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## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Copyright (C) 2012 - 2022 Reza Mohammadi |
# |
# This file is part of BDgraph package. |
# |
# BDgraph is free software: you can redistribute it and/or modify it under |
# the terms of the GNU General Public License as published by the Free |
# Software Foundation; see <https://cran.r-project.org/web/licenses/GPL-3>.|
# |
# Maintainer: Reza Mohammadi <a.mohammadi@uva.nl> |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# posterior predict function for "bdgraph" object |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
posterior.predict = function( object, iter = 1, ... )
{
if( is.null( object $ all_graphs ) )
stop( "'bdgraph.obj' must be an object of function 'bdgraph()' or 'ssgraph()' with option 'save = TRUE'" )
method = object $ method
data = object $ data
n_data = nrow( data )
p = ncol( data )
if( isSymmetric( data ) )
{
S = data
}else{
S = t( data ) %*% data
}
sample_graphs = object $ sample_graphs
all_graphs = object $ all_graphs
graph_weights = object $ graph_weights
sample_G = sample( x = sample_graphs, size = iter, replace = TRUE, prob = graph_weights )
G_i = matrix( 0, nrow = p, ncol = p )
upper_G_i = G_i[ upper.tri( G_i ) ]
Z = matrix( 0, nrow = iter, ncol = p )
for( i in 1:iter )
{
upper_G_i = upper_G_i * 0
upper_G_i[ which( unlist( strsplit( as.character( sample_G[i] ), "" ) ) == 1 ) ] = 1
G_i[ upper.tri( G_i ) ] = upper_G_i
K_i = BDgraph::rgwish( n = 1, adj = G_i, b = 3 + n_data, D = diag( p ) + S )
sigma_i = solve( K_i )
Z[ i, ] = BDgraph::rmvnorm( n = 1, mean = 0, sigma = sigma_i )
}
if( method == "ggm" )
sample = Z
if( method == "tgm" )
{
mean = 0
nu = 1
tau_gamma = stats::rgamma( n = iter, shape = nu / 2, rate = nu / 2 )
sample = mean + Z / sqrt( tau_gamma )
}
if( method == "gcgm" )
{
K = object $ K_hat
if( is.null( K ) )
{
G = BDgraph::select( bdgraph.obj = object )
sample_K = BDgraph::rgwish( n = 500, adj = G, b = 3 + n_data, D = diag( p ) + S )
K = 0 * G
for( i in 1:dim( sample_K )[3] )
K = K + sample_K[[i]]
K = K / dim( sample_K )[3]
}
sample = 0 * Z
for( j in 1:p )
{
sdj = sqrt( 1 / K[ j, j ] ) # 2a: # variance of component j (given the rest!)
muj = - sum( Z[ , -j, drop = FALSE ] %*% K[ -j, j, drop = FALSE ] / K[ j, j ] )
table_j = table( data[ , j ] )
cat_y_j = as.numeric( names( table_j ) )
len_cat_y_j = length( cat_y_j )
if( len_cat_y_j > 1 )
{
cum_prop_yj = cumsum( table_j[ -len_cat_y_j ] ) / n_data
#cut_j = vector( length = len_cat_y_j - 1 )
# for( k in 1:length( cut_j ) ) cut_j[ k ] = stats::qnorm( cum_prop_yj[ k ] )
cut_j = stats::qnorm( cum_prop_yj, mean = 0, sd = 1 )
breaks = c( min( Z[ , j ] ) - 1, cut_j, max( Z[ , j ] ) + 1 )
ind_sj = as.integer( cut( Z[ , j ], breaks = breaks, right = FALSE ) )
sample[ , j ] = cat_y_j[ ind_sj ]
}else{
sample[ , j ] = cat_y_j
}
}
}
if( method == "dw" )
{
q = object $ q.est
beta = object $ beta.est
mean = rep( 0, p )
#Z = tmvtnorm::rtmvnorm( n = iter, mean = mean, sigma = sigma, lower = rep( -5, length = p ), upper = rep( 5, length = p ) )
pnorm_Z = stats::pnorm( Z )
if( is.matrix( q ) && is.matrix( beta ) )
{
for( j in 1 : p )
sample[ ,j ] = BDgraph::qdweibull( pnorm_Z[ , j ], q = q[ , j ], beta = beta[ , j ], zero = TRUE )
}
if( is.vector( q ) && is.vector( beta ) )
{
for( j in 1 : p )
sample[ , j ] = BDgraph::qdweibull( pnorm_Z[ , j ], q = q[ j ], beta = beta[ j ], zero = TRUE )
}
}
return( sample )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
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