id_me: 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

id_me(object, ...)

Arguments

object	A fitted idealstan model
...	Other values passed on to methods.

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

Returns a tibble that has one row per posterior draw per item-specific marginal effect in the scale of the outcome.

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.