ols/logit marginal effects

gistfile1.txt

``` r
library(marginaleffects)
library(modelsummary)

set.seed(20220722)
S <- diag(3)
S[1,2] <- S[2,1] <- 0.6
S[1,3] <- S[3,1] <- 0.6
data <- MASS::mvrnorm(1000, rep(0, 3), S) |> 
  as.data.frame() |> 
  # make outcome, group, baseline binary
  transform(outcome = V1 < 0,
            rx = V2 < 0,
            baseline = V3 < 0) |> 
  subset(select = c(outcome, rx, baseline))
```

OLS and Logit produce identical results:

``` r
mod <- list(
    "glm 1" = glm(outcome ~ rx, data = data, family = binomial()),
    "ols 1" = lm(outcome ~ rx, data = data),
    "glm 2" = glm(outcome ~ rx + baseline, data = data, family = binomial()),
    "ols 2" = lm(outcome ~ rx + baseline, data = data))

mfx <- lapply(mod, marginaleffects)

modelsummary(mfx, "markdown")
```

|          |   glm 1   |   ols 1   |   glm 2   |   ols 2   |
| :------- | :-------: | :-------: | :-------: | :-------: |
| rx       |   0.404   |   0.404   |   0.387   |   0.387   |
|          |  (0.029)  |  (0.029)  |  (0.027)  |  (0.026)  |
| baseline |           |           |   0.390   |   0.390   |
|          |           |           |  (0.027)  |  (0.026)  |
| Num.Obs. |   1000    |   1000    |   1000    |   1000    |
| R2       |           |   0.163   |           |   0.315   |
| R2 Adj.  |           |   0.162   |           |   0.314   |
| AIC      |  1220.8   |  1277.9   |  1032.4   |  1079.6   |
| BIC      |  1230.6   |  1292.6   |  1047.1   |  1099.3   |
| Log.Lik. | \-608.404 | \-635.940 | \-513.181 | \-535.820 |
| F        |  153.571  |  194.743  |  111.275  |  229.355  |
| RMSE     |   0.46    |   0.46    |   0.41    |   0.41    |

Therefore, the slope is equal to the effect of a 1 unit change, as is always the case in linear models:

``` r
marginaleffects(mod[[1]]) |> summary()
#> Average marginal effects 
#>   Term     Contrast Effect Std. Error z value   Pr(>|z|)  2.5 % 97.5 %
#> 1   rx TRUE - FALSE 0.4038    0.02891   13.97 < 2.22e-16 0.3471 0.4605
#> 
#> Model type:  glm 
#> Prediction type:  response

comparisons(mod[[1]]) |> summary()
#> Average contrasts 
#>   Term     Contrast Effect Std. Error z value   Pr(>|z|)  2.5 % 97.5 %
#> 1   rx TRUE - FALSE 0.4038    0.02891   13.97 < 2.22e-16 0.3471 0.4605
#> 
#> Model type:  glm 
#> Prediction type:  response
```

Right, of course! Should have been a *

@mattansb see the discussion above. The model with 1 dummy is saturated because it has intercept and coefficient. The model with 2 dummies and an interaction is saturated because it has an intercept, two coefficients, and the parameter for the interaction. The model with only 2 dummies is not saturated, but as noted by Noah it is consistent, so it should be similar in large samples.