Bootstrap regression
Estimate the beta parameter by wild or smoothed bootstrap procedure
fregre.bootstrap( model, nb = 500, wild = TRUE, type.wild = "golden", newX = NULL, smo = 0.1, smoX = 0.05, alpha = 0.95, kmax.fix = FALSE, draw = TRUE, ... )
model |
|
nb |
Number of bootstrap samples. |
wild |
Naive or smoothed bootstrap depending of the |
type.wild |
Type of distribution of V in wild bootstrap procedure, see
|
newX |
A |
smo |
If >0, smoothed bootstrap on the residuals (proportion of response variance). |
smoX |
If >0, smoothed bootstrap on the explanatory functional
variable |
alpha |
Significance level used for graphical option, |
kmax.fix |
The number of maximum components to consider in each bootstrap iteration. =TRUE, the bootstrap procedure considers the same number of components used in the previous fitted model. =FALSE, the bootstrap procedure estimates the best components in each iteration. |
draw |
=TRUE, plot the bootstrap estimated beta, and (optional) the CI for the predicted response values. |
... |
Further arguments passed to or from other methods. |
Estimate the beta parameter by wild or smoothed bootstrap procedure using
principal components representation fregre.pc, Partial least
squares components (PLS) representation fregre.pls or basis
representation fregre.basis.
If a new curves are in
newX argument the bootstrap method estimates the response using the
bootstrap resamples.
If the model exhibits heteroskedasticity, the use of wild bootstrap procedure is recommended (by default).
Return:
model fregre.pc, fregre.pls or fregre.basis object.
beta.boot functional beta estimated by the nb bootstrap regressions.
norm.boot norm of diferences beetween the nboot betas estimated by bootstrap and beta estimated by regression model.
coefs.boot matrix with the bootstrap estimated basis coefficients.
kn.boot vector or list of length nb with index of the basis, PC or PLS factors selected in each bootstrap
regression.
y.pred predicted response values using newX covariates.
y.boot matrix of bootstrap predicted response values using newX covariates.
newX a fdata class containing the values of the model covariates at which predictions are required (only
for smoothed bootstrap).
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Febrero-Bande, M., Galeano, P. and Gonzalez-Manteiga, W. (2010). Measures of influence for the functional linear model with scalar response. Journal of Multivariate Analysis 101, 327-339.
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
See Also as: fregre.pc, fregre.pls,
fregre.basis, .
## Not run: data(tecator) iest<-1:165 x=tecator$absorp.fdata[iest] y=tecator$y$Fat[iest] nb<-25 ## Time-consuming res.pc=fregre.pc(x,y,1:6) # Fix the compontents used in the each regression res.boot1=fregre.bootstrap(res.pc,nb=nb,wild=FALSE,kmax.fix=TRUE) # Select the "best" compontents used in the each regression res.boot2=fregre.bootstrap(res.pc,nb=nb,wild=FALSE,kmax.fix=FALSE) res.boot3=fregre.bootstrap(res.pc,nb=nb,wild=FALSE,kmax.fix=10) ## predicted responses and bootstrap confidence interval newx=tecator$absorp.fdata[-iest] res.boot4=fregre.bootstrap(res.pc,nb=nb,wild=FALSE,newX=newx,draw=TRUE) ## End(Not run)
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