Release fixes (#206)

* fixes diagnostics plots in RoBMA * fix missing value treatment in PEMA
jasp-stats · Nov 30, 2023 · c8853b2 · c8853b2
1 parent 924cbb0
commit c8853b2
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Showing 2 changed files with 18 additions and 9 deletions.
diff --git a/R/penalizedmetaanalysis.R b/R/penalizedmetaanalysis.R
@@ -23,7 +23,7 @@ PenalizedMetaAnalysis <- function(jaspResults, dataset = NULL, options, ...) {
 
   if (.pemaCheckReady(options)) {
     # get the data
-    dataset <- .pemaGetData(dataset, options)
+    dataset <- .pemaGetData(jaspResults, dataset, options)
 
     # fit the models
     .pemaFit(jaspResults, dataset, options)
@@ -57,7 +57,7 @@ PenalizedMetaAnalysis <- function(jaspResults, dataset = NULL, options, ...) {
 .pemaCheckReady                <- function(options) {
   return(options[["effectSize"]] != "" && options[["effectSizeSe"]] != "" && length(options[["modelTerms"]]) > 0)
 }
-.pemaGetData                   <- function(dataset, options) {
+.pemaGetData                   <- function(jaspResults, dataset, options) {
 
   if (!is.null(dataset))
     return(dataset)
@@ -73,8 +73,7 @@ PenalizedMetaAnalysis <- function(jaspResults, dataset = NULL, options, ...) {
     factor.variables  <- Filter(function(s) s != "", c(factNames, studyLabels, clustNames))
 
     dataset <- .readDataSetToEnd(columns.as.factor   = factor.variables,
-                                 columns.as.numeric  = numeric.variables,
-                                 exclude.na.listwise = numeric.variables)
+                                 columns.as.numeric  = numeric.variables)
 
     # precompute variance
     dataset$JASP_computed_variance__ <- dataset[,stderrName]^2
@@ -328,6 +327,9 @@ PenalizedMetaAnalysis <- function(jaspResults, dataset = NULL, options, ...) {
   if (minESS < 100 * options$mcmcChains || is.nan(minESS))
     summaryTable$addFootnote(.pemaMessageMinESS(minESS, 100 * options$mcmcChains), symbol = gettext("Warning:"))
 
+  if (!is.null(jaspResults[["nOmitted"]]))
+    summaryTable$addFootnote(gettextf("%1$s observations were ommited due to missing values.", jaspResults[["nOmitted"]]$object))
+
   return()
 }
 .pemaSummaryTableTau           <- function(jaspResults, options) {

diff --git a/R/robustbayesianmetaanalysis.R b/R/robustbayesianmetaanalysis.R
@@ -1475,9 +1475,15 @@ RobustBayesianMetaAnalysis <- function(jaspResults, dataset, options, state = NU
 
