Empirical Bayes multivariate functional regression

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