bfpca_optimization: Internal main optimization for bfpca

Description

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).

Usage

bfpca_optimization(
  npc,
  npc_varExplained = NULL,
  Kt,
  maxiter,
  print.iter,
  seed,
  periodic,
  error_thresh,
  verbose,
  Y,
  rows,
  I,
  knots,
  Theta_phi,
  xi,
  alpha_coefs
)

Value

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.

Arguments

npc: The number of functional principal components (FPCs) has to be specified either directly as npc or based on their explained share of variance. In the latter case, npc_varExplained has to be set to a share between 0 and 1.
npc_varExplained: The number of functional principal components (FPCs) has to be specified either directly as npc or based on their explained share of variance. In the latter case, npc_varExplained has to be set to a share between 0 and 1.
Kt: Number of B-spline basis functions used to estimate mean functions and functional principal components. Default is 8. If npc_varExplained is used, Kt is set to 20.
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 bfpca.