| loglik.dmm {drimmR} | R Documentation |
Evaluate the log-likelihood of a drifting Markov Model
Description
Evaluate the log-likelihood of a drifting Markov Model
Usage
## S3 method for class 'dmm'
loglik(x, sequences, ncpu = 2)
Arguments
x |
An object of class |
sequences |
A character vector or a list of character vectors representing the sequence |
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 log-likelihood (numeric)
Author(s)
Annthomy Gilles, 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
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")
loglik(dmm,sequence)