pumBayes (version 1.0.2)

tune_hyper: Generate Probability Samples for Voting "Yes"

Description

This function generates probability samples for Voting "Yes". It uses predefined hyperparameters and simulates data based on the specified number of members (`n_leg`) and issues (`n_issue`).

Usage

tune_hyper(hyperparams = hyperparams, n_leg, n_issue)

Value

A numeric vector containing the simulated probabilities of voting "Yes" for legislators across issues.

Arguments

hyperparams

A list of hyperparameter values: - `beta_mean`: The prior mean of the `beta` parameter, representing legislator positions. - `beta_var`: The prior variance of `beta`. - `alpha_mean`: A vector of length two, specifying the prior means of the item discrimination parameters, `alpha1` and `alpha2`. - `alpha_scale`: The scale parameter for `alpha1` and `alpha2`. - `delta_mean`: A vector of length two, indicating the prior means of the item difficulty parameters, `delta1` and `delta2`. - `delta_scale`: The scale parameter for `delta1` and `delta2`.

n_leg

Integer, representing the number of legislators (members) to be simulated.

n_issue

Integer, indicating the number of issues to be simulated.

Examples

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))
theta = tune_hyper(hyperparams, n_leg = 10, n_issue = 10)