Add -2LL as the Criterion column

R/test_likelihoodratio.R

@@ -189,12 +189,14 @@ test_likelihoodratio.ListNestedRegressions <- function(
   } else {
     # lmtest::lrtest()
     lls <- sapply(objects, insight::get_loglikelihood, REML = REML, check_response = TRUE)
-    chi2 <- abs(c(NA, -2 * diff(lls)))
+    criterion <- -2 * lls
+    chi2 <- abs(c(NA, diff(criterion)))
     p <- stats::pchisq(chi2, abs(dfs_diff), lower.tail = FALSE)
 
     out <- data.frame(
       df = dfs,
       df_diff = dfs_diff,
+      Criterion = criterion,
       Chi2 = chi2,
       p = p,
       stringsAsFactors = FALSE
@@ -228,10 +230,13 @@ test_likelihoodratio.ListNestedRegressions <- function(
 test_likelihoodratio_ListLavaan <- function(..., objects = NULL) {
   insight::check_if_installed("lavaan")
 
-  # Create data frame with info about model name and class
+  # Create data frame with info about model name, class, and criterion
   names_types <- data.frame(
     Model = names(objects),
     Type = sapply(objects, function(x) class(x)[1]),
+    Criterion = sapply(objects, function(x) {
+      -2 * as.numeric(lavaan::fitMeasures(x, "logl"))
+    }),
     stringsAsFactors = FALSE
   )
 
@@ -250,11 +255,13 @@ test_likelihoodratio_ListLavaan <- function(..., objects = NULL) {
   # Bind all data
   out <- merge(names_types, out[c("Model", "df", "df_diff", "Chi2", "p")], by = "Model")
 
+  # Reorder columns so Criterion is next to Chi2
+  out <- out[c("Model", "Type", "df", "df_diff", "Criterion", "Chi2", "p")]
+
   class(out) <- c("test_likelihoodratio", "see_test_likelihoodratio", "data.frame")
   out
 }
 
-
 # helper ----------------------
 
 .is_lmer_reml <- function(x) {

R/test_performance.R

@@ -113,19 +113,18 @@
 #'   the Wald test for small sample sizes (under or about 30) or if the
 #'   parameters are large.
 #'
+#'   The test statistic is calculated by comparing the -2 * log-likelihood
+#'   (-2LL) values for each model. In the output table, the `Criterion` column
+#'   represents this -2LL value. The difference in the criterion values
+#'   between the nested models corresponds to the `Chi2` statistic. This
+#'   Chi-square value is then used to compute the p-value based on the
+#'   difference in degrees of freedom (`df_diff`).
+#'
 #'   Note: for regression models, this is similar to
 #'   `anova(..., test="LRT")` (on models) or `lmtest::lrtest(...)`, depending
 #'   on the `estimator` argument. For **lavaan** models (SEM, CFA), the function
 #'   calls `lavaan::lavTestLRT()`.
 #'
-#'   For models with transformed response variables (like `log(x)` or `sqrt(x)`),
-#'   `logLik()` returns a wrong log-likelihood. However, `test_likelihoodratio()`
-#'   calls `insight::get_loglikelihood()` with `check_response=TRUE`, which
-#'   returns a corrected log-likelihood value for models with transformed
-#'   response variables. Furthermore, since the LRT only accepts nested
-#'   models (i.e. models that differ in their fixed effects), the computed
-#'   log-likelihood is always based on the ML estimator, not on the REML fits.
-#'
 #' - **Vuong's Test** - `test_vuong()`: Vuong's (1989) test can
 #'   be used both for nested and non-nested models, and actually consists of two
 #'   tests.

tests/testthat/_snaps/model_performance.psych.md

@@ -102,7 +102,7 @@
 
       Chi2(1) | p (Chi2) |  RMSR
       --------------------------
-      4.119   |    0.042 | 0.035
+      4.119   |    0.042 | 0.025
 
 ---
 
@@ -113,8 +113,8 @@
 
       Model             |    Chi2 | df | p (Chi2) |  RMSR | RMSR_corrected |   TLI | RMSEA |   RMSEA 90% CI |   BIC |    R2 | Correlation
       -----------------------------------------------------------------------------------------------------------------------------------
-      3-factor solution |  31.796 | 25 |   0.164  | 0.015 |          0.023 |       | 0.087 | [0.000, 0.181] | -54.8 |       |            
-      g-model           | 264.781 | 44 |   < .001 | 0.393 |          0.440 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.761 |       0.873
+      3-factor solution |  31.796 | 25 |   0.164  | 0.011 |          0.016 |       | 0.087 | [0.000, 0.181] | -54.8 |       |            
+      g-model           | 264.781 | 44 |   < .001 | 0.278 |          0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 |       0.941
 
