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