id_me-idealstan-method

Calculate ideal point marginal effects

Description

This function allows you to calculate ideal point marginal effects for a given person-level hierarchical covariate.

Usage

## S4 method for signature 'idealstan'
id_me(
  object,
  covariate = NULL,
  group_effects = NULL,
  pred_outcome = NULL,
  eps = 1e-04,
  draws = 100,
  cores = 1,
  lb = 0.05,
  upb = 0.95
)

Arguments

`object`	A fitted `idealstan` model
`covariate`	The character value for a covariate passed to the ‘id_make’ function before model fitting. Only one covariate can be processed at a time.
`group_effects`	character value of a covariate included in the formula passed to ‘id_make’ for which marginal effect summaries should be grouped by. Useful when looking at the marginal effect of an interaction. Note that grouping by a covariate with many values will result in slow performance.
`pred_outcome`	Numeric value for level of outcome to predict for ordinal responses. Defaults to top level.
`eps`	The value used for numerical differentiation. Default is 1e-4. Usually does not need to be changed.
`draws`	The total number of draws to use when calculating the marginal effects. Defaults to 100. Use option "all" to use all available MCMC draws.
`cores`	The total number of cores to use when calculating the marginal effects. Defaults to 1.
`lb`	The quantile for the lower bound of the aggregated effects (default is 0.05)
`upb`	The quantile for the upper bound of the aggregated effects (default is 0.95)

Details

This function will calculate item-level ideal point marginal effects for a given covariate that was passed to the ‘id_make’ function using the ‘person_cov’ option. The function will iterate over all items in the model and use numerical differentiation to calculate responses in the scale of the outcome for each item. Note: if the covariate is binary (i.e., only has two values), then the function will calculate the difference between these two values instead of using numerical differentation.

Value

A list with two objects, ideal_effects with one estimate of the marginal effect per item and posterior draw and sum_ideal_effects with one row per item with that item’s median ideal point marginal effect with the quantiles defined by the upb and lb parameters.