bayesCountMargEffF {bayesMeanScale} | R Documentation |
Bayesian marginal effects on count probabilities.
Description
Computes Bayesian marginal effects on count probabilities. Models must have a family of "poisson"" or "neg_binomial_2" and be fit using the "rstanarm" package. Marginal effects can be averaged over the values of the covariates in the data (average marginal effects), or the covariates can be held at their means (marginal effects at the means). Also, "at" values can be specified to fix covariates at particular values. The marginal effects must be specified in terms of discrete changes. When investigating continuous variables this might be specified as the change between the mean and 1 standard deviation above the mean, for example.
Usage
bayesCountMargEffF(model,
counts,
marginal_effect,
start_value,
end_value,
n_draws = 2000,
ci = .95,
hdi_interval = TRUE,
centrality = 'mean',
digits = 4,
at = NULL,
at_means = FALSE)
Arguments
model |
A model object of class "stanreg." |
counts |
Vector of counts to get the predicted probabilities for. |
marginal_effect |
A character vector of terms that you want to estimate the marginal effect for. |
start_value |
The start value(s) for the marginal effect(s). Must be a list when specifying multiple values. |
end_value |
The end value(s) for the marginal effect(s). Must be a list when specifying multiple values. |
n_draws |
The number of draws to take from the posterior distribution of the expectation. |
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 |
Optional list of covariate values to estimate the marginal effect at. |
at_means |
If FALSE, the default, the marginal effects are averaged across the rows of the data. If TRUE, the covariate values that are not specified in the "at" argument are held at their means. |
Details
Marginal effects on count probabilities give you useful information to complement post estimation summaries on the mean scale.
Value
A list of class "bayes_mean_scale_marg" with the following components:
diffTable |
summary table of the marginal effects |
diffDraws |
posterior draws of the marginal effects |
Author(s)
David Dalenberg
References
Long, J. Scott and Jeremy Freese. 2001. "Predicted Probabilities for Count Models." Stata Journal 1(1): 51-57.
Examples
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)
# marginal effect holding covariates at their means #
bayesCountMargEffF(poissonModel,
counts = 1,
marginal_effect = 'width',
start_value = 25,
end_value = 20,
at_means = TRUE,
n_draws = 500)
}