mHMMbayes (version 1.1.0)

pd_RW_emiss_count: Proposal distribution settings RW Metropolis sampler for mHMM Poisson-lognormal emission distribution(s)

Description

pd_RW_emiss_count provides a framework to manually specify the settings of the proposal distribution of the random walk (RW) Metropolis sampler of Poisson emission distribution(s) of the multilevel hidden Markov model, and creates on object of the class mHMM_pdRW_emiss. The RW metropolis sampler is used for sampling the subject level parameter estimates relating to the emission distributions of the dependent variables k, that is, the Poisson parameters lambda.

Usage

pd_RW_emiss_count(gen, emiss_scalar, emiss_w)

Value

pd_RW_emiss_count returns an object of class

mHMM_pdRW_emiss, containing settings of the proposal distribution of the random walk (RW) Metropolis sampler on the categorical emission distribution(s) of the multilevel hidden Markov model. The object is specifically created and formatted for use by the function mHMM, and checked for correct input dimensions. The object contains the following components:

gen

A list containing the elements m and n_dep, used for checking equivalent general model properties specified under pd_RW_emiss_count and mHMM.

emiss_scalar

A list containing n_dep elements denoting the scale factor s of the proposal distribution.

emiss_w

A list containing n_dep elements denoting denoting the weight for the overall log likelihood in the fractional likelihood.

Arguments

gen

List containing the following elements denoting the general model properties:

• m: numeric vector with length 1 denoting the number of hidden states

• n_dep: numeric vector with length 1 denoting the number of dependent variables

• q_emiss: only to be specified if the data represents categorical data. Numeric vector with length n_dep denoting the number of observed categories for the categorical emission distribution for each of the dependent variables.

emiss_scalar

A list containing n_dep elements corresponding to each of the dependent variables, where each element is a numeric vector with length 1 denoting the scale factor s. That is, the scale of the proposal distribution is composed of a covariance matrix Sigma, which is then tuned by multiplying it by a scaling factor s^2.

emiss_w

A list containing n_dep elements corresponding to each of the dependent variables, where each element is a numeric vector with length 1 denoting the weight for the overall log likelihood (i.e., log likelihood based on the pooled data over all subjects) in the fractional likelihood.

Details

When no manual values for the settings of the proposal distribution of the random walk (RW) Metropolis sampler are specified at all (that is, the function pd_RW_emiss_count is not used), emiss_scalar set to 2.38, and emiss_w set to 0.1. See the section Scaling the proposal distribution of the RW Metropolis sampler in vignette("estimation-mhmm") for details.

Within the function mHMM, the acceptance rate of the RW metropolis sampler relating to the emission distribution(s) can be tracked using the output parameter emiss_naccept. An acceptance rate of about 45% is considered optimal when a single parameter is being updated (Gelman, Carlin, Stern & Rubin, 2014).

References

gelman2014mHMMbayes

rossi2012mHMMbayes

Examples

###### Example using simulated data
# specifying general model properties:
n_t     <- 200     # Number of observations on the dependent variable
m       <- 3        # Number of hidden states
n_dep   <- 2        # Number of dependent variables
n_subj  <- 30        # Number of subjects

gamma   <- matrix(c(0.9, 0.05, 0.05,
                    0.2, 0.7, 0.1,
                    0.2,0.3, 0.5), ncol = m, byrow = TRUE)

emiss_distr <- list(matrix(c(20,
                             10,
                             5), nrow = m, byrow = TRUE),
                    matrix(c(50,
                             3,
                             20), nrow = m, byrow = TRUE))

# Define between subject variance to use on the simulating function:
# here, the variance is varied over states within the dependent variable.
var_emiss <- list(matrix(c(5.0, 3.0, 1.5), nrow = m),
                  matrix(c(5.0, 5.0, 5.0), nrow = m))

# Simulate count data:
data_count <- sim_mHMM(n_t = n_t,
                       n = n_subj,
                       data_distr = "count",
                       gen = list(m = m, n_dep = n_dep),
                       gamma = gamma,
                       emiss_distr = emiss_distr,
                       var_gamma = 0.1,
                       var_emiss = var_emiss,
                       return_ind_par = TRUE)


# Transition probabilities
start_gamma <- diag(0.8, m)
start_gamma[lower.tri(start_gamma) | upper.tri(start_gamma)] <- (1 - diag(start_gamma)) / (m - 1)

# Emission distribution
start_emiss <- list(matrix(c(20,10, 5), nrow = m, byrow = TRUE),
                    matrix(c(50, 3,20), nrow = m, byrow = TRUE))

# Specify hyper-prior for the count emission distribution
manual_prior_emiss <- prior_emiss_count(
  gen = list(m = m, n_dep = n_dep),
  emiss_mu0 = list(matrix(c(20, 10, 5), byrow = TRUE, ncol = m),
                   matrix(c(50, 3, 20), byrow = TRUE, ncol = m)),
  emiss_K0  = rep(list(0.1),n_dep),
  emiss_nu  = rep(list(0.1),n_dep),
  emiss_V   = rep(list(rep(10, m)),n_dep)
)

# Specify the desired values for the sampler
manual_emiss_sampler <- pd_RW_emiss_count(gen = list(m = m, n_dep = n_dep),
                                        emiss_scalar = rep(list(2.38),n_dep),
                                        emiss_w = rep(list(0.1), n_dep))

# Run model
# Note that for reasons of running time, J is set at a ridiculous low value.
# One would typically use a number of iterations J of at least 1000,
# and a burn_in of 200.
out_3st_count_RWemiss <- mHMM(s_data = data_count$obs,
                          data_distr = 'count',
                          gen = list(m = m, n_dep = n_dep),
                          start_val = c(list(start_gamma), start_emiss),
                          emiss_hyp_prior = manual_prior_emiss,
                          emiss_sampler = manual_emiss_sampler,
                          mcmc = list(J = 11, burn_in = 5),
                          show_progress = TRUE)

# Examine acceptance rates over dependent variable, individual, and states:
lapply(out_3st_count_RWemiss$emiss_naccept, function(e) e/out_3st_count_RWemiss$input$J)

# Finally, take the average acceptance rate by dependent variable and state:
lapply(out_3st_count_RWemiss$emiss_naccept, function(e) colMeans(e/out_3st_count_RWemiss$input$J))