_lm for more detailed results, PLS_lm_kfoldcv for cross-validating models and PLS_glm_wvc for the same function dedicated to plsRglm models

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
PLS_lm_wvc(dataY=yCornell,dataX=XCornell,nt=3,dataPredictY=XCornell[1,])
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
verbose=FALSE)
PLS_lm_wvc(dataY=yCornell[-c(1,2)],dataX=XCornell[-c(1,2),],nt=3,dataPredictY=XCornell[c(1,2),],
keepcoeffs=TRUE, verbose=FALSE)
rm("XCornell","yCornell")

## With an incomplete dataset (X[1,2] is NA)
data(pine)
ypine <- pine[,11]
data(XpineNAX21)
PLS_lm_wvc(dataY=ypine[-1],dataX=XpineNAX21[-1,],nt=3, verbose=FALSE)
PLS_lm_wvc(dataY=ypine[-1],dataX=XpineNAX21[-1,],nt=3,dataPredictY=XpineNAX21[1,], verbose=FALSE)
PLS_lm_wvc(dataY=ypine[-2],dataX=XpineNAX21[-2,],nt=3,dataPredictY=XpineNAX21[2,], verbose=FALSE)
PLS_lm_wvc(dataY=ypine,dataX=XpineNAX21,nt=3, verbose=FALSE)
rm("ypine")

Partial least squares Regression models with leave one out cross validation

Description

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

Usage

plsR(object, ...)
## Default S3 method:
plsRmodel(object, dataX, nt = 2, limQ2set = 0.0975, 
dataPredictY = dataX, modele = "pls", family = NULL, typeVC = "none", 
EstimXNA = FALSE, scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE, 
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12), weights,
sparse = FALSE, sparseStop = TRUE, naive = FALSE,verbose=TRUE,...)
## S3 method for class 'formula'
plsRmodel(object, data, nt = 2, limQ2set = 0.0975,
dataPredictY, modele = "pls", family = NULL, typeVC = "none",
EstimXNA = FALSE, scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE, 
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12), weights,
subset, contrasts = NULL, sparse = FALSE, sparseStop = TRUE, naive = FALSE,
verbose=TRUE,...)
PLS_lm(dataY, dataX, nt = 2, limQ2set = 0.0975, dataPredictY = dataX, 
modele = "pls", family = NULL, typeVC = "none", EstimXNA = FALSE, 
scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE, 
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12),
weights,sparse=FALSE,sparseStop=FALSE,naive=FALSE,verbose=TRUE)
PLS_lm_formula(formula,data=NULL,nt=2,limQ2set=.0975,dataPredictY=dataX,
modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,scaleX=TRUE,
scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,MClassed=FALSE,
tol_Xi=10^(-12),weights,subset,contrasts=NULL,sparse=FALSE,
sparseStop=FALSE,naive=FALSE,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.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which plsR is called.
nt

number of components to be extracted
limQ2set

limit value for the Q2
dataPredictY

predictor(s) (testing) dataset
modele

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

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

type of leave one out cross validation. Several procedures are available. If cross validation is required, one needs to selects the way of predicting the response for left out observations. For complete rows, without any missing value, there are two different ways of computing these predictions. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).

none

no cross validation

standard

as in SIMCA for datasets without any missing value. For datasets with any missing value, it is the as using missingdata

missingdata

all values predicted as those with missing values for datasets with any missing values

adaptative

predict a response value for an x with any missing value as those with missing values and for an x without any missing value as those without missing values.
EstimXNA

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

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

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

should individual p-values be reported to tune model selection ?
alpha.pvals.expli

level of significance for predictors when pvals.expli=TRUE
MClassed

number of missclassified cases, should only be used for binary responses
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
subset

an optional vector specifying a subset of observations to be used in the fitting process.
contrasts

an optional list. See the contrasts.arg of model.matrix.default.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
naive

Use the naive estimates for the Degrees of Freedom in plsR? Default is FALSE.
verbose

should info messages be displayed ?
...

arguments to pass to plsRmodel.default or to plsRmodel.formula

Details

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

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.

The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one. Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see N. Kraemer and M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705, 2011.

Value

nr

Number of observations
nc

Number of predictors
nt

Number of requested components
ww

raw weights (before L2-normalization)
wwnorm

L2 normed weights (to be used with deflated matrices of predictor variables)
wwetoile

modified weights (to be used with original matrix of predictor variables)
tt

PLS components
pp

loadings of the predictor variables
CoeffC

coefficients of the PLS components
uscores

scores of the response variable
YChapeau

predicted response values for the dataX set
residYChapeau

residuals of the deflated response on the standardized scale
RepY

scaled response vector
na.miss.Y

is there any NA value in the response vector
YNA

indicatrix vector of missing values in RepY
residY

deflated scaled response vector
ExpliX

scaled matrix of predictors
na.miss.X

is there any NA value in the predictor matrix
XXNA

indicator of non-NA values in the predictor matrix
residXX

deflated predictor matrix
PredictY

response values with NA replaced with 0
press.ind

individual PRESS value for each observation (scaled scale)
press.tot

total PRESS value for all observations (scaled scale)
family

glm family used to fit PLSGLR model
ttPredictY

PLS components for the dataset on which prediction was requested
typeVC

type of leave one out cross-validation used
dataX

predictor values
dataY

response values
computed_nt

number of components that were computed
CoeffCFull

matrix of the coefficients of the predictors
CoeffConstante

value of the intercept (scaled scale)
Std.Coeffs

Vector of standardized regression coefficients
press.ind2

individual PRESS value for each observation (original scale)
RSSresidY

residual sum of squares (scaled scale)
Coeffs

Vector of regression coefficients (used with the original data scale)
Yresidus

residuals of the PLS model
RSS

residual sum of squares (original scale)
residusY

residuals of the deflated response on the standardized scale
AIC.std

AIC.std vs number of components (AIC computed for the standardized model
AIC

AIC vs number of components
optional

If the response is assumed to be binary:
i.e. MClassed=TRUE.

MissClassed

Number of miss classed results

Probs

"Probability" predicted by the model. These are not true probabilities since they may lay outside of [0,1]

Probs.trc

Probability predicted by the model and constrained to belong to [0,1]
ttPredictFittedMissingY

Description of 'comp2'
optional

If cross validation was requested:
i.e. typeVC="standard", typeVC="missingdata" or typeVC="adaptative".

R2residY

R2 coefficient value on the standardized scale

R2

R2 coefficient value on the original scale

press.tot2

total PRESS value for all observations (original scale)

Q2

Q2 value (standardized scale)

limQ2

limit of the Q2 value

Q2_2

Q2 value (original scale)

Q2cum

cumulated Q2 (standardized scale)

Q2cum_2

cumulated Q2 (original scale)
InfCrit

table of Information Criteria
Std.ValsPredictY

predicted response values for supplementary dataset (standardized scale)
ValsPredictY

predicted response values for supplementary dataset (original scale)
Std.XChapeau

estimated values for missing values in the predictor matrix (standardized scale)
XXwotNA

predictor matrix with missing values replaced with 0

Note

Use cv.plsR to cross-validate the plsRglm models and bootpls to bootstrap them.

