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,...}?
Names of the states don't really need to have any statistical meaning as long as they are different. So, while simulating transitions between states, it's perfectly fine to choose states <- 1:length(init)
or any other names for them. Ultimately, though, for practical purposes we often have in mind some labels in mind, such as -1, 0, ..., n, as in your example. You can provide those names as the labels
parameter and then labels[simlist]
will rename 1:length(init)
to labels
, element by element. I.e., if initially we had c(1, 2, 3)
and you provided labels
as c(5, 10, 12)
, then the output will be in terms of the latter vector. For instance,
(states <- sample(1:3, 10, replace = TRUE))
# [1] 1 3 3 2 2 1 2 1 3 3
labels <- c(5, 10, 12)
labels[states]
# [1] 5 12 12 10 10 5 10 5 12 12
来源:https://stackoverflow.com/questions/56061603/understanding-markov-chain-source-code-in-r