Functional Principal Component Analysis Diagnostics plot
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.
CreateDiagnosticsPlot(...) ## S3 method for class 'FPCA' plot(x, openNewDev = FALSE, addLegend = TRUE, ...)
... |
passed into |
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. |
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.