Author(s)

Frederic Bertrand
[email protected]
https://fbertran.github.io/homepage/

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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

See also plsRglm to fit PLSGLR models.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

#maximum 6 components could be extracted from this dataset
#trying 10 to trigger automatic stopping criterion
modpls10<-plsR(yCornell,XCornell,10)
modpls10

#With iterated leave one out CV PRESS
modpls6cv<-plsR(Y~.,data=Cornell,6,typeVC="standard")
modpls6cv
cv.modpls<-cv.plsR(Y~.,data=Cornell,6,NK=100, verbose=FALSE)
res.cv.modpls<-cvtable(summary(cv.modpls))
plot(res.cv.modpls)

rm(list=c("XCornell","yCornell","modpls10","modpls6cv"))


#A binary response example
data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
modpls.aze <- plsR(yaze_compl,Xaze_compl,10,MClassed=TRUE,typeVC="standard")
modpls.aze

#Direct access to not cross-validated values
modpls.aze$AIC
modpls.aze$AIC.std
modpls.aze$MissClassed

#Raw predicted values (not really probabily since not constrained in [0,1]
modpls.aze$Probs
#Truncated to [0;1] predicted values (true probabilities)
modpls.aze$Probs.trc
modpls.aze$Probs-modpls.aze$Probs.trc

#Repeated cross validation of the model (NK=100 times)
cv.modpls.aze<-cv.plsR(y~.,data=aze_compl,10,NK=100, verbose=FALSE)
res.cv.modpls.aze<-cvtable(summary(cv.modpls.aze,MClassed=TRUE))
#High discrepancy in the number of component choice using repeated cross validation
#and missclassed criterion
plot(res.cv.modpls.aze)

rm(list=c("Xaze_compl","yaze_compl","modpls.aze","cv.modpls.aze","res.cv.modpls.aze"))

#24 predictors
dimX <- 24
#2 components
Astar <- 2
simul_data_UniYX(dimX,Astar)
dataAstar2 <- data.frame(t(replicate(250,simul_data_UniYX(dimX,Astar))))
modpls.A2<- plsR(Y~.,data=dataAstar2,10,typeVC="standard")
modpls.A2
cv.modpls.A2<-cv.plsR(Y~.,data=dataAstar2,10,NK=100, verbose=FALSE)
res.cv.modpls.A2<-cvtable(summary(cv.modpls.A2,verbose=FALSE))
#Perfect choice for the Q2 criterion in PLSR
plot(res.cv.modpls.A2)

#Binarized data.frame
simbin1 <- data.frame(dicho(dataAstar2))
modpls.B2 <- plsR(Y~.,data=simbin1,10,typeVC="standard",MClassed=TRUE, verbose=FALSE)
modpls.B2
modpls.B2$Probs
modpls.B2$Probs.trc
modpls.B2$MissClassed
plsR(simbin1$Y,dataAstar2[,-1],10,typeVC="standard",MClassed=TRUE,verbose=FALSE)$InfCrit
cv.modpls.B2<-cv.plsR(Y~.,data=simbin1,2,NK=100,verbose=FALSE)
res.cv.modpls.B2<-cvtable(summary(cv.modpls.B2,MClassed=TRUE))
#Only one component found by repeated CV missclassed criterion
plot(res.cv.modpls.B2)

rm(list=c("dimX","Astar","dataAstar2","modpls.A2","cv.modpls.A2",
"res.cv.modpls.A2","simbin1","modpls.B2","cv.modpls.B2","res.cv.modpls.B2"))

Computation of the Degrees of Freedom

Description

This function computes the Degrees of Freedom using the Krylov representation of PLS and other quantities that are used to get information criteria values. For the time present, it only works with complete datasets.

Usage

## S3 method for class 'dof'
plsR(modplsR, naive = FALSE)

Arguments

modplsR

A plsR model i.e. an object returned by one of the functions plsR, plsRmodel.default, plsRmodel.formula, PLS_lm or PLS_lm_formula.
naive

A boolean.

Details

If naive=FALSE returns values for estimated degrees of freedom and error dispersion. If naive=TRUE returns returns values for naive degrees of freedom and error dispersion. The original code from Nicole Kraemer and Mikio L. Braun was unable to handle models with only one component.

Value

DoF

Degrees of Freedom
sigmahat

Estimates of dispersion
Yhat

Predicted values
yhat

Square Euclidean norms of the predicted values
RSS

Residual Sums of Squares

Author(s)

Nicole Kraemer, Mikio L. Braun with improvements from Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

N. Kraemer, M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705.
N. Kraemer, M. Sugiyama, M.L. Braun. (2009). Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 272-279.

See Also

aic.dof and infcrit.dof for computing information criteria directly from a previously fitted plsR model.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsR(yCornell,XCornell,4)
plsR.dof(modpls) 
plsR.dof(modpls,naive=TRUE)

Partial least squares Regression generalized linear models

Description

This function implements Partial least squares Regression generalized linear models complete or incomplete datasets.

Usage

plsRglm(object, ...)
## Default S3 method:
plsRglmmodel(object,dataX,nt=2,limQ2set=.0975,
dataPredictY=dataX,modele="pls",family=NULL,typeVC="none",
EstimXNA=FALSE,scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,
alpha.pvals.expli=.05,MClassed=FALSE,tol_Xi=10^(-12),weights,
sparse=FALSE,sparseStop=TRUE,naive=FALSE,fit_backend="stats",
verbose=TRUE,...)
## S3 method for class 'formula'
plsRglmmodel(object,data=NULL,nt=2,limQ2set=.0975,
dataPredictY,modele="pls",family=NULL,typeVC="none",
EstimXNA=FALSE,scaleX=TRUE,scaleY=NULL,pvals.expli=FALSE,
alpha.pvals.expli=.05,MClassed=FALSE,tol_Xi=10^(-12),weights,subset,
start=NULL,etastart,mustart,offset,method="glm.fit",control= list(),
contrasts=NULL,sparse=FALSE,sparseStop=TRUE,naive=FALSE,
fit_backend="stats",verbose=TRUE,...)
PLS_glm(dataY, dataX, nt = 2, limQ2set = 0.0975, dataPredictY = dataX, 
modele = "pls", family = NULL, typeVC = "none", EstimXNA = FALSE, 
scaleX = TRUE, scaleY = NULL, pvals.expli = FALSE, 
alpha.pvals.expli = 0.05, MClassed = FALSE, tol_Xi = 10^(-12), weights, 
method, sparse = FALSE, sparseStop=FALSE, naive=FALSE,
fit_backend="stats",verbose=TRUE)
PLS_glm_formula(formula,data=NULL,nt=2,limQ2set=.0975,dataPredictY=dataX,
modele="pls",family=NULL,typeVC="none",EstimXNA=FALSE,scaleX=TRUE,
scaleY=NULL,pvals.expli=FALSE,alpha.pvals.expli=.05,MClassed=FALSE,
tol_Xi=10^(-12),weights,subset,start=NULL,etastart,mustart,offset,method,
control= list(),contrasts=NULL,sparse=FALSE,sparseStop=FALSE,naive=FALSE,
fit_backend="stats",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.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which plsRglm is called.
nt

number of components to be extracted
limQ2set

limit value for the Q2
dataPredictY

predictor(s) (testing) dataset
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 the family option.
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 family for details of family functions.) To use the family option, please set modele="pls-glm-family". User defined families can also be defined. See details.
typeVC

type of leave one out cross validation. For back compatibility purpose.

none

no cross validation
EstimXNA

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

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

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

should individual p-values be reported to tune model selection ?
alpha.pvals.expli

level of significance for predictors when pvals.expli=TRUE
MClassed

number of missclassified cases, should only be used for binary responses
tol_Xi

minimal value for Norm2(Xi) and det(pp′×pp)\mathrm{det}(pp' \times pp) if there is any missing value in the dataX. It defaults to 10−1210^{-12}
weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.
fit_backend

backend used for repeated non-ordinal score-space GLM fits. Use "stats" for the compatibility path or "fastglm" to opt into the accelerated complete-data backend. Unsupported cases fall back to "stats" with a warning.
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 NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See model.offset.
method

For non-ordinal GLM modes this argument is kept for backward compatibility; use fit_backend to choose the score-space fitting backend. For a polr model (modele="pls-glm-polr"), logistic or probit or (complementary) log-log (loglog or cloglog) or cauchit (corresponding to a Cauchy latent variable).
control

a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.
contrasts

an optional list. See the contrasts.arg of model.matrix.default.
sparse

should the coefficients of non-significant predictors (<alpha.pvals.expli) be set to 0
sparseStop

should component extraction stop when no significant predictors (<alpha.pvals.expli) are found
naive

Use the naive estimates for the Degrees of Freedom in plsR? Default is FALSE.
verbose

Should details be displayed ?
...

arguments to pass to plsRmodel.default or to plsRmodel.formula

Details

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 gaussian family

accepts the links (as names) identity, log and inverse.

