# New variants of marginal effects available for logistical regressions

*It is now possible to calculate marginal effects for logistic regression analyzes in other ways than the default value “overall”, using the new option “mfx_at ()”.*

Logistic regression analyzes run by the `logit`

, `probit`

and `mlogit`

commands provide logistic coefficient estimates by default. However, many people prefer to look at marginal effects, as these are easier to interpret. For this, the option `mfx()`

is used, where you can select the variants `dydx`

, `dyex`

, `eydx`

, and `eyex`

. More info about these variants can be found in the user manual chapter 5.6.2 or by using the command `help logit`

(or one of the other logistical commands).

By using the new option `mfx_at()`

, you can override the default measure. The following variants are available:

`mfx_at(overall)`

(mean of the marginal effects measured over all x values) (default measure if this option is not used)`mfx_at(mean)`

(marginal effect measured at mean value of x)`mfx_at(median)`

(marginal effect measured at median value of x)`mfx_at(zero)`

(marginal effect measured at 0-value of x)

The `mfx_at()`

option is usually used in combination with `mfx()`

, for example:

`logit high_income male married age high_wealth, mfx(dydx) mfx_at(mean)`

However, you may also just use `mfx_at()`

. Thus, the standard variant `mfx(dydx)`

is used.

The following alternative regression expressions will present the same marginal effect values:

```
logit high_income male married age high_wealth, mfx(dydx) mfx_at(overall)
logit high_income male married age high_wealth, mfx_at(overall)
logit high_income male married age high_wealth, mfx(dydx)
```