Modelbased-like API to create marginaleffects objects. This is Work-in-progress.
Arguments
- model
A statistical model.
- trend
A character indicating the name of the variable for which to compute the slopes.
- by
The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).
- fixed
A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.
- at
Deprecated, use
byinstead.- ...
Other arguments passed for instance to
insight::get_datagrid().
Examples
if (require("marginaleffects")) {
model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
get_marginaleffects(model, trend = "Petal.Length", by = "Species")
get_marginaleffects(model, trend = "Petal.Length", by = "Petal.Length")
get_marginaleffects(model, trend = "Petal.Length", by = c("Species", "Petal.Length"))
}
#> Loading required package: marginaleffects
#>
#> Term Species Petal.Length Estimate Std. Error z Pr(>|z|) S
#> Petal.Length setosa 1.00 0.388 0.2602 1.49 0.13601 2.9
#> Petal.Length setosa 1.66 0.388 0.2601 1.49 0.13596 2.9
#> Petal.Length versicolor 3.62 0.374 0.0963 3.89 < 0.001 13.3
#> Petal.Length versicolor 4.28 0.374 0.0963 3.89 < 0.001 13.3
#> Petal.Length versicolor 4.93 0.374 0.0963 3.89 < 0.001 13.3
#> Petal.Length virginica 4.93 0.234 0.0819 2.86 0.00420 7.9
#> Petal.Length virginica 5.59 0.234 0.0819 2.86 0.00421 7.9
#> Petal.Length virginica 6.24 0.234 0.0818 2.86 0.00418 7.9
#> Petal.Length virginica 6.90 0.234 0.0819 2.86 0.00420 7.9
#> 2.5 % 97.5 %
#> -0.1221 0.898
#> -0.1220 0.898
#> 0.1856 0.563
#> 0.1856 0.563
#> 0.1856 0.563
#> 0.0739 0.395
#> 0.0739 0.395
#> 0.0740 0.395
#> 0.0739 0.395
#>
#> Columns: rowid, term, estimate, std.error, statistic, p.value, s.value, conf.low, conf.high, predicted_lo, predicted_hi, predicted, Species, Petal.Length, Sepal.Width
#> Type: response
#>