Exponentiation of clmm coefficients in the ordinal package

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.5.5
+Version: 0.21.5.6
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

NEWS.md

@@ -14,6 +14,10 @@
 * Fixed issue with parameter names for `model_parameters()` and objects from
   package *epiR*.
 
+* Fixed issue with `exponentiate = TRUE` for `model_parameters()` with models
+  of class `clmm` (package *ordinal*), when model had no `component` column
+  (e.g., no scale or location parameters were returned).
+
 # parameters 0.21.5
 
 ## Bug fixes

R/p_function.R

@@ -58,7 +58,7 @@
 #' (i.e. assumptions are taken as given). The unconditional interpretation (B),
 #' however, questions all these assumptions.
 #'
 #' \if{html}{\cr \figure{unconditional_interpretation.png}{options: alt="Conditional versus unconditional interpretations of P-values"} \cr}
 #'
 #' "Emphasizing unconditional interpretations helps avoid overconfident and
 #' misleading inferences in light of uncertainties about the assumptions used
@@ -92,6 +92,17 @@
 #' (which is in line with the "unconditional" interpretation in the sense of
 #' Rafi and Greenland).
 #'
+#' Ascribing a probabilistic interpretation to one realized confidence interval
+#' is possible without repeated sampling of the specific experiment. Important
+#' is the assumption that a _sampling distribution_ is a good description of the
+#' variability of the parameter (_Vos and Holbert 2022_). At the core, the
+#' interpretation of a confidence interval is "I assume that this sampling
+#' distribution is a good description of the uncertainty of the parameter. If
+#' that's a good assumption, then the values in this interval are the most
+#' plausible or compatible with the data". The source of confidence in
+#' probability statements is the assumption that the selected sampling
+#' distribution is appropriate.
+#'
 #' "The realized confidence distribution is clearly an epistemic probability
 #' distribution" (_Schweder 2018_). In Bayesian words, compatibility intervals
 #' (or confidence distributons, or consonance curves) are "posteriors without
@@ -156,20 +167,21 @@
 #' - Schweder T, Hjort NL. Confidence, Likelihood, Probability: Statistical
 #'   inference with confidence distributions. Cambridge University Press, 2016.
 #'
-#' @examples
+#' - Vos P, Holbert D. Frequentist statistical inference without repeated sampling.
+#'   Synthese 200, 89 (2022). \doi{10.1007/s11229-022-03560-x}
+#'
+#' @examplesIf requireNamespace("see")
 #' model <- lm(Sepal.Length ~ Species, data = iris)
 #' p_function(model)
 #'
-#' if (requireNamespace("see") && packageVersion("see") > "0.7.3") {
-#'   model <- lm(mpg ~ wt + as.factor(gear) + am, data = mtcars)
-#'   result <- p_function(model)
+#' model <- lm(mpg ~ wt + as.factor(gear) + am, data = mtcars)
+#' result <- p_function(model)
 #'
-#'   # single panels
-#'   plot(result, n_columns = 2)
+#' # single panels
+#' plot(result, n_columns = 2)
 #'
-#'   # integrated plot, the default
-#'   plot(result)
-#' }
+#' # integrated plot, the default
+#' plot(result)
 #' @export
 p_function <- function(model,
                        ci_levels = c(0.25, 0.5, 0.75, emph = 0.95),

R/utils_model_parameters.R

@@ -2,7 +2,7 @@
 # usually used for printing etc.
 
