Fixed Fixes

R/bayesianmetaanalysiscommon.R

@@ -301,7 +301,7 @@
     SE <- dataset[, options[["effectSizeSe"]]]
     .hasErrors(dataset              = dataset,
                seCheck.target       = options[["effectSizeSe"]],
-               custom               = .metaAnalysisCheckSE,
+               custom               = .maCheckStandardErrors,
                exitAnalysisIfErrors = TRUE)
   }
 

R/bayesianpredictionperformance.R

@@ -21,7 +21,7 @@ BayesianPredictionPerformance  <- function(jaspResults, dataset, options, state
   ready <- .metamiscReady(options)
 
   if (ready) {
-    dataset <- .metamiscGetData(options, dataset)
+    .metamiscCheckData(options, dataset)
     .metamiscFitModelBayesian(jaspResults, options, dataset)
   }
 

R/classicalmetaanalysiscommon.R

@@ -557,12 +557,13 @@
   modelSummaryContainer[["fitMeasuresTable"]] <- fitMeasuresTable
 
 
-  fitMeasuresTable$addColumnInfo(name = "model", title = "",                  type = "string")
-  fitMeasuresTable$addColumnInfo(name = "ll",    title = gettext("Log Lik."), type = "number")
-  fitMeasuresTable$addColumnInfo(name = "dev",   title = gettext("Deviance"), type = "number")
-  fitMeasuresTable$addColumnInfo(name = "AIC",   title = gettext("AIC"),      type = "number")
-  fitMeasuresTable$addColumnInfo(name = "BIC",   title = gettext("BIC"),      type = "number")
-  fitMeasuresTable$addColumnInfo(name = "AICc",  title = gettext("AICc"),     type = "number")
+  fitMeasuresTable$addColumnInfo(name = "model",         title = "",                      type = "string")
+  fitMeasuresTable$addColumnInfo(name = "observations",  title = gettext("Observations"), type = "integer")
+  fitMeasuresTable$addColumnInfo(name = "ll",            title = gettext("Log Lik."),     type = "number")
+  fitMeasuresTable$addColumnInfo(name = "dev",           title = gettext("Deviance"),     type = "number")
+  fitMeasuresTable$addColumnInfo(name = "AIC",           title = gettext("AIC"),          type = "number")
+  fitMeasuresTable$addColumnInfo(name = "BIC",           title = gettext("BIC"),          type = "number")
+  fitMeasuresTable$addColumnInfo(name = "AICc",          title = gettext("AICc"),         type = "number")
 
   if (.maIsMetaregressionEffectSize(options) && !.maIsMultilevelMultivariate(options))
     fitMeasuresTable$addColumnInfo(name = "R2",  title = gettext("R\U00B2"),   type = "number")
@@ -571,7 +572,7 @@
   if (is.null(fit) || jaspBase::isTryError(fit) || !is.null(.maCheckIsPossibleOptions(options)))
     return()
 
-  fitSummary <- cbind("model" = colnames(fit[["fit.stats"]]), data.frame(t(fit[["fit.stats"]])))
+  fitSummary <- cbind("model" = colnames(fit[["fit.stats"]]), observations = fit[["k"]], data.frame(t(fit[["fit.stats"]])))
 
   if (.maIsMetaregressionEffectSize(options) && !.maIsMultilevelMultivariate(options))
     fitSummary$R2 <- fit[["R2"]]

R/classicalpredictionperformance.R

@@ -21,7 +21,7 @@ ClassicalPredictionPerformance   <- function(jaspResults, dataset, options, stat
   ready <- .metamiscReady(options)
 
   if (ready) {
-    dataset <- .metamiscGetData(options, dataset)
+    .metamiscCheckData(options, dataset)
     .metamiscFitModel(jaspResults, options, dataset)
   }
 
@@ -76,39 +76,23 @@ ClassicalPredictionPerformance   <- function(jaspResults, dataset, options, stat
 
   return(FALSE)
 }
-.metamiscGetData             <- function(options, dataset) {
-
-  if (!is.null(dataset))
-    return(dataset)
-
+.metamiscCheckData           <- function(options, dataset) {
 
   varNames <- c(options[["effectSize"]], options[["effectSizeSe"]], unlist(options[["effectSizeCi"]]),
                 options[["numberOfParticipants"]], options[["numberOfObservedEvents"]], options[["numberOfExpectedEvents"]])
   varNames <- varNames[varNames != ""]
 
