choose.best {Clomial} | R Documentation |
Given the output of Clomial function, the likelihoods of all models are compared, and the best model is determined.
choose.best(models, U = NULL, PTrue = NULL, compareTo = NULL, upto = "All", doTalk=FALSE)
models |
The models trained by |
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 |
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. |
If compareTo
, U
, and PTrue
are NULL
no comparison will be done, and the function runs considerably faster.
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 |
bestModel |
The best model in terms of log-likelihood. |
seconds |
A vector of the time taken, in seconds, to train each model. |
When the number of assumed clones, C
, is greater than 6,
the comparison will be time taking because all possible permutations
of clones should be considered. The running time will be slowed down
by C!
.
Habil Zare
Inferring clonal composition from multiple sections of a breast cancer, Zare et al., Submitted.
Clomial
,
Clomial.likelihood
, Clomial.iterate
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)