The following source code is from a book. Comments are written by me to understand the code better.
#==================================================================
# markov(init,mat,n,states) = Simulates n steps of a Markov chain
#------------------------------------------------------------------
# init = initial distribution
# mat = transition matrix
# labels = a character vector of states used as label of data-frame;
# default is 1, .... k
#-------------------------------------------------------------------
markov <- function(init,mat,n,labels)
{
if (missing(labels)) # check if 'labels' argument is missing
{
labels <- 1:length(init) # obtain the length of init-vecor, and number them accordingly.
}
simlist <- numeric(n+1) # create an empty vector of 0's
states <- 1:length(init)# ???? use the length of initial distribution to generate states.
simlist[1] <- sample(states,1,prob=init) # sample function returns a random permutation of a vector.
# select one value from the 'states' based on 'init' probabilities.
for (i in 2:(n+1))
{
simlist[i] <- sample(states, 1, prob = mat[simlist[i-1],]) # simlist is a vector.
# so, it is selecting all the columns
# of a specific row from 'mat'
}
labels[simlist]
}
#==================================================================
I have a few confusions regarding this source code.
Why is states <- 1:length(init) used to generate states? What if states are like S ={-1, 0, 1, 2,...}?
