boxplots.bootpls: Boxplot bootstrap distributions

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

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
[e-mail]
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)

plsRglm documentation built on May 3, 2022, 1:07 a.m.