Description Usage Arguments Value
Main optimization function for bfpca
. If npc_varExplained
is specified, the function simply returns a list with elements npc
(chosen number of FPCs), evalues
(estimated variances of the first 'npc'
FPCs) and evalues_sum
(sum of the estimated variances of the first 20
FPCs, as approximation of the overall variance).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | bfpca_optimization(
npc,
npc_varExplained = NULL,
Kt,
maxiter,
print.iter,
seed,
periodic,
error_thresh,
verbose,
Y,
rows,
I,
knots,
Theta_phi,
xi,
alpha_coefs
)
|
npc |
The number of functional principal components (FPCs)
has to be specified either directly as |
npc_varExplained |
The number of functional principal components (FPCs)
has to be specified either directly as |
Kt |
Number of B-spline basis functions used to estimate mean functions
and functional principal components. Default is 8. If |
maxiter |
Maximum number of iterations to perform for EM algorithm. Default is 50. |
print.iter |
Prints current error and iteration |
seed |
Set seed for reproducibility. Defaults to 1988. |
periodic |
If TRUE, uses periodic b-spline basis functions. Default is FALSE. |
error_thresh |
Error threshold to end iterations. Defaults to 0.0001. |
verbose |
Can be set to integers between 0 and 4 to control the level of detail of the printed diagnostic messages. Higher numbers lead to more detailed messages. Defaults to 1. |
Y, rows, I, knots, Theta_phi, xi, alpha_coefs |
Internal objects created in
|
list with elements t_vec
, Theta_phi_mean
, alpha_coefs
,
efunctions
, evalues
, evalues_sum
, scores
,
subject_coef
, fittedVals
, error
. See documentation of
fpca_gauss
for details.
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