The binomial family

accepts the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log).

The Gamma family

accepts the links inverse, identity and log.

The poisson family

accepts the links log, identity, and sqrt.

The inverse.gaussian family

accepts the links 1/mu^2, inverse, identity and log.

The quasi family

accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt.

The function power

can be used to create a power link function.

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.

The default estimator for Degrees of Freedom is the Kramer and Sugiyama's one which only works for classical plsR models. For these models, Information criteria are computed accordingly to these estimations. Naive Degrees of Freedom and Information Criteria are also provided for comparison purposes. For more details, see N. Kraemer and M. Sugiyama. (2011). The Degrees of Freedom of Partial Least Squares Regression. Journal of the American Statistical Association, 106(494), 697-705, 2011.

Value

Depends on the model that was used to fit the model. You can generally at least find these items.

nr

Number of observations
nc

Number of predictors
nt

Number of requested components
ww

raw weights (before L2-normalization)
wwnorm

L2 normed weights (to be used with deflated matrices of predictor variables)
wwetoile

modified weights (to be used with original matrix of predictor variables)
tt

PLS components
pp

loadings of the predictor variables
CoeffC

coefficients of the PLS components
uscores

scores of the response variable
YChapeau

predicted response values for the dataX set
residYChapeau

residuals of the deflated response on the standardized scale
RepY

scaled response vector
na.miss.Y

is there any NA value in the response vector
YNA

indicatrix vector of missing values in RepY
residY

deflated scaled response vector
ExpliX

scaled matrix of predictors
na.miss.X

is there any NA value in the predictor matrix
XXNA

indicator of non-NA values in the predictor matrix
residXX

deflated predictor matrix
PredictY

response values with NA replaced with 0
RSS

residual sum of squares (original scale)
RSSresidY

residual sum of squares (scaled scale)
R2residY

R2 coefficient value on the standardized scale
R2

R2 coefficient value on the original scale
press.ind

individual PRESS value for each observation (scaled scale)
press.tot

total PRESS value for all observations (scaled scale)
Q2cum

cumulated Q2 (standardized scale)
family

glm family used to fit PLSGLR model
ttPredictY

PLS components for the dataset on which prediction was requested
typeVC

type of leave one out cross-validation used
dataX

predictor values
dataY

response values
weights

weights of the observations
fit_backend

backend used for repeated non-ordinal score-space GLM fits
computed_nt

number of components that were computed
AIC

AIC vs number of components
BIC

BIC vs number of components
Coeffsmodel_vals
ChisqPearson
CoeffCFull

matrix of the coefficients of the predictors
CoeffConstante

value of the intercept (scaled scale)
Std.Coeffs

Vector of standardized regression coefficients
Coeffs

Vector of regression coefficients (used with the original data scale)
Yresidus

residuals of the PLS model
residusY

residuals of the deflated response on the standardized scale
InfCrit

table of Information Criteria:

AIC

AIC vs number of components

BIC

BIC vs number of components

MissClassed

Number of miss classed results

Chi2_Pearson_Y

Q2 value (standardized scale)

RSS

residual sum of squares (original scale)

R2

R2 coefficient value on the original scale

R2residY

R2 coefficient value on the standardized scale

RSSresidY

residual sum of squares (scaled scale)
Std.ValsPredictY

predicted response values for supplementary dataset (standardized scale)
ValsPredictY

predicted response values for supplementary dataset (original scale)
Std.XChapeau

estimated values for missing values in the predictor matrix (standardized scale)
FinalModel

final GLR model on the PLS components
XXwotNA

predictor matrix with missing values replaced with 0
call

call
AIC.std

AIC.std vs number of components (AIC computed for the standardized model

Note

Use cv.plsRglm to cross-validate the plsRglm models and bootplsglm to bootstrap them.

Author(s)

Frederic Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frederic Bertrand (2010). Comparaison de la regression PLS et de la regression logistique PLS : application aux donnees d'allelotypage. Journal de la Societe Francaise de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

See also plsR.

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

modplsglm <- plsRglm(yCornell,XCornell,10,modele="pls-glm-gaussian")

#To retrieve the final GLR model on the PLS components
finalmod <- modplsglm$FinalModel
#It is a glm object.
plot(finalmod)


#Cross validation
cv.modplsglm<-cv.plsRglm(Y~.,data=Cornell,6,NK=100,modele="pls-glm-gaussian", verbose=FALSE)
res.cv.modplsglm<-cvtable(summary(cv.modplsglm))
plot(res.cv.modplsglm)

#If no model specified, classic PLSR model
modpls <- plsRglm(Y~.,data=Cornell,6)
modpls
modpls$tt
modpls$uscores
modpls$pp
modpls$Coeffs

#rm(list=c("XCornell","yCornell",modpls,cv.modplsglm,res.cv.modplsglm))


data(aze_compl)
Xaze_compl<-aze_compl[,2:34]
yaze_compl<-aze_compl$y
plsRglm(yaze_compl,Xaze_compl,nt=10,modele="pls",MClassed=TRUE, verbose=FALSE)$InfCrit
modpls <- plsRglm(yaze_compl,Xaze_compl,nt=10,modele="pls-glm-logistic",
MClassed=TRUE,pvals.expli=TRUE, verbose=FALSE)
modpls
colSums(modpls$pvalstep)
modpls$Coeffsmodel_vals

plot(plsRglm(yaze_compl,Xaze_compl,4,modele="pls-glm-logistic")$FinalModel)
plsRglm(yaze_compl[-c(99,72)],Xaze_compl[-c(99,72),],4,
modele="pls-glm-logistic",pvals.expli=TRUE)$pvalstep
plot(plsRglm(yaze_compl[-c(99,72)],Xaze_compl[-c(99,72),],4,
modele="pls-glm-logistic",pvals.expli=TRUE)$FinalModel)
rm(list=c("Xaze_compl","yaze_compl","modpls"))


data(bordeaux)
Xbordeaux<-bordeaux[,1:4]
ybordeaux<-factor(bordeaux$Quality,ordered=TRUE)
modpls <- plsRglm(ybordeaux,Xbordeaux,10,modele="pls-glm-polr",pvals.expli=TRUE)
modpls
colSums(modpls$pvalstep)


XbordeauxNA<-Xbordeaux
XbordeauxNA[1,1] <- NA
modplsNA <- plsRglm(ybordeaux,XbordeauxNA,10,modele="pls-glm-polr",pvals.expli=TRUE)
modpls
colSums(modpls$pvalstep)
rm(list=c("Xbordeaux","XbordeauxNA","ybordeaux","modplsNA"))


data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
modpls1 <- plsRglm(ypine,Xpine,1)
modpls1$Std.Coeffs
modpls1$Coeffs
modpls4 <- plsRglm(ypine,Xpine,4)
modpls4$Std.Coeffs
modpls4$Coeffs
modpls4$PredictY[1,]
plsRglm(ypine,Xpine,4,dataPredictY=Xpine[1,])$PredictY[1,]

XpineNAX21 <- Xpine
XpineNAX21[1,2] <- NA
modpls4NA <- plsRglm(ypine,XpineNAX21,4)
modpls4NA$Std.Coeffs
modpls4NA$YChapeau[1,]
modpls4$YChapeau[1,]
modpls4NA$CoeffC
plsRglm(ypine,XpineNAX21,4,EstimXNA=TRUE)$XChapeau
plsRglm(ypine,XpineNAX21,4,EstimXNA=TRUE)$XChapeauNA

