post_mat_mean: Compute posterior mean under mixture normal prior

In stephenslab/susiF.alpha: Sum of Single Functions

Compute posterior mean under mixture normal prior

Description

Add description here.

Usage

post_mat_mean(G_prior, Bhat, Shat, lBF, lowc_wc, indx_lst, e, ...)

## S3 method for class 'mixture_normal'
post_mat_mean(
  G_prior,
  Bhat,
  Shat,
  lBF = NULL,
  lowc_wc,
  indx_lst,
  e = 0.001,
  ...
)

## S3 method for class 'mixture_normal_per_scale'
post_mat_mean(
  G_prior,
  Bhat,
  Shat,
  lBF = NULL,
  lowc_wc,
  indx_lst,
  e = 0.001,
  ...
)

Arguments

G_prior	mixture normal prior
Bhat	matrix pxJ regression coefficient, Bhat[j,t] corresponds to regression coefficient of Y[,t] on X[,j]
Shat	matrix pxJ standard error, Shat[j,t] corresponds to standard error of the regression coefficient of Y[,t] on X[,j]
lBF	log BF
lowc_wc	wavelet coefficient with low count to be discarded
indx_lst	list generated by gen_wavelet_indx for the given level of resolution, used only with class mixture_normal_per_scale
e	threshold value to avoid computing posterior that have low alpha value. Set it to 0 to compute the entire posterior. default value is 0.001
...	Other arguments.

Value

pxJ matrix of posterior mean

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