id_sim_gen | R Documentation |
A function designed to simulate IRT ideal point data.
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,
spline_knots = NULL,
spline_degree = 2,
spline_intercept_sd = 0.5,
spline_basis_sd = 0.5,
phi = 1
)
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 |
latent_space |
Whether to use the latent space formulation of the ideal point model
|
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 |
time_sd |
The standard deviation of the change in ideal points over time (should be low relative to
|
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 |
inflate |
If |
sigma_sd |
If a normal or log-normal distribution is being fitted, this parameter gives the standard |
spline_knots |
Number of knots (essentially, number of points
at which to calculate time-varying ideal points given T time points).
Default is NULL, which means that the spline is equivalent to
polynomial time trend of degree |
spline_degree |
The degree of the spline polynomial. The default is 2 which is a quadratic polynomial. A value of 1 will result in independent knots (essentially pooled across time points T). A higher value will result in wigglier time series. |
spline_intercept_sd |
The SD of the Normal distribution (centered on 0) used to draw the intercept for the basis spline function |
spline_basis_sd |
The SD of the Normal distribution (centered on 0) for the coefficients used to create the simulated ideal points from the spline function |
phi |
The phi (dispersion) parameter for the ordered beta distribution deviation of the outcome (i.e. the square root of the variance). |
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.
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.
id_plot_sims for plotting fitted models versus true values.
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