improved footnote posthoc anova

R/ancova.R

@@ -930,7 +930,10 @@ AncovaInternal <- function(jaspResults, dataset = NULL, options) {
                                                                                 options$postHocTypeStandardBootstrap)
 
       postHocRef <- emmeans::lsmeans(model, postHocVariablesListV)
-      postHocCorrections <- c("tukey", "scheffe", "bonferroni", "holm", "sidak")
+      wantsCorrections <- c(options[["postHocCorrectionTukey"]], options[["postHocCorrectionScheffe"]],
+                            options[["postHocCorrectionBonferroni"]], options[["postHocCorrectionHolm"]],
+                            options[["postHocCorrectionSidak"]])
+      postHocCorrections <- c("tukey", "scheffe", "bonferroni", "holm", "sidak")[wantsCorrections]
 
       ## Computation
       resultPostHoc <- lapply(postHocCorrections, function(x)
@@ -950,7 +953,9 @@ AncovaInternal <- function(jaspResults, dataset = NULL, options) {
       # if there is p-adjustment, then add footnote
       if (nrow(resultPostHoc[[1]]) > 1 && any(grepl(attr(resultPostHoc[[1]], "mesg"), pattern = "P value adjustment")))
         postHocStandardContainer[[thisVarNameRef]]$addFootnote(.getCorrectionFootnoteAnova(resultPostHoc[[1]],
-                                                                                           (options$postHocCi && isFALSE(options$postHocTypeStandardBootstrap))))
+                                                                                           (options$postHocCi && isFALSE(options$postHocTypeStandardBootstrap)),
+                                                                                           includeEffectSize = options[["postHocTypeStandardEffectSize"]],
+                                                                                           isBetween = TRUE))
       avFootnote <- attr(resultPostHoc[[1]], "mesg")[grep(attr(resultPostHoc[[1]], "mesg"), pattern = "Results are averaged")]
       if (length(avFootnote) != 0) {
         avTerms <- .unv(strsplit(gsub(avFootnote, pattern = "Results are averaged over the levels of: ", replacement = ""),

R/anovarepeatedmeasures.R

@@ -966,7 +966,7 @@ AnovaRepeatedMeasuresInternal <- function(jaspResults, dataset = NULL, options)
       thisVarNameRef <- paste0(thisVarName, termIndex)
       byVariable <- if (length(termsToLoop) > 1) postHocVariables[[postHocVarIndex]] else NULL
 
-      resultPostHoc <- summary(pairs(referenceGrid[[thisVarName]], adjust="bonferroni", by = byVariable[termIndex]),
+      resultPostHoc <- summary(pairs(referenceGrid[[thisVarName]], adjust="holm", by = byVariable[termIndex]),
                                infer = TRUE, level = options$postHocCiLevel)
       numberOfLevels <- nrow(as.data.frame(referenceGrid[[thisVarName]]))
       bonfAdjustCIlevel <- .computeBonferroniConfidence(options$postHocCiLevel,
@@ -978,15 +978,15 @@ AnovaRepeatedMeasuresInternal <- function(jaspResults, dataset = NULL, options)
                                                           level = bonfAdjustCIlevel,
                                                           by = byVariable[termIndex]))
 
-      resultPostHoc[["bonferroni"]] <- resultPostHoc[["p.value"]]
+      resultPostHoc[["holm"]] <- resultPostHoc[["p.value"]]
 
       resultPostHoc[["tukey"]] <-  summary(pairs(referenceGrid[[thisVarName]], adjust="tukey",
                                                  by = byVariable[termIndex]))[["p.value"]]
 
       resultPostHoc[["scheffe"]] <-  summary(pairs(referenceGrid[[thisVarName]], adjust="scheffe",
                                                    by = byVariable[termIndex]))[["p.value"]]
 
-      resultPostHoc[["holm"]] <-  summary(pairs(referenceGrid[[thisVarName]], adjust="holm",
+      resultPostHoc[["bonferroni"]] <-  summary(pairs(referenceGrid[[thisVarName]], adjust="bonferroni",
                                                 by = byVariable[termIndex]))[["p.value"]]
 
       resultPostHoc[["cohenD"]] <- effectSizeResult[["effect.size"]]
@@ -1014,9 +1014,8 @@ AnovaRepeatedMeasuresInternal <- function(jaspResults, dataset = NULL, options)
       resultPostHoc[["contrast_B"]] <- lapply(comparisons, function(x) paste(.unv(strsplit(x[[2]], "[ ,]")[[1]]),
                                                                              collapse = ", "))
 
