Calculate average contrasts by taking the mean of all the
unit-level contrasts computed by the `predictions`

function.

```
# S3 method for comparisons
tidy(x, conf_level = 0.95, transform_avg = NULL, ...)
```

- x
An object produced by the

`comparisons`

function.- conf_level
numeric value between 0 and 1. Confidence level to use to build a confidence interval.

- transform_avg
(experimental) A function applied to the estimates and confidence intervals

*after*the unit-level estimates have been averaged.- ...
Additional arguments are passed to the

`predict()`

method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the`marginaleffects`

website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the`?marginaleffects`

documentation for a non-exhaustive list of available arguments.

A "tidy" `data.frame`

of summary statistics which conforms to the
`broom`

package specification.

To compute standard errors around the average marginaleffects, we begin by applying the mean function to each column of the Jacobian. Then, we use this matrix in the Delta method to obtained standard errors.

In Bayesian models (e.g., `brms`

), we compute Average Marginal
Effects by applying the mean function twice. First, we apply it to all
marginal effects for each posterior draw, thereby estimating one Average (or
Median) Marginal Effect per iteration of the MCMC chain. Second, we
calculate the mean and the `quantile`

function to the results of Step 1 to
obtain the Average Marginal Effect and its associated interval.

```
mod <- lm(mpg ~ factor(gear), data = mtcars)
contr <- comparisons(mod, variables = list(gear = "sequential"))
tidy(contr)
#> type term contrast estimate std.error statistic p.value conf.low
#> 1 response gear 4 - 3 8.426667 1.823417 4.621361 3.812306e-06 4.852836
#> 2 response gear 5 - 4 -3.153333 2.506046 -1.258290 2.082869e-01 -8.065094
#> conf.high
#> 1 12.000498
#> 2 1.758428
```