```
library(marginaleffects)
mod <- glm(am ~ vs + scale(drat), family = binomial, mtcars)
avg_comparisons(mod,
variables = "vs",
comparison = "lnoravg",
type = "response")
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> -0.901 0.368 -2.45 0.0143 6.1 -1.62 -0.18
#>
#> Term: vs
#> Type: response
#> Comparison: ln(odds(1) / odds(0))
#> Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted
```

# FAQ

## Questions & Answers

- Error: Matrix columns are not supported
- Wiggly (non-smooth) confidence intervals in plots.
`plot_predictions()`

over a range of unobserved values- Plot the marginal effects from a
`plm`

package model - Models with demeaned, polynomials, or transformed variables
`nlme::lme`

problem with character predictors- Controlling facet rows and columns in
`plot_*()`

functions. - Bayesian diagnostics (Rhat, etc.) with
`brms`

- Pooling predictions with multiple imputation

## Calling `marginaleffects`

in functions, loops, environments, or after re-assigning variables

Functions from the `marginaleffects`

package can sometimes fail when they are called inside a function, loop, or other environments. To see why, it is important to know that `marginaleffects`

often needs to operate on the original data that was used to fit the model. To extract this original data, we use the `get_data()`

function from the `insight`

package.

In most cases, `get_data()`

can extract the data which is stored inside the model object created by the modeling package. However, some modeling packages do *not* save the original data in the model object (in order to save memory). In those cases, `get_data()`

will parse the call to find the name of the data object, and will search for that data object in the global environment. When users fit models in a different environment (e.g., function calls), `get_data()`

may not be able to retrieve the original data.

A related problem can arise if users fit a model, but then assign a new value to the variable that used to store the dataset.

Recommendations:

- Supply your dataset explicitly to the
`newdata`

argument of`slopes`

functions. - Avoid assigning a new value to a variable that you use to store a dataset for model fitting.

## Equivalence between `avg_comparisons()`

and `avg_predictions()`

Say we estimate a simple logit regression and want to compute the average log-odds ratio associated with a change from 0 to 1 on the `vs`

variable. We can proceed as follows using the `comparisons()`

function:

We can specify the function to compare “hi” predictions to “lo” predictions manually and get the same results:

```
fun <- function(hi, lo) log((mean(hi) / (1 - mean(hi))) / (mean(lo) / (1 - mean(lo))))
comparisons(mod,
variables = "vs",
comparison = fun,
type = "response")
#>
#> Estimate Std. Error z Pr(>|z|) S 2.5 % 97.5 %
#> -0.901 0.368 -2.45 0.0143 6.1 -1.62 -0.18
#>
#> Term: vs
#> Type: response
#> Comparison: 1, 0
#> Columns: term, contrast, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted
```

The same results can be obtained using the `predictions()`

function. First, we replicate the entire dataset, and substitute the values of `vs`

. Then, we make predictions. Finally, we compute the log odds ratios:

```
dat0 <- transform(mtcars, vs = 0)
dat1 <- transform(mtcars, vs = 1)
p0 <- predictions(mod, newdata = dat0, type = "response")
p1 <- predictions(mod, newdata = dat1, type = "response")
fun(p1$estimate, p0$estimate)
#> [1] -0.9006949
```

Notice that the following command gives us a different result. This is because instead of duplicating the full dataset, and computing the “hi” and “lo” as the mean predictions over the full data, we only compute the averages on the subsets of rows where `vs`

is 0 or 1, respectively:

```
p <- avg_predictions(mod, by = "vs", type = "response")
fun(p$estimate[2], p$estimate[1])
#> [1] 0.6931472
```

Which is equivalent to:

```
dat0 <- subset(mtcars, vs == 0)
dat1 <- subset(mtcars, vs == 1)
p0 <- predictions(mod, newdata = dat0, type = "response")
p1 <- predictions(mod, newdata = dat1, type = "response")
fun(p1$estimate, p0$estimate)
#> [1] 0.6931472
```

## More plot customization

As described in the Plot vignette, there are currently three ways to customize plots in `marginaleffects`

:

- Using the arguments provided by
`plot_*()`

functions in the`marginaleffects`

package. - Modifying the plot objects using
`ggplot2`

or an extension package like`ggExtra`

. - Setting
`draw=FALSE`

to extract the plotting data, and then feeding that data to a standalone plotting software like`ggplot2`

,`plot`

, or`lattice`

.

Options 2 and 3 are extremely flexible, and make it trivial to reproduce the default plots or to draw highly customized visualizations.

Of course, some users might still prefer to use function arguments directly available in `marginaleffects::plot_*()`

, and the unavailability of some arguments can be frustrating. Nevertheless, a decision was made to keep the number of arguments in `plot_*()`

functions quite low. This is because `ggplot2`

functions like `geom_*()`

, `facet_*()`

, and `aes()`

each accept a very large number of options, and because users’ needs are incredibly varied. This makes it difficult to accomodate all needs and possibilities, without developing unwieldy functions with dozens of arguments.^{1}

Thus, the `marginaleffects`

package is designed with limited scope, focusing on *computation* while offering a few *visualization* options for quick exploration. Users who need highly customized or publication-ready figures are encouraged to use the `draw=FALSE`

argument with standalone plotting software.

In an ideal world, someone else would build a richer visualization package on top of `marginaleffects`

. I think this would be a very cool project, and I would be happy to collaborate, advise, and help if I can. In the meantime, feel free to keep the discussion going by opening an issue on Github. I will be happy to consider counter-arguments, suggestions, or pull requests.