Description Usage Arguments Value See Also Examples
View source: R/MFPCAfit_methods.R
This function plots the proportion of variance explained by the leading eigenvalues in an MFPCA against the number of the principal component.
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x 
An object of class MFPCAfit, typically returned by a call to

npcs 
The number of eigenvalued to be plotted. Defaults to all eigenvalues if their number is less or equal to 10, otherwise show only the leading first 10 eigenvalues. 
type 
The type of screeplot to be plotted. Can be either

ylim 
The limits for the y axis. Can be passed either as a vector
of length 2 or as 
main 
The title of the plot. Defaults to the variable name of

... 
Other graphic parameters passed to

A screeplot, showing the decrease of the principal component score.
1 2 3 4 5 6 7 8 9 10 11 12 13  # 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")))
# screeplot
screeplot(PCA) # default options
screeplot(PCA, npcs = 3, type = "barplot", main= "Screeplot")

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