| post_process {bggum} | R Documentation |
Post-process a Posterior Sample
Description
Post-process the results of ggumMCMC or ggumMC3
using an artificial identifiability constraint (AIC).
Usage
post_process(sample, constraint, expected_sign)
Arguments
sample |
A numeric matrix of posterior draws as returned by
|
constraint |
An integer vector of length one giving the column number of the parameter to constrain, or a character vector of length one giving the column name for the constraint. |
expected_sign |
A character vector of length one giving the sign for the constraint; it should be either "-" if the constrained parameter is to be negative or "+" if the constrained parameter is to be positive. |
Details
Since under the GGUM the probability of a response is the same for any given choice of theta and delta parameters and the negative of that choice; i.e.
Pr(z | \theta, \alpha, \delta, \tau) = Pr(z | -\theta, \alpha, -\delta, \tau),
if symmetric priors are used, the posterior has a reflective mode. This function transforms a posterior sample by enforcing a constraint that a particular parameter is of a given sign, essentially transforming it into a sample from only one of the reflective modes if a suitable constraint is chosen; using a sufficiently extreme parameter is suggested.
Please see the vignette (via vignette("bggum")) for a full in-depth
practical guide to Bayesian estimation of GGUM parameters.
Value
A numeric matrix, the post-processed sample.
See Also
Examples
## 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 generate posterior draws
## (for the purposes of example, we use 100 iterations,
## though in practice you would use much more)
draws <- ggumMC3(data = sim_data$response_matrix, n_temps = 2,
sd_tune_iterations = 100, temp_tune_iterations = 100,
temp_n_draws = 50,
burn_iterations = 100, sample_iterations = 100)
## Then you can post-process the output
processed_draws <- post_process(sample = draws,
constraint = which.min(sim_data$theta),
expected_sign = "-")