
Coefficients for bootstrap computations of PLSBeta models
Source:R/coefs.plsRbetanp.R
coefs.plsRbetanp.Rd
A function passed to boot
to perform bootstrap.
Usage
coefs.plsRbetanp(
dataRepYtt,
ind,
nt,
modele,
family = NULL,
method = "logistic",
link = NULL,
link.phi = NULL,
type = "ML",
verbose = TRUE,
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
- method
method for beta regression
- link
link for beta regression
- link.phi
link.phi for beta regression
- type
type of estimates
- verbose
should info messages be displayed ?
- 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")
bootplsbeta(plsRbeta(yield~.,data=GasolineYield,nt=3, modele="pls-beta"), typeboot="fmodel_np",
R=250, statistic=coefs.plsRbetanp)
#> ____************************************************____
#>
#> Model: pls-beta
#>
#> Link: logit
#>
#> Link.phi:
#>
#> Type: ML
#>
#> ____Component____ 1 ____
#> ____Component____ 2 ____
#> ____Component____ 3 ____
#> ____Predicting X without NA neither in X or Y____
#> ****________________________________________________****
#>
#>
#> 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 -6.917754e-04 0.008434712
#> t2* 0.0129719714 -1.791867e-03 0.015879635
#> t3* -0.0437412382 5.654104e-04 0.030145370
#> t4* 0.1099552630 5.071299e-04 0.050028234
#> t5* 0.0043782576 -1.333676e-03 0.015709145
#> t6* 0.0068269103 5.293340e-04 0.012730336
#> t7* 0.0105674939 3.558548e-04 0.011115232
#> t8* -0.0000215689 -1.773027e-04 0.002033377
#> t9* 0.0034693882 -6.139327e-04 0.013252135
#> t10* 0.0044693385 -1.775854e-04 0.005557751
#> t11* -0.0037252887 1.122405e-03 0.020672308
#> t12* -0.0020821555 3.158314e-04 0.002934637
#> t13* -0.0031646188 -9.781348e-05 0.006505060
#> t14* -0.0089486739 3.823206e-04 0.004190039
# }