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 one-dimensonal domains
# and calculate MFPCA (cf. MFPCA help)
set.seed(1)
# simulate data (one-dimensional 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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