Simulates animals of each class from the reference allele frequencies of
the diagnostic markers, adds genotyping error, and classifies them with
classify_hybrids(). The result shows how often each class can be called
correctly with these markers, and what it is mistaken for.
Arguments
- markers
Output of
diagnostic_markers()(columnsfreq1,freq2).- n
Animals simulated per class.
- error
Genotyping error rate used to simulate and classify.
- alpha
Passed to
classify_hybrids().- seed
Random seed.
Value
A matrix of proportions: true classes in rows, called classes in columns. The diagonal is the expected accuracy.
Examples
ex <- hybridclock_example()
mk <- diagnostic_markers(ex$genotypes, ex$groups, "species1", "species2", locus = ex$locus)
round(classification_power(mk[1:15, ], n = 200), 2)
#> called
#> true pure1 pure2 F1 F2 BC1_species1 BC1_species2 BC2_species1
#> pure1 0.96 0.00 0 0.00 0.00 0.00 0.04
#> pure2 0.00 0.96 0 0.00 0.00 0.00 0.00
#> F1 0.00 0.00 1 0.00 0.00 0.00 0.00
#> F2 0.00 0.00 0 0.94 0.04 0.01 0.00
#> BC1_species1 0.00 0.00 0 0.00 0.88 0.00 0.12
#> BC1_species2 0.00 0.00 0 0.03 0.00 0.83 0.00
#> BC2_species1 0.01 0.00 0 0.00 0.18 0.00 0.80
#> BC2_species2 0.00 0.03 0 0.00 0.00 0.14 0.00
#> called
#> true BC2_species2 later
#> pure1 0.00 0
#> pure2 0.04 0
#> F1 0.00 0
#> F2 0.00 0
#> BC1_species1 0.00 0
#> BC1_species2 0.14 0
#> BC2_species1 0.00 0
#> BC2_species2 0.84 0