Coefficients for bootstrap computations of PLSBeta models

Source: R/coefs.plsRbetanp.R

A function passed to boot to perform bootstrap.

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

Value

estimates on a bootstrap sample or ifbootfail value if the bootstrap computation fails.

Note

~~some notes~~

See also

See also bootplsbeta

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 -8.475618e-04 0.007835618
#> t2*   0.0129719714 -1.322232e-03 0.015578140
#> t3*  -0.0437412382  2.162199e-03 0.029320315
#> t4*   0.1099552630  2.647930e-03 0.042843717
#> t5*   0.0043782576 -6.610652e-05 0.017398728
#> t6*   0.0068269103 -5.143025e-04 0.013758681
#> t7*   0.0105674939 -3.637492e-04 0.011353774
#> t8*  -0.0000215689 -5.166384e-05 0.002127845
#> t9*   0.0034693882  8.810699e-04 0.015678654
#> t10*  0.0044693385  5.520933e-04 0.006762267
#> t11* -0.0037252887 -1.058912e-03 0.024123222
#> t12* -0.0020821555  1.555461e-04 0.003058469
#> t13* -0.0031646188  5.282972e-04 0.007190695
#> t14* -0.0089486739  2.323208e-04 0.004088138
# }