# compare pls-glm-gaussian with classic plsR
modplsglm4 <- plsRglm(ypine,Xpine,4,modele="pls-glm-gaussian")
cbind(modpls4$Std.Coeffs,modplsglm4$Std.Coeffs)

# without missing data
cbind(ypine,modpls4$ValsPredictY,modplsglm4$ValsPredictY)

# with missing data
modplsglm4NA <- plsRglm(ypine,XpineNAX21,4,modele="pls-glm-gaussian")
cbind((ypine),modpls4NA$ValsPredictY,modplsglm4NA$ValsPredictY)
rm(list=c("Xpine","ypine","modpls4","modpls4NA","modplsglm4","modplsglm4NA"))

data(fowlkes)
Xfowlkes <- fowlkes[,2:13]
yfowlkes <- fowlkes[,1]
modpls <- plsRglm(yfowlkes,Xfowlkes,4,modele="pls-glm-logistic",pvals.expli=TRUE)
modpls
colSums(modpls$pvalstep)
rm(list=c("Xfowlkes","yfowlkes","modpls"))


if(require(chemometrics)){
data(hyptis)
yhyptis <- factor(hyptis$Group,ordered=TRUE)
Xhyptis <- as.data.frame(hyptis[,c(1:6)])
options(contrasts = c("contr.treatment", "contr.poly"))
modpls2 <- plsRglm(yhyptis,Xhyptis,6,modele="pls-glm-polr")
modpls2$Coeffsmodel_vals
modpls2$InfCrit
modpls2$Coeffs
modpls2$Std.Coeffs

table(yhyptis,predict(modpls2$FinalModel,type="class"))
rm(list=c("yhyptis","Xhyptis","modpls2"))
}

dimX <- 24
Astar <- 6
dataAstar6 <- t(replicate(250,simul_data_UniYX(dimX,Astar)))
ysimbin1 <- dicho(dataAstar6)[,1]
Xsimbin1 <- dicho(dataAstar6)[,2:(dimX+1)]
modplsglm <- plsRglm(ysimbin1,Xsimbin1,10,modele="pls-glm-logistic")
modplsglm

simbin=data.frame(dicho(dataAstar6))
cv.modplsglm <- suppressWarnings(cv.plsRglm(Y~.,data=simbin,nt=10,
modele="pls-glm-logistic",NK=100, verbose=FALSE))
res.cv.modplsglm <- cvtable(summary(cv.modplsglm,MClassed=TRUE,
verbose=FALSE))
plot(res.cv.modplsglm) #defaults to type="CVMC"

rm(list=c("dimX","Astar","dataAstar6","ysimbin1","Xsimbin1","modplsglm","cv.modplsglm",
"res.cv.modplsglm"))

Experimental multivariate-response PLS2 models

Description

plsRmulti() implements an experimental complete-case linear PLS2 fit for multivariate numeric responses. It is intentionally separate from plsR so the current PLS1 API remains unchanged.

Usage

plsRmulti(object, ...)

## Default S3 method:
plsRmultiModel(
  object,
  dataX,
  nt = 2,
  limQ2set = 0.0975,
  dataPredictY,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

## S3 method for class 'formula'
plsRmultiModel(
  object,
  data,
  nt = 2,
  limQ2set = 0.0975,
  modele = "pls",
  family = NULL,
  typeVC = "none",
  EstimXNA = FALSE,
  scaleX = TRUE,
  scaleY = NULL,
  pvals.expli = FALSE,
  alpha.pvals.expli = 0.05,
  MClassed = FALSE,
  tol_Xi = 10^(-12),
  weights,
  subset,
  contrasts = NULL,
  sparse = FALSE,
  sparseStop = FALSE,
  naive = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object

For the default method, a numeric multivariate response matrix or data frame with at least two columns. For the formula method, a formula of the form cbind(y1, y2, ...) ~ ..
...

Not used. Extra arguments are rejected in this experimental release.
dataX

Numeric predictor matrix or data frame.
nt

Number of components to extract.
limQ2set

Kept for interface compatibility. Not supported by plsRmulti.
dataPredictY

Kept for interface compatibility. Not supported by plsRmulti; fit first and then use predict.
modele

Only "pls" is supported.
family

Not supported in this experimental release.
typeVC

Only "none" is supported.
EstimXNA

Not supported in this experimental release.
scaleX

Should predictors be scaled?
scaleY

Should responses be scaled? Defaults to TRUE.
pvals.expli

Not supported in this experimental release.
alpha.pvals.expli

Not supported in this experimental release.
MClassed

Not supported in this experimental release.
tol_Xi

Tolerance used for degeneracy checks during component extraction.
weights

Not supported in this experimental release.
sparse

Not supported in this experimental release.
sparseStop

Not supported in this experimental release.
naive

Not supported in this experimental release.
verbose

Should informational messages be displayed?
data

An optional data frame for the formula method.
subset

An optional subset for the formula method.
contrasts

Optional contrasts for the formula method.

Details

This experimental release supports complete-case linear PLS2 fitting, prediction, repeated k-fold cross-validation via cv.plsRmulti, and bootstrap resampling via bootpls. It still does not support missing values, weights, sparse extraction, classification diagnostics, or GLM families.

Value

An object of class "plsRmultiModel" with multivariate analogues of the linear plsR outputs, including the extracted scores tt, X loadings pp, response score coefficients CoeffC, coefficient matrix Coeffs, intercept vector CoeffConstante, scaled response matrix RepY, and fitted response matrices YChapeau, Std.ValsPredictY, and ValsPredictY.

See Also

predict.plsRmultiModel, cv.plsRmulti, bootpls, plsR

Examples

set.seed(123)
X <- matrix(rnorm(60 * 4), ncol = 4)
Y <- cbind(
  y1 = X[, 1] - 0.5 * X[, 2] + rnorm(60, sd = 0.1),
  y2 = 0.3 * X[, 2] + X[, 3] + rnorm(60, sd = 0.1)
)

fit <- plsRmulti(Y, X, nt = 2, verbose = FALSE)
fit
head(predict(fit))

Print method for plsRglm models

Description

This function provides a predict method for the class "plsRglmmodel"

Usage

## S3 method for class 'plsRglmmodel'
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("link", "response", "terms", "scores", "class", "probs"),
  se.fit = FALSE,
  weights,
  dispersion = NULL,
  methodNA = "adaptative",
  verbose = TRUE,
  ...
)

Arguments

object

An object of the class "plsRmodel".
newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
comps

A value with a single value of component to use for prediction.
type

Type of predicted value. Available choices are the glms ones ("link", "response", "terms"), the polr ones ("class", "probs") or the scores ("scores").
se.fit

If TRUE, pointwise standard errors are produced for the predictions using the Cox model.
weights

Vector of case weights. If weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.
dispersion

the dispersion of the GLM fit to be assumed in computing the standard errors. If omitted, that returned by summary applied to the object is used.
methodNA

Selects the way of predicting the response or the scores of the new data. For complete rows, without any missing value, there are two different ways of computing the prediction. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).
verbose

should info messages be displayed ?
...

Arguments to be passed on to stats::glm and plsRglm::plsRglm.

