1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
|
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# 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> |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# BDMCMC algorithm for graphical models based on marginal pseudo-likelihood
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
bdgraph.mpl = function( data, n = NULL, method = "ggm", transfer = TRUE, algorithm = "bdmcmc",
iter = 5000, burnin = iter / 2, g.prior = 0.2, g.start = "empty",
jump = NULL, alpha = 0.5, save = FALSE,
cores = NULL, operator = "or", verbose = TRUE )
{
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 )
}
if( inherits( data, "sim" ) )
data <- data $ data
colnames_data = colnames( data )
if( !is.matrix( data ) & !is.data.frame( data ) )
stop( "Data must be a matrix or dataframe" )
if( is.data.frame( data ) ) data <- data.matrix( data )
if( any( is.na( data ) ) )
stop( "'bdgraph.mpl()' does not deal with missing values. You could use 'bdgraph()' function with option method = 'gcgm'" )
p <- ncol( data )
if( p < 3 )
stop( "Number of variables/nodes ('p') must be more than 2" )
if( is.null( n ) )
n <- nrow( data )
if( ( is.null( cores ) ) & ( p < 16 ) )
cores = 1
cores = BDgraph::get_cores( cores = cores, verbose = verbose )
if( method == "ggm" )
{
if( isSymmetric( data ) )
{
if ( is.null( n ) )
stop( "Please specify the number of observations 'n'" )
cat( "Input is identified as the covariance matrix \n" )
S <- data
}else{
S <- t( data ) %*% data
}
}
if( ( method == "dgm" ) || ( method == "dgm-binary" ) )
{
if( transfer == TRUE )
data = transfer( r_data = data )
p = ncol( data ) - 1
freq_data = data[ , p + 1 ]
data = data[ , -( p + 1 ) ]
n = sum( freq_data )
max_range_nodes = apply( data, 2, max )
max_range_nodes = max_range_nodes + 1
length_f_data = length( freq_data )
}
if( method == "dgm-binary" )
if( ( min( data ) != 0 ) || ( max( data ) != 1 ) )
stop( "For the case 'method = \"dgm-binary\"', data must be binary, 0 or 1" )
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 )
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 ( 1, 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 needs to run this function is around: " )
print( ( iter - burnin ) * utils::object.size( string_g ), units = "auto" )
}
last_graph = matrix( 0, p, p )
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 ) && ( algorithm != "hc" ) )
cat( paste( c( iter, " MCMC sampling ... in progress: \n" ), collapse = "" ) )
print = floor( iter / 20 )
# - - - main BDMCMC algorithms implemented in C++ - - - - - - - - - - - - -|
if( save == TRUE )
{
if( ( method == "ggm" ) && ( algorithm == "rjmcmc" ) )
{
result = .C( "ggm_rjmcmc_mpl_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "ggm" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "ggm_bdmcmc_mpl_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "ggm" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
counter_all_g = 0
result = .C( "ggm_bdmcmc_mpl_map_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
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(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "rjmcmc" ) )
{
result = .C( "dgm_rjmcmc_mpl_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm-binary" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_binary_map", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.double(alpha), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
sample_graphs = as.character(sample_graphs), graph_weights = as.double(graph_weights), size_sample_g = as.integer(size_sample_g),
as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
counter_all_g = 0
result = .C( "dgm_bdmcmc_mpl_map_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
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(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm-binary" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
counter_all_g = 0
result = .C( "dgm_bdmcmc_mpl_binary_map_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.double(alpha), as.integer(n), as.integer(p),
all_graphs = as.integer(all_graphs), all_weights = as.double(all_weights),
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(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
}else{
if( ( method == "ggm" ) && ( algorithm == "rjmcmc" ) )
{
result = .C( "ggm_rjmcmc_mpl_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
p_links = as.double(p_links), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "ggm" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "ggm_bdmcmc_mpl_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
p_links = as.double(p_links), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "ggm" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
result = .C( "ggm_bdmcmc_mpl_ma_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior), as.double(S), as.integer(n), as.integer(p),
p_links = as.double(p_links), as.integer(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "rjmcmc" ) )
{
result = .C( "dgm_rjmcmc_mpl_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha),
as.integer(n), as.integer(p), p_links = as.double(p_links), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha),
as.integer(n), as.integer(p), p_links = as.double(p_links), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm-binary" ) && ( algorithm == "bdmcmc" ) && ( jump == 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_binary_ma", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.double(alpha),
as.integer(n), as.integer(p), p_links = as.double(p_links), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_ma_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.integer(max_range_nodes), as.double(alpha), as.integer(n), as.integer(p),
p_links = as.double(p_links), as.integer(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
if( ( method == "dgm-binary" ) && ( algorithm == "bdmcmc" ) && ( jump != 1 ) )
{
result = .C( "dgm_bdmcmc_mpl_binary_ma_multi_update", as.integer(iter), as.integer(burnin), G = as.integer(G), as.double(g_prior),
as.integer(data), as.integer(freq_data), as.integer(length_f_data), as.double(alpha), as.integer(n), as.integer(p),
p_links = as.double(p_links), as.integer(jump), as.integer(trace_mcmc), PACKAGE = "BDgraph" )
}
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -|
if( algorithm != "hc" )
{
last_graph = matrix( result $ G, p, p )
colnames( last_graph ) = colnames_data[1:p]
if( save == TRUE )
{
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,
all_graphs = all_graphs, all_weights = all_weights, last_graph = last_graph,
data = data, method = method )
}else{
p_links = matrix( result $ p_links, p, p )
if( algorithm == "rjmcmc" )
p_links = p_links / ( iter - burnin )
p_links[ lower.tri( p_links ) ] = 0
colnames( p_links ) = colnames_data[1:p]
output = list( p_links = p_links, last_graph = last_graph,
data = data, method = method )
}
}else{
if( method == "dgm" )
selected_graph = hill_climb_mpl( data = data, freq_data = freq_data, n = n, max_range_nodes = max_range_nodes, alpha = alpha, operator = operator )
if( method == "dgm-binary" )
selected_graph = hill_climb_mpl_binary( data = data, freq_data = freq_data, n = n, alpha = alpha, operator = operator )
colnames( selected_graph ) = colnames_data[ 1:p ]
output = list( selected_graph = selected_graph,
data = data, method = method )
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -|
class( output ) = "bdgraph"
return( output )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
|
