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I have a multinomial logit model created with the nnet R package, using the multinom command. The dependent variable has three categories/choice options. I am modelling the probability of selecting a certain irrigation type (no irrigation, surface irrigation, drip irrigation) based on farmer characteristics.

I would like to estimate marginal effects, i.e. by how much does the probability of selecting irrigation type Y change when I increase independent variable X by one unit? I have tried doing this with the margins package (marginal_effects), but this gives only 1 value per observation in the dataset. I was expecting three values, since I want the marginal effect for each of the three irrigation types.

Does someone know if there is a better R package to use for this? Or whether I am doing something wrong with the margins packages? Thank you.

jay.sf
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Charlotte
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2 Answers2

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You can use the marginaleffects package to do that (disclaimer: I am the maintainer). Please note the warning.

library(nnet)
library(marginaleffects)

mod <- multinom(factor(cyl) ~ hp + mpg, data = mtcars, quiet = true)

mfx <- marginaleffects(mod, type = "probs")

## Warning in sanity_model_specific.multinom(model, ...): The standard errors
## estimated by `marginaleffects` do not match those produced by Stata for
## `nnet::multinom` models. Please be very careful when interpreting the results.

summary(mfx)

## Average marginal effects 
##    type Group Term     Effect Std. Error     z value   Pr(>|z|)      2.5 %
## 1 probs     6   hp  2.792e-04  0.000e+00         Inf < 2.22e-16  2.792e-04
## 2 probs     6  mpg -1.334e-03  0.000e+00        -Inf < 2.22e-16 -1.334e-03
## 3 probs     8   hp  2.396e-05 1.042e-126  2.298e+121 < 2.22e-16  2.396e-05
## 4 probs     8  mpg -2.180e-04 1.481e-125 -1.472e+121 < 2.22e-16 -2.180e-04
##       97.5 %
## 1  2.792e-04
## 2 -1.334e-03
## 3  2.396e-05
## 4 -2.180e-04
## 
## Model type:  multinom 
## Prediction type:  probs
Vincent
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The marginaleffects package should work in theory, but my example doesn't compile because of file size restrictions (meaning I don't have enough RAM for the 1.5 GB vector it tries to use). It's not even that large of a dataset, which is odd.

If you use marginal_effects() (margins package) for multinomial models, it only displays the output for a default category. You have to manually set each category you want to see. You can clean up the output with broom and then combine some other way. It's clunky, but it can work.

marginal_effects(model, category = 'cat1')

Jeffrey Harding
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  • You can set several arguments to considerably reduce the computational requirements. For example, `variables`, `newdata="mean"` , and `vcov=FALSE` – Vincent Sep 10 '22 at 01:25