id_plot_cov – idealstan

Marginal Effects Plot for Hierarchical Covariates

Description

This function will calculate and plot the ideal point marginal effects, or the first derivative of the IRT/ideal point model with respect to the hierarchical covariate, for each item in the model. The function id_me is used to first calculate the ideal point marginal effects.

Usage

id_plot_cov(
  object,
  calc_param = NULL,
  label_high = "High",
  label_low = "Low",
  group_effects = NULL,
  plot_model_id = NULL,
  pred_outcome = NULL,
  lb = 0.05,
  upb = 0.95,
  facet_ncol = 2,
  cov_type = "person_cov",
  ...
)

Arguments

`object`	A fitted `idealstan` object
`calc_param`	Whether to calculate ideal point marginal effects for a given covariate. If NULL, the default, the function will instead produce a plot of the raw coefficients from the ideal point model. If passing the name of a covariate, should be a character value of a column in the data passed to the `id_make` function.
`label_high`	What label to use on the plot for the high end of the latent scale
`label_low`	What label to use on the plot for the low end of the latent scale
`plot_model_id`	The integer of the model ID to plot. If NULL and there are multiple model types, `facet_wrap` will be used to produce multiple plots with one for each model type.
`pred_outcome`	For discrete models with more than 2 categories, or binary models with missing data, which outcome to predict. This should be the value that matches what the outcome was coded as in the data passed to `id_make`.
`lb`	The lower limit of the posterior density to use for calculating credible intervals
`upb`	The upper limit of the posterior density to use for calculating credible intervals
`facet_ncol`	If facetting by multiple models or grouped factors, sets the number of columns in the multiple plots
`cov_type`	Either `‘person_cov’` for person or group-level hierarchical parameters, `‘discrim_reg_cov’` for bill/item discrimination parameters from regular (non-inflated) model, and `‘discrim_infl_cov’` for bill/item discrimination parameters from inflated model.
`…`	Additional argument passed on to `id_me`

Details

The ends of the latent variable can be specified via the label_low and label_high options, which will use those labels for item discrimination.

Note that the function produces a ggplot2 object, which can be further modified with ggplot2 functions.

Value

A ggplot2 plot that can be further customized with ggplot2 functions if need be.