rmAnova: fixed emmeans to use multivariate model instead of univariate

jamovi · Mar 22, 2021 · a9c8cb0 · a9c8cb0
1 parent d0c3257
commit a9c8cb0
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Showing 2 changed files with 48 additions and 2 deletions.
diff --git a/R/anovarm.b.R b/R/anovarm.b.R
@@ -593,7 +593,7 @@ anovaRMClass <- R6::R6Class(
                     table$setStatus('running')
 
                     emmeans::emm_options(sep = ",", parens = "a^")
-                    referenceGrid <- emmeans::emmeans(result, formula)
+                    referenceGrid <- emmeans::emmeans(result, formula, model="multivariate")
                     none <- summary(pairs(referenceGrid, adjust='none'))
                     tukey <- summary(pairs(referenceGrid, adjust='tukey'))
                     scheffe <- summary(pairs(referenceGrid, adjust='scheffe'))
@@ -757,7 +757,8 @@ anovaRMClass <- R6::R6Class(
                         emmeans::emm_options(sep = ",", parens = "a^")
 
                         mm <- try(
-                            emmeans::emmeans(model, formula, options=list(level=self$options$ciWidthEmm / 100), weights = weights),
+                            emmeans::emmeans(model, formula, options=list(level=self$options$ciWidthEmm / 100),
+                                             weights = weights, model = "multivariate"),
                             silent = TRUE
                         )
                     })

diff --git a/tests/testthat/testanovarm.R b/tests/testthat/testanovarm.R
@@ -82,3 +82,48 @@ test_that('anovarm works', {
     spher <- r$assump$spherTable$asDF
     testthat::expect_equal(spher$mauch, NaN)
 })
+
+test_that('emmeans work for unbalanced data', {
+    set.seed(1337)
+    N <- 100
+    data <- data.frame(
+        measure1 = rnorm(N, 0, 1),
+        measure2 = rnorm(N, 1, 1),
+        measure3 = rnorm(N, 2, 1),
+        bsFactor = sample(letters[1:2], replace=TRUE, prob=c(0.3, 0.7), size=N),
+        stringsAsFactors = TRUE
+    )
+
+    rm = list(list(
+        label="rmFactor",
+        levels=c("measure1", "measure2", "measure3")
+    ))
+
+    rmCells = list(
+        list(
+            measure="measure1",
+            cell="measure1"),
+        list(
+            measure="measure2",
+            cell="measure2"),
+        list(
+            measure="measure3",
+            cell="measure3")
+    )
+
+    r <- jmv::anovaRM(
+        data=data, rm=rm, rmCells=rmCells, bs="bsFactor",
+        rmTerms=list("rmFactor"), bsTerms=list("bsFactor"),
+        emMeans = ~bsFactor:rmFactor, emmPlots = FALSE, emmTables = TRUE
+    )
+
+    means <- aggregate(data[, -4], data[4], mean)
+    emmeans <- r$emm[[1]]$emmTable$asDF
+
+    expect_equal(means[1, 2], emmeans[1, "mean"], tolerance = 1e-4)
+    expect_equal(means[2, 2], emmeans[2, "mean"], tolerance = 1e-4)
+    expect_equal(means[1, 3], emmeans[3, "mean"], tolerance = 1e-4)
+    expect_equal(means[2, 3], emmeans[4, "mean"], tolerance = 1e-4)
+    expect_equal(means[1, 4], emmeans[5, "mean"], tolerance = 1e-4)
+    expect_equal(means[2, 4], emmeans[6, "mean"], tolerance = 1e-4)
+})