tune_temperatures {bggum} | R Documentation |

Find Optimal Temperatures for the GGUM MCMCMC Sampler

tune_temperatures(data, n_temps, temp_tune_iterations = 5000, n_draws = 2500, K = NULL, proposal_sds = NULL, sd_tune_iterations = 5000, 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))

`data` |
An integer matrix giving the response by each respondent to each item |

`n_temps` |
How many temperatures to make? |

`temp_tune_iterations` |
How many iterations should the temperature tuning algorithm run for each temperature? (default is 5000) |

`n_draws` |
How many draws should be used to determine each temperature? (specifying n_draws < temp_tune_iterations will result in an error; default is 2500). |

`K` |
(Optional) A numeric vector with an element for each item giving the number of options for the item; if not provided, it is generated by taking the number of unique options observed in the data |

`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. |

`sd_tune_iterations` |
A numeric vector of length one; if proposal standard deviations are not given, this provides the number of iterations to use to tune the proposals before the temperature finding algorithm begins (default is 5000) |

`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) |

AtchadÃ©, Roberts, and Rosenthal (2011) determine the optimal swap-acceptance rate for Metropolis-coupled MCMC and provide an algorithm for building optimal temperature schedules. We implement this algorithm in the context of the GGUM to provide a temperature schedule that should result in approximately 0.234 swap acceptance rate between adjacent chains.

Please see the vignette (via `vignette("bggum")`

) for a full in-depth
practical guide to Bayesian estimation of GGUM parameters.

A numeric vector of temperatures

AtchadÃ©, Yves F., Gareth O. Roberts, and Jeffrey S. Rosenthal.
2011. “Towards Optimal Scaling of Metropolis-Coupled Markov Chain
Monte Carlo.” *Statistics and Computing* 21(4): 555–68.

## NOTE: This is a toy example just to demonstrate the function, which uses ## a small dataset and an unreasonably low number of sampling interations. ## For a longer practical guide on Bayesian estimation of GGUM parameters, ## please see the vignette ( via vignette("bggum") ). ## We'll simulate data to use for this example: set.seed(123) sim_data <- ggum_simulation(100, 10, 2) ## Now we can tune the temperature schedule: ## (for the purposes of example, we use 100 iterations, ## though in practice you would use much more) temps <- tune_temperatures(data = sim_data$response_matrix, n_temps = 5, temp_tune_iterations = 100, n_draws = 50, sd_tune_iterations = 100)

[Package *bggum* version 1.0.2 Index]