## Why is the output from Stata different from the output from ggeffect?

Stata’s equivalent to the marginal effects produced by *ggeffects* is the `margins`

-command. However, the results are not always identical. For models from non-gaussian families, point estimates for the marginal effects are identical, but the confidence intervals differ.

Here is an explanation, why there is a difference. First, we fit a logistic regression model.

```
library(magrittr)
set.seed(5)
data <- data.frame(
outcome = rbinom(100, 1, 0.5),
var1 = rbinom(100, 1, 0.1),
var2 = rnorm(100, 10, 7)
)
m <- glm(
outcome ~ var1 * var2,
data = data,
family = binomial(link = "logit")
)
```

### Example with graphical output

#### The Stata plot

This is the code in Stata to produce a marginal effects plot.

```
use data.dta, clear
quietly logit outcome c.var1##c.var2
quietly margins, at(var2 = (-8(0.5)28) var1 = (0 1))
marginsplot
```

The resulting image looks like this.

#### The ggeffects plot

When we use *ggeffects*, the plot slighlty differs.

As we can see, the confidence intervals in the Stata plot are outside the plausible range of `[0, 1]`

, which means that the predicted uncertainty from the Stata output has a probability higher than 1 and lower than 0, while `ggpredict()`

computes confidence intervals *within* the possible range.

### Conclusion

It seems like Stata is getting the confidence intervals wrong. Predictions and standard errors returned in Stata are on the (transformed) response scale. Obviously, the confidence intervals are then computed by `estimate +/- 1.96 * standard error`

, which may lead to confidence intervals that are out of reasonable bounds (e.g. above 1 or below 0 for predicted probabilities).

The *transformed estimate* (on the response scale) is always between 0 and 1, and the same is true for the *transformed standard errors*. However, adding or subtracting approx. 2 * *transformed* SE to the *transformed* estimate does no longer ensure that the confidence intervals are within the correct range.

The precise way to do the calculation is to calculate estimates, standard errors and confidence intervals on the (untransformed) scale of the linear predictor and then back-transform.