https://github.com/cran/BDgraph
Tip revision: 8c39889a9a3f88629878e2f4caca9cea1ea67fa1 authored by Reza Mohammadi on 05 September 2019, 11:40:03 UTC
version 2.61
version 2.61
Tip revision: 8c39889
compare.R
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# Copyright (C) 2012 - 2019 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> |
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# This function reports below measures to assess the performance of estimated graphs: |
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compare = function( target, est, est2 = NULL, est3 = NULL, est4 = NULL, main = NULL, vis = FALSE )
{
G = BDgraph::get_graph( target )
est = BDgraph::get_graph( est )
p = ncol( G )
result = matrix( 1, 8, 2 )
result[ 1 , 1 ] = sum( G[ upper.tri( G ) == 1 ] )
result[ 2 , 1 ] = p * ( p - 1 ) / 2 - result[ 1, 1 ]
result[ c( 3, 4 ), 1 ] = 0
result[ , 2 ] = compute_measures( G = G, est_G = est )
if( !is.null( est2 ) )
{
est2 = BDgraph::get_graph( est2 )
result = cbind( result, compute_measures( G = G, est_G = est2 ) )
}
if( !is.null( est3 ) )
{
est3 = BDgraph::get_graph( est3 )
result = cbind( result, compute_measures( G = G, est_G = est3 ) )
}
if( !is.null( est4 ) )
{
est4 = BDgraph::get_graph( est4 )
result = cbind( result, compute_measures( G = G, est_G = est4 ) )
}
result[ is.na( result ) ] = 0
if( is.null( main ) )
{
main = c( "Target", "estimate1" )
if( !is.null( est2 ) ) main = c( main, "estimate2" )
if( !is.null( est3 ) ) main = c( main, "estimate3" )
if( !is.null( est4 ) ) main = c( main, "estimate4" )
}
colnames( result ) <- main
rownames( result ) <- c( "true positive", "true negative", "false positive", "false negative",
"F1-score", "specificity", "sensitivity", "MCC" )
if( vis == TRUE )
{
row_plot = ifelse( is.null( est2 ), 1, 2 )
op = graphics::par( mfrow = c( row_plot, 2 ), pty = "s", omi = c( 0.3, 0.3, 0.3, 0.3 ), mai = c( 0.3, 0.3, 0.3, 0.3 ) )
BDgraph::plot.graph( G , main = main[1] )
BDgraph::plot.graph( est, main = main[2] )
if( !is.null( est2 ) ) BDgraph::plot.graph( est2, main = main[3] )
if( !is.null( est3 ) ) BDgraph::plot.graph( est3, main = main[4] )
if( !is.null( est4 ) ) BDgraph::plot.graph( est4, main = main[5] )
graphics::par( op )
}
return( round( result, 3 ) )
}
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# To compare measures the performance of estimated graphs based on true graph
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compute_measures = function( G, est_G )
{
if( sum( dim( G ) == dim( est_G ) ) != 2 ) stop( " 'target' and 'est' have non-conforming size" )
upper_G = G[ upper.tri( G ) ]
upper_est_G = est_G[ upper.tri( est_G ) ]
tp = sum( ( upper_G == 1 ) * ( upper_est_G == 1 ) ) # True Positive
tn = sum( ( upper_G == 0 ) * ( upper_est_G == 0 ) ) # True Negative
fp = sum( ( upper_G == 0 ) * ( upper_est_G == 1 ) ) # False Positive
fn = sum( ( upper_G == 1 ) * ( upper_est_G == 0 ) ) # False Negative
# harmonic mean of precision and recall, called F-measure or balanced F-score
F1score = ( 2 * tp ) / ( 2 * tp + fp + fn )
specificity = tn / ( tn + fp )
sensitivity = tp / ( tp + fn )
# Matthews Correlation Coefficients (MCC)
mcc = ( ( tp * tn ) - ( fp * fn ) ) / ( sqrt( ( tp + fp ) * ( tp + fn ) ) * sqrt( ( tn + fp ) * ( tn + fn ) ) )
return( c( tp, tn, fp, fn, F1score, specificity, sensitivity, mcc ) )
}
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