Description Usage Arguments Value See Also Examples
View source: R/MFPCAfit_methods.R
Plots the eigenfunctions as perturbations of the mean (i.e. the mean function plus/minus a constant factor times each eigenfunction separately). If all elements have a onedimensional domain, the plots can be combined, otherwise the effects of adding and subtracting are shown in two separate rows for each eigenfunction.
1 2 3 
x 
An object of class 
plotPCs 
The principal components to be plotted. Defaults to all
components in the 
stretchFactor 
The factor by which the principal components are
multiplied before adding / subtracting them from the mean function. If

combined 
Logical: Should the plots be combined? (Works only if all
dimensions are onedimensional). Defaults to 
... 
Further graphical parameters passed to the plot.funData functions for functional data. 
A plot of the principal components as perturbations of the mean.
1 2 3 4 5 6 7 8 9 10 11 12  # Simulate multivariate functional data on onedimensonal domains
# and calculate MFPCA (cf. MFPCA help)
set.seed(1)
# simulate data (onedimensional domains)
sim < simMultiFunData(type = "split", argvals = list(seq(0,1,0.01), seq(0.5,0.5,0.02)),
M = 5, eFunType = "Poly", eValType = "linear", N = 100)
# MFPCA based on univariate FPCA
PCA < MFPCA(sim$simData, M = 5, uniExpansions = list(list(type = "uFPCA"),
list(type = "uFPCA")))
# Plot the results
plot(PCA, combined = TRUE) # combine addition and subtraction in one plot

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