bic.dmm {drimmR}

Evaluate the BIC of a drifting Markov Model

Description

Computation of the Bayesian Information Criterion.

Usage

## S3 method for class 'dmm'
bic(x, sequences, ncpu = 2)

Arguments

x

An object of class dmm

sequences

A character vector or a list of character vector representing the sequences for which the BIC will be computed based on x.

ncpu

Default=2. Represents the number of cores used to parallelized computation. If ncpu=-1, then it uses all available cores.

Value

A list of BIC (numeric).

Author(s)

Victor Mataigne, Alexandre Seiller

References

Barbu VS, Vergne N (2018). “Reliability and survival analysis for drifting Markov models: modelling and estimation.” Methodology and Computing in Applied Probability, 1–33. doi: 10.1007/s11009-018-9682-8, https://doi.org/10.1007/s11009-018-9682-8. Vergne N (2008). “Drifting Markov models with polynomial drift and applications to DNA sequences.” Statistical Applications in Genetics Molecular Biology , 7(1) . doi: 10.2202/1544-6115.1326, https://doi.org/10.2202/1544-6115.1326.

See Also

fitdmm, getTransitionMatrix, loglik, bic

Examples

data(lambda, package = "drimmR")
sequence <- c("a","g","g","t","c","g","a","t","a","a","a")
dmm<- fitdmm(lambda, 1, 1, c('a','c','g','t'), init.estim = "freq", fit.method="sum")
bic(dmm,sequence)