Given a data frame or a matrix (rows are observations, by cols the temporal sequence), it fits a non - homogeneous discrete time markov chain process (storing row). In particular a markovchainList of size = ncol - 1 is obtained estimating transitions from the n samples given by consecutive column pairs.
markovchainListFit(data, byrow = TRUE, laplacian = 0, name)
| data | Either a matrix or a data.frame or a list object. |
|---|---|
| byrow | Indicates whether distinc stochastic processes trajectiories are shown in distinct rows. |
| laplacian | Laplacian correction (default 0). |
| name | Optional name. |
A list containing two slots: estimate (the estimate) name
If data contains NAs then the transitions containing NA will be ignored.
# using holson dataset data(holson) # fitting a single markovchain singleMc <- markovchainFit(data = holson[,2:12]) # fitting a markovchainList mclistFit <- markovchainListFit(data = holson[, 2:12], name = "holsonMcList")