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## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Copyright (C) 2012 - 2021 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> |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Get graph from class objects "sim", "graph", "bdgraph", "ssgraph", or "select"
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_graph = function( obj_G, cut = 0.5 )
{
if( is.matrix( obj_G ) )
{
if( nrow( obj_G ) != ncol( obj_G ) ) stop( "Adjacency matrix must be squere" )
if( ( sum( obj_G == 0 ) + sum( obj_G == 1 ) ) != ( ncol( obj_G ) ^ 2 ) )
stop( "Elements of adjacency matrix must be 0 or 1" )
G = unclass( obj_G )
}else{
if( inherits( obj_G, "sim" ) ) G <- unclass( obj_G $ G )
if( inherits( obj_G, "graph" ) ) G <- unclass( obj_G )
if( ( inherits( obj_G, "bdgraph" ) ) | ( inherits( obj_G, "ssgraph" ) ) ) G <- BDgraph::select( obj_G, cut = cut )
if( inherits( obj_G, "select" ) ) G <- obj_G $ refit
G = as.matrix( G )
}
return( G )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_g_prior = function( g.prior, p )
{
if( is.data.frame( g.prior ) ) g.prior <- data.matrix( g.prior )
if( inherits( g.prior, "dtCMatrix" ) ) g.prior = as.matrix( g.prior )
if( ( inherits( g.prior, "bdgraph" ) ) | ( inherits( g.prior, "ssgraph" ) ) ) g.prior <- BDgraph::plinks( g.prior )
if( inherits( g.prior, "sim" ) )
{
K = as.matrix( g.prior $ K )
g.prior = abs( K / diag( K ) )
}
if( !is.matrix( g.prior ) )
{
if( ( g.prior <= 0 ) | ( g.prior >= 1 ) ) stop( "'g.prior' must be between 0 and 1" )
g.prior = matrix( g.prior, p, p )
}else{
if( ( nrow( g.prior ) != p ) | ( ncol( g.prior ) != p ) ) stop( "'g.prior' and 'data' have non-conforming size" )
if( any( g.prior < 0 ) || any( g.prior > 1 ) ) stop( "Elements of matrix 'g.prior' must be between 0 and 1" )
}
g.prior[ lower.tri( g.prior, diag = TRUE ) ] <- 0
g.prior = g.prior + t( g.prior )
return( g.prior )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_g_start = function( g.start, g_prior, p )
{
if( is.matrix( g.start ) )
{
if( ( sum( g.start == 0 ) + sum( g.start == 1 ) ) != ( p ^ 2 ) )
stop( "Elements of matrix 'g.start' must be 0 or 1" )
G = g.start
}
if( inherits( g.start, "sim" ) ) G <- unclass( g.start $ G )
if( inherits( g.start, "graph" ) ) G <- unclass( g.start )
if( ( inherits( g.start, "bdgraph" ) ) | ( inherits( g.start, "ssgraph" ) ) ) G <- g.start $ last_graph
if( ( inherits( g.start, "character" ) ) && ( g.start == "empty" ) ) G = matrix( 0, p, p )
if( ( inherits( g.start, "character" ) ) && ( g.start == "full" ) ) G = matrix( 1, p, p )
if( ( nrow( G ) != p ) | ( ncol( G ) != p ) )
stop( "'g.start' and 'data' have non-conforming size" )
G[ g_prior == 1 ] = 1
G[ g_prior == 0 ] = 0
G[ lower.tri( G, diag( TRUE ) ) ] <- 0
G = G + t( G )
return( G = G )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_K_start = function( G, g.start, Ts, b_star, threshold )
{
p = ncol( G )
if( ( inherits( g.start, "bdgraph" ) ) | ( inherits( g.start, "ssgraph" ) ) )
K <- g.start $ last_K
if( inherits( g.start, "sim" ) ) K <- g.start $ K
if( ( !inherits( g.start, "bdgraph" ) ) && ( !inherits( g.start, "ssgraph" ) ) && ( !inherits( g.start, "sim" ) ) )
{
K = G
result = .C( "rgwish_c", as.integer(G), as.double(Ts), K = as.double(K), as.integer(b_star), as.integer(p), as.double(threshold), PACKAGE = "BDgraph" )
