bayesOrdinalPredsF {bayesMeanScale} | R Documentation |
Bayesian predictions for ordinal models on the mean scale.
Description
Computes Bayesian outcome predictions for cumulative link models with a proportional odds structure fit using the package "rstanarm". Predictions can be averaged over the values of the covariates in the data (average marginal predictions), or the covariates can be held at their means (marginal predictions at the means). Also, "at" values must be specified to fix at least one covariate at particular values.
Usage
bayesOrdinalPredsF(model,
at,
n_draws = 2000,
ci = .95,
hdi_interval = TRUE,
centrality = 'mean',
digits = 4,
at_means = FALSE,
data_slice = 'full')
Arguments
model |
A model object of class "stanreg" and "polr." |
at |
List of covariate values to estimate the predictions at. |
n_draws |
The number of draws to take from the joint posterior distribution. |
ci |
The level for the credible intervals. |
hdi_interval |
If TRUE, the default, computes the highest density credible interval. If FALSE, computes the equal-tailed interval. |
centrality |
Centrality measure for the posterior distribution. Options are "mean" or "median". |
digits |
The number of digits to report in the summary table. |
at_means |
If FALSE, the default, the predictions are averaged across the rows of the model data for each unique combination of "at" values. If TRUE, the covariate values that are not specified in the "at" argument are held at their means. |
data_slice |
The number of rows of data to average over for the predictions. Defaults to all rows. This can be useful for very large data sets. |
Details
The following links for fixed-effect ordinal models fit using "rstanarm" are supported: logit, probit, and clogclog.
Value
A list of class "bayesmeanscale_pred" with the following components:
predTable |
summary table of the predictions |
predDraws |
posterior draws of the predictions |
Author(s)
David Dalenberg
References
Agresti, Alan. 2013. Categorical Data Analysis. Third Edition. New York: Wiley
Long, J. Scott and Sarah A. Mustillo. 2018. "Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes." Sociological Methods & Research 50(3): 1284-1320.
Mize, Trenton D. 2019. "Best Practices for Estimating, Interpreting, and Presenting Non-linear Interaction Effects." Sociological Science 6: 81-117.
Examples
## Proportional odds logistic regression ##
if(require(rstanarm) & require(MASS)){
m1 <- stan_polr(Sat ~ Infl + Type,
data = MASS::housing,
prior = rstanarm::R2(0.2, 'mean'),
refresh = 0,
iter = 500)
# marginal predictions holding covariates at means #
bayesOrdinalPredsF(m1,
at = list(Type = c("Tower", "Apartment")),
at_means = TRUE,
n_draws = 500)
}