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## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Copyright (C) 2012 - 2024 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> |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# BDMCMC algorithm for graphical models based on Discrete Weibull |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_bounds_dw = function( data, q, beta, pii, n, p, zero = TRUE )
{
lower_bounds = matrix( 0, nrow = n, ncol = p )
upper_bounds = matrix( 0, nrow = n, ncol = p )
if( is.vector(beta) & is.vector(pii))
{
for ( j in 1:p )
{
for( r in sort( unique( data[ , j ] ) ) )
{
ir = ( 1:n )[ data[ , j ] == r & !is.na( data[ , j ] ) ]
pdw_lb = BDgraph::pdweibull( r - 1, q = q[ j ], beta = beta[ j ], zero = zero )
pdw_ub = BDgraph::pdweibull( r , q = q[ j ], beta = beta[ j ], zero = zero )
lower_bounds[ ir, j ] = stats::qnorm( ( 1 - pii[ j ] ) * ( r != 0 ) + pii[ j ] * pdw_lb )
upper_bounds[ ir, j ] = stats::qnorm( ( 1 - pii[ j ] ) + pii[ j ] * pdw_ub )
}
}
}
if(is.matrix(beta) & is.vector(pii))
{
for(j in 1:p)
{
for(r in sort(unique(data[, j])))
{
ir = (1:n)[data[, j] == r & !is.na(data[, j])]
pdw_lb = BDgraph::pdweibull(r - 1, q = q[ir, j], beta = beta[ir, j], zero = zero)
pdw_ub = BDgraph::pdweibull(r , q = q[ir, j], beta = beta[ir, j], zero = zero)
lower_bounds[ir, j] = stats::qnorm((1 - pii[j]) * (r != 0) + pii[j] * pdw_lb)
upper_bounds[ir, j] = stats::qnorm((1 - pii[j]) + pii[j] * pdw_ub)
}
}
}
if(is.matrix(beta) & is.matrix(pii))
{
for(j in 1:p)
{
for(r in sort(unique(data[, j])))
{
ir = (1:n)[data[, j] == r & !is.na(data[, j])]
pdw_lb = BDgraph::pdweibull(r - 1, q = q[ir, j], beta = beta[ir, j], zero = zero)
pdw_ub = BDgraph::pdweibull(r , q = q[ir, j], beta = beta[ir, j], zero = zero)
lower_bounds[ir, j] = stats::qnorm((1 - pii[ir, j]) * (r != 0) + pii[ir, j] * pdw_lb)
upper_bounds[ir, j] = stats::qnorm((1 - pii[ir, j]) + pii[ir, j] * pdw_ub)
}
}
}
lower_bounds [ lower_bounds == -Inf ] = - .Machine $ double.xmax
upper_bounds [ upper_bounds == Inf ] = .Machine $ double.xmax
lower_bounds [ lower_bounds == Inf ] = .Machine $ double.xmax
upper_bounds [ upper_bounds == -Inf ] = - .Machine $ double.xmax
return( list( lower_bounds = lower_bounds, upper_bounds = upper_bounds ) )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
bdgraph.dw = function( data, x = NULL, formula = y ~ .,
n = NULL, algorithm = "bdmcmc", iter = 5000,
burnin = iter / 2, g.prior = 0.2, df.prior = 3,
ZI = FALSE, iter_bdw = 5000,
g.start = "empty",jump = NULL, save = FALSE,
q = NULL, beta = NULL, pii = NULL,
cores = NULL, threshold = 1e-8, verbose = TRUE )
{
if( is.matrix( data ) | is.data.frame( data ) )
if( any( data < 0 ) )
stop( "'data' should not have negative values" )
if( df.prior < 3 ) stop( "'prior.df' must be >= 3" )
if( iter < burnin ) stop( "'iter' must be higher than 'burnin'" )
burnin = floor( burnin )
if( is.numeric( verbose ) )
{
if( ( verbose < 1 ) | ( verbose > 100 ) )
stop( "'verbose' (for numeric case) must be between ( 1, 100 )" )
trace_mcmc = floor( verbose )
verbose = TRUE
}else{
trace_mcmc = ifelse( verbose == TRUE, 10, iter + 1000 )
}
list_S_n_p = BDgraph::get_S_n_p( data = data, method = "dw", n = n, not.cont = NULL )
S = list_S_n_p $ S
n = list_S_n_p $ n
p = list_S_n_p $ p
method = list_S_n_p $ method
colnames_data = list_S_n_p $ colnames_data
if( ( is.null( cores ) ) & ( p < 16 ) )
cores = 1
cores = BDgraph::get_cores( cores = cores, verbose = verbose )
not.cont = list_S_n_p $ not.cont
Z = list_S_n_p $ Z
data = list_S_n_p $ data
