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.
- at
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.
- ...
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", at = "Species")
get_marginaleffects(model, trend = "Petal.Length", at = "Petal.Length")
get_marginaleffects(model, trend = "Petal.Length", at = c("Species", "Petal.Length"))
}
#> Loading required package: marginaleffects
#>
#> Term Estimate Std. Error z Pr(>|z|) 2.5 % 97.5 % Species
#> Petal.Length 0.388 0.2602 1.49 0.1360 -0.1220 0.898 setosa
#> Petal.Length 0.388 0.2602 1.49 0.1360 -0.1220 0.898 setosa
#> Petal.Length 0.374 0.0961 3.89 <0.001 0.1859 0.563 versicolor
#> Petal.Length 0.374 0.0961 3.89 <0.001 0.1859 0.563 versicolor
#> Petal.Length 0.374 0.0962 3.89 <0.001 0.1859 0.563 versicolor
#> Petal.Length 0.234 0.0819 2.86 0.0042 0.0739 0.395 virginica
#> Petal.Length 0.234 0.0819 2.86 0.0042 0.0739 0.395 virginica
#> Petal.Length 0.234 0.0819 2.86 0.0042 0.0739 0.395 virginica
#> Petal.Length 0.234 0.0819 2.86 0.0042 0.0739 0.395 virginica
#> Petal.Length
#> 1.00
#> 1.66
#> 3.62
#> 4.28
#> 4.93
#> 4.93
#> 5.59
#> 6.24
#> 6.90
#>
#> Columns: rowid, term, estimate, std.error, statistic, p.value, conf.low, conf.high, predicted, predicted_hi, predicted_lo, Species, Petal.Length, Sepal.Width
#>