getStationaryLaw.dmm {drimmR}

Get the stationary laws of the DMM

Description

Evaluate the stationary law of the DMM at a given position or at every position

Usage

## S3 method for class 'dmm'
getStationaryLaw(x, pos, all.pos = FALSE, internal = FALSE, ncpu = 2)

Arguments

x

An object of class dmm

pos

A positive integer giving the position along the sequence on which the stationary law of the DMM should be computed

all.pos

'FALSE' (default, evaluation at position index) ; 'TRUE' (evaluation for all position indices)

internal

'FALSE' (default) ; 'TRUE' (for internal use of th initial law of fitdmm and word applications)

ncpu

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

Details

Stationary law at position t is evaluated by solving \mu_t \ \pi_{\frac{t}{n}} = \mu

Value

A vector or matrix of stationary law probabilities

Author(s)

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, stationary_distributions, getDistribution

Examples

data(lambda, package = "drimmR")
dmm <- fitdmm(lambda, 1, 1, c('a','c','g','t'), init.estim = "freq", fit.method="sum")
t <- 10
getStationaryLaw(dmm,pos=t)