K = matrix( result $ K, p, p )
}
return( K = K )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_S_n_p = function( data, method, n, not.cont = NULL )
{
if( inherits( data, "sim" ) )
{
not.cont <- data $ not.cont # Do not change the order of these links
data <- data $ 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 ) ) )
{
if( method == "dw" ) stop( "'bdgraph.dw()' does not deal with missing values" )
if( method == "ggm" ) stop( "'ggm' method does not deal with missing values. You could choose option 'method = \"gcgm\"'" )
if( method == "tgm" ) stop( "'tgm' method does not deal with missing values. You could choose option 'method = \"gcgm\"'" )
gcgm_NA = 1
}else{
gcgm_NA = 0
}
if( isSymmetric( data ) )
{
if( method == "gcgm" ) stop( "'method = \"gcgm\"' requires all data" )
if( method == "dw" ) stop( "'method = \"dw\"' requires all data" )
if( method == "tgm" ) stop( "'method = \"tgm\"' requires all data" )
if( is.null( n ) ) stop( "Please specify the number of observations 'n'" )
}
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( method == "ggm" )
{
if( isSymmetric( data ) )
{
cat( "Input is identified as the covariance matrix \n" )
S <- data
}else{
S <- t( data ) %*% data
}
}
if( method == "gcgm" )
{
if( is.null( not.cont ) )
{
not.cont = c( rep( 1, p ) )
for( j in 1:p )
if( length( unique( data[ , j ] ) ) > min( n / 2 ) )
not.cont[ j ] = 0
}else{
if( !is.vector( not.cont ) )
stop( "'not.cont' must be a vector with length of number of variables" )
if( length( not.cont ) != p )
stop( "'not.cont' must be a vector with length of number of variables" )
if( ( sum( not.cont == 0 ) + sum( not.cont == 1 ) ) != p )
stop( "Elements of vector 'not.cont' must be 0 or 1" )
}
R <- 0 * data
for( j in 1:p )
if( not.cont[ j ] )
R[ , j ] = match( data[ , j ], sort( unique( data[ , j ] ) ) )
R[ is.na( R ) ] = -1000 # dealing with missing values
# copula for continuous non-Gaussian data
if( ( gcgm_NA == 0 ) && ( min( apply( R, 2, max ) ) > ( n - 5 * n / 100 ) ) )
{
# copula transfer
data = stats::qnorm( apply( data, 2, rank ) / ( n + 1 ) )
data = t( ( t( data ) - apply( data, 2, mean ) ) / apply( data, 2, stats::sd ) )
method = "ggm"
}else{
# for non-Gaussian data
Z <- stats::qnorm( apply( data, 2, rank, ties.method = "random" ) / ( n + 1 ) )
Zfill <- matrix( stats::rnorm( n * p ), n, p ) # for missing values
Z[ is.na( data ) ] <- Zfill[ is.na( data ) ] # for missing values
Z <- t( ( t( Z ) - apply( Z, 2, mean ) ) / apply( Z, 2, stats::sd ) )
S <- t( Z ) %*% Z
}
}
if( method == "dw" )
{
if( is.null( not.cont ) )
{
not.cont = c( rep( 1, p ) )
}else{
if( !is.vector( not.cont ) ) stop( "'not.cont' must be a vector with length of number of variables" )
if( length( not.cont ) != p ) stop( "'not.cont' must be a vector with length of number of variables" )
if( ( sum( not.cont == 0 ) + sum( not.cont == 1 ) ) != p ) stop( "Elements of vector 'not.cont' must be 0 or 1" )
}
# for non-Gaussian data
Z <- stats::qnorm( apply( data, 2, rank, ties.method = "random" ) / ( n + 1 ) )
Zfill <- matrix( stats::rnorm( n * p ), n, p ) # for missing values
Z[ is.na( data ) ] <- Zfill[ is.na( data ) ] # for missing values
Z <- t( ( t( Z ) - apply( Z, 2, mean ) ) / apply( Z, 2, stats::sd ) )
