tune_hyper {pumBayes} | R Documentation |
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)
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. |
Value
A numeric vector containing the simulated probabilities of voting "Yes" for legislators across issues.
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)