bic.dmm: Evaluate the BIC of a drifting Markov Model

In drimmR: Estimation, Simulation and Reliability of Drifting Markov Models

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

\insertRef

BaVe2018drimmR \insertRefVer08drimmR

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)

drimmR documentation built on Oct. 9, 2026, 5:08 p.m.