Feature Request: Contrasting Simple Slopes

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: modelbased
 Title: Estimation of Model-Based Predictions, Contrasts and Means
-Version: 0.8.9.8
+Version: 0.8.9.10
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",

R/estimate_slopes.R

@@ -170,7 +170,7 @@
   # Summarize and clean
   trends <- parameters::parameters(estimated, ci = ci, ...)
   # remove redundant columns
-  trends <- datawizard::data_remove(trends, c("Parameter", "Statistic", "SE", "S", "CI", "df", "rowid_dedup"), verbose = FALSE) # nolint
+  trends <- datawizard::data_remove(trends, c("Statistic", "SE", "S", "CI", "df", "rowid_dedup"), verbose = FALSE) # nolint
   trends <- datawizard::data_relocate(trends, "p", after = -1, verbose = FALSE)
   # Restore factor levels
   datawizard::data_restoretype(trends, insight::get_data(model, verbose = FALSE))
@@ -232,7 +232,7 @@
   ends <- nrow(data)
   # Iterate through all rows to find blocks
   for (i in 2:nrow(data)) {
     if ((data$Confidence[i] != sig) || ((centrality_signs[i] != centrality_sign) && data$Confidence[i] == "Uncertain")) {
       centrality_sign <- centrality_signs[i]
       sig <- data$Confidence[i]
       starts <- c(starts, i)

R/get_marginalcontrasts.R

@@ -19,18 +19,42 @@
   # Guess arguments
   my_args <- .guess_marginaleffects_arguments(model, by, contrast, ...)
 
-  out <- estimate_means(
-    model = model,
-    ## TODO: once .format_marginaleffects_contrasts() is working, we have to
-    ## pass only "contrast" to the `by` argument, and use `my_args$by` for
-    ## filtering...
-    by = unique(c(my_args$contrast, my_args$by)),
-    ci = ci,
-    hypothesis = method,
-    predict = predict,
-    backend = "marginaleffects",
-    ...
-  )
+  # check whether contrasts should be made for numerics or categorical
+  model_data <- insight::get_data(model, source = "mf", verbose = FALSE)
+  on_the_fly_factors <- attributes(model_data)$factors
+
+  # extract first focal term
+  first_focal <- my_args$contrast[1]
+
+  # if first focal term is numeric, we contrast slopes
+  if (is.numeric(model_data[[first_focal]]) && !first_focal %in% on_the_fly_factors) {
+    out <- estimate_slopes(
+      model = model,
+      trend = my_args$contrast,
+      ## TODO: once .format_marginaleffects_contrasts() is working, we have to
+      ## pass only "contrast" to the `by` argument, and use `my_args$by` for
+      ## filtering...
+      by = my_args$by,
+      ci = ci,
+      hypothesis = method,
+      backend = "marginaleffects",
+      ...
+    )
+  } else {
+    # for contrasts of categorical predictors, we call avg_predictions
+    out <- estimate_means(
+      model = model,
+      ## TODO: once .format_marginaleffects_contrasts() is working, we have to
+      ## pass only "contrast" to the `by` argument, and use `my_args$by` for
+      ## filtering...
+      by = unique(c(my_args$contrast, my_args$by)),
+      ci = ci,
+      hypothesis = method,
+      predict = predict,
+      backend = "marginaleffects",
+      ...
+    )
+  }
 
   # adjust p-values
   out <- .p_adjust(model, out, p_adjust, ...)
@@ -71,7 +95,7 @@
       # base R adjustments
       params[["p"]] <- stats::p.adjust(params[["p"]], method = p_adjust)
     } else if (tolower(p_adjust) == "tukey") {
       if (!is.null(statistic)) {
         # tukey adjustment
         params[["p"]] <- suppressWarnings(stats::ptukey(
           sqrt(2) * abs(statistic),

tests/testthat/test-estimate_contrasts.R

@@ -295,3 +295,19 @@ test_that("estimate_contrasts - on-the-fly factors", {
   expect_identical(nrow(out4), 3L)
   expect_equal(out3$Difference, out4$Difference, tolerance = 1e-4)
 })
+
+
+test_that("estimate_contrasts - works with slopes", {
+  data(iris)
+  fit <- lm(Sepal.Width ~ Petal.Length * Species, data = iris)
+
+  out1 <- estimate_slopes(fit, trend = "Petal.Length", backend = "marginaleffects")
+  out2 <- suppressMessages(as.data.frame(emmeans::emtrends(fit, specs = ~Petal.Length, var = "Petal.Length")))
+  expect_equal(out1$Coefficient, out2$Petal.Length.trend, tolerance = 1e-3)
+
+  out3 <- estimate_slopes(fit, trend = "Petal.Length", by = "Species", backend = "marginaleffects")
+  out4 <- estimate_contrasts(fit, contrast = "Petal.Length", by = "Species", backend = "marginaleffects")
+  out5 <- emmeans::emtrends(fit, specs = pairwise ~ Species, var = "Petal.Length")
+  expect_equal(out3$Coefficient, as.data.frame(out5$emtrends)$Petal.Length.trend, tolerance = 1e-3)
+  expect_equal(out4$Coefficient, as.data.frame(out5$contrasts)$estimate, tolerance = 1e-3)
+})