View source: R/fittingFunctions.R
rmarkovchain | R Documentation |
Provided any markovchain
or markovchainList
objects, it returns a sequence of
states coming from the underlying stationary distribution.
rmarkovchain(
n,
object,
what = "data.frame",
useRCpp = TRUE,
parallel = FALSE,
num.cores = NULL,
...
)
n |
Sample size |
object |
Either a |
what |
It specifies whether either a |
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 Cores to be used |
... |
additional parameters passed to the internal sampler |
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.
Character Vector, data.frame, list or matrix
Check the type of input
Giorgio Spedicato
A First Course in Probability (8th Edition), Sheldon Ross, Prentice Hall 2010
markovchainFit
, markovchainSequence
# 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")
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