       Compare the model fit of the 3-factor solution with the g-only model.
         If the g-model has smaller RMSR and RMSEA then your items are more
@@ -131,8 +131,8 @@
 
       Model             |    Chi2 | df | p (Chi2) |  RMSR | RMSR_corrected |   TLI | RMSEA |   RMSEA 90% CI |   BIC |    R2 | Correlation
       -----------------------------------------------------------------------------------------------------------------------------------
-      3-factor solution |  31.796 | 25 |   0.164  | 0.015 |          0.023 |       | 0.087 | [0.000, 0.181] | -54.8 |       |            
-      g-model           | 264.781 | 44 |   < .001 | 0.393 |          0.440 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.761 |       0.873
+      3-factor solution |  31.796 | 25 |   0.164  | 0.011 |          0.016 |       | 0.087 | [0.000, 0.181] | -54.8 |       |            
+      g-model           | 264.781 | 44 |   < .001 | 0.278 |          0.311 | 0.195 | 0.395 | [0.356, 0.450] | 112.3 | 0.886 |       0.941
 
       Compare the model fit of the 3-factor solution with the g-only model.
         If the g-model has smaller RMSR and RMSEA then your items are more

tests/testthat/test-test_likelihoodratio.R

@@ -125,3 +125,73 @@ test_that("test_likelihoodratio - print p-digits", {
   expect_snapshot(test_likelihoodratio(m1, m2))
   expect_snapshot(print_md(test_likelihoodratio(m1, m2), p_digits = 3))
 })
+
+test_that("test_likelihoodratio - Criterion values (lm)", {
+  m1 <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
+  m2 <- lm(mpg ~ wt + cyl + gear, data = mtcars)
+
+  # Check ML estimator since OLS defaults to .test_wald()
+  rez <- test_likelihoodratio(m2, m1, estimator = "ML")
+
+  # Check if column exists
+  expect_true("Criterion" %in% colnames(rez))
+
+  # Check exact values against manual -2LL calculation
+  ll1 <- as.numeric(stats::logLik(m1))
+  ll2 <- as.numeric(stats::logLik(m2))
+  expect_equal(rez$Criterion, -2 * c(ll2, ll1), tolerance = 1e-3)
+
+  # Check that the difference in Criterion matches Chi2 exactly
+  expect_equal(rez$Chi2[2], rez$Criterion[1] - rez$Criterion[2], tolerance = 1e-3)
+})
+
+test_that("test_likelihoodratio - Criterion values (lme4)", {
+  skip_if_not_installed("lme4")
+
+  m1 <- suppressMessages(lme4::lmer(
+    Sepal.Length ~ Petal.Width + (1 | Species),
+    data = iris,
+    REML = FALSE
+  ))
+  m2 <- suppressMessages(lme4::lmer(
+    Sepal.Length ~ Petal.Width + Petal.Length + (1 | Species),
+    data = iris,
+    REML = FALSE
+  ))
+
+  rez <- test_likelihoodratio(m1, m2, estimator = "ML")
+
+  expect_true("Criterion" %in% colnames(rez))
+
+  # Check values
+  ll1 <- as.numeric(stats::logLik(m1))
+  ll2 <- as.numeric(stats::logLik(m2))
+  expect_equal(rez$Criterion, -2 * c(ll1, ll2), tolerance = 1e-3)
+
+  # Check math
+  expect_equal(rez$Chi2[2], rez$Criterion[1] - rez$Criterion[2], tolerance = 1e-3)
+})
+
+test_that("test_likelihoodratio - Criterion values (lavaan)", {
+  skip_if_not_installed("lavaan")
+
+  structure1 <- " visual  =~ x1 + x2 + x3
+                  textual =~ x4 + x5 + x6
+                  speed   =~ x7 + x8 + x9
+                  visual ~~ textual + speed "
+  m1 <- suppressWarnings(lavaan::cfa(structure1, data = lavaan::HolzingerSwineford1939))
+
+  structure2 <- " visual  =~ x1 + x2 + x3
+                  textual =~ x4 + x5 + x6
+                  speed   =~ x7 + x8 + x9
+                  visual ~~ 0 * textual + speed "
+  m2 <- suppressWarnings(lavaan::cfa(structure2, data = lavaan::HolzingerSwineford1939))
+
+  rez <- test_likelihoodratio(m1, m2)
+
+  expect_true("Criterion" %in% colnames(rez))
+
+  ll1 <- as.numeric(lavaan::fitMeasures(m1, "logl"))
+  ll2 <- as.numeric(lavaan::fitMeasures(m2, "logl"))
+  expect_equal(rez$Criterion, -2 * c(ll1, ll2), tolerance = 1e-3)
+})