# plot.pca.fd: Plot Functional Principal Components In fda: Functional Data Analysis

## Description

Display the types of variation across a sample of functions. Label with the eigenvalues that indicate the relative importance of each mode of variation.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```#plot.pca.fd(x, nx = 128, pointplot = TRUE, harm = 0, # expand = 0, cycle = FALSE, ...) #NOTE: The following is required by CRAN rules that # function names like "as.numeric" must follow the documentation # standards for S3 generics, even when they are not. # Please ignore the following line: ## S3 method for class 'pca.fd' plot(x, nx = 128, pointplot = TRUE, harm = 0, expand = 0, cycle = FALSE, ...) ```

## Arguments

 `x` a functional data object. `nx` Number of points to plot or vector (if length > 1) to use as `evalarg` in evaluating and plotting the functional principal components. `pointplot` logical: If TRUE, the harmonics / principal components are plotted as '+' and '-'. Otherwise lines are used. `harm` Harmonics / principal components to plot. If 0, plot all. If length(harm) > sum(par("mfrow")), the user advised, "Waiting to confirm page change..." and / or 'Click or hit ENTER for next page' for each page after the first. `expand` nonnegative real: If expand == 0 then effect of +/- 2 standard deviations of each pc are given otherwise the factor expand is used. `cycle` logical: If cycle=TRUE and there are 2 variables then a cycle plot will be drawn If the number of variables is anything else, cycle will be ignored. `...` other arguments for 'plot'.

## Details

Produces one plot for each principal component / harmonic to be plotted.

## Value

invisible(NULL)

`cca.fd`, `pda.fd` `plot.pca.fd`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```# carry out a PCA of temperature # penalize harmonic acceleration, use varimax rotation daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365) harmaccelLfd <- vec2Lfd(c(0,(2*pi/365)^2,0), c(0, 365)) harmfdPar <- fdPar(daybasis65, harmaccelLfd, lambda=1e5) daytempfd <- smooth.basis(day.5, CanadianWeather\$dailyAv[,,"Temperature.C"], daybasis65, fdnames=list("Day", "Station", "Deg C"))\$fd daytemppcaobj <- pca.fd(daytempfd, nharm=4, harmfdPar) # plot harmonics, asking before each new page after the first: plot.pca.fd(daytemppcaobj) # plot 4 on 1 page op <- par(mfrow=c(2,2)) plot.pca.fd(daytemppcaobj, cex.main=0.9) par(op) ```