simulate.dmm {drimmR}

Simulate a sequence under a drifting Markov model

Description

Simulate a sequence under a k-th order DMM.

Usage

## S3 method for class 'dmm'
simulate(x, output_file = NULL, model_size = NULL, ncpu = 2)

Arguments

x

An object of class dmm

output_file

(Optional) File containing the simulated sequence (e.g, "C:/.../SIM.txt")

model_size

Size of the model

ncpu

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

Value

the vector of simulated sequence

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

fitdmm, getTransitionMatrix, getStationaryLaw

Examples

data(lambda, package = "drimmR")
dmm <- fitdmm(lambda, 1, 1, c('a','c','g','t'), init.estim = "freq", fit.method="sum")
simulate(dmm, model_size=100)