-  dataset <- readDataSetToEnd(
-    columns.as.numeric = varNames,
-    columns            = if (options[["studyLabel"]] != "") options[["studyLabel"]]
-  )
-
-  if (options[["studyLabel"]] != "") {
-    dataset[[options[["studyLabel"]]]] <- as.character(dataset[[options[["studyLabel"]]]])
-    if (any(!validUTF8(dataset[[options[["studyLabel"]]]])))
-      .quitAnalysis(gettext("The study labels contain invalid characters. Please, remove them before running the analysis."))
-  }
-
   .hasErrors(dataset               = dataset[,varNames],
              type                  = c("infinity", "observations", "negativeValues"),
              observations.amount   = "< 2",
              exitAnalysisIfErrors  = TRUE)
 
   .hasErrors(dataset              = dataset,
              seCheck.target       = varNames[varNames %in% c(options[["effectSizeSe"]],options[["numberOfParticipants"]])],
-             custom               = .metaAnalysisCheckSE,
+             custom               = .maCheckStandardErrors,
              exitAnalysisIfErrors = TRUE)
 
-
-  return(dataset)
+  return()
 }
 .metamiscFitModel            <- function(jaspResults, options, dataset) {
 
@@ -133,7 +117,7 @@ ClassicalPredictionPerformance   <- function(jaspResults, dataset, options, stat
     O          = if (options[["numberOfObservedEvents"]] != "")      dataset[, options[["numberOfObservedEvents"]]],
     E          = if (options[["numberOfExpectedEvents"]] != "")      dataset[, options[["numberOfExpectedEvents"]]],
     slab       = if (options[["studyLabel"]] != "") dataset[, options[["studyLabel"]]],
-    method     = .metaAnalysisGetMethod(options),
+    method     = .maGetMethodOptions(options),
     pars       = list(
       model.oe    = if (options[["measure"]] == "oeRatio")    options[["withinStudyVariation"]],
       model.cstat = if (options[["measure"]] == "cStatistic") options[["withinStudyVariation"]])
@@ -340,7 +324,7 @@ ClassicalPredictionPerformance   <- function(jaspResults, dataset, options, stat
   fatFits <- modelsFat[["object"]]
 
   # switch the theta / theta.se location according to the link (poisson/log derives and stores the values at different place)
-  if (options[["withinStudyVariation"]] == "poisson/log" && options$method != "BAYES" && .metaAnalysisGetMethod(options) != "FE") {
+  if (options[["withinStudyVariation"]] == "poisson/log" && options$method != "BAYES" && .maGetMethodOptions(options) != "FE") {
     theta    <- "theta.blup"
     theta.se <- "theta.se.blup"
   } else {

R/selectionmodels.R

@@ -18,8 +18,7 @@
 SelectionModels <- function(jaspResults, dataset, options, state = NULL) {
 
   if (.smCheckReady(options)) {
-    # get the data
-    dataset <- .smGetData(dataset, options)
+    # check the data
     dataset <- .smCheckData(jaspResults, dataset, options)
 
     # fit the models
@@ -57,18 +56,6 @@ SelectionModels <- function(jaspResults, dataset, options, state = NULL) {
     return(options[["effectSize"]] != "" && options[["sampleSize"]] != "")
   }
 }
-.smGetData                 <- function(dataset, options) {
-  if (!is.null(dataset)) {
-    return(dataset)
-  } else {
-    return(.readDataSetToEnd(columns.as.numeric = c(
-      options[["effectSize"]],
-      if (options[["effectSizeSe"]] != "") options[["effectSizeSe"]],
-      if (options[["sampleSize"]] != "")  options[["sampleSize"]],
-      if (options[["pValue"]] != "")  options[["pValue"]]
-    )))
-  }
-}
 .smCheckData               <- function(jaspResults, dataset, options) {
 
   dataset_old <- dataset
@@ -94,14 +81,14 @@ SelectionModels <- function(jaspResults, dataset, options, state = NULL) {
   if (options[["effectSizeSe"]] != "")
     .hasErrors(dataset              = dataset,
                seCheck.target       = options[["effectSizeSe"]],
-               custom               = .metaAnalysisCheckSE,
+               custom               = .maCheckStandardErrors,
                exitAnalysisIfErrors = TRUE)
 
 
   if (options[["sampleSize"]] != "")
     .hasErrors(dataset              = dataset,
                seCheck.target       = options[["sampleSize"]],
-               custom               = .metaAnalysisCheckSE,
+               custom               = .maCheckStandardErrors,
                exitAnalysisIfErrors = TRUE)
 
