add df to posthoc tables (#388)

* add df to posthoc tables * update unit tests * rm anova * another update unit test * last update and remove "LSD" from standard post hoc test * dynamic name, allow no adjustment
jasp-stats · Oct 14, 2024 · 7ca9295 · 7ca9295
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diff --git a/R/ancova.R b/R/ancova.R
@@ -900,7 +900,8 @@ AncovaInternal <- function(jaspResults, dataset = NULL, options) {
   if (!is.null(postHocContainer[["postHocStandardContainer"]]))
     return()
 
-  postHocStandardContainer <- createJaspContainer(title = gettext("Standard (LSD)"))
+  myTitle <- if (options[["postHocCorrectionTukey"]])  gettext("Standard (HSD)") else  gettext("Standard")
+  postHocStandardContainer <- createJaspContainer(title = myTitle)
   postHocStandardContainer$dependOn(c("postHocTerms", "postHocTypeStandardEffectSize", "postHocTypeStandard",
                                       "postHocCorrectionBonferroni", "postHocCorrectionHolm", "postHocCorrectionScheffe",
                                       "postHocCorrectionTukey", "postHocCorrectionSidak", "postHocSignificanceFlag",
@@ -933,6 +934,8 @@ AncovaInternal <- function(jaspResults, dataset = NULL, options) {
       wantsCorrections <- c(options[["postHocCorrectionTukey"]], options[["postHocCorrectionScheffe"]],
                             options[["postHocCorrectionBonferroni"]], options[["postHocCorrectionHolm"]],
                             options[["postHocCorrectionSidak"]])
+      if (sum(wantsCorrections) == 0)
+        wantsCorrections <- "none"
       postHocCorrections <- c("tukey", "scheffe", "bonferroni", "holm", "sidak")[wantsCorrections]
 
       ## Computation

diff --git a/R/anovarepeatedmeasures.R b/R/anovarepeatedmeasures.R
@@ -932,8 +932,9 @@ AnovaRepeatedMeasuresInternal <- function(jaspResults, dataset = NULL, options)
     thisVarName <- paste(postHocVariables[[postHocVarIndex]], collapse = ":")
     byVariable <- if (options[["postHocConditionalTable"]] && length(postHocVariables[[postHocVarIndex]]) > 1) postHocVariables[[postHocVarIndex]] else NULL
     termsToLoop <- if (options[["postHocConditionalTable"]]) postHocVariables[[postHocVarIndex]] else 1
+    dfType <- if (options[["poolErrorTermFollowup"]] && (length(postHocVariables[[postHocVarIndex]]) > 1)) "number" else "integer"
     for (termIndex in seq_along(termsToLoop))
-      postHocContainer[[ paste0(thisVarName, termIndex)]] <- .createPostHocStandardTable(thisTitle, byVariable[termIndex], options)
+      postHocContainer[[ paste0(thisVarName, termIndex)]] <- .createPostHocStandardTable(thisTitle, byVariable[termIndex], options, dfType = dfType)
 
     if (options[["postHocLetterTable"]]) {
       letterTable <- createJaspTable(title = paste0("Letter-Based Grouping - ", thisVarName))

diff --git a/R/commonAnovaFreq.R b/R/commonAnovaFreq.R
@@ -65,7 +65,7 @@
   return(coefTable)
 }
 
-.createPostHocStandardTable <- function(myTitle, byVariable = NULL, options, makeBootstrapTable = FALSE) {
+.createPostHocStandardTable <- function(myTitle, byVariable = NULL, options, makeBootstrapTable = FALSE, dfType = "integer") {
 
   preTitle <- if (!makeBootstrapTable) gettext("Post Hoc Comparisons - ") else gettext("Bootstrapped Post Hoc Comparisons - ")
   postHocTable <- createJaspTable(title = paste0(preTitle, myTitle)) #this paste is ok
@@ -92,6 +92,7 @@
   }
 
   postHocTable$addColumnInfo(name="SE", title=gettext("SE"), type="number")
+  postHocTable$addColumnInfo(name="df", title=gettext("df"), type = dfType)
 
   if (makeBootstrapTable)
     postHocTable$addColumnInfo(name="bias", title=gettext("bias"), type="number")

