Model selection with abc - cross validation - based on 100 samples
Confusion matrix based on 100 samples for each model.

$tol0.005
        Fitness LG Neutral
Fitness      86  6       8
LG           10 88       2
Neutral       2  1      97


Mean model posterior probabilities (rejection)

$tol0.005
        Fitness     LG Neutral
Fitness  0.7760 0.1648  0.0592
LG       0.1377 0.8319  0.0304
Neutral  0.0783 0.0657  0.8561




Model selection with abc - Real data
Call: 
postpr(target = FullRealmatrix, index = ModelsVector, sumstat = FullSSmatrix, 
    tol = ABC_Tolerance, method = ABC_Method)
Data:
 postpr.out$values (150 posterior samples)
Models a priori:
 Fitness, LG, Neutral
Models a posteriori:
 Fitness, LG, Neutral

Proportion of accepted simulations (rejection):
Fitness      LG Neutral 
 0.8533  0.0333  0.1133 

Bayes factors:
        Fitness      LG Neutral
Fitness  1.0000 25.6000  7.5294
LG       0.0391  1.0000  0.2941
Neutral  0.1328  3.4000  1.0000





Goodness of fit of Real data

   -Fitness:
$pvalue
[1] 0.27

$s.dist.sim
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.817   4.056   4.313   5.995   4.994  72.531 

$dist.obs
[1] 4.924284

   -LG:
$pvalue
[1] 0.3

$s.dist.sim
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.497   3.742   3.964   4.926   4.445  22.260 

$dist.obs
[1] 4.355899

   -Neutral:
$pvalue
[1] 0.14

$s.dist.sim
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.705   4.001   4.283   5.597   5.002  20.465 

$dist.obs
[1] 8.371825

