So, I calculated a negative binomial regression model and I am trying to estimate the mean marginal effects in R.
To do this, I used the
mfx package and wrote the following code:
library(mfx) negbinmfx(formula = attack ~ cinc + alliance_memberships + maj.pow.fac + I(log(int.users2)) + myedu + I(log(gdp_pc)) + polity2 + years_since_dispute, data = data, atmean = TRUE)
My dependent variable measures the number of cyberattacks a country initiates per year.
The output from this command is the following:
Marginal Effects: dF/dx Std. Err. z P>|z| cinc 3.2182e-33 1.8067e-27 0 1 alliance_memberships 5.6471e-35 3.1703e-29 0 1 maj.pow.facyes -7.5705e-35 4.2501e-29 0 1 I(log(int.users2)) 2.2773e-34 1.2785e-28 0 1 myedu -4.2954e-35 2.4115e-29 0 1 I(log(gdp_pc)) -1.5224e-34 8.5471e-29 0 1 polity2 -2.6135e-35 1.4672e-29 0 1 years_since_dispute -1.4429e-32 7.9944e-27 0 1 dF/dx is for discrete change for the following variables:  "maj.pow.facyes"
Now, what I am unclear about is the last part.
maj.pow.fac is a simple dummy variable with the levels "yes" and "no".
dF/dx is for discrete change for the following variables:  "maj.pow.facyes"
...mean that these marginal effects are calculated by holding the dummy
maj.pow.fac constant at the level
"yes", or what do the effects mean in relation to the dummy?
I am sorry if this question is simple to some. I am a complete beginner and have not been able to figure this out using the
mfx package's documentation.