post_mat_sd | R Documentation |
Compute posterior standard deviation under mixture normal prior
post_mat_sd(G_prior, Bhat, Shat, lBF, lowc_wc, indx_lst, e, ...)
## S3 method for class 'mixture_normal'
post_mat_sd(G_prior, Bhat, Shat, lBF = NULL, lowc_wc, indx_lst, e = 0.001, ...)
## S3 method for class 'mixture_normal_per_scale'
post_mat_sd(G_prior, Bhat, Shat, lBF = NULL, lowc_wc, indx_lst, e = 0.001, ...)
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 |
e |
threshold value to avoid computing posterior that have low alpha value |
... |
Other arguments. |
pxJ matrix of posterior standard deviation
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