S <- t( Z ) %*% Z
}
if( method == "tgm" )
S <- t( data ) %*% data
list_out = list( method = method, S = S, n = n, p = p, colnames_data = colnames( data ), data = data )
if( method == "gcgm" )
{
list_out$not.cont = not.cont
list_out$gcgm_NA = gcgm_NA
list_out$Z = Z
list_out$R = R
}
if( method == "dw" )
{
list_out$not.cont = not.cont
list_out$gcgm_NA = gcgm_NA
list_out$Z = Z
}
return( list_out )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_Ds_tgm_R = function( data, tu, mu, D, n, p, in_C = TRUE )
{
if( in_C == TRUE )
{
S = matrix( 0, p, p )
Ds = matrix( 0, p, p )
# void get_Ds_tgm( double data[], double D[], double mu[], double tu[], double Ds[], double S[], int *n, int *p )
result = .C( "get_Ds_tgm", as.double(data), as.double(D), as.double (mu), as.double(tu),
Ds = as.double(Ds), S = as.double(S), as.integer(n), as.integer(p), PACKAGE = "BDgraph" )
S = matrix( result $ S , p, p )
Ds = matrix( result $ Ds, p, p )
}else{
#X_new = matrix( nrow = n, ncol = p )
#for( i in 1:n ){
# sqrt_tu_i = sqrt( tu[ i ] );
# for( j in 1:p ) X_new[ i, j ] = sqrt_tu_i * ( data[ i, j ] - mu[ j ] );
#}
#S = t( X_new ) %*% X_new
S = matrix( 0, p, p )
for( i in 1:p )
for( j in 1:p )
for( k in 1:n )
S[ i, j ] = S[ i, j ] + tu[ k ] * ( data[ k, i ] - mu[ i ] ) * ( data[ k, j ] - mu[ j ] );
Ds = D + S
}
return( list( S = S, Ds = Ds ) )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
get_Ts_R = function( Ds, in_C = TRUE )
{
if( in_C == TRUE )
{
p = ncol( Ds )
Ts = matrix( 0, p, p )
inv_Ds = matrix( 0, p, p )
copy_Ds = matrix( 0, p, p )
# void get_Ts( double Ds[], double Ts[], double inv_Ds[], double copy_Ds[], int *p )
result = .C( "get_Ts", as.double(Ds), Ts = as.double(Ts), as.double(inv_Ds), as.double(copy_Ds), as.integer(p), PACKAGE = "BDgraph" )
Ts = matrix( result $ Ts, p, p )
}else{
invDs = solve( Ds )
Ts = chol( invDs )
}
return( Ts )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
update_tu_R = function( tu, data, K, mu, nu, n, p, in_C = TRUE )
{
if( in_C == TRUE )
{
Ts = matrix( 0, p, p )
inv_Ds = matrix( 0, p, p )
copy_Ds = matrix( 0, p, p )
# void update_tu( double data[], double K[], double tu[], double mu[], double *nu, int *n, int *p )
result = .C( "update_tu", as.double(data), as.double(K), tu = as.double(tu), as.double(mu), as.double(nu), as.integer(n), as.integer(p), PACKAGE = "BDgraph" )
tu = c( result $ tu )
}else{
d_mu_i = numeric( p )
for( i in 1:n )
{
for( j in 1:p )
d_mu_i[ j ] = data[ i, j ] - mu[ j ];
#d_mu_i_x_K = d_mu_i %*% K;
#delta_y_i = sum( d_mu_i_x_K * d_mu_i );
delta_y_i = 0.0;
for( k in 1:p )
for( l in 1:p )
delta_y_i = delta_y_i + d_mu_i[ l ] * K[ l, k ] * d_mu_i[ k ];
shape_tu_i = ( nu + p ) / 2.0;
rate_tu_i = ( nu + delta_y_i ) / 2.0;
tu[ i ] = stats::rgamma( 1, shape = shape_tu_i, scale = 1.0 / rate_tu_i )
}
}
return( tu )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
update_mu_R = function( data, mu, tu, n, p, in_C = TRUE )
{
if( in_C == TRUE )
{
# void update_mu( double data[], double mu[], double tu[], int *n, int *p )
result = .C( "update_mu", as.double(data), mu = as.double(mu), as.double(tu), as.integer(n), as.integer(p), PACKAGE = "BDgraph" )
mu = c( result $ mu )
}else{
mu = c( tu %*% data / sum( tu ) )
}
return( mu )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
|
