predict_irt: Calculate Probabilities for Dynamic Item Response Theory...

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

Calculate Probabilities for Dynamic Item Response Theory Model

Description

This function computes the probability matrix for a dynamic item response theory (IRT) 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_irt(vote_info, years_v, post_samples)

Arguments

vote_info	A logical vote matrix where 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.
post_samples	MCMC results obtained from ‘wnominate’ function in ‘wnominate’ 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(scotus.1937.2021)
library(MCMCpack)
special_judge_ind = sapply(c("HLBlack", "PStewart", "WHRehnquist"),
                           function(name){grep(name, rownames(mqVotes))})
e0_v = rep(0, nrow(mqVotes))
E0_v = rep(1, nrow(mqVotes))
e0_v[special_judge_ind] = c(-2, 1, 3)
E0_v[special_judge_ind] = c(10, 10, 10)
theta.start = rep(0, nrow(mqVotes))
indices = c(2, 5, 8, 9, 12, 22, 23, 24, 25, 29, 30, 33, 36, 39,
            42, 43, 44)
values = c(1, 1, -1, -2, -2, 1, -1, 1, 1, -1, 1, 3, 3, 3, 1, 1, -1)
theta.start[indices] = values
data(scotus.1937.2021)
scotus.MQ = MCMCdynamicIRT1d(mqVotes, mqTime, mcmc = 2,
                             burnin = 0, thin = 1, tau2.start = 0.1,
                             theta.start = theta.start, a0 = 0, A0 = 1, b0 = 0, B0 = 1, c0 = -10,
                             d0 = -2, e0 = e0_v, E0 = E0_v,
                             theta.constraints=list(CThomas = "+", SAAlito = "+", WJBrennan = "-",
                                                    WODouglas = "-", CEWhittaker = "+"))
scotus.MQ.predprob = predict_irt(mqVotes, mqTime, scotus.MQ)

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