EBmvFR.workhorse: Empirical Bayes multivariate functional regression

In stephenslab/susiF.alpha: Sum of Single Functions

Empirical Bayes multivariate functional regression

Description

Empirical Bayes multivariate functional regression

Usage

EBmvFR.workhorse(
  obj,
  W,
  X,
  tol,
  lowc_wc,
  init_pi0_w,
  control_mixsqp,
  indx_lst,
  nullweight,
  cal_obj,
  verbose,
  maxit,
  max_step_EM
)

Arguments

obj	an object of class EBmvFR
W	a list in which element D contains matrix of wavelet d coefficients and element C contains the vector of scaling coefficients
X	matrix of size n by p contains the covariates
tol	a small, non-negative number specifying the convergence tolerance for the IBSS fitting procedure. The fitting procedure will halt when the difference in the variational lower bound, or “ELBO” (the objective function to be maximized), is less than tol.
lowc_wc	list of wavelet coefficients that exhibit too little variance
init_pi0_w	starting value of weight on null compoenent in mixsqp (between 0 and 1)
control_mixsqp	list of parameter for mixsqp function see mixsqp package
indx_lst	list generated by gen_wavelet_indx for the given level of resolution
nullweight	numeric value for penalizing likelihood at point mass 0 (useful in small sample size)
cal_obj	logical if set as TRUE compute ELBO for convergence monitoring
verbose	If verbose = TRUE, the algorithm's progress, and a summary of the optimization settings are printed to the console.
maxit	Maximum number of IBSS iterations.
max_step_EM	see susiF function

Details

Empirical Bayes multivariate functional regression

stephenslab/susiF.alpha documentation built on March 1, 2025, 4:28 p.m.