Cross-entropy criterion for snmf runs

Description

Return the cross-entropy criterion for runs of snmfcwith K ancestral populations. The cross-entropy criterion is based on the prediction of masked genotypes to evaluate the fit of a model with K populations. The cross-entropy criterion helps choosing the number of ancestral populations or a best run for a fixed value of K. A smaller value of cross-entropy means a better run in terms of prediction capability. The cross-entropy criterion is computed by the snmf function when the entropy Boolean option is TRUE.

Usage

cross.entropy(object, K, run)

Arguments

Value

Author(s)

Eric Frichot

See Also

geno snmf G Q

Examples

### Example of analyses using snmf ###

# creation of a genotype file: genotypes.geno.
# The data contains 400 SNPs for 50 individuals.
data("tutorial")
write.geno(tutorial.R, "genotypes.geno")

################
# running snmf #
################

# Runs with K = 3 populations 
# cross-entropy is computed for 2 runs.
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)