Value

When type is "response", a matrix of predicted response values is returned.
When type is "scores", a score matrix is returned.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

See Also predict.glm

Examples

data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
data(pine_sup)
Xpine_sup<-pine_sup[,1:10]
Xpine_supNA<-Xpine_sup
Xpine_supNA[1,1]<-NA

modpls=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",family="gaussian",
verbose=FALSE)
modplsform=plsRglm(x11~.,data=pine,nt=6,modele="pls-glm-family",family="gaussian", verbose=FALSE)
modpls2=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_sup,family="gaussian", verbose=FALSE)
modpls2NA=plsRglm(object=ypine,dataX=Xpine,nt=6,modele="pls-glm-family",
dataPredictY=Xpine_supNA,family="gaussian", verbose=FALSE)

#Identical to predict(modpls,type="link") or modpls$Std.ValsPredictY
cbind(modpls$Std.ValsPredictY,modplsform$Std.ValsPredictY,
predict(modpls),predict(modplsform))

#Identical to predict(modpls,type="response") or modpls$ValsPredictY
cbind(modpls$ValsPredictY,modplsform$ValsPredictY,
predict(modpls,type="response"),predict(modplsform,type="response"))

#Identical to modpls$ttPredictY
predict(modpls,type="scores")
predict(modplsform,type="scores")


#Identical to modpls2$ValsPredictY
cbind(predict(modpls,newdata=Xpine_sup,type="response"),
predict(modplsform,newdata=Xpine_sup,type="response"))

#Select the number of components to use to derive the prediction
predict(modpls,newdata=Xpine_sup,type="response",comps=1)    
predict(modpls,newdata=Xpine_sup,type="response",comps=3)    
predict(modpls,newdata=Xpine_sup,type="response",comps=6)    
try(predict(modpls,newdata=Xpine_sup,type="response",comps=8))

#Identical to modpls2$ttValsPredictY
predict(modpls,newdata=Xpine_sup,type="scores")    

#Select the number of components in the scores matrix
predict(modpls,newdata=Xpine_sup,type="scores",comps=1)    
predict(modpls,newdata=Xpine_sup,type="scores",comps=3)    
predict(modpls,newdata=Xpine_sup,type="scores",comps=6)    
try(predict(modpls,newdata=Xpine_sup,type="scores",comps=8))

#Identical to modpls2NA$ValsPredictY
predict(modpls,newdata=Xpine_supNA,type="response",methodNA="missingdata")    

cbind(predict(modpls,newdata=Xpine_supNA,type="response"),
predict(modplsform,newdata=Xpine_supNA,type="response"))

predict(modpls,newdata=Xpine_supNA,type="response",comps=1)    
predict(modpls,newdata=Xpine_supNA,type="response",comps=3)    
predict(modpls,newdata=Xpine_supNA,type="response",comps=6)    
try(predict(modpls,newdata=Xpine_supNA,type="response",comps=8))

#Identical to modpls2NA$ttPredictY
predict(modpls,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
predict(modplsform,newdata=Xpine_supNA,type="scores",methodNA="missingdata")

predict(modpls,newdata=Xpine_supNA,type="scores")    
predict(modplsform,newdata=Xpine_supNA,type="scores")    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=1)    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=3)    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=6)    
try(predict(modpls,newdata=Xpine_supNA,type="scores",comps=8))

Print method for plsR models

Description

This function provides a predict method for the class "plsRmodel"

Usage

## S3 method for class 'plsRmodel'
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("response", "scores"),
  weights,
  methodNA = "adaptative",
  verbose = TRUE,
  ...
)

Arguments

object

An object of the class "plsRmodel".
newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
comps

A value with a single value of component to use for prediction.
type

Type of predicted value. Available choices are the response values ("response") or the scores ("scores").
weights

Vector of case weights. If weights is a vector of integers, then the estimated coefficients are equivalent to estimating the model from data with the individual cases replicated as many times as indicated by weights.
methodNA

Selects the way of predicting the response or the scores of the new data. For complete rows, without any missing value, there are two different ways of computing the prediction. As a consequence, for mixed datasets, with complete and incomplete rows, there are two ways of computing prediction : either predicts any row as if there were missing values in it (missingdata) or selects the prediction method accordingly to the completeness of the row (adaptative).
verbose

should info messages be displayed ?
...

Arguments to be passed on to plsRglm::plsR.

Value

When type is "response", a matrix of predicted response values is returned.
When type is "scores", a score matrix is returned.

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

Examples

data(pine)
Xpine<-pine[,1:10]
ypine<-pine[,11]
data(pine_sup)
Xpine_sup<-pine_sup[,1:10]
Xpine_supNA<-Xpine_sup
Xpine_supNA[1,1]<-NA

modpls=plsR(object=ypine,dataX=Xpine,nt=6,modele="pls", verbose=FALSE)
modplsform=plsR(x11~.,data=pine,nt=6,modele="pls", verbose=FALSE)
modpls2=plsR(object=ypine,dataX=Xpine,nt=6,modele="pls",dataPredictY=Xpine_sup, verbose=FALSE)
modpls2NA=plsR(object=ypine,dataX=Xpine,nt=6,modele="pls",dataPredictY=Xpine_supNA, verbose=FALSE)

#Identical to predict(modpls,type="response") or modpls$ValsPredictY
cbind(predict(modpls),predict(modplsform))

#Identical to modpls$ttPredictY
predict(modpls,type="scores")
predict(modplsform,type="scores")


#Identical to modpls2$ValsPredictY
cbind(predict(modpls,newdata=Xpine_sup,type="response"),
predict(modplsform,newdata=Xpine_sup,type="response"))

#Select the number of components to use to derive the prediction
predict(modpls,newdata=Xpine_sup,type="response",comps=1)    
predict(modpls,newdata=Xpine_sup,type="response",comps=3)    
predict(modpls,newdata=Xpine_sup,type="response",comps=6)    
try(predict(modpls,newdata=Xpine_sup,type="response",comps=8))

#Identical to modpls2$ttValsPredictY
predict(modpls,newdata=Xpine_sup,type="scores")    

#Select the number of components in the scores matrix
predict(modpls,newdata=Xpine_sup,type="scores",comps=1)    
predict(modpls,newdata=Xpine_sup,type="scores",comps=3)    
predict(modpls,newdata=Xpine_sup,type="scores",comps=6)    
try(predict(modpls,newdata=Xpine_sup,type="scores",comps=8))

#Identical to modpls2NA$ValsPredictY
predict(modpls,newdata=Xpine_supNA,type="response",methodNA="missingdata")    

cbind(predict(modpls,newdata=Xpine_supNA,type="response"),
predict(modplsform,newdata=Xpine_supNA,type="response"))

predict(modpls,newdata=Xpine_supNA,type="response",comps=1)    
predict(modpls,newdata=Xpine_supNA,type="response",comps=3)    
predict(modpls,newdata=Xpine_supNA,type="response",comps=6)    
try(predict(modpls,newdata=Xpine_supNA,type="response",comps=8))

#Identical to modpls2NA$ttPredictY
predict(modpls,newdata=Xpine_supNA,type="scores",methodNA="missingdata")
predict(modplsform,newdata=Xpine_supNA,type="scores",methodNA="missingdata")

predict(modpls,newdata=Xpine_supNA,type="scores")    
predict(modplsform,newdata=Xpine_supNA,type="scores")    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=1)    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=3)    
predict(modpls,newdata=Xpine_supNA,type="scores",comps=6)    
try(predict(modpls,newdata=Xpine_supNA,type="scores",comps=8))

Predict method for experimental plsRmulti models

Description

Prediction method for "plsRmultiModel" objects.

Usage

## S3 method for class 'plsRmultiModel'
predict(
  object,
  newdata,
  comps = object$computed_nt,
  type = c("response", "scores"),
  verbose = TRUE,
  ...
)

Arguments

object

An object of class "plsRmultiModel".
newdata

Optional predictor matrix or data frame. For formula-fitted models, a data frame with the predictor variables used at fit time.
comps

Number of extracted components to use.
type

Either "response" or "scores".
verbose

Should informational messages be displayed?
...

Not used.

Value

If type = "response", a matrix of predicted responses. If type = "scores", a matrix of latent score coordinates.

