id_me,idealstan-method | R Documentation |
This function allows you to calculate ideal point marginal effects for a given person-level hierarchical covariate.
## 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,
...
)
object |
A fitted |
covariate |
The character value for a covariate passed to the
|
group_effects |
character value of a covariate included in the formula passed
to |
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 0.05) |
upb |
The quantile for the upper bound of the aggregated effects (default is 0.95) |
... |
Additional arguments passed on to id_post_pred |
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.
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.
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