fda.usc (version 0.9.4)

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

Description

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

Usage

## S3 method for class 'fregre.fd':
summary(object,times.influ=3,times.sigma=3,draw=TRUE,\dots)
## S3 method for class 'fregre.fd':
print(x, digits = max(3, getOption("digits") - 3),...)

Arguments

object,x

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.

digits

a non-null value for digits specifies the minimum number of significant digits to be printed in values. The default, NULL, uses getOption(digits).

...

Further arguments passed to or from other methods.

Value

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

Details

Shows: ll{ -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: ll{ -Variability of explicative variables explained by Principal Components. -Variability for each principal components -PC-. } If draw=TRUE plot: ll{ -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 defalult. If ask=TRUE, draw each graph in a window, waiting to confirm.

See Also

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

Examples

# Ex 1. Simulated data
library(e1071)
n=500
t=seq(0,1,len=101)
beta=t*sin(2*pi*t)^2 # beta con 2 colinas
x=matrix(NA,ncol=101,nrow=n)
y=numeric(n)
for(i in 1:n){
	x[i, ]=rwiener(1,101)*3+rnorm(101,sd=0.1)
	y[i]=mean(x[i,]*beta)+ rnorm(1,sd=0.1)
}
x.fdata<-fdata(x,t)

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