This function is a wrapper for boot.ci to derive
bootstrap-based confidence intervals from a "boot" object.
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. Column names
are added so that the returned matrix can be reused by downstream helpers
such as confints2signifind.
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
#> Normal.Lower Normal.Upper Basic.Lower Basic.Upper Percentile.Lower
#> X1 -0.2305299 -0.03654653 -0.2146155 -0.01243502 -0.2657483
#> X2 -0.3824730 -0.12056633 -0.4240731 -0.16474400 -0.2526435
#> X3 -0.2262325 -0.03807142 -0.2115428 -0.01464437 -0.2604663
#> X4 -0.4336032 -0.19861671 -0.4793055 -0.22524165 -0.3610949
#> X5 -0.2895056 0.13307318 -0.3083408 0.07915147 -0.1560125
#> X6 0.3197348 0.65767612 0.3256605 0.67125328 0.2415264
#> X7 -0.2387634 -0.03963758 -0.2590735 -0.03271142 -0.2540574
#> Percentile.Upper BCa.Lower BCa.Upper
#> X1 -0.063567788 -0.2867282 -0.07494113
#> X2 0.006685662 -0.2795110 -0.11744873
#> X3 -0.063567788 -0.2795040 -0.07955903
#> X4 -0.107030999 -0.4109452 -0.17018880
#> X5 0.231479782 -0.1803183 0.17569760
#> X6 0.587119147 0.3172633 0.64752609
#> X7 -0.027695351 -0.2222602 0.03146667
#> attr(,"typeBCa")
#> [1] TRUE
confints.bootpls(Cornell.bootYX,2:8,typeBCa=FALSE)
#> Normal.Lower Normal.Upper Basic.Lower Basic.Upper Percentile.Lower
#> X1 -0.2305299 -0.03654653 -0.2146155 -0.01243502 -0.2657483
#> X2 -0.3824730 -0.12056633 -0.4240731 -0.16474400 -0.2526435
#> X3 -0.2262325 -0.03807142 -0.2115428 -0.01464437 -0.2604663
#> X4 -0.4336032 -0.19861671 -0.4793055 -0.22524165 -0.3610949
#> X5 -0.2895056 0.13307318 -0.3083408 0.07915147 -0.1560125
#> X6 0.3197348 0.65767612 0.3256605 0.67125328 0.2415264
#> X7 -0.2387634 -0.03963758 -0.2590735 -0.03271142 -0.2540574
#> Percentile.Upper
#> X1 -0.063567788
#> X2 0.006685662
#> X3 -0.063567788
#> X4 -0.107030999
#> X5 0.231479782
#> X6 0.587119147
#> X7 -0.027695351
#> attr(,"typeBCa")
#> [1] FALSE
# }