Deal with / fix tidy() method in the _marginaleffects_ methods for model_parameters() (#1157)

* Deal with / fix `tidy()` method in the _marginaleffects_ methods for `model_parameters()`
Fixes #1156

* comment

Author: strengejacke

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.28.0.7
+Version: 0.28.0.9
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

R/methods_marginaleffects.R

@@ -25,6 +25,18 @@ model_parameters.marginaleffects <- function(model,
   } else {
     # handle non-Bayesian models
     tidy_model <- marginaleffects::tidy(model, conf_level = ci, ...)
+
+    # all columns in data grid and model data, we only want to keep "by" variables
+    all_data_cols <- union(
+      colnames(marginaleffects::components(model, "newdata")),
+      colnames(marginaleffects::components(model, "modeldata"))
+    )
+    # columns we want to keep
+    by_cols <- marginaleffects::components(model, "variable_names_by")
+
+    # remove redundant columns
+    to_remove <- setdiff(all_data_cols, by_cols)
+    tidy_model <- tidy_model[, !colnames(tidy_model) %in% to_remove, drop = FALSE]
   }
 
   out <- .rename_reserved_marginaleffects(tidy_model)

tests/testthat/test-marginaleffects.R

@@ -41,6 +41,31 @@ test_that("marginaleffects()", {
     variables = "Petal.Length"
   )
   expect_identical(nrow(parameters(model)), 1L)
+
+  # remove redundant columns
+  skip_if_not_installed("mgcv")
+  data(iris)
+  model <- mgcv::gam(Sepal.Width ~ s(Petal.Length, by = Species), data = iris)
+  mfx <- marginaleffects::avg_slopes(model, variables = "Petal.Length")
+  out <- model_parameters(mfx)
+  expect_identical(dim(out), c(1L, 11L))
+  expect_named(
+    out,
+    c(
+      "Parameter", "Comparison", "Coefficient", "SE", "Statistic",
+      "p", "S", "CI", "CI_low", "CI_high", "Predicted"
+    )
+  )
+  mfx <- marginaleffects::avg_slopes(model, variables = "Petal.Length", by = "Species")
+  out <- model_parameters(mfx)
+  expect_identical(dim(out), c(3L, 11L))
+  expect_named(
+    out,
+    c(
+      "Parameter", "Comparison", "Species", "Coefficient", "SE", "Statistic",
+      "p", "S", "CI", "CI_low", "CI_high"
+    )
+  )
 })