
Coefficients for permutation bootstrap computations of PLSBeta models
Source:R/permcoefs.plsRbetanp.R
permcoefs.plsRbetanp.Rd
A function passed to boot
to perform bootstrap.
Usage
permcoefs.plsRbetanp(
dataRepYtt,
ind,
nt,
modele,
family = NULL,
maxcoefvalues,
wwetoile,
ifbootfail
)
Arguments
- dataRepYtt
components' coordinates to bootstrap
- ind
indices for resampling
- nt
number of components to use
- modele
type of modele to use, see plsRbeta
- family
glm family to use, see plsRbeta
- maxcoefvalues
maximum values allowed for the estimates of the coefficients to discard those coming from singular bootstrap samples
- wwetoile
values of the Wstar matrix in the original fit
- ifbootfail
value to return if the estimation fails on a bootstrap sample
See also
See also bootplsbeta
Author
Frédéric Bertrand
frederic.bertrand@lecnam.net
https://fbertran.github.io/homepage/
Examples
# \donttest{
data("GasolineYield",package="betareg")
modplsbeta <- plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta")
#> ____************************************************____
#>
#> Model: pls-beta
#>
#> Link: logit
#>
#> Link.phi:
#>
#> Type: ML
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
bootplsbeta(modplsbeta, R=250, statistic=permcoefs.plsRbetanp, typeboot="fmodel_np")
#>
#> ORDINARY NONPARAMETRIC BOOTSTRAP
#>
#>
#> Call:
#> boot(data = dataRepYtt, statistic = statistic, R = 250, sim = sim,
#> stype = stype, nt = nt, modele = modele, family = family,
#> maxcoefvalues = maxcoefvalues[-(1:(length(object$Coeffs) -
#> ncol(object$dataX)))], wwetoile = wwetoile, ifbootfail = ifbootfail)
#>
#>
#> Bootstrap Statistics :
#> original bias std. error
#> t1* 0.0139841003 -1.431575e-02 0.0038229899
#> t2* 0.0129719714 -1.382620e-02 0.0077454137
#> t3* -0.0437412382 4.400794e-02 0.0134061619
#> t4* 0.1099552630 -1.100199e-01 0.0209100182
#> t5* 0.0043782576 -5.044095e-03 0.0080290774
#> t6* 0.0068269103 -6.554950e-03 0.0061983660
#> t7* 0.0105674939 -1.039483e-02 0.0051347876
#> t8* -0.0000215689 -6.398954e-05 0.0009976748
#> t9* 0.0034693882 -3.817977e-03 0.0070556227
#> t10* 0.0044693385 -4.589373e-03 0.0030738554
#> t11* -0.0037252887 4.333924e-03 0.0108662479
#> t12* -0.0020821555 2.235699e-03 0.0014725934
#> t13* -0.0031646188 3.099113e-03 0.0032364535
#> t14* -0.0089486739 9.144411e-03 0.0019703566
# }