id_post_pred-idealstan-method: Posterior Prediction for 'idealstan' objects

In idealstan: Robust Measurement with 'Stan'

Posterior Prediction for idealstan objects

Description

This function will draw from the posterior distribution, whether in terms of the outcome (prediction) or to produce the log-likelihood values.

This function can also produce either distribution of the outcomes (i.e., predictions) or the log-likelihood values of the posterior (set option type to 'log_lik'. For more information, see the package vignette How to Evaluate Models.

You can then use functions such as id_plot_ppc() to see how well the model does returning the correct number of categories in the score/vote matrix. Also see help("posterior_predict", package = "rstanarm")

Usage

## S4 method for signature 'idealstan'
id_post_pred(
  object,
  newdata = NULL,
  draws = 100,
  output = "observed",
  type = "predict",
  covar = "person",
  sample_scores = NULL,
  item_subset = NULL,
  pred_outcome = NULL,
  use_cores = 1,
  use_chain = NULL,
  skip_cov = FALSE,
  ...
)

Arguments

object	A fitted idealstan object
newdata	Optional: pass a data frame that must have all of the predictors that were given to the id_make function. Used to generate predictions from person or item covariates on to items.
draws	The number of draws to use from the total number of posterior draws (default is 100). Set to "all" to use all draws in the chains. For reproducibility, you can also pass a vector of specific draws to use.
output	If the model has an unbounded outcome (Poisson, continuous, etc.), then specify whether to show the 'observed' data (the default) or the binary output 'missing' showing whether an observation was predicted as missing or not
type	Whether to produce posterior predictive values ('predict', the default), the posterior expected (average) values ('epred'), or log-likelihood values ('log_lik'). See the How to Evaluate Models vignette for more info.
covar	What kind of covariates to include as part of the prediction – either "person" (the default) or "items" if you included predictors for item discriminations.
sample_scores	In addition to reducing the number of posterior draws used to calculate the posterior predictive distribution, which will reduce computational overhead. Only available for calculating predictive distributions, not log-likelihood values.
item_subset	Whether to calculate marginal effects for only a subset of items. Should be item IDs that match the item_id column passed to the id_make function.
pred_outcome	In the case of ordinal responses, the number of the category to predict. Defaults to top category.
use_cores	Number of cores to use for multicore parallel processing with the base R parallel package
use_chain	ID of MCMC chain to use rather than all chains (the default).
skip_cov	Whether to skip adding in hierarchical person-level covariates. Defaults to FALSE.
...	Any other arguments passed on to posterior_predict (currently none available)

Value

An object of class id_pred_obj (a list of matrices of posterior-predicted draws, one per model type). If type="log_lik", a draws-by-observations matrix of class log_lik with a chain_order attribute for use with derive_chain.

Examples

sim <- id_sim_gen()
est <- id_estimate(sim, model_type=1, fixtype='vb_full',
                   use_method="pathfinder", nchains=2, ncores=2)
# posterior predictive distribution (100 draws)
ppc <- id_post_pred(est, draws=50)
# log-likelihood matrix (for loo)
ll <- id_post_pred(est, type='log_lik')

idealstan documentation built on May 13, 2026, 1:08 a.m.