item_char: Generate Data for Item Characteristic Curves

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

Generate Data for Item Characteristic Curves

Description

This function calculates the data needed to plot the item characteristic curve for a specific issue based on posterior samples.

Usage

item_char(vote_num, x = NULL, post_samples)

Arguments

vote_num	The vote number of the issue to be reviewed. This refers to numbers in the column names of the input vote matrix, not the clerk session vote number.
x	A vector showing the range of beta in the x axis.
post_samples	A list of posterior samples of parameters obtained from 'sample_pum_static' in 'pumBayes'.

Value

A data frame containing 'beta_samples', mean probabilities ('means'), and confidence intervals ('ci_lower' and 'ci_upper') for the input issue, which can be used to plot the item characteristic curve.

Examples

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)
item_data <- item_char(vote_num = 5, x = c(-4,2), post_samples = h116.c.pum)

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