 #' @keywords internal
 .add_model_parameters_attributes <- function(params,
                                              model,
                                              ci,
                                              exponentiate = FALSE,
@@ -239,7 +239,7 @@
   )
 }
 
 .find_coefficient_type <- function(info, exponentiate, model = NULL) {
   # column name for coefficients
   coef_col <- "Coefficient"
   if (!is.null(model) && inherits(model, "emmGrid")) {
@@ -323,12 +323,12 @@
   if (any(columns)) {
     if (inherits(model, "mvord")) {
       rows <- params$Component != "correlation"
-    } else if (inherits(model, c("clm", "clm2", "clmm"))) {
-      ## TODO: make sure we catch all ordinal models properly here
-      rows <- !tolower(params$Component) %in% c("location", "scale")
     } else if (is.null(params$Component)) {
       # don't exponentiate dispersion
       rows <- seq_len(nrow(params))
+    } else if (inherits(model, c("clm", "clm2", "clmm"))) {
+      ## TODO: make sure we catch all ordinal models properly here
+      rows <- !tolower(params$Component) %in% c("location", "scale")
     } else {
       rows <- !tolower(params$Component) %in% c("dispersion", "residual")
     }

tests/testthat/test-marginaleffects.R

@@ -1,15 +1,16 @@
 skip_if_not_installed("marginaleffects", minimum_version = "0.18.0")
+skip_if_not_installed("insight", minimum_version = "0.19.8.8")
 skip_if_not_installed("rstanarm")
 
 test_that("marginaleffects()", {
   # Frequentist
   x <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
   model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
   out <- parameters(model)
   expect_identical(nrow(out), 1L)
   expect_named(out, c(
     "Parameter", "Coefficient", "SE", "Statistic",
-    "p", "s.value", "CI", "CI_low", "CI_high"
+    "p", "S", "CI", "CI_low", "CI_high"
   ))
   out <- model_parameters(model, exponentiate = TRUE)
   expect_equal(out$Coefficient, 1.394, tolerance = 1e-3)
@@ -24,7 +25,7 @@
       chains = 1
     )
   )
   model <- marginaleffects::avg_slopes(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
   expect_identical(nrow(parameters(model)), 1L)
 })
 
@@ -35,8 +36,8 @@
   out <- parameters(p)
   expect_identical(nrow(out), 3L)
   expect_named(out, c(
-    "Predicted", "SE", "CI", "CI_low", "CI_high", "Statistic",
-    "p", "Species", "s.value"
+    "Predicted", "SE", "CI", "CI_low", "CI_high", "S", "Statistic",
+    "p", "Species"
   ))
   out <- parameters(p, exponentiate = TRUE)
   expect_equal(out$Predicted, c(30.81495, 15.95863, 19.57004), tolerance = 1e-4)
@@ -47,7 +48,7 @@
   data(iris)
   # Frequentist
   x <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
   m <- marginaleffects::avg_comparisons(x, newdata = insight::get_datagrid(x, at = "Species"), variables = "Petal.Length")
   expect_identical(nrow(parameters(m)), 1L)
   out <- parameters(m, exponentiate = TRUE)
   expect_equal(out$Coefficient, 1.393999, tolerance = 1e-4)
@@ -71,7 +72,6 @@
 })
 
 
-
 test_that("hypotheses()", {
   data(mtcars)
   x <- lm(mpg ~ hp + wt, data = mtcars)

tests/testthat/test-model_parameters_ordinal.R

@@ -91,3 +91,14 @@ test_that("model_parameters.clm2", {
 
   expect_snapshot(print(mp))
 })
+
+test_that("model_parameters.clmm, exponentiate works w/o component column", {
+  data(wine, package = "ordinal")
+  mox <- ordinal::clmm(rating ~ temp + contact + (1 | judge), data = wine)
+  out1 <- model_parameters(mox, exponentiate = FALSE)
+  out2 <- model_parameters(mox, exponentiate = TRUE)
+  expect_equal(out1$Coefficient, c(-1.62367, 1.51337, 4.22853, 6.08877, 3.063, 1.83488, 1.13113), tolerance = 1e-4)
+  expect_equal(out2$Coefficient, c(0.19717, 4.54199, 68.61606, 440.87991, 21.39156, 6.26441, 1.13113), tolerance = 1e-4)
+  expect_identical(attributes(out1)$coefficient_name, "Log-Odds")
+  expect_identical(attributes(out2)$coefficient_name, "Odds Ratio")
+})