fda.usc (version 1.5.0)

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.

Usage

# S3 method for fregre.fd
summary(object,times.influ=3,times.sigma=3,draw=TRUE,…)
# S3 method for fregre.fd
print(x, digits = max(3, getOption("digits") - 3),…)
# S3 method for fregre.lm
summary(object, correlation = FALSE, symbolic.cor = FALSE, 
times.influ = 3,times.sigma = 3,…)
# S3 method for summary.lm
plot(x,times.influ=3,times.sigma=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).

correlation

logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.

symbolic.cor

logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.

…

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:

	-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.

See Also

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

Examples

# NOT RUN {
# 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)

# }