See Also

plsRmulti

Examples

set.seed(123)
X <- matrix(rnorm(40 * 3), ncol = 3)
Y <- cbind(
  y1 = X[, 1] + rnorm(40, sd = 0.1),
  y2 = X[, 2] - X[, 3] + rnorm(40, sd = 0.1)
)
fit <- plsRmulti(Y, X, nt = 2, verbose = FALSE)

predict(fit, type = "response")
predict(fit, type = "scores")

Print method for plsRglm models

Description

This function provides a print method for the class "coef.plsRglmmodel"

Usage

## S3 method for class 'coef.plsRglmmodel'
print(x, ...)

Arguments

x

an object of the class "coef.plsRglmmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modplsglm <- plsRglm(yCornell,XCornell,3,modele="pls-glm-family",family=gaussian())
class(modplsglm)
print(coef(modplsglm))
rm(list=c("XCornell","yCornell","modplsglm"))

Print method for plsR models

Description

This function provides a print method for the class "coef.plsRmodel"

Usage

## S3 method for class 'coef.plsRmodel'
print(x, ...)

Arguments

x

an object of the class "coef.plsRmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsRglm(yCornell,XCornell,3,modele="pls")
class(modpls)
print(coef(modpls))
rm(list=c("XCornell","yCornell","modpls"))

Print method for plsRglm models

Description

This function provides a print method for the class "cv.plsRglmmodel"

Usage

## S3 method for class 'cv.plsRglmmodel'
print(x, ...)

Arguments

x

an object of the class "cv.plsRglmmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparaison de la régression PLS et de la régression logistique PLS : application aux données d'allélotypage. Journal de la Société Française de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
print(cv.plsRglm(object=yCornell,dataX=XCornell,nt=10,NK=1,
modele="pls-glm-family",family=gaussian(), verbose=FALSE))
rm(list=c("XCornell","yCornell","bbb"))

Print method for plsR models

Description

This function provides a print method for the class "cv.plsRmodel"

Usage

## S3 method for class 'cv.plsRmodel'
print(x, ...)

Arguments

x

an object of the class "cv.plsRmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
print(cv.plsR(object=yCornell,dataX=XCornell,nt=10,K=6, verbose=FALSE))
rm(list=c("XCornell","yCornell","bbb"))

Print method for plsRglm models

Description

This function provides a print method for the class "plsRglmmodel"

Usage

## S3 method for class 'plsRglmmodel'
print(x, ...)

Arguments

x

an object of the class "plsRglmmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparaison de la régression PLS et de la régression logistique PLS : application aux données d'allélotypage. Journal de la Société Française de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modplsglm <- plsRglm(yCornell,XCornell,3,modele="pls-glm-gaussian")
class(modplsglm)
print(modplsglm)
rm(list=c("XCornell","yCornell","modplsglm"))

Print method for plsR models

Description

This function provides a print method for the class "plsRmodel"

Usage

## S3 method for class 'plsRmodel'
print(x, ...)

Arguments

x

an object of the class "plsRmodel"
...

not used

Value

NULL

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparaison de la régression PLS et de la régression logistique PLS : application aux données d'allélotypage. Journal de la Société Française de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsRglm(yCornell,XCornell,3,modele="pls")
class(modpls)
print(modpls)
rm(list=c("XCornell","yCornell","modpls"))

Print method for summaries of plsRglm models

Description

This function provides a print method for the class "summary.plsRglmmodel"

Usage

## S3 method for class 'summary.plsRglmmodel'
print(x, ...)

Arguments

x

an object of the class "summary.plsRglmmodel"
...

not used

Value

language

call of the model

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparaison de la régression PLS et de la régression logistique PLS : application aux données d'allélotypage. Journal de la Société Française de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print and summary

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modplsglm <- plsRglm(yCornell,XCornell,3,modele="pls-glm-gaussian")
class(modplsglm)
print(summary(modplsglm))
rm(list=c("XCornell","yCornell","modplsglm"))

Print method for summaries of plsR models

Description

This function provides a print method for the class "summary.plsRmodel"

Usage

## S3 method for class 'summary.plsRmodel'
print(x, ...)

Arguments

x

an object of the class "summary.plsRmodel"
...

not used

Value

language

call of the model

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric Bertrand (2010). Comparaison de la régression PLS et de la régression logistique PLS : application aux données d'allélotypage. Journal de la Société Française de Statistique, 151(2), pages 1-18. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

print and summary

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsRglm(yCornell,XCornell,3,modele="pls")
class(modpls)
print(summary(modpls))
rm(list=c("XCornell","yCornell","modpls"))

Graphical assessment of the stability of selected variables

Description

This fonctions plots, for each of the model, the

Usage

signpred(
  matbin,
  pred.lablength = max(sapply(rownames(matbin), nchar)),
  labsize = 1,
  plotsize = 12
)

Arguments

matbin

Matrix with 0 or 1 entries. Each row per predictor and a column for every model. 0 means the predictor is not significant in the model and 1 that, on the contrary, it is significant.
pred.lablength

Maximum length of the predictors labels. Defaults to full label length.
labsize

Size of the predictors labels.
plotsize

Global size of the graph.

Details

This function is based on the visweb function from the bipartite package.

Value

A plot window.

Author(s)

Bernd Gruber with minor modifications from Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Vazquez, P.D., Chacoff, N.,P. and Cagnolo, L. (2009) Evaluating multiple determinants of the structure of plant-animal mutualistic networks. Ecology, 90:2039-2046.

See Also

See Also visweb

Examples

signpred(matrix(rbinom(160,1,.2),ncol=8,dimnames=list(as.character(1:20),as.character(1:8))))

Data generating detailed process for multivariate plsR models

Description

This function generates a single multivariate response value Y\boldsymbol{Y} and a vector of explinatory variables (X1,…,Xtotdim)(X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_complete(totdim, ncomp)

Arguments

totdim

Number of columns of the X vector (from ncomp to hardware limits)
ncomp

Number of latent components in the model (from 2 to 6)

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

simX

Vector of explanatory variables
HH

Dimension of the response Y\boldsymbol{Y}
eta

See Li et al.
r

See Li et al.
epsilon

See Li et al.
ksi

See Li et al.
f

See Li et al.
z

See Li et al.
Y

See Li et al.

Note

The value of rr depends on the value of ncomp :

ncomp rr
2 3
3 3
4 4

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576.
Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

simul_data_YX for data simulation purpose

Examples

simul_data_complete(20,6)                          

dimX <- 6
Astar <- 2
simul_data_complete(dimX,Astar)


dimX <- 6
Astar <- 3
simul_data_complete(dimX,Astar)


dimX <- 6
Astar <- 4
simul_data_complete(dimX,Astar)

rm(list=c("dimX","Astar"))

Data generating function for univariate plsR models

Description

This function generates a single univariate response value YY and a vector of explanatory variables (X1,…,Xtotdim)(X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_UniYX(totdim, ncomp)

Arguments

totdim

Number of columns of the X vector (from ncomp to hardware limits)
ncomp

Number of latent components in the model (from 2 to 6)

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

vector

(Y,X1,…,Xtotdim)(Y,X_1,\ldots,X_{totdim})

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576.
Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

simul_data_YX and simul_data_complete for generating multivariate data

Examples

simul_data_UniYX(20,6)                          


dimX <- 6
Astar <- 2
simul_data_UniYX(dimX,Astar)
(dataAstar2 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar2,5,NK=100, verbose=FALSE)))

dimX <- 6
Astar <- 3
simul_data_UniYX(dimX,Astar)
(dataAstar3 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar3,5,NK=100, verbose=FALSE)))

dimX <- 6
Astar <- 4
simul_data_UniYX(dimX,Astar)
(dataAstar4 <- data.frame(t(replicate(50,simul_data_UniYX(dimX,Astar)))))
cvtable(summary(cv.plsR(Y~.,data=dataAstar4,5,NK=100, verbose=FALSE)))

rm(list=c("dimX","Astar","dataAstar2","dataAstar3","dataAstar4"))

Data generating function for univariate binomial plsR models

Description

This function generates a single univariate binomial response value YY and a vector of explanatory variables (X1,…,Xtotdim)(X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_UniYX_binom(totdim, ncomp, link = "logit", offset = 0)

