Average Marginal Effects — frames • MarginalMediation

Provides the average marginal effects of a GLM model with bootstrapped confidence intervals. Similar results would be obtained from using margins::margins().

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), 309–329. <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>

Author

Tyson S. Barrett

Examples


library(furniture)
data(nhanes_2010)
fit = glm(marijuana ~ home_meals + gender + age + asthma, 
           data = nhanes_2010, 
           family = "binomial")
frames(fit)
#> 
#> Bootstrapping...Done.
                                                                                 

#> ┌────────────────────────────┐
#> │  Average Marginal Effects  │
#> └────────────────────────────┘
#> 
#>              Estimate   Lower   Upper
#> home_meals    -0.0097 -0.0169 -0.0031
#> genderFemale   0.1043  0.0379  0.1646
#> age            0.0007 -0.0075  0.0079
#> asthmaNo      -0.0017 -0.0893  0.0893
#> ────