-      if (nrow(resultPostHoc[[1]]) > 1 && any(grepl(attr(resultPostHoc[[1]], "mesg"), pattern = "P value adjustment")))
-        postHocContainer[[thisVarNameRef]]$addFootnote(.getCorrectionFootnoteAnova(resultPostHoc[[1]],
-                                                                                   (options$postHocCi && isFALSE(options$postHocTypeStandardBootstrap))))
+      if (nrow(resultPostHoc) > 1 && any(grepl(attr(resultPostHoc, "mesg"), pattern = "P value adjustment")))
+        postHocContainer[[thisVarNameRef]]$addFootnote(.getCorrectionFootnoteAnova(resultPostHoc, options[["postHocCi"]], options[["postHocEffectSize"]]))
 
       avFootnote <- attr(resultPostHoc, "mesg")[grep(attr(resultPostHoc, "mesg"), pattern = "Results are averaged")]
       if (length(avFootnote) != 0) {

R/commonAnovaFreq.R

@@ -133,7 +133,7 @@
   return(postHocTable)
 }
 
-.getCorrectionFootnoteAnova <- function(postHocObject, includeCI = FALSE) {
+.getCorrectionFootnoteAnova <- function(postHocObject, includeCI = FALSE, includeEffectSize = FALSE, isBetween = FALSE) {
 
   pvalAdjust <- attr(postHocObject, "mesg")[grep(attr(postHocObject, "mesg"), pattern = "P value adjustment")]
   nEstimates <- regmatches(pvalAdjust, gregexpr("[[:digit:]]+", pvalAdjust))[[1]]
@@ -143,8 +143,11 @@
 
   if (!includeCI) {
     correctionFootnote <- gettextf("P-value adjusted for comparing a family of %s", as.character(nEstimates))
-  } else {
+  } else if (isFALSE(includeEffectSize) || isFALSE(isBetween)) {
     correctionFootnote <- gettextf("P-value and confidence intervals adjusted for comparing a family of %1$s estimates (confidence intervals corrected using the %2$s method).", nEstimates, confAdjust)
+  } else {
+    correctionFootnote <- gettextf("P-value and confidence intervals adjusted for comparing a family of %1$s estimates (ci for mean difference corrected using the %2$s method; ci for effect size corrected using the bonferroni method).",
+                                   nEstimates, confAdjust)
   }
 
   return(correctionFootnote)

inst/help/Ancova.md

@@ -85,7 +85,7 @@ ANCOVA allows the user to analyze the difference between multiple group means, w
     <img src="%HELP_FOLDER%/gif/labelediting.gif"/>
   </details> 
 
-  - Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value are available:  
+  - Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value and confidence interval for mean difference are available (note that the confidence intervals for effect sizes can only be adjusted using the Bonferroni method):  
       - Tukey: Compare all possible pairs of group means. This correction can be used when the groups of the independent variable have an equal sample size and variance. This method is commonly used and is selected by default. 
       - Scheffe: Adjusting significance levels in a linear regression, to account for multiple comparisons. This method is considered to be quite conservative. 
       - Bonferroni: This correction is considered conservative. The risk of Type I error is reduced, however the statistical power decreases as well. 

inst/help/Anova.md

@@ -84,7 +84,7 @@ ANOVA allows the user to analyze the difference between multiple group means.
     <img src="%HELP_FOLDER%/gif/labelediting.gif"/>
   </details> 
 
-  - Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value are available:  
+  - Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value and confidence interval for mean difference are available (note that the confidence intervals for effect sizes can only be adjusted using the Bonferroni method):
       - Tukey: Compare all possible pairs of group means. This correction can be used when the groups of the independent variable have an equal sample size and variance. This method is commonly used and is selected by default. 
       - Scheffe: Adjusting significance levels in a linear regression, to account for multiple comparisons. This method is considered to be quite conservative. 
       - Bonferroni: This correction is considered conservative. The risk of Type I error is reduced, however the statistical power decreases as well. 

inst/help/AnovaRepeatedMeasures.md

@@ -71,7 +71,7 @@ To perform a post hoc test, drag one or more factor names to the right column. S
 - 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`.
 - Conditional comparisons for interactions: Instead of pairwise comparisons for all possible combination of cells in the interaction, list pairwise comparisons conditional on each of the interaction terms. This provides as many tables as there are terms in the interaction effect. 
 - Pool error term for follow-up tests:  By selecting this option, the univariate linear model, rather than the multivariate model, will be used for follow-up tests (contrasts, post-hoc tests, marginal means). Caution: multivariate models (i.e., unpooled error terms) handle departures from sphericity better, since these models allow the standard errors to differ for each level of the repeated measure(s) factor(s).
-- Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value are available:  
+- Correction: To correct for multiple comparison testing and avoid Type I errors, different methods for correcting the p-value are available (note that the confidence intervals can only be adjusted using the Bonferroni method):  
     - Tukey: Compare all possible pairs of group means. This correction can be used when the groups of the repeated measures have an equal sample size and variance. This method is commonly used and is selected by default.
     - Scheffe: Adjusting significance levels in a linear regression, to account for multiple comparisons. This method is considered to be quite conservative.
     - Bonferroni: This correction is considered conservative. The risk of Type I error is reduced, however the statistical power decreases as well.