View source: R/influence.quan.R
influence_quan | R Documentation |
Estimate the quantile of measures of influence for each observation.
influence_quan(model,out.influ,mue.boot=500,
smo=0.1,smoX=0.05,alpha=0.95,kmax.fix=FALSE,...)
model |
|
out.influ |
|
mue.boot |
Number of bootstrap samples |
smo |
Smoothing parameter as a proportion of response variance. |
smoX |
Smoothing parameter for |
alpha |
Significance level. |
kmax.fix |
The maximum number of principal comoponents or number of
basis is fixed by |
... |
Further arguments passed to or from other methods. |
Compute the quantile of measures of influence estimated in
influence.fregre.fd
for functional regression using principal
components representation (fregre.pc
) or basis
representation
(fregre.basis
or
fregre.basis.cv
).
A smoothed bootstrap method is used to estimate the quantiles of the influence measures, which allows to point out which observations have the larger influence on estimation and prediction.
Return:
quan.cook.for
: Distance Cook Prediction Quantile.
quan.cook.est
: Distance Cook Estimation Quantile.
quan.cook.Pena
: Pena Distance Quantile.
mues.est
: Sample Cook generated.
mues.pena
: Sample Pena generated.
beta.boot
: Functional beta estimated by bootstrap method.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.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.
See Also as: influence.fregre.fd
,
fregre.basis
, fregre.pc
.
## Not run:
data(tecator)
x=tecator$absorp.fdata
y=tecator$y$Fat
res=fregre.pc(x,y,1:6)
#time consuming
res.infl=influence.fregre.fd(res)
resquan=influence_quan(res,res.infl,4,0.01,0.05,0.95)
plot(res.infl$betas,type="l",col=2)
lines(res$beta.est,type="l",col=3)
lines(resquan$betas.boot,type="l",col="gray")
res=fregre.basis(x,y)
res.infl=influence.fregre.fd(res)
resquan=influence_quan(res,res.infl,mue.boot=4,kmax.fix=T)
plot(resquan$betas.boot,type="l",col=4)
lines(res.infl$betas,type="l",col=2)
lines(resquan$betas.boot,type="l",col="gray")
## End(Not run)
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