snmf
.
The cross-entropy criterion is a value based on the prediction of masked
genotypes to evaluate the error of ancestry estimation. The criterion will
help to choose the best number of ancestral population (K) and the best run
among a set of runs in snmf
. A smaller value of cross-entropy
means a better run in terms of prediction capacity.
The cross-entropy criterion can be automatically calculated by the
snmf
function with the entropy
option.
cross.entropy(object, K, run)
geno
snmf
G
Q
### Example of analyses using snmf ###
# creation of the genotype file, genotypes.geno.
# It contains 400 SNPs for 50 individuals.
data("tutorial")
write.geno(tutorial.R, "genotypes.geno")
################
# runs of snmf #
################
# main options, K: (the number of ancestral populations),
# entropy: calculate the cross-entropy criterion,
# CPU: the number of CPUs.
# Runs with K = 3 with cross-entropy and 2 repetitions.
project = NULL
project = snmf("genotypes.geno", K = 3, entropy = TRUE, repetitions = 2,
project = "new")
# get the cross-entropy for all runs for K = 3
ce = cross.entropy(project, K = 3)
# get the cross-entropy for the 2nd run for K = 3
ce = cross.entropy(project, K = 3, run = 2)
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