diff --git a/inst/help/Ancova.md b/inst/help/Ancova.md
@@ -73,7 +73,7 @@ ANCOVA allows the user to analyze the difference between multiple group means, w
 ### Post Hoc Tests
 - To perform a post hoc test, drag one or more factor names to the right column. Several options are available:    
   - Type: Different types of post hoc tests can be selected. 
-  -  Standard (LSD): Pairwise t-tests are performed. All the corrections can be applied to this method. This option is selected by default. 
+  -  Standard: Pairwise t-tests are performed. All the corrections can be applied to this method. When Tukey's p-value correction is selected, this is equivalent to Tukey's HSD. This option is selected by default. 
       - Confidence intervals: When this option is selected, the confidence interval for the mean difference is calculated. This is done for every post hoc method except for Dunn. By default this is set to 95% but this can be adjusted into the desired percentage.  
       - From `...` bootstraps: By selecting this option, the bootstrapped post hoc test is applied. By default, the number of replications is set to 1000. This can be changed into the desired number. 
       - Effect size: By selecting this option, the effect size (i.e., the magnitude of the observed effect) will be displayed. The used measure for the effect size is Cohen's d. The effect size will only be displayed for the post hoc type `Standard`. 

diff --git a/inst/help/Ancova_nl.md b/inst/help/Ancova_nl.md
@@ -77,7 +77,7 @@ Met een ANCOVA kan men het verschil tussen meerdere groepsgemiddelden analyseren
       - Bonferroni: Deze correctie wordt gezien als vrij conservatief. Het risico op een type 1 fout wordt verminderd, maar de statistische kracht (power) wordt ook lager.
       - Holm: Deze methode wordt ook wel sequentiële Bonferroni genoemd, en wordt gezien als minder conservatief dan de Bonferroni methode.
   - Type: Er kunnen verschillende typen post-hoc toetsen worden geselecteerd. 
-	  -  Standaard (LSD): Paarsgewijze t-toetsen worden uitgevoerd. Alle correcties kunnen op deze methode worden toegepast. Dit is de standaardoptie.
+	  -  Standaard: Paarsgewijze t-toetsen worden uitgevoerd. Alle correcties kunnen op deze methode worden toegepast. Als Tukey's p-waarde correctie is geselecteerd, is dit equivalent aan Tukey's HSD. Dit is de standaardoptie.
       -  Games-Howell: Deze methode kan worden gebruikt wanneer groeps-/niveauvarianties niet gelijk zijn. De p-waarden worden gecorrigeerd met de Tukey methode.
       -  Dunett: Als u deze methode selecteert, worden alle niveaus vergeleken met een specifiek niveau, bijvoorbeeld met de controlegroep. Momenteel is het nog niet mogelijk om handmatig te specificeren met welk niveau de andere niveaus worden vergeleken, maar dit is gebaseerd op de volgorde van de niveaus. Om de volgorde aan te passen kunnen de niveaulabels worden veranderd.
         <details>

diff --git a/inst/help/Anova.md b/inst/help/Anova.md
@@ -72,7 +72,7 @@ ANOVA allows the user to analyze the difference between multiple group means.
 ### Post Hoc Tests 
 - To perform a post hoc test, drag one or more factor names to the right column. Several options are available:    
   - Type: Different types of post hoc tests can be selected. 
-  -  Standard (LSD): Pairwise t-tests are performed. All the corrections can be applied to this method. This option is selected by default. 
+  -  Standard: Pairwise t-tests are performed. All the corrections can be applied to this method. When Tukey's p-value correction is selected, this is equivalent to Tukey's HSD. This option is selected by default. 
       - Confidence intervals: When this option is selected, the confidence interval for the mean difference is calculated. This is done for every post hoc method except for Dunn. By default this is set to 95% but this can be adjusted into the desired percentage.  
       - From `...` bootstraps: By selecting this option, the bootstrapped post hoc test is applied. By default, the number of replications is set to 1000. This can be changed into the desired number. 
       - Effect size: By selecting this option, the effect size (i.e., the magnitude of the observed effect) will be displayed. The used measure for the effect size is Cohen's d. The effect size will only be displayed for the post hoc type `Standard`. 

