boxplots.bootpls: Boxplot bootstrap distributions
In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Description Usage Arguments Value Author(s) See Also Examples

Boxplots for bootstrap distributions.

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

`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.

NULL

Frédéric Bertrand
frederic.bertrand@math.unistra.fr
https://fbertran.github.io/homepage/

bootpls

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)