item_char {pumBayes} | R Documentation |
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)
[Package pumBayes version 1.0.0 Index]