issue #1209: tidymodels indexing

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: marginaleffects
 Title: Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests
-Version: 0.22.0.5
+Version: 0.22.0.6
 Authors@R: 
     c(person(given = "Vincent",
              family = "Arel-Bundock",

NEWS.md

@@ -15,6 +15,7 @@ Bugs:
 * `hypotheses(joint = TRUE)` would throw an error if sample sizes could not be computed, even if they were not needed. Thanks to Noah Greifer.
 * `hypotheses(joint = TRUE)` respects the `vcov` argument. Thanks to @kennchua for report #1214.
 * `ordbetareg` models in `glmmTMB` are now supported. Thanks to @jgeller112 for code contribution #1221.
+* `tidymodels()`: Indexing overrode the value of predictors in the output data frame. The numerical estimates were unaffected. Thanks to @agmath for report #1209.
 
 ## 0.22.0
 

R/methods_tidymodels.R

@@ -41,10 +41,10 @@ get_predict.model_fit <- function(model, newdata, type = NULL, ...) {
 
     if (type == "numeric") {
         v <- intersect(c(".pred", ".pred_res"), colnames(out))[1]
-        out <- data.frame(rowid = seq_along(out), estimate = out[[v]])
+        out <- data.frame(rowid = seq_len(nrow(out)), estimate = out[[v]])
 
     } else if (type == "class") {
-        out <- data.frame(rowid = seq_along(out), estimate = out[[".pred_class"]])
+        out <- data.frame(rowid = seq_len(nrow(out)), estimate = out[[".pred_class"]])
 
     } else if (type == "prob") {
         colnames(out) <- substr(colnames(out), 7, nchar(colnames(out)))

inst/tinytest/test-pkg-tidymodels.R

@@ -78,6 +78,24 @@ p <- plot_comparisons(fit,
 expect_inherits(p, "data.frame")
 
 
+# Issue 1209
+nobs <- 50
+my_data <- tibble(
+  x = runif(nobs, 0, 10),
+  y = -(x - 11)^2 + 100 + rnorm(nobs, 0, 25)
+)
+lr_spec <- linear_reg()
+lr_rec <- recipe(y ~ x, data = my_data) |>
+  step_poly(x, degree = 2)
+lr_wf <- workflow() |>
+  add_model(lr_spec) |>
+  add_recipe(lr_rec)
+lr_fit <- lr_wf |>
+  fit(my_data)
+mfx <- slopes(lr_fit, newdata = my_data, variable = "x")
+expect_equivalent(mfx$x, my_data$x)
+expect_equivalent(mfx$y, my_data$y)
+
 
 
 rm(list = ls())