id_me-idealstan-method: Calculate ideal point marginal effects

In idealstan: Robust Measurement with 'Stan'

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 = 0.025,
  upb = 0.975,
  ...
)

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	Parameter for numerical differentiation (usually does not need to be changed) passed on to id_post_pred
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 0.025 for a 95% interval)
upb	The quantile for the upper bound of the aggregated effects (default is 0.975 for a 95% interval)
...	Additional arguments passed on to id_post_pred

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.

Examples

data('senate114')
senate114$cast_code <- ifelse(senate114$cast_code=="Absent", NA,
                              as.integer(senate114$cast_code) - 1L)
senate114$age <- (2018 - senate114$born - 
                 mean(2018 - senate114$born)) / 10
sen_cov <- id_make(senate114, outcome_disc='cast_code',
                   person_id='bioname', item_id='rollnumber',
                   group_id='party_code', person_cov=~party_code+age)
sen_cov_est <- id_estimate(sen_cov, model_type=1, fixtype='vb_full',
                           use_method="pathfinder", ncores=4)
me <- id_me(sen_cov_est, covariate='age', draws=50)
me$sum_ideal_effects

idealstan documentation built on May 13, 2026, 1:08 a.m.