Description Usage Arguments Value See Also Examples
View source: R/MFPCAfit_methods.R
This function plots two scores of a multivariate functional principal component analysis for each observation.
1 2 |
PCAobject |
An object of class |
choices |
The indices of the scores that should by displayed. Defaults
to |
scale |
Logical. Should the scores be scaled by the estimated
eigenvalues to emphasize the proportions of total variance explained by the
components. Defaults to |
... |
Further parameters passed to the
|
A bivariate plot of scores.
1 2 3 4 5 6 7 8 9 10 11 12 | # 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")))
# Plot the first two scores
scoreplot(PCA) # no scaling (default)
scoreplot(PCA, scale = TRUE) # scale the scores by the first two eigenvalues
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