Arguments

totdim

Number of columns of the X vector (from ncomp to hardware limits)
ncomp

Number of latent components in the model (from 2 to 6)
link

Character specification of the link function in the mean model (mu). Currently, "logit", "probit", "cloglog", "cauchit", "log", "loglog" are supported. Alternatively, an object of class "link-glm" can be supplied.
offset

Offset on the linear scale

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a modification of a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

vector

(Y,X1,…,Xtotdim)(Y,X_1,\ldots,X_{totdim})

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576.
Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002), 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

simul_data_UniYX

Examples

layout(matrix(1:6,nrow=2))
# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit")))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog")))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit")))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog")))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log")))[,1])
layout(1)


layout(matrix(1:6,nrow=2))
# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,offset=5)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit",offset=5)))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog",offset=5)))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit",offset=5)))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog",offset=5)))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log",offset=5)))[,1])
layout(1)


layout(matrix(1:6,nrow=2))
# logit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,offset=-5)))[,1])
# probit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="probit",offset=-5)))[,1])
# cloglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cloglog",offset=-5)))[,1])
# cauchit link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="cauchit",offset=-5)))[,1])
# loglog link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="loglog",offset=-5)))[,1])
# log link
hist(t(replicate(100,simul_data_UniYX_binom(4,4,link="log",offset=-5)))[,1])
layout(1)

Data generating function for multivariate plsR models

Description

This function generates a single multivariate response value Y\boldsymbol{Y} and a vector of explinatory variables (X1,…,Xtotdim)(X_1,\ldots,X_{totdim}) drawn from a model with a given number of latent components.

Usage

simul_data_YX(totdim, ncomp)

Arguments

totdim

Number of column of the X vector (from ncomp to hardware limits)
ncomp

Number of latent components in the model (from 2 to 6)

Details

This function should be combined with the replicate function to give rise to a larger dataset. The algorithm used is a port of the one described in the article of Li which is a multivariate generalization of the algorithm of Naes and Martens.

Value

vector

(Y1,…,Yr,X1,…,Xtotdim)(Y_1,\ldots,Y_r,X_1,\ldots,X_{totdim})

Note

The value of rr depends on the value of ncomp :

ncomp rr
2 3
3 3
4 4

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

T. Naes, H. Martens, Comparison of prediction methods for multicollinear data, Commun. Stat., Simul. 14 (1985) 545-576.
Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89, doi:10.1016/S0169-7439(02)00051-5.

See Also

simul_data_complete for highlighting the simulations parameters

Examples

simul_data_YX(20,6)                          


if(require(plsdepot)){
dimX <- 6
Astar <- 2
(dataAstar2 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plsdepot)
resAstar2 <- plsreg2(dataAstar2[,4:9],dataAstar2[,1:3],comps=5)
resAstar2$Q2
resAstar2$Q2[,4]>0.0975

dimX <- 6
Astar <- 3
(dataAstar3 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plsdepot)
resAstar3 <- plsreg2(dataAstar3[,4:9],dataAstar3[,1:3],comps=5)
resAstar3$Q2
resAstar3$Q2[,4]>0.0975

dimX <- 6
Astar <- 4
(dataAstar4 <- t(replicate(50,simul_data_YX(dimX,Astar))))
library(plsdepot)
resAstar4 <- plsreg2(dataAstar4[,5:10],dataAstar4[,1:4],comps=5)
resAstar4$Q2
resAstar4$Q2[,5]>0.0975

rm(list=c("dimX","Astar"))
}

Summary method for plsRglm models

Description

This function provides a summary method for the class "cv.plsRglmmodel"

Usage

## S3 method for class 'cv.plsRglmmodel'
summary(object, ...)

Arguments

object

an object of the class "cv.plsRglmmodel"
...

further arguments to be passed to or from methods.

Value

An object of class "summary.cv.plsRmodel" if model is missing or model="pls". Otherwise an object of class "summary.cv.plsRglmmodel".

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
summary(cv.plsRglm(Y~.,data=Cornell,nt=10,NK=1,
modele="pls-glm-family",family=gaussian(), verbose=FALSE))
rm(list=c("XCornell","yCornell","bbb"))

Summary method for plsR models

Description

This function provides a summary method for the class "cv.plsRmodel"

Usage

## S3 method for class 'cv.plsRmodel'
summary(object, ...)

Arguments

object

an object of the class "cv.plsRmodel"
...

further arguments to be passed to or from methods.

Value

An object of class "summary.cv.plsRglmmodel".

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
summary(cv.plsR(Y~.,data=Cornell,nt=10,K=6, verbose=FALSE), verbose=FALSE)

Summary method for experimental multivariate PLS2 CV models

Description

Summarizes repeated k-fold cross-validation results from cv.plsRmulti.

Usage

## S3 method for class 'cv.plsRmultiModel'
summary(object, verbose = TRUE, ...)

Arguments

object

An object of class "cv.plsRmultiModel".
verbose

Should progress information be displayed?
...

Further arguments passed to methods.

Details

The returned object inherits from "summary.cv.plsRmodel" so that cvtable and the existing plot method can be reused for the aggregated multivariate criteria.

Value

A list of per-partition summary matrices with the same aggregate columns used by summary.cv.plsRmodel for Q2, PRESS, and RSS, plus response-specific PRESS, RSS, Q2, and R2 columns.

See Also

cv.plsRmulti, cvtable

Examples

set.seed(123)
X <- matrix(rnorm(60 * 4), ncol = 4)
Y <- cbind(
  y1 = X[, 1] - 0.5 * X[, 2] + rnorm(60, sd = 0.1),
  y2 = 0.3 * X[, 2] + X[, 3] + rnorm(60, sd = 0.1)
)

cv_fit <- cv.plsRmulti(Y, X, nt = 2, K = 3, NK = 1, verbose = FALSE)
summary(cv_fit, verbose = FALSE)

Summary method for plsRglm models

Description

This function provides a summary method for the class "plsRglmmodel"

Usage

## S3 method for class 'plsRglmmodel'
summary(object, ...)

Arguments

object

an object of the class "plsRglmmodel"
...

further arguments to be passed to or from methods.

Value

call

function call of plsRglmmodel

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modplsglm <- plsRglm(yCornell,XCornell,3,modele="pls-glm-gaussian")
class(modplsglm)
summary(modplsglm)
rm(list=c("XCornell","yCornell","modplsglm"))

Summary method for plsR models

Description

This function provides a summary method for the class "plsRmodel"

Usage

## S3 method for class 'plsRmodel'
summary(object, ...)

Arguments

object

an object of the class "plsRmodel"
...

further arguments to be passed to or from methods.

Value

call

function call of plsRmodel

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

References

Nicolas Meyer, Myriam Maumy-Bertrand et Frédéric 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. https://www.numdam.org/item/JSFS_2010__151_2_1_0/

See Also

summary

Examples

data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]
modpls <- plsR(yCornell,XCornell,3,modele="pls")
class(modpls)
summary(modpls)
rm(list=c("XCornell","yCornell","modpls"))

Non-parametric tilted bootstrap for PLS regression models

Description

Provides a wrapper for the bootstrap function tilt.boot from the boot R package.
Implements non-parametric tilted bootstrap for PLS regression models by case resampling : the tilt.boot function will run an initial bootstrap with equal resampling probabilities (if required) and will use the output of the initial run to find resampling probabilities which put the value of the statistic at required values. It then runs an importance resampling bootstrap using the calculated probabilities as the resampling distribution.

