Skip to contents

Bootstrap confidence intervals

Source: R/confints.bootpls.R
confints.bootpls.Rd

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.

See also

See also bootpls and bootplsglm.

Author

Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/

Examples


# \donttest{
data(Cornell)

#Lazraq-Cleroux PLS (Y,X) bootstrap
set.seed(250)
modpls <- plsR(Y~.,data=Cornell,3)
#> ____************************************************____
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X nor in Y____
#> ****________________________________________________****
#> 
Cornell.bootYX <- bootpls(modpls, R=250, verbose=FALSE)
confints.bootpls(Cornell.bootYX,2:8)
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#>                                                                         
#> X1 -0.2305299 -0.03654653 -0.2146155 -0.01243502 -0.2657483 -0.063567788
#> X2 -0.3824730 -0.12056633 -0.4240731 -0.16474400 -0.2526435  0.006685662
#> X3 -0.2262325 -0.03807142 -0.2115428 -0.01464437 -0.2604663 -0.063567788
#> X4 -0.4336032 -0.19861671 -0.4793055 -0.22524165 -0.3610949 -0.107030999
#> X5 -0.2895056  0.13307318 -0.3083408  0.07915147 -0.1560125  0.231479782
#> X6  0.3197348  0.65767612  0.3256605  0.67125328  0.2415264  0.587119147
#> X7 -0.2387634 -0.03963758 -0.2590735 -0.03271142 -0.2540574 -0.027695351
#>                          
#> X1 -0.2867282 -0.07494113
#> X2 -0.2795110 -0.11744873
#> X3 -0.2795040 -0.07955903
#> X4 -0.4109452 -0.17018880
#> X5 -0.1803183  0.17569760
#> X6  0.3172633  0.64752609
#> X7 -0.2222602  0.03146667
#> attr(,"typeBCa")
#> [1] TRUE
confints.bootpls(Cornell.bootYX,2:8,typeBCa=FALSE)
#>                                                                         
#> X1 -0.2305299 -0.03654653 -0.2146155 -0.01243502 -0.2657483 -0.063567788
#> X2 -0.3824730 -0.12056633 -0.4240731 -0.16474400 -0.2526435  0.006685662
#> X3 -0.2262325 -0.03807142 -0.2115428 -0.01464437 -0.2604663 -0.063567788
#> X4 -0.4336032 -0.19861671 -0.4793055 -0.22524165 -0.3610949 -0.107030999
#> X5 -0.2895056  0.13307318 -0.3083408  0.07915147 -0.1560125  0.231479782
#> X6  0.3197348  0.65767612  0.3256605  0.67125328  0.2415264  0.587119147
#> X7 -0.2387634 -0.03963758 -0.2590735 -0.03271142 -0.2540574 -0.027695351
#> attr(,"typeBCa")
#> [1] FALSE
# }