# summary.fdata.comp: Correlation for functional data by Principal Component... In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 summary.fdata.comp R Documentation

## Correlation for functional data by Principal Component Analysis

### Description

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.

### Author(s)

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

### 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.

See Also as `fdata2pc`, `fdata2pls` and cor

### Examples

```## Not run:
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)

## End(Not run)

```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.