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

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Aug 25, 2025
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Deal with / fix tidy() method in the _marginaleffects_ methods for model_parameters() #1157

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Deal with / fix `tidy()` method in the _marginaleffects_ methods for …
strengejacke Aug 25, 2025
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strengejacke Aug 25, 2025
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -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",
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12 changes: 12 additions & 0 deletions R/methods_marginaleffects.R
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Expand Up @@ -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"))
)
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Copilot AI Aug 25, 2025

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The marginaleffects::components() function calls may fail if the model object doesn't have the expected components. Consider adding error handling or checking if these components exist before calling colnames() on them.

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# 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]
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If to_remove is empty or contains non-existent column names, this subsetting could behave unexpectedly. Consider adding a check to ensure to_remove contains valid column names that exist in tidy_model.

Suggested change
tidy_model <- tidy_model[, !colnames(tidy_model) %in% to_remove, drop = FALSE]
# Only remove columns that exist in tidy_model
valid_to_remove <- intersect(to_remove, colnames(tidy_model))
if (length(setdiff(to_remove, valid_to_remove)) > 0 && length(to_remove) > 0) {
warning("Some columns specified for removal do not exist in tidy_model: ", paste(setdiff(to_remove, valid_to_remove), collapse = ", "))
}
tidy_model <- tidy_model[, !colnames(tidy_model) %in% valid_to_remove, drop = FALSE]

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}

out <- .rename_reserved_marginaleffects(tidy_model)
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25 changes: 25 additions & 0 deletions tests/testthat/test-marginaleffects.R
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Expand Up @@ -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"
)
)
})


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