ggumMC3: GGUM MC3

Description Usage Arguments Value See Also

Description

Metropolis Coupled Markov Chain Monte Carlo Sampling for the GGUM

Usage

1
2
3
4
5
6
7
8
9
ggumMC3(data, sample_iterations = 10000, burn_iterations = 10000,
  sd_tune_iterations = 5000, temp_tune_iterations = 5000,
  temp_n_draws = 2500, swap_interval = 1, flip_interval = NA,
  n_temps = length(temps), temps = NULL, optimize_temps = TRUE,
  temp_multiplier = 0.1, proposal_sds = NULL, theta_init = NULL,
  alpha_init = NULL, delta_init = NULL, tau_init = NULL,
  theta_prior_params = c(0, 1), alpha_prior_params = c(1.5, 1.5, 0.25,
  4), delta_prior_params = c(2, 2, -5, 5), tau_prior_params = c(2, 2,
  -6, 6), return_sds = TRUE, return_temps = TRUE)

Arguments

data

A numeric matrix giving the individuals' responses

sample_iterations

A vector of length one giving the number of iterations the sampler should complete (default is 10000)

burn_iterations

A vector of length one giving the number of iterations to burn in (default is 10000)

sd_tune_iterations

A numeric vector of length one; the number of iterations to use to tune the proposals before the burn-in period begins (default is 5000). If 0 is given, the proposals are not tuned.

temp_tune_iterations

A numeric vector of length one; if a temperature schedule is not provided in the temps argument and optimize_temps = TRUE, temp_tune_iterations gives the number of iterations to use to tune each temperature before the burn-in period begins (default is 5000) – see tune_temperatures

temp_n_draws

A numeric vector of length one; if a temperature schedule is not provided in the temps argument and optimize_temps = TRUE, temp_n_draws gives the number of draws from the temperature finding algorithm to calculate each temperature (default is 2500) – see tune_temperatures

swap_interval

The period by which to attempt chain swaps; e.g. if swap_interval = 100, a state swap will be proposed between two adjacent chains every 100 iterations (default is 1)

flip_interval

(Optional) If given, provides the number of iterations after which the sign of the thetas and deltas should be changed. For example, if flip_interval = 1000, every 1000 iterations the theta and delta parameters will be multiplied by -1 (a valid parameter value change as discussed in Geyer (1991)).

n_temps

The number of chains; should only be given if temps is not specified

temps

(Optional) A numeric vector giving the temperatures; if not provided and optimize_temps = FALSE, each temperature T_t for t > 1 is given by 1 + temp_multiplier * (t-1), and T_1 = 1, while if optimize_temps = TRUE, the temperature schedule is determined according to an optimal temperature finding algorithm – see tune_temperatures

optimize_temps

A logical vector of length one; if TRUE and a temperature schedule is not provided in the temps argument, an algorithm is run to determine the optimal temperature schedule (default is TRUE) – see tune_temperatures

temp_multiplier

A numeric vector of length one; if a temperature schedule is not provided and optimize_temps = FALSE, controls the differences between temperatures as described in the description of the temps argument (default is 0.1)

proposal_sds

(Optional) A list of length four where is element is a numeric vector giving standard deviations for the proposals; the first element should be a numeric vector with a standard deviation for the proposal for each respondent's theta parameter (the latent trait), the second a vector with a standard deviation for each item's alpha (discrimination) parameter, the third a vector with a standard deviation for each item's delta (location) parameter, and the fourth a vector with a standard deviation for each item's tau (option threshold) parameters. If not given, the standard deviations are all set to 1.0 before any tuning begins.

theta_init

(Optional) Either a numeric vector giving an initial value for each respondent's theta parameter, or a numeric matrix giving an initial value for each respondent's theta parameter for each parallel chain; if not given, the initial values are drawn from the prior distribution

alpha_init

(Optional) Either a numeric vector giving an initial value for each item's alpha parameter, or a numeric matrix giving an initial value for each item's alpha parameter for each parallel chain; if not given, the initial values are drawn from the prior distribution

delta_init

(Optional) Either a numeric vector giving an initial value for each item's delta parameter, or a numeric matrix giving an initial value for each item's delta parameter for each parallel chain; if not given, the initial values are drawn from the prior distribution

tau_init

(Optional) Either a list giving an initial value for each item's tau vector, or a list of lists giving an initial value for each item's tau vector for each parallel chain; if not given, the initial values are drawn from the prior distribution

theta_prior_params

A numeric vector of length two; the mean and standard deviation of theta parameters' prior distribution (where the theta parameters have a normal prior; the default is 0 and 1)

alpha_prior_params

A numeric vector of length four; the two shape parameters and a and b values for alpha parameters' prior distribution (where the alpha parameters have a four parameter beta prior; the default is 1.5, 1.5, 0.25, and 4)

delta_prior_params

A numeric vector of length four; the two shape parameters and a and b values for delta parameters' prior distribution (where the delta parameters have a four parameter beta prior; the default is 2, 2, -5, and 5)

tau_prior_params

A numeric vector of length four; the two shape parameters and a and b values for tau parameters' prior distribution (where the tau parameters have a four parameter beta prior; the default is 2, 2, -6, and 6)

return_sds

A logical vector of length one; if TRUE, the proposal standard deviations are stored in an attribute of the returned object named "proposal_sds." The default is TRUE.

return_temps

A logical vector of length one; if TRUE, the temperatures of the parallel chains are stored in an attribute of the returned object named "proposal_temps." The default is TRUE.

Value

A numeric matrix giving the parameter values at each iteration for the cold chain

See Also

ggumProbability, ggumMCMC, tune_temperatures


duckmayr/bggum documentation built on June 5, 2019, 5:14 a.m.