rmarkovchain: Function to generate a sequence of states from homogeneous or...

In markovchain: Easy Handling Discrete Time Markov Chains

Function to generate a sequence of states from homogeneous or non-homogeneous Markov chains.

Description

Provided any markovchain or markovchainList objects, it returns a sequence of states coming from the underlying stationary distribution.

Usage

rmarkovchain(
  n,
  object,
  what = "data.frame",
  useRCpp = TRUE,
  parallel = FALSE,
  num.cores = NULL,
  ...
)

Arguments

n	Sample size
object	Either a markovchain or a markovchainList object
what	It specifies whether either a data.frame or a matrix (each rows represent a simulation) or a list is returned.
useRCpp	Boolean. Should RCpp fast implementation being used? Default is yes.
parallel	Boolean. Should parallel implementation being used? Default is yes.
num.cores	Number of threads used when parallel = TRUE. If NULL (the default) the thread count is read from getOption("RcppParallel.numThreads"), then getOption("Ncpus"), then RCPP_PARALLEL_NUM_THREADS, then OMP_NUM_THREADS, falling back to min(2, cores) as CRAN policy requires. Previous versions defaulted to parallel::detectCores() - 1, which could grab all available cores unexpectedly.
...	additional parameters passed to the internal sampler

Details

When a homogeneous process is assumed (markovchain object) a sequence is sampled of size n. When a non - homogeneous process is assumed, n samples are taken but the process is assumed to last from the begin to the end of the non-homogeneous markov process.

Value

Character Vector, data.frame, list or matrix

Note

Check the type of input

Author(s)

Giorgio Spedicato

References

A First Course in Probability (8th Edition), Sheldon Ross, Prentice Hall 2010

See Also

markovchainFit, markovchainSequence

Examples

# define the markovchain object
statesNames <- c("a", "b", "c")
mcB <- new("markovchain", states = statesNames, 
   transitionMatrix = matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), 
   nrow = 3, byrow = TRUE, dimnames = list(statesNames, statesNames)))

# show the sequence
outs <- rmarkovchain(n = 100, object = mcB, what = "list")


#define markovchainList object
statesNames <- c("a", "b", "c")
mcA <- new("markovchain", states = statesNames, transitionMatrix = 
   matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
   byrow = TRUE, dimnames = list(statesNames, statesNames)))
mcB <- new("markovchain", states = statesNames, transitionMatrix = 
   matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
   byrow = TRUE, dimnames = list(statesNames, statesNames)))
mcC <- new("markovchain", states = statesNames, transitionMatrix = 
   matrix(c(0.2, 0.5, 0.3, 0, 0.2, 0.8, 0.1, 0.8, 0.1), nrow = 3, 
   byrow = TRUE, dimnames = list(statesNames, statesNames)))
mclist <- new("markovchainList", markovchains = list(mcA, mcB, mcC)) 

# show the list of sequence
rmarkovchain(100, mclist, "list")
     

markovchain documentation built on Oct. 10, 2026, 9:07 a.m.