Clomial (version 1.8.0)

choose.best: Chooses the best trained Clomial model.

Description

Given the output of Clomial function, the likelihoods of all models are compared, and the best model is determined.

Usage

choose.best(models, U = NULL, PTrue = NULL, compareTo = NULL, upto =
"All", doTalk=FALSE)

Arguments

models

The models trained by Clomial function.

U

The optional genotype matrix used for comparison.

PTrue

The optional clone frequency matrix used for comparison.

compareTo

The index of the model against which all other models are compared. Set to NULL to disable.

upto

The models with index less than this value are considered. Set to "All" to include every model.

doTalk

If TRUE, information on number of analyzed models is reported.

Value

A list will be made with the following entries:

err

A list with 2 entries; err$P and err$U the vectors of clonal frequency errors, and genotype errors, accordingly.

Li

A vector of the best obtained log-likelihood for each model.

bestInd

The index of the best model in terms of log-likelihood.

comparison

If compareTo is not NULL, the result of comparison with the corresponding model is reported.

bestModel

The best model in terms of log-likelihood.

seconds

A vector of the time taken, in seconds, to train each model.

Details

If compareTo, U, and PTrue are NULL no comparison will be done, and the function runs considerably faster.

References

Inferring clonal composition from multiple sections of a breast cancer, Zare et al., Submitted.

See Also

Clomial, Clomial.likelihood, Clomial.iterate

Examples

set.seed(4)
data(breastCancer)
Dc <- breastCancer$Dc
Dt <- breastCancer$Dt
ClomialResult <-Clomial(Dc=Dc,Dt=Dt,maxIt=20,C=4,doParal=FALSE,binomTryNum=5)
chosen <- choose.best(models=ClomialResult$models)
M1 <- chosen$bestModel
print("Genotypes:")
round(M1$Mu)
print("Clone frequencies:")
M1$P
bestInd <- chosen$bestInd
plot(chosen$Li,ylab="Log-likelihood",type="l")
points(x=bestInd,y=chosen$Li[bestInd],col="red",pch=19)