Description Usage Arguments Details Value Note Author(s) References See Also Examples

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

1 2 | ```
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`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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)
``` |

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