gcgm_NA = list_S_n_p $ gcgm_NA
sample_marginals = NULL
if( is.null( q ) ) if( inherits( data, "sim" ) ) q = data $ q
if( is.null( beta ) ) if( inherits( data, "sim" ) ) beta = data $ beta
if( is.null( pii ) ) pii = rep( 1, p )
if( is.null( q ) & is.null( beta ) )
{
if( length( ZI ) == 1 ) ZI = rep( ZI, p )
if( length( ZI ) != p ) stop( "'ZI', as a vector, must be of length equal to the number of variables, 'ncol( data )'" )
if( is.null( x ) )
{
q = NULL
beta = NULL
pii = NULL
sample_marginals = vector( "list", p )
cat( paste( c( " MCMC sampling of DW regression parameters ... in progress: \n" ), collapse = "" ) )
for( j in 1 : p )
{
cat( paste( c(" Marginal regression for node ", j, " " ), collapse = "" ) , "\r" )
xy_j = data.frame( y = data[ , j ] )
est_qbeta = BDgraph::bdw.reg( data = xy_j, formula = formula, ZI = ZI[ j ], iter = iter_bdw )
q[ j ] = est_qbeta $ q.est
beta[ j ] = est_qbeta $ beta.est
pii[ j ] = est_qbeta $ pi.est
sample_marginals[[ j ]] = est_qbeta $ sample
}
}
if( !is.null( x ) )
{
q = data * 0
beta = data * 0
pii = vector( length = p )
sample_marginals = vector( "list", p )
cat( paste( c( " MCMC sampling of DW regression parameters... in progress: \n" ), collapse = "" ) )
for( j in 1 : p )
{
cat( paste( c(" Marginal regression for node ", j," " ), collapse = "" ) , "\r" )
xy_j = data.frame( x, y = data[ , j ] )
est_qbeta = BDgraph::bdw.reg( data = xy_j, formula = formula, ZI = ZI[ j ], iter = iter_bdw)
q[ , j ] = est_qbeta $ q.est
beta[ , j ] = est_qbeta $ beta.est
pii[ j ] = est_qbeta $ pi.est
sample_marginals[[ j ]] = est_qbeta $ sample
}
}
}
b = df.prior
b_star = b + n
D = diag( p )
Ds = D + S
Ts = chol( solve( Ds ) )
Ti = chol( solve( D ) ) # only for double Metropolis-Hastings algorithms
g_prior = BDgraph::get_g_prior( g.prior = g.prior, p = p )
G = BDgraph::get_g_start( g.start = g.start, g_prior = g_prior, p = p )
K = BDgraph::get_K_start( G = G, g.start = g.start, Ts = Ts, b_star = b_star, threshold = threshold )
if( save == TRUE )
{
qp1 = ( p * ( p - 1 ) / 2 ) + 1
string_g = paste( c( rep( 0, qp1 ) ), collapse = '' )
sample_graphs = c( rep ( string_g, iter - burnin ) ) # vector of numbers like "10100"
graph_weights = c( rep ( 0, iter - burnin ) ) # waiting time for every state
all_graphs = c( rep ( 0, iter - burnin ) ) # vector of numbers like "10100"
all_weights = c( rep ( 1, iter - burnin ) ) # waiting time for every state
size_sample_g = 0
}else{
p_links = matrix( 0, p, p )
}
if( ( verbose == TRUE ) && ( save == TRUE ) && ( p > 50 & iter > 20000 ) )
{
cat( " WARNING: Memory needed to run this function is around " )
print( ( iter - burnin ) * utils::object.size( string_g ), units = "auto" )
}
K_hat = matrix( 0, p, p )
last_graph = K_hat
last_K = K_hat
if( ( is.null( jump ) ) && ( p > 10 & iter > ( 5000 / p ) ) )
jump = floor( p / 10 )
if( is.null( jump ) ) jump = 1
if( ( p < 10 ) && ( jump > 1 ) ) cat( " WARNING: the value of jump should be 1 " )
if( jump > min( p, sqrt( p * 11 ) ) ) cat( " WARNING: the value of jump should be smaller " )
if( verbose == TRUE )
cat( paste( c( iter, " MCMC sampling ... in progress: \n" ), collapse = "" ) )
bounds = BDgraph::get_bounds_dw( data = data, q = q, beta = beta, pii = pii, n = n, p = p )
lower_bounds = bounds $ lower_bounds
upper_bounds = bounds $ upper_bounds
# - - main BDMCMC algorithms implemented in C++ - - - - - - - - - - - - - |
if( save == TRUE )
{
if( ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "gcgm_dw_bdmcmc_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(Ts),
