increase precision

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.22.1
+Version: 0.22.1.1
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

R/equivalence_test.R

@@ -654,11 +654,13 @@ equivalence_test.ggeffects <- function(x,
     # then, the range from mean value to lower/upper limit, for a normal
     # distribution is approximately 3.3 SD (3 SD cover 99.7% of the probability
     # mass of the normal distribution). Thus, assuming that half of the ci_range
-    # refers to ~ 3.3 SD, we "normalize" the value (i.e. divide by 3.29) to get
-    # the value for one SD, which we need to build the normal distribution.
-    sd = (diff_ci / 2) / 3.29
+    # refers to ~ 3.3 SD, we "normalize" the value (i.e. divide by 3.290525) to
+    # get the value for one SD, which we need to build the normal distribution.
+    sd = (diff_ci / 2) / 3.290525
   )
 
+  # compare: ci_range and range(out)
+
   # The SGPV refers to the proportion of the confidence interval inside the
   # full ROPE - thus, we set ci = 1 here
   rc <- bayestestR::rope(out, range = range_rope, ci = 1)

tests/testthat/test-equivalence_test.R

@@ -1,3 +1,5 @@
+skip_if_not_installed("bayestestR")
+
 test_that("equivalence_test", {
   data(mtcars)
   m <- lm(mpg ~ gear + wt + cyl + hp, data = mtcars)
@@ -28,6 +30,23 @@ test_that("equivalence_test, unequal rope-range", {
     rez$ROPE_Equivalence,
     c("Rejected", "Accepted", "Undecided", "Rejected", "Accepted", "Undecided")
   )
+
+  # validate thet range of CI equals approximated  normal distribution
+  diff_ci <- abs(diff(c(rez$CI_low[3], rez$CI_high[3])))
+  set.seed(123)
+  out <- bayestestR::distribution_normal(
+    n = 1000,
+    mean = rez$CI_high[3] - (diff_ci / 2),
+    sd = (diff_ci / 2) / 3.290525
+  )
+  expect_equal(range(out)[1], rez$CI_low[3], tolerance = 1e-4)
+  expect_equal(range(out)[2], rez$CI_high[3], tolerance = 1e-4)
+  expect_equal(
+    rez$SGPV[3],
+    bayestestR::rope(out, range = c(-Inf, 0.5), ci = 1)$ROPE_Percentage,
+    tolerance = 1e-4
+  )
+
   rez <- equivalence_test(m, range = c(-0.5, 0.5))
   expect_identical(
     rez$ROPE_Equivalence,