 
   # create waiting plot
-  if (!(options[["mcmcDiagnosticsPlotEffect"]] || options[["mcmcDiagnosticsPlotHeterogeneity"]] || options[["mcmcDiagnosticsPlotWeights"]] || options[["mcmcDiagnosticsPlotPet"]] || options[["mcmcDiagnosticsPlotPeese"]]) && (options[["mcmcDiagnosticsPlotTypeTrace"]] || options[["mcmcDiagnosticsPlotTypeAutocorrelation"]] || options[["mcmcDiagnosticsPlotTypePosteriorSamplesDensity"]]) || is.null(jaspResults[["model"]])) {
+  if (
+    (!(options[["mcmcDiagnosticsPlotEffect"]] || options[["mcmcDiagnosticsPlotHeterogeneity"]] || options[["mcmcDiagnosticsPlotWeights"]] || options[["mcmcDiagnosticsPlotPet"]] || options[["mcmcDiagnosticsPlotPeese"]]) &&
+      (options[["mcmcDiagnosticsPlotTypeTrace"]] || options[["mcmcDiagnosticsPlotTypeAutocorrelation"]] || options[["mcmcDiagnosticsPlotTypePosteriorSamplesDensity"]])) ||
+    ((options[["mcmcDiagnosticsPlotEffect"]] || options[["mcmcDiagnosticsPlotHeterogeneity"]] || options[["mcmcDiagnosticsPlotWeights"]] || options[["mcmcDiagnosticsPlotPet"]] || options[["mcmcDiagnosticsPlotPeese"]]) &&
+     !(options[["mcmcDiagnosticsPlotTypeTrace"]] || options[["mcmcDiagnosticsPlotTypeAutocorrelation"]] || options[["mcmcDiagnosticsPlotTypePosteriorSamplesDensity"]])) ||
+      is.null(jaspResults[["model"]])) {
     tempWait  <- createJaspPlot(title = "")
-    tempWait$dependOn(c("mcmcDiagnosticsPlotEffect", "mcmcDiagnosticsPlotHeterogeneity", "mcmcDiagnosticsPlotWeights", "mcmcDiagnosticsPlotPet", "mcmcDiagnosticsPlotPeese", "mcmcDiagnosticsPlotTypeTrace", "mcmcDiagnosticsPlotTypeAutocorrelation", "mcmcDiagnosticsPlotTypePosteriorSamplesDensity"))
+    tempWait$dependOn(c("mcmcDiagnosticsPlotEffect", "mcmcDiagnosticsPlotHeterogeneity", "mcmcDiagnosticsPlotWeights", "mcmcDiagnosticsPlotPet", "mcmcDiagnosticsPlotPeese",
+                        "mcmcDiagnosticsPlotTypeTrace", "mcmcDiagnosticsPlotTypeAutocorrelation", "mcmcDiagnosticsPlotTypePosteriorSamplesDensity"))
     diagnostics[["tempWait"]] <- tempWait
     return()
   }
@@ -1554,8 +1560,8 @@ RobustBayesianMetaAnalysis <- function(jaspResults, dataset, options, state = NU
           "mu"    = "mcmcDiagnosticsPlotEffect",
           "tau"   = "mcmcDiagnosticsPlotHeterogeneity",
           "omega" = "mcmcDiagnosticsPlotWeights",
-          "PET"   = "diagnosticsPET",
-          "PEESE" = "diagnosticsPEESE"
+          "PET"   = "mcmcDiagnosticsPlotPet",
+          "PEESE" = "mcmcDiagnosticsPlotPeese"
         ))
         tempModel[[par]] <- tempPar
       } else {
@@ -1709,7 +1715,8 @@ RobustBayesianMetaAnalysis <- function(jaspResults, dataset, options, state = NU
     # show error if only one model is selected but doesn't contain any of the diagnostics
     if (noPars && options[["mcmcDiagnosticsPlotSingleModelNumber"]]) {
       tempError  <- createJaspPlot(title = "")
-      tempError$dependOn(c("mcmcDiagnosticsPlotEffect", "mcmcDiagnosticsPlotHeterogeneity", "mcmcDiagnosticsPlotWeights", "diagnosticsPET", "diagnosticsPEESE", "mcmcDiagnosticsPlotTypeTrace", "mcmcDiagnosticsPlotTypeAutocorrelation", "mcmcDiagnosticsPlotTypePosteriorSamplesDensity"))
+      tempError$dependOn(c("mcmcDiagnosticsPlotEffect", "mcmcDiagnosticsPlotHeterogeneity", "mcmcDiagnosticsPlotWeights", "mcmcDiagnosticsPlotPet", "mcmcDiagnosticsPlotPeese",
+                           "mcmcDiagnosticsPlotTypeTrace", "mcmcDiagnosticsPlotTypeAutocorrelation", "mcmcDiagnosticsPlotTypePosteriorSamplesDensity"))
       tempError$setError(gettextf("Model %i does not contain any of the selected parameters.", i))
       tempModel[["tempError"]] <- tempError
     }