Clinton-Jackman-Rivers Bayesian IRT (stub)

Usage

morie_spatial_voting_cjr_irt(
  votes,
  n_dims = 1L,
  n_samples = 1000L,
  burn_in = 200L
)

Arguments

votes: Binary roll-call matrix.
n_dims: Ideal-point dimensions.
n_samples: MCMC samples. @param burn_in Burn-in length.
burn_in: Integer; MCMC burn-in iterations.

Value

List: ideal_points, ideal_sd, discrimination, difficulty, engine.

References

Clinton, Jackman & Rivers (2004).

Examples

set.seed(1)
votes <- matrix(rbinom(200, 1, 0.5), 20, 10)
fit <- morie_spatial_voting_cjr_irt(votes, n_samples = 100L,
                                    burn_in = 50L)
#> ideal: analysis of roll call data via Markov chain Monte Carlo methods.
#> 
#> Ideal Point Estimation
#> 
#> Number of Legislators		 20 
#> Number of Items			 10 
#> 
#> 
#> Starting MCMC Iterations...
#> 
head(fit$ideal_points)
#>            [,1]
#> [1,]  1.0233366
#> [2,] -0.2253761
#> [3,] -0.5287431
#> [4,] -0.2987930
#> [5,]  0.4167965
#> [6,] -0.5859076