plot.mHMM: Plotting the posterior densities for a fitted multilevel HMM

In mHMMbayes: Multilevel Hidden Markov Models Using Bayesian Estimation

Plotting the posterior densities for a fitted multilevel HMM

Description

plot.mHMM plots the posterior densities for a fitted multilevel hidden Markov model for the group and subject level parameters simultaneously. The plotted posterior densities are either for the transition probability matrix gamma, or for the emission distribution probabilities (categorical data) or means and standard deviation (continuous data).

Usage

## S3 method for class 'mHMM'
plot(
  x,
  component = "gamma",
  dep = 1,
  col,
  dep_lab,
  cat_lab,
  lwd1 = 2,
  lwd2 = 1,
  lty1 = 1,
  lty2 = 3,
  legend_cex,
  burn_in,
  ...
)

Arguments

x	Object of class mHMM, generated by the function mHMM.
component	String specifying if the displayed posterior densities should be for the transition probability matrix gamma (component = "gamma"), or for the emission distribution probabilities (component = "emiss"). In case of the latter and the model is based on multiple dependent variables, the user has to indicate for which dependent variable the posterior densities have to be plotted, see dep.
dep	Integer specifying for which dependent variable the posterior densities should be plotted. Only required if one wishes to plot the emission distribution probabilities and the model is based on multiple dependent variables. Defaults to dep = 1.
col	Vector of colors for the posterior density lines. If one is plotting the posterior densities for gamma, or the posterior densities of Normally distributed emission probabilities, the vector has length m (i.e., number of hidden states). If one is plotting the posterior densities for categorical emission probabilities, the vector has length q_emiss[k] (i.e., the number of outcome categories for the dependent variable k).
dep_lab	Optional string when plotting the posterior densities of the emission probabilities with length 1, denoting the label for the dependent variable plotted. Automatically obtained from the input object x when not specified.
cat_lab	Optional vector of strings when plotting the posterior densities of categorical emission probabilities, denoting the labels of the categorical outcome values. Automatically generated when not provided.
lwd1	Positive number indicating the line width of the posterior density at the group level.
lwd2	Positive number indicating the line width of the posterior density at the subject level.
lty1	Positive number indicating the line type of the posterior density at the group level.
lty2	Positive number indicating the line type of the posterior density at the subject level.
legend_cex	A numerical value giving the amount by which plotting text and symbols in the legend should be magnified relative to the default.
burn_in	An integer which specifies the number of iterations to discard when obtaining the model parameter summary statistics. When left unspecified, the burn in period specified at creating the mHMM object with the function mHMM will be used.
...	Arguments to be passed to methods (see par)

Details

Note that the standard deviation of the (variable and) state specific Normal emission distribution in case of continuous data is fixed over subjects. Hence, for the standard deviation, only the posterior distribution at the group level is plotted.

Value

plot.mHMM returns a plot of the posterior densities. Depending on whether (component = "gamma") or (component = "emiss"), the plotted posterior densities are either for the transition probability matrix gamma or for the emission distribution probabilities, respectively.

See Also

mHMM for fitting the multilevel hidden Markov model, creating the object mHMM.

Examples

###### Example on package example data, see ?nonverbal
# First run the function mHMM on example 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))

## plot the posterior densities for gamma
plot(out_2st, component = "gamma")

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