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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:

[1] "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".

Does...

dF/dx is for discrete change for the following variables: [1] "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.

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