I have been looking around for a way to calculate marginal effects/predictions at specific values in R, while the remaining variables are kept as observed. I believe this is the default in Stata (for example, when using margins, at(age=(30 35 40 45 50)) (see Stata manual).
From what I can tell, marginaleffects and ggeffects automatically sets the other variables to the mean. Is there a good way to achieve this (preferably using the above packages)?
You can do this easily with the marginaleffects package. Please refer
to the documentation for details. In particular, note that the at
notation in Stata replicates the full dataset several times for each
value of the specified variable, and then averages. This is equivalent
to using the datagridcf() function in marginaleffects to create
“counter-factual” datasets, and the by argument to marginalize. You
may also find those two vignettes useful:
library(marginaleffects)
mod <- lm(mpg ~ hp + am, data = mtcars)
predictions(mod, newdata = datagridcf(hp = c(110, 120, 130)), by = "hp")
type hp predicted std.error statistic p.value conf.low conf.high
1 response 110 22.25107 0.5895479 37.74260 0 21.09558 23.40656
2 response 120 21.66219 0.5553795 39.00431 0 20.57367 22.75072
3 response 130 21.07332 0.5307275 39.70647 0 20.03311 22.11352
marginaleffects(mod, newdata = datagridcf(hp = c(110, 120, 130)), by = "hp")
type term contrast dydx std.error statistic p.value
1 response hp mean(dY/dX) -0.0588878 0.007856747 -7.495189 6.620280e-14
2 response hp mean(dY/dX) -0.0588878 0.007856747 -7.495189 6.620280e-14
3 response hp mean(dY/dX) -0.0588878 0.007856745 -7.495191 6.620172e-14
4 response am mean(dY/dX) 5.2770853 1.079540509 4.888270 1.017260e-06
5 response am mean(dY/dX) 5.2770853 1.079540509 4.888270 1.017260e-06
6 response am mean(dY/dX) 5.2770853 1.079540509 4.888270 1.017260e-06
conf.low conf.high predicted predicted_hi predicted_lo hp
1 -0.07428674 -0.04348886 25.38434 25.38267 25.38434 110
2 -0.07428674 -0.04348886 24.79546 24.79380 24.79546 120
3 -0.07428674 -0.04348887 24.20658 24.20492 24.20658 130
4 3.16122479 7.39294583 25.38434 25.38487 25.38434 110
5 3.16122479 7.39294583 24.79546 24.79599 24.79546 120
6 3.16122479 7.39294583 24.20658 24.20711 24.20658 130