simulate.dmm {drimmR} | R Documentation |
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 |
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)