Print on screen a simple description of the content of objects of class phylterinitial.
Usage
# S3 method for class 'phylterinitial'
print(x, ...)Arguments
- x
Object present in the
$initialelement of the object returned by functionphylter().- ...
Additional arguments.
Examples
data(carnivora)
res <- phylter(carnivora, parallel = FALSE)
#>
#> Number of Genes: 125
#> Number of Species: 53
#> --------
#> Initial score: 0.86235
#> 28 new cells to remove -> New score: 0.90272 -> OK
#> 18 new cells to remove -> New score: 0.90833 -> OK
#> 16 new cells to remove -> New score: 0.91501 -> OK
#> 18 new cells to remove -> New score: 0.92561 -> OK
#> 5 new cells to remove -> New score: 0.93404 -> OK
#> 4 new cells to remove -> New score: 0.93692 -> OK
#> 2 new cells to remove -> New score: 0.93712 -> OK
#> 1 new cells to remove -> New score: 0.94392 -> OK
#> 1 new cells to remove -> New score: 0.94417 -> OK
#> 1 new cells to remove -> New score: 0.94426 -> OK
#> => No more outliers detected -> Checking for complete gene outliers
#> => No more outliers detected -> STOPPING OPTIMIZATION
#> --------
#>
#> Total number of outliers detected: 94
#> Number of complete gene outliers : 0
#> Number of complete species outliers : 0
#>
#> Gain (concordance between matrices): 8.19%
#> Loss (data filtering): 1.42%
print(res$Initial)
#> Phylter Analysis - initial state
#> List of class phylterinitial
#>
#> Object Dimension Content
#> 1 $mat.data 125 List of original distance matrices, one per gene
#> 2 $WR 53 x 125 Species x Genes reference matrix
#> 3 $RV 125 x 125 Genes x Genes RV correlation coefficients matrix
#> 4 $weights 125 Weight of each gene in the compromise
#> 5 $compromise 53 x 53 Species x Species compromise matrix
#> 6 $F 53 x 6 Distatis coordinates of compromise
#> 7 $matrices 125 Distatis coordinates of gene matrices (list)
#> 8 $PartialF 125 Species x Species gene matrices (list)