Usage

tilt.bootpls(
  object,
  typeboot = "plsmodel",
  statistic = coefs.plsR,
  R = c(499, 250, 250),
  alpha = c(0.025, 0.975),
  sim = "ordinary",
  stype = "i",
  index = 1,
  stabvalue = 1e+06,
  ...
)

Arguments

object

An object of class plsRmodel to bootstrap
typeboot

The type of bootstrap. Either (Y,X) boostrap (typeboot="plsmodel") or (Y,T) bootstrap (typeboot="fmodel_np"). Defaults to (Y,T) resampling.
statistic

A function which when applied to data returns a vector containing the statistic(s) of interest. statistic must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the ... argument.
R

The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
alpha

The alpha level to which tilting is required. This parameter is ignored if R[1] is 0 or if theta is supplied, otherwise it is used to find the values of theta as quantiles of the initial uniform bootstrap. In this case R[1] should be large enough that min(c(alpha, 1-alpha))*R[1] > 5, if this is not the case then a warning is generated to the effect that the theta are extreme values and so the tilted output may be unreliable.
sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".
stype

A character string indicating what the second argument of statistic represents. Possible values of stype are "i" (indices - the default), "f" (frequencies), or "w" (weights).
index

The index of the statistic of interest in the output from statistic. By default the first element of the output of statistic is used.
stabvalue

Upper bound for the absolute value of the coefficients.
...

ny further arguments can be passed to statistic.

Value

An object of class "boot".

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

tilt.boot

Examples

## Not run: 
data(Cornell)
XCornell<-Cornell[,1:7]
yCornell<-Cornell[,8]

set.seed(1385)
Cornell.tilt.boot <- tilt.bootpls(plsR(yCornell,XCornell,1), statistic=coefs.plsR, 
typeboot="fmodel_np", R=c(499, 100, 100), alpha=c(0.025, 0.975), sim="ordinary", 
stype="i", index=1)
Cornell.tilt.boot
str(Cornell.tilt.boot)

boxplots.bootpls(Cornell.tilt.boot,indices=2:7)

rm(Cornell.tilt.boot)

## End(Not run)

Non-parametric tilted bootstrap for PLS generalized linear regression models

Description

Provides a wrapper for the bootstrap function tilt.boot from the boot R package.
Implements non-parametric tilted bootstrap for PLS generalized linear regression models by case resampling : the tilt.boot function will run an initial bootstrap with equal resampling probabilities (if required) and will use the output of the initial run to find resampling probabilities which put the value of the statistic at required values. It then runs an importance resampling bootstrap using the calculated probabilities as the resampling distribution.

Usage

tilt.bootplsglm(
  object,
  typeboot = "fmodel_np",
  statistic = coefs.plsRglm,
  R = c(499, 250, 250),
  alpha = c(0.025, 0.975),
  sim = "ordinary",
  stype = "i",
  index = 1,
  stabvalue = 1e+06,
  ...
)

Arguments

object

An object of class plsRbetamodel to bootstrap
typeboot

The type of bootstrap. Either (Y,X) boostrap (typeboot="plsmodel") or (Y,T) bootstrap (typeboot="fmodel_np"). Defaults to (Y,T) resampling.
statistic

A function which when applied to data returns a vector containing the statistic(s) of interest. statistic must take at least two arguments. The first argument passed will always be the original data. The second will be a vector of indices, frequencies or weights which define the bootstrap sample. Further, if predictions are required, then a third argument is required which would be a vector of the random indices used to generate the bootstrap predictions. Any further arguments can be passed to statistic through the ... argument.
R

The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights.
alpha

The alpha level to which tilting is required. This parameter is ignored if R[1] is 0 or if theta is supplied, otherwise it is used to find the values of theta as quantiles of the initial uniform bootstrap. In this case R[1] should be large enough that min(c(alpha, 1-alpha))*R[1] > 5, if this is not the case then a warning is generated to the effect that the theta are extreme values and so the tilted output may be unreliable.
sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "balanced", "permutation", or "antithetic".
stype

A character string indicating what the second argument of statistic represents. Possible values of stype are "i" (indices - the default), "f" (frequencies), or "w" (weights).
index

The index of the statistic of interest in the output from statistic. By default the first element of the output of statistic is used.
stabvalue

Upper bound for the absolute value of the coefficients.
...

ny further arguments can be passed to statistic.

Value

An object of class "boot".

Author(s)

Frédéric Bertrand
[email protected]
https://fbertran.github.io/homepage/

See Also

tilt.boot

Examples

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

dataset <- cbind(y=yaze_compl,Xaze_compl)

# Lazraq-Cleroux PLS bootstrap Classic

aze_compl.tilt.boot <- tilt.bootplsglm(plsRglm(yaze_compl,Xaze_compl,3, 
modele="pls-glm-logistic", family=NULL), statistic=coefs.plsRglm, R=c(499, 100, 100), 
alpha=c(0.025, 0.975), sim="ordinary", stype="i", index=1)
boxplots.bootpls(aze_compl.tilt.boot,1:2)

aze_compl.tilt.boot2 <- tilt.bootplsglm(plsRglm(yaze_compl,Xaze_compl,3, 
modele="pls-glm-logistic"), statistic=coefs.plsRglm, R=c(499, 100, 100), 
alpha=c(0.025, 0.975), sim="ordinary", stype="i", index=1)
boxplots.bootpls(aze_compl.tilt.boot2,1:2)

aze_compl.tilt.boot3 <- tilt.bootplsglm(plsRglm(yaze_compl,Xaze_compl,3, 
modele="pls-glm-family", family=binomial), statistic=coefs.plsRglm, R=c(499, 100, 100), 
alpha=c(0.025, 0.975), sim="ordinary", stype="i", index=1)
boxplots.bootpls(aze_compl.tilt.boot3,1:2)


# PLS bootstrap balanced

aze_compl.tilt.boot4 <- tilt.bootplsglm(plsRglm(yaze_compl,Xaze_compl,3, 
modele="pls-glm-logistic"), statistic=coefs.plsRglm, R=c(499, 100, 100), 
alpha=c(0.025, 0.975), sim="balanced", stype="i", index=1)
boxplots.bootpls(aze_compl.tilt.boot4,1:2)

Incomplete dataset for the quality of wine dataset

Description

Quality of Bordeaux wines (Quality) and four potentially predictive variables (Temperature, Sunshine, Heat and Rain).
The value of Temperature for the first observation was remove from the matrix of predictors on purpose.

Format

A data frame with 34 observations on the following 4 variables.

Temperature

a numeric vector

Sunshine

a numeric vector

Heat

a numeric vector

Rain

a numeric vector

Source

P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.

References

M. Tenenhaus. (2005). La regression logistique PLS. In J.-J. Droesbeke, M. Lejeune, and G. Saporta, editors, Modeles statistiques pour donnees qualitatives. Editions Technip, Paris.

Examples

data(XbordeauxNA)
str(XbordeauxNA)

Incomplete dataset from the pine caterpillars example

Description

The caterpillar dataset was extracted from a 1973 study on pine processionary caterpillars. It assesses the influence of some forest settlement characteristics on the development of caterpillar colonies. There are k=10 potentially explanatory variables defined on n=33 areas.
The value of x2 for the first observation was remove from the matrix of predictors on purpose.

Format

A data frame with 33 observations on the following 10 variables and one missing value.

x1

altitude (in meters)

x2

slope (en degrees)

x3

number of pines in the area

x4

height (in meters) of the tree sampled at the center of the area

x5

diameter (in meters) of the tree sampled at the center of the area

x6

index of the settlement density

x7

orientation of the area (from 1 if southbound to 2 otherwise)

x8

height (in meters) of the dominant tree

x9

number of vegetation strata

x10

mix settlement index (from 1 if not mixed to 2 if mixed)

Details

These caterpillars got their names from their habit of moving over the ground in incredibly long head-to-tail processions when leaving their nest to create a new colony.
The XpineNAX21 is a dataset with a missing value for testing purpose.

Source

Tomassone R., Audrain S., Lesquoy-de Turckeim E., Millier C. (1992). “La régression, nouveaux regards sur une ancienne méthode statistique”, INRA, Actualités Scientifiques et Agronomiques, Masson, Paris.

Examples

data(XpineNAX21)
str(XpineNAX21)