pumBayes (version 1.0.2)

predict_pum: Calculate Probabilities for Probit Unfolding Models

Description

This function computes the probability matrix for both static and dynamic Probit Unfolding Models. Specifically, it calculates the probabilities of voting "Yea" for each legislator (member), issue, (and time period) based on the posterior samples of model parameters.

Usage

predict_pum(vote_info, years_v = NULL, post_samples)

Value

An array of probabilities with three dimensions. The first one represents to members, the second one refers to issues, and the third one refers to MCMC iterations.

Arguments

vote_info

A logical vote matrix (or a rollcall object) in which rows represent members and columns represent issues. The entries should be FALSE ("No"), TRUE ("Yes"), or NA (missing data).

years_v

A vector representing the time period for each vote in the model. This is defultly set as `NULL` for a static model.

post_samples

A list of posterior samples of parameters obtained from MCMC.

Examples

# \donttest{
# Long-running example
data(h116)
h116.c = preprocess_rollcall(h116)
hyperparams <- list(beta_mean = 0, beta_var = 1, alpha_mean = c(0, 0),
                    alpha_scale = 5, delta_mean = c(-2, 10), delta_scale = sqrt(10))
control <- list(num_iter = 2, burn_in = 0, keep_iter = 1, flip_rate = 0.1)
h116.c.pum <- sample_pum_static(h116.c, hyperparams,
                                  control, pos_leg = grep("SCALISE", rownames(h116.c$votes)),
                                  verbose = FALSE, pre_run = NULL, appended = FALSE)
h116.c.pum.predprob = predict_pum(h116.c, years_v = NULL, h116.c.pum)
# }