obtain_gamma: Obtain the transition probabilities gamma for a fitted...

In mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation

Obtain the transition probabilities gamma for a fitted multilevel HMM

Description

obtain_gamma obtains the transition probability matrix (TPM) for an object containing a fitted multilevel hidden Markov model. The TPM can be obtained either at the group level, i.e., representing the average transition probability matrix over all subjects, or at the subject level, returning the transition probability matrices for each subject.

Usage

obtain_gamma(object, level = "group", burn_in = NULL)

Arguments

object	An object of class mHMM, generated by the function mHMM.
level	String specifying if the returned transition probability matrix gamma should be at the group level (level = "group"), i.e., representing the average transition probability matrix over all subjects, or at the subject level (level = "subject").
burn_in	An integer which specifies the number of iterations to discard when obtaining the model parameter summary statistics. When left unspecified (burn_in = NULL), the burn in period specified when creating the mHMM object will be used.

Value

obtain_gamma creates an object of the class mHMM_gamma. This object can be directly plotted using the function plot.mHMM_gamma(), or simply plot(). Depending on the specification at the input variable level, the output is either a matrix with the transition probabilities at the group level (if level = "group"), or a list of matrices (with the number of elements equal to the number of subjects analyzed, if level = 'subject'), where each matrix in the list represents a subject specific transition probability matrix.

See Also

mHMM for fitting the multilevel hidden Markov model, and plot.mHMM_gamma for plotting the obtained transition probabilities.

Examples

###### Example on package data

# specifying general model properties:
m <- 2
n_dep <- 4
q_emiss <- c(3, 2, 3, 2)

# specifying starting values
start_TM <- diag(.8, m)
start_TM[lower.tri(start_TM) | upper.tri(start_TM)] <- .2
start_EM <- list(matrix(c(0.05, 0.90, 0.05,
                          0.90, 0.05, 0.05), byrow = TRUE,
                        nrow = m, ncol = q_emiss[1]), # vocalizing patient
                 matrix(c(0.1, 0.9,
                          0.1, 0.9), byrow = TRUE, nrow = m,
                        ncol = q_emiss[2]), # looking patient
                 matrix(c(0.90, 0.05, 0.05,
                          0.05, 0.90, 0.05), byrow = TRUE,
                        nrow = m, ncol = q_emiss[3]), # vocalizing therapist
                 matrix(c(0.1, 0.9,
                          0.1, 0.9), byrow = TRUE, nrow = m,
                        ncol = q_emiss[4])) # looking therapist

# Run a model without covariate(s):
out_2st <- mHMM(s_data = nonverbal,
                gen = list(m = m, n_dep = n_dep, q_emiss = q_emiss),
                start_val = c(list(start_TM), start_EM),
                mcmc = list(J = 11, burn_in = 5))

out_2st
summary(out_2st)

# obtaining the transition probabilities at the group and subject level
obtain_gamma(out_2st, level = "group")
obtain_gamma(out_2st, level = "subject")

mHMMbayes documentation built on Oct. 2, 2023, 5:06 p.m.