bayesMeanScale (version 0.2.1)

bayesCountPredsF: Bayesian predictions of count probabilities.

Description

Computes Bayesian predictions for count probabilities. Models must have a family of "poisson"" or "neg_binomial_2" and be fit using the "rstanarm" package. 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

bayesCountPredsF(model,
                 counts,
                 at,
                 n_draws      = 2000,
                 ci           = .95,
                 hdi_interval = TRUE,
                 centrality   = 'mean',
                 digits       = 4,
                 at_means     = FALSE,
                 data_slice   = 'full')

Value

A list of class "bayesmeanscale_pred" with the following components:

predTable

summary table of the predictions

predDraws

posterior draws of the predictions

Arguments

model

A model object of class "stanreg."

counts

Vector of counts to get the predicted probabilities for.

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.

Author

David Dalenberg

Details

Predicted count probabilities give you useful information to complement post estimation summaries of the mean.

References

Long, J. Scott and Jeremy Freese. 2001. "Predicted Probabilities for Count Models." Stata Journal 1(1): 51-57.

Examples

# \donttest{

if(require(rstanarm)){

crabs <- read.table("https://users.stat.ufl.edu/~aa/cat/data/Crabs.dat", 
                    header = TRUE)

poissonModel  <- stan_glm(sat ~ weight + width, 
                          data    = crabs, 
                          family  = poisson, 
                          refresh = 0,
                          iter    = 500)

bayesCountPredsF(poissonModel,
                 counts  = c(0,1),
                 at      = list(weight=c(2,3)),
                 n_draws = 500)
                 
}
                 
# }