plsRglm (version 1.2.5)

bootpls: 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=coefs.plsR, sim="ordinary", 
stype="i", stabvalue=1e6, verbose=TRUE,...)

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,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.

Value

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

Details

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

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

# NOT RUN {
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)


# }
# NOT RUN {
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)
# }