id_sim_gen – idealstan

Simulate IRT ideal point data

Description

A function designed to simulate IRT ideal point data.

Usage

id_sim_gen(
  num_person = 20,
  num_items = 50,
  cov_effect = NULL,
  model_type = "binary",
  latent_space = FALSE,
  absence_discrim_sd = 3,
  absence_diff_mean = 0,
  discrim_reg_upb = 1,
  discrim_reg_lb = -1,
  discrim_miss_upb = 1,
  discrim_miss_lb = -1,
  discrim_reg_scale = 2,
  discrim_reg_shape = 2,
  discrim_miss_scale = 2,
  discrim_miss_shape = 2,
  diff_sd = 3,
  time_points = 1,
  time_process = "random",
  time_sd = 0.1,
  ideal_pts_sd = 3,
  prior_type = "gaussian",
  ordinal_outcomes = 3,
  inflate = FALSE,
  sigma_sd = 1
)

Arguments

`num_person`	The number of persons/persons
`num_items`	The number of items (bills in the canonical ideal point model)
`cov_effect`	The effect of a hierarchical/external covariate on the person ideal points. The covariate will be a uniformly-distributed random variable on the [0,1] scale, so covariate effects in the [-2,2] approximate range would result in noticeable effects on the ideal point scale.
`model_type`	One of `‘binary’`, `‘ordinal_rating’`, `‘ordinal_grm’`, `‘poisson’` `‘normal’`, or `‘lognormal’`
`latent_space`	Whether to use the latent space formulation of the ideal point model `FALSE` by default. NOTE: currently, the package only has estimation for a binary response with the latent space formulation.
`absence_discrim_sd`	The SD of the discrimination parameters for the inflated model
`absence_diff_mean`	The mean intercept for the inflated model; increasing it will lower the total number of missing data
`discrim_reg_upb`	The upper bound of the generalized Beta distribution for the observed discrimination parameters (gamma)
`discrim_reg_lb`	The lower bound of the generalized Beta distribution for the observed discrimination parameters (gamma)
`discrim_miss_upb`	The upper bound of the generalized Beta distribution for the missingness discrimination parameters (nu)
`discrim_miss_lb`	The lower bound of the generalized Beta distribution for the missingness discrimination parameters (nu)
`discrim_reg_scale`	The scale parameter for the generalized Beta distribution for the observed discrimination parameters (gamma)
`discrim_reg_shape`	The shape parameter for the generalized Beta distribution for the observed discrimination parameters (gamma)
`discrim_miss_scale`	The scale parameter for the generalized Beta distribution for the missingness discrimination parameters (nu)
`discrim_miss_shape`	The shape parameter for the generalized Beta distribution for the missingness discrimination parameters (nu)
`diff_sd`	The SD of the difficulty parameters (bill/item intercepts) for both missing and observed parameters (beta and omega)
`time_points`	The number of time points for time-varying legislator/person parameters
`time_process`	The process used to generate the ideal points: either `‘random’` for a random walk, `‘AR’` for an AR1 process, or `‘GP’` for a Gaussian process.
`time_sd`	The standard deviation of the change in ideal points over time (should be low relative to `ideal_pts_sd`)
`ideal_pts_sd`	The SD for the person/person ideal points
`prior_type`	The statistical distribution that generates the data for ideal point parameters (alpha) and difficulty intercepts (beta and omega). Currently only ‘gaussian’ is supported.
`ordinal_outcomes`	If `model` is `‘ordinal’`, an integer giving the total number of categories
`inflate`	If `TRUE`, an missing-data-inflated dataset is produced.
`sigma_sd`	If a normal or log-normal distribution is being fitted, this parameter gives the standard deviation of the outcome (i.e. the square root of the variance).

Details

This function produces simulated data that matches (as closely as possible) the models used in the underlying Stan code. Currently the simulation can produce inflated and non-inflated models with binary, ordinal (GRM and rating-scale), Poisson, Normal and Log-Normal responses.

Value

The results is a idealdata object that can be used in the id_estimate function to run a model. It can also be used in the simulation plotting functions.