summary.fregre.fd: Summarizes information from fregre.fd objects.

Description

Summary function for fregre.pc, fregre.basis, fregre.pls, fregre.np
and fregre.plm functions.

Shows:

	-Call.
	-R squared.
	-Residual variance.
	-Index of possible atypical curves or possible outliers.
	-Index of possible influence curves.

If the fregre.fd object comes from the fregre.pc then shows:

	-Variability of explicative variables explained by Principal Components.
	-Variability for each principal components -PC-.

If draw=TRUE plot:

	-y vs y fitted values.
	-Residuals vs fitted values.
	-Standarized residuals vs fitted values.
	-Levarage.
	-Residual boxplot.
	-Quantile-Quantile Plot (qqnorm).

If ask=FALSE draw graphs in one window, by default. If ask=TRUE, draw each graph in a window, waiting to confirm.

Usage

# S3 method for fregre.fd
summary(object, times.influ = 3, times.sigma = 3, draw = TRUE, ...)

Value

Influence Vector of influence measures.
i.influence Index of possible influence curves.
i.atypical Index of possible atypical curves or possible outliers.

Arguments

object: Estimated by functional regression, fregre.fd object.
times.influ: Limit for detect possible infuence curves.
times.sigma: Limit for detect possible oultiers or atypical curves.
draw: =TRUE draw estimation and residuals graphics.
...: Further arguments passed to or from other methods.

Author

Manuel Febrero-Bande and Manuel Oviedo de la Fuente manuel.oviedo@udc.es

Examples

Run this code

if (FALSE) {
# Ex 1. Simulated data
n= 200;tt= seq(0,1,len=101)
x0<-rproc2fdata(n,tt,sigma="wiener")
x1<-rproc2fdata(n,tt,sigma=0.1)
x<-x0*3+x1
beta = tt*sin(2*pi*tt)^2
fbeta = fdata(beta,tt)
y<-inprod.fdata(x,fbeta)+rnorm(n,sd=0.1)

# Functional regression
res=fregre.pc(x,y,l=c(1:5))
summary(res,3,ask=TRUE)

res2=fregre.pls(x,y,l=c(1:4))
summary(res2)

res3=fregre.pls(x,y)
summary(res3)
}