diff --git a/inst/help/Anova_nl.md b/inst/help/Anova_nl.md
@@ -81,7 +81,7 @@ Met een ANOVA berekent men het verschil tussen meerdere groepsgemiddelden.
       - Bonferroni: Deze correctie wordt gezien als vrij conservatief. Het risico op een type 1 fout wordt verminderd, maar statistische kracht (power) wordt ook lager.
       - Holm: Deze methode wordt ook wel sequentiële Bonferroni genoemd, en wordt gezien als minder conservatief dan de Bonferroni methode.
   - Type: Er kunnen verschillende typen post-hoc toets worden geselecteerd. 
-	  -  Standaard (LSD): Paarsgewijze t-toetsen worden uitgevoerd. Alle correcties kunnen op deze methode worden toegepast. Dit is de standaardoptie.
+	  -  Standaard: Paarsgewijze t-toetsen worden uitgevoerd. Alle correcties kunnen op deze methode worden toegepast. Als Tukey's p-waarde correctie is geselecteerd, is dit equivalent aan Tukey's HSD. Dit is de standaardoptie.
       -  Games-Howell: Deze methode kan worden gebruikt wanneer groeps-/niveauvarianties niet gelijk zijn. De p-waarden worden gecorrigeerd met de Tukey methode.
       -  Dunett: Als u deze methode selecteert, worden alle niveaus vergeleken met een specifiek niveau, bijvoorbeeld met de controlegroep. Momenteel is het nog niet mogelijk om handmatig te specificeren met welk niveau de andere niveaus worden vergeleken, maar dit is gebaseerd op de volgorde van de niveaus. Om de volgorde aan te passen, kunnen de niveaulabels worden veranderd.
         <details>

diff --git a/tests/testthat/test-ancova.R b/tests/testthat/test-ancova.R
@@ -201,7 +201,7 @@ test_that("Post Hoc table results match", {
   jaspTools::expect_equal_tables(table,
                                  list("TRUE", 0.21391801479091, 0.698555762823947, -0.0779045273305165,
                                       -0.476129796984023, 0.32032074232299, "control", "experimental",
-                                      -0.0830902357515325, 0.698555762823947, -0.507658279613134,
+                                      -0.0830902357515325, 0.698555762823947, -0.507658279613134, 97,
                                       0.698555762823947, 0.698555762823947, -0.388420937024623, 0.698555762823947,
                                       0.341477808110069)
   )
@@ -354,9 +354,9 @@ test_that("Field - Chapter 6 results match", {
   table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection[[1]]$data
   jaspTools::expect_equal_tables(table,
                                  list("TRUE", 0.538336190074754, -0.0194100366350722, 1, 2, -1.78324124481148,
-                                      -2.83630538964887, 0.129983128349044, -2.1023947223315, -0.858078871455782,
-                                      "FALSE", 0.763324066789791, -0.0155779657803694, 1, 3, -2.18556663740395,
-                                      -3.97107607430337, 0.0302155022603601, -2.77136409916095, -0.844312084720958,
+                                      -2.83630538964887, 0.129983128349044, -2.1023947223315, -0.858078871455782,26,
+                                      "FALSE", 0.763324066789791, -0.0155779657803694, 1, 3, -2.18556663740395, 26,
+                                      -3.97107607430337, 0.0302155022603601, -2.77136409916095, -0.844312084720958,26,
                                       "FALSE", 0.775278163963213, 0.00383207085470289, 2, 3, -0.410252544853603,
                                       -2.12122714048355, 0.932499470707816, -0.541407321622228, 0.972524781215359
                                  ))

