calcMFPCA {MFPCA} | R Documentation |
Internal function that implements the MFPCA algorithm for given univariate decompositions
Description
Internal function that implements the MFPCA algorithm for given univariate decompositions
Usage
calcMFPCA(
N,
p,
Bchol,
M,
type,
weights,
npc,
argvals,
uniBasis,
fit = FALSE,
approx.eigen = FALSE
)
Arguments
N |
Number of observations. |
p |
Number of elements in multivariate functional data. |
Bchol |
Cholesky decomposition of B = block diagonal of Cholesky decompositions. |
M |
The number of multivariate functional principal components to calculate. |
type |
Vector of univariate decompositions to use. |
weights |
Vector of weights. |
npc |
Vector giving the number of univariate basis functions used. |
argvals |
List of argument values for each of the univariate basis functions. |
uniBasis |
List of univariate basis functions. |
fit |
Logical. If |
approx.eigen |
Logical. If |
Value
A list containing the following components:
values |
A vector of estimated eigenvalues |
functions |
A
|
scores |
A matrix of dimension |
vectors |
A matrix representing the eigenvectors associated with the combined univariate score vectors. This might be helpful for calculating predictions. |
normFactors |
The normalizing factors used for calculating the multivariate eigenfunctions and scores. This might be helpful when calculation predictions. |
meanFunction |
A multivariate functional
data object, corresponding to the mean function. The MFPCA is applied to
the de-meaned functions in |
fit |
A
|