Description Usage Arguments Value See Also

Metropolis Coupled Markov Chain Monte Carlo Sampling for the GGUM

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)
``` |

`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 |

`temp_n_draws` |
A numeric vector of length one; if a temperature
schedule is not provided in the |

`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 |

`n_temps` |
The number of chains; should only be given if |

`temps` |
(Optional) A numeric vector giving the temperatures;
if not provided and |

`optimize_temps` |
A logical vector of length one; if TRUE and a
temperature schedule is not provided in the |

`temp_multiplier` |
A numeric vector of length one; if a temperature
schedule is not provided and |

`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. |

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

`ggumProbability`

, `ggumMCMC`

,
`tune_temperatures`

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