plot.FPCA: Functional Principal Component Analysis Diagnostics plot

In hadjipantelis/tPACE: Functional Data Analysis and Empirical Dynamics

Functional Principal Component Analysis Diagnostics plot

Description

Deprecated. Use plot.FPCA instead.

Plotting the results of an FPCA, including printing the design plot, mean function, scree-plot and the first three eigenfunctions for a functional sample. If provided with a derivative options object (?FPCAder), it will return the differentiated mean function and first two principal modes of variation for 50%, 75%, 100%, 125% and 150% of the defined bandwidth choice.

Usage

CreateDiagnosticsPlot(...)

## S3 method for class 'FPCA'
plot(x, openNewDev = FALSE, addLegend = TRUE, ...)

Arguments

...	passed into plot.FPCA.
x	An FPCA class object returned by FPCA().
openNewDev	A logical specifying if a new device should be opened - default: FALSE
addLegend	A logical specifying whether to add legend.

Details

The black, red, and green curves stand for the first, second, and third eigenfunctions, respectively. plot.FPCA is currently implemented only for the original function, but not a derivative FPCA object.

Examples

set.seed(1)
n <- 20
pts <- seq(0, 1, by=0.05)
sampWiener <- Wiener(n, pts)
sampWiener <- Sparsify(sampWiener, pts, 10)
res1 <- FPCA(sampWiener$Ly, sampWiener$Lt, 
            list(dataType='Sparse', error=FALSE, kernel='epan', verbose=FALSE))
plot(res1)

hadjipantelis/tPACE documentation built on Aug. 16, 2022, 10:45 a.m.