predict_ideal: Calculate Probabilities for the IDEAL Model

In pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data

Calculate Probabilities for the IDEAL Model

Description

This function computes the probability matrix for the IDEAL Model. 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_ideal(vote_info, post_samples)

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).
post_samples	Posterior samples obtained from function 'ideal' in 'pscl' package.

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.

Examples

# Long-running example
data(h116)
h116.c = preprocess_rollcall(h116)
require(pscl)
cl = constrain.legis(h116.c, x = list("CLYBURN" = -1, "SCALISE" = 1),
                     d = 1)
h116.c.ideal = ideal(h116.c, d = 1, priors = cl, startvals = cl,
                     maxiter = 2, thin = 1, burnin = 0,
                     store.item = TRUE)
h116.c.ideal.predprob = predict_ideal(h116.c, h116.c.ideal)

pumBayes documentation built on June 8, 2025, 11:29 a.m.