cross.entropy {LEA} | R Documentation |
Return the cross-entropy criterion for runs of snmf
cwith 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.
cross.entropy(object, K, run)
object |
A snmfProject object. |
K |
The number of ancestral populations. |
run |
A vector of run labels. |
res |
A matrix containing the cross-entropy criterion for runs with K ancestral populations. |
Eric Frichot
### 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)