Correlation for functional data by Principal Component Analysis

Summary of functional principal components

Usage

# S3 method for class 'fdata.comp'
summary(object, biplot = TRUE, ...)

Arguments

object: fdata.comp class object calculated by: fdata2pc, fdata2pls, fregre.pc or fregre.pls.
biplot: =TRUE draw the biplot and PC (or PLS) components.
...: Further arguments passed to or from other methods.

Value

If corplot=TRUE, are displaying the biplot between the PC (or PLS) components.
If ask=TRUE, draw each graph in a window, waiting to confirm the change of page with a click of the mouse or pressing ENTER. If ask=FALSE draw graphs in one window.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole.

Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S. Springer-Verlag.

Author

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

Examples

if (FALSE) { # \dontrun{
library(fda.usc)
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)
pc1 <- fdata2pc(x,3)
summary.fdata.comp(pc1)
y <- inprod.fdata(x,fbeta) #+ rnorm(n,sd=0.1)
pls1 <- fdata2pls(x,y,2)
summary(pls1)
} # }