Provides the average marginal effects of a GLM model with
bootstrapped confidence intervals. Similar results would be obtained from using
margins::margins().
Usage
frames(model, ci_type = "perc", boot = 100, ci = 0.95)
Arguments
model
the model object
ci_type
the type of boostrapped confidence interval; options are "perc", "basic", "bca"
boot
the number of bootstrapped samples; default is 100
ci
the confidence interval; the default is .975 which is the 95% confidence interval.
Details
Using the average marginal effects as discussed by Tamas Bartus (2005),
the coefficients are transformed into probabilities (for binary outcomes) or remain
in their original units (continuous outcomes).
References
Bartus, T. (2005). Estimation of marginal effects using margeff.
The Stata Journal, 5(3), 309<U+2013>329. <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>
# NOT RUN {library(furniture)
data(nhanes_2010)
fit = glm(marijuana ~ home_meals + gender + age + asthma,
data = nhanes_2010,
family = "binomial")
frames(fit)
# }