diff --git a/tests/testthat/test-anova.R b/tests/testthat/test-anova.R
@@ -150,7 +150,7 @@ test_that("Post Hoc table results match", {
   table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_contBinom$data
   jaspTools::expect_equal_tables(table,
                                  list("TRUE", 0.214904085649005, 0.448976320466698, 0.15401876311258,
-                                      -0.24864690950018, 0.55668443572534, 0, 1, 0.163364220743842,
+                                      -0.24864690950018, 0.55668443572534, 0, 1, 0.163364220743842, 98,
                                       0.448976320466698, -0.263105943067512, 0.448976320466698, 0.448976320466698,
                                       0.760172707980336, 0.448976320466698, 0.589834384555196)
     )
@@ -507,11 +507,12 @@ test_that("Post Hoc Comparisons - Species table results match and contrast names
    table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_postHocContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer_Species1"]][["data"]]
    jaspTools::expect_equal_tables(table,
                                   list("TRUE", 0.286589136802729, "First species", "Second species",
-                                       -1.6, -5.58290526239083, 0.000324812090923943, "FALSE", 0.286589136802729,
-                                       "First species", "Third species", -1.54, -5.37354631505117,
-                                       0.000453440993214982, "FALSE", 0.286589136802729, "Second species",
-                                       "Third species", 0.0599999999999998, 0.209358947339655, 0.976174158311121
-                                  ))
+                                       12, -1.6, -5.58290526239083, 0.000324812090923943, "FALSE",
+                                       0.286589136802729, "First species", "Third species", 12, -1.54,
+                                       -5.37354631505117, 0.000453440993215093, "FALSE", 0.286589136802729,
+                                       "Second species", "Third species", 12, 0.0600000000000003, 0.209358947339657,
+                                       0.97617415831112)
+                                  )
 
    # following https://github.com/jasp-stats/jasp-issues/issues/1295, assert that these names do not contain commas.
    contrast_A <- vapply(table, `[[`, "contrast_A", FUN.VALUE = character(1L))
@@ -581,11 +582,11 @@ test_that("Field - Chapter 5 results match", {
   # standard post hoc (tukey)
   table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Dose$data
   jaspTools::expect_equal_tables(table,
-                                 list("TRUE", 0.886942313043338, 1, 2, -1, -3.36624115850686, -1.12746904200424,
-                                      0.516276123508473, 1.36624115850686, "FALSE", 0.886942313043338,
-                                      1, 3, -2.8, -5.16624115850687, -3.15691331761188, 0.0209243994922408,
-                                      -0.433758841493136, "FALSE", 0.886942313043338, 2, 3, -1.8,
-                                      -4.16624115850686, -2.02944427560764, 0.147457622995377, 0.566241158506864
+                                 list("TRUE", 0.886942313043338, 1, 2, 12, -1, -3.36624115850687, -1.12746904200424,
+                                      0.516276123508473, 1.36624115850687, "FALSE", 0.886942313043338,
+                                      1, 3, 12, -2.8, -5.16624115850687, -3.15691331761188, 0.020924399492241,
+                                      -0.433758841493132, "FALSE", 0.886942313043338, 2, 3, 12, -1.8,
+                                      -4.16624115850687, -2.02944427560764, 0.147457622995377, 0.566241158506867
                                  ))
 
   # games-howell post hoc
@@ -658,14 +659,14 @@ test_that("Field - Chapter 7 results match", {
   # removed both post hoc table and contrast table because bootstrap results are now in same table
   table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Alcohol$data
   jaspTools::expect_equal_tables(table,
-                                 list("TRUE", 0.392000159227314, -0.00376914898826786, 0.230950085511107,
-                                      0, 1, -0.769579725829724, -1.48733254329573, -1.8129965586672,
-                                      0.177726007657148, 0.0548973391001529, "FALSE", 0.435378856593833,
-                                      0.0142934574412505, 0.00359956767679779, 0, 2, -1.43536324786325,
-                                      -2.31465226049576, -3.47491007077881, 0.00337043014651417, -0.602581004497449,
-                                      "FALSE", 0.407611902363181, 0.0180626064295184, 0.311931949521,
-                                      1, 2, -0.690674603174603, -1.43846891737073, -1.66191351211161,
-                                      0.231712504393661, 0.214248742598683))
+                                 list("TRUE", 0.392000159227314, -0.00376914898826863, 0.230950085511108,
+                                      0, 1, 42, -0.769579725829724, -1.48733254329573, -1.8129965586672,
+                                      0.177726007657148, 0.0548973391001528, "FALSE", 0.435378856593833,
+                                      0.014293457441249, 0.00359956767679781, 0, 2, 42, -1.43536324786325,
+                                      -2.31465226049576, -3.47491007077881, 0.00337043014651439, -0.602581004497448,
+                                      "FALSE", 0.407611902363181, 0.0180626064295178, 0.311931949521002,
+                                      1, 2, 42, -0.690674603174603, -1.43846891737072, -1.6619135121116,
+                                      0.231712504393662, 0.214248742598684))
 
 
   table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data