Description Usage Arguments Value See Also Examples
View source: R/MFPCAfit_methods.R
Predict functions based on a truncated multivariate Karhunen-Loeve representation:
\hat x = \hat mu + ∑_{m = 1}^M ρ_m \hat ψ_m
with estimated mean function \hat μ and principal components ψ_m. The scores ρ_m can be either estimated (reconstruction of observed functions) or user-defined (construction of new functions).
1 2 |
object |
An object of class |
scores |
A matrix containing the score values. The number of columns in
|
... |
Arguments passed to or from other methods. |
A multiFunData
object containing the predicted functions.
MFPCA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | #' # 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$simData, M = 5, uniExpansions = list(list(type = "uFPCA"),
list(type = "uFPCA")))
# Reconstruct the original data
pred <- predict(PCA) # default reconstructs data used for the MFPCA fit
# plot the results: 1st element
plot(sim$simData[[1]]) # original data
plot(pred[[1]], add = TRUE, lty = 2) # reconstruction
# plot the results: 2nd element
plot(sim$simData[[2]]) # original data
plot(pred[[2]], add = TRUE, lty = 2) # reconstruction
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