K = as.double(K), as.integer(p), as.double(threshold),
as.double(Z), as.integer(data), as.double(lower_bounds), as.double(upper_bounds), as.integer(n), as.integer(gcgm_NA),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights), K_hat = as.double(K_hat),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(b), as.integer(b_star), as.double(D), as.double(Ds), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
counter_all_g = 0
result = .C( "gcgm_dw_bdmcmc_map_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(Ts), K = as.double(K), as.integer(p), as.double(threshold),
as.double(Z), as.integer(data), as.double(lower_bounds), as.double(upper_bounds), as.integer(n), as.integer(gcgm_NA),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights), K_hat = as.double(K_hat),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g), counter_all_g = as.integer(counter_all_g),
as.integer(b), as.integer(b_star), as.double(D), as.double(Ds), as.integer(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
}else{
if( ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "gcgm_dw_bdmcmc_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(Ts), K = as.double(K), as.integer(p), as.double(threshold),
as.double(Z), as.integer(data), as.double(lower_bounds), as.double(upper_bounds), as.integer(n), as.integer(gcgm_NA),
K_hat = as.double(K_hat), p_links = as.double(p_links),
as.integer(b), as.integer(b_star), as.double(D), as.double(Ds), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
result = .C( "gcgm_dw_bdmcmc_ma_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(Ts), K = as.double(K), as.integer(p), as.double(threshold),
as.double(Z), as.integer(data), as.double(lower_bounds), as.double(upper_bounds), as.integer(n), as.integer(gcgm_NA),
K_hat = as.double(K_hat), p_links = as.double(p_links),
as.integer(b), as.integer(b_star), as.double(D), as.double(Ds), as.integer(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -|
K_hat = matrix( result $ K_hat, p, p, dimnames = list( colnames_data, colnames_data ) )
last_graph = matrix( result $ G , p, p, dimnames = list( colnames_data, colnames_data ) )
last_K = matrix( result $ K , p, p )
if( save == TRUE )
{
if( algorithm == "rjmcmc" ) K_hat = K_hat / ( iter - burnin )
size_sample_g = result $ size_sample_g
sample_graphs = result $ sample_graphs[ 1 : size_sample_g ]
graph_weights = result $ graph_weights[ 1 : size_sample_g ]
all_graphs = result $ all_graphs + 1
all_weights = result $ all_weights
if( ( algorithm != "rjmcmc" ) & ( jump != 1 ) )
{
all_weights = all_weights[ 1 : ( result $ counter_all_g ) ]
all_graphs = all_graphs[ 1 : ( result $ counter_all_g ) ]
}
output = list( sample_graphs = sample_graphs, graph_weights = graph_weights, K_hat = K_hat,
all_graphs = all_graphs, all_weights = all_weights, last_graph = last_graph, last_K = last_K,
q.est = q, beta.est = beta, pi.est = pii,
data = data, method = "dw" )
}else{
p_links = matrix( result $ p_links, p, p, dimnames = list( colnames_data, colnames_data ) )
if( ( algorithm == "rjmcmc" ) | ( algorithm == "rj-dmh" ) )
{
p_links = p_links / ( iter - burnin )
K_hat = K_hat / ( iter - burnin )
}
p_links[ lower.tri( p_links ) ] = 0
output = list( p_links = p_links, K_hat = K_hat, last_graph = last_graph, last_K = last_K,
q.est = q, beta.est = beta, pi.est = pii,
data = data, method = "dw" )
}
if( !is.null( sample_marginals ) )
output $ sample_marginals = sample_marginals
class( output ) = "bdgraph"
return( output )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
|