 

R/waapwlspetpeese.R

@@ -61,13 +61,13 @@
   if (options[["effectSizeSe"]] != "")
     .hasErrors(dataset              = dataset,
                seCheck.target       = options[["effectSizeSe"]],
-               custom               = .metaAnalysisCheckSE,
+               custom               = .maCheckStandardErrors,
                exitAnalysisIfErrors = TRUE)
 
   if (options[["sampleSize"]] != "")
     .hasErrors(dataset              = dataset,
                seCheck.target       = options[["sampleSize"]],
-               custom               = .metaAnalysisCheckSE,
+               custom               = .maCheckStandardErrors,
                exitAnalysisIfErrors = TRUE)
 
 

inst/Description.qml

@@ -101,6 +101,7 @@ Description
 		title:			qsTr("Bayesian Meta-Analysis")
 		func:			"BayesianMetaAnalysis"
 		requiresData:	true
+		preloadData:	false
 	}
 
 	Analysis
@@ -109,6 +110,7 @@ Description
 		title:			qsTr("Bayesian Binomial Meta-Analysis")
 		func:			"BayesianBinomialMetaAnalysis"
 		requiresData:	true
+		preloadData:	false
 	}
 
 	Analysis
@@ -117,6 +119,7 @@ Description
 		title:			qsTr("Penalized Meta-Analysis")
 		func:			"PenalizedMetaAnalysis"
 		requiresData:	true
+		preloadData:	false
 	}
 
 	Analysis
@@ -125,12 +128,14 @@ Description
 		title:			qsTr("Bayesian Prediction Model Performance")
 		func:			"BayesianPredictionPerformance"
 		requiresData:	true
+		preloadData:	false
 	}
 
 	Analysis
 	{
 		title:			qsTr("Robust Bayesian Meta-Analysis")
 		func:			"RobustBayesianMetaAnalysis"
 		requiresData:	false
+		preloadData:	false
 	}
 }

inst/Upgrades.qml

@@ -651,9 +651,9 @@ Upgrades
 
 		ChangeJS
 		{
-			name:		"inferenceOutputScale"
 			jsFunction:	function(options)
 			{
+				name:		"inferenceOutputScale"
 				switch(options["inferenceOutputScale"])
 				{
 					case "correlation":	return "r";
@@ -662,4 +662,72 @@ Upgrades
 			}
 		}
 	}
+
+	Upgrade
+	{
+		functionName:	"ClassicalPredictionPerformance"
+		fromVersion:	"0.19.1"
+		toVersion:		"0.19.2"
+
+		ChangeJS
+		{
+			name:		"withinStudyVariation"
+			jsFunction:	function(options)
+			{
+				if (options[["measure"]] == "cStatistic") {
+					switch(options["withinStudyVariation"])
+					{
+						case "normal/log":	return "normal/logit";
+						default:			return options["withinStudyVariation"];
+					}
+				} else {
+					return options["withinStudyVariation"]
+				}
+			}
+		}
+
+		ChangeJS
+		{
+			name:		"method"
+			jsFunction:	function(options)
+			{
+				switch(options["withinStudyVariation"])
+				{
+					case "Fixed Effects"		: return "fixedEffects";
+					case "Maximum Likelihood"	: return "maximumLikelihood";
+					case "Restricted ML"		: return "restrictedML";
+					case "DerSimonian-Laird"	: return "derSimonianLaird";
+					case "Hedges"				: return "hedges";
+					case "Hunter-Schmidt"		: return "hunterSchmidt";
+					case "Sidik-Jonkman"		: return "sidikJonkman";
+					case "Empirical Bayes"		: return "empiricalBayes";
+					case "Paule-Mandel"			: return "pauleMandel";
+				}
+			}
+		}
+	}
+
+	Upgrade
+	{
+		functionName:	"BayesianPredictionPerformance"
+		fromVersion:	"0.19.1"
+		toVersion:		"0.19.2"
+
+		ChangeJS
+		{
+			name:		"withinStudyVariation"
+			jsFunction:	function(options)
+			{
+				if (options[["measure"]] == "cStatistic") {
+					switch(options["withinStudyVariation"])
+					{
+						case "normal/log":	return "normal/logit";
+						default:			return options["withinStudyVariation"];
+					}
+				} else {
+					return options["withinStudyVariation"]
+				}
+			}
+		}
+	}
 }

inst/qml/qml_components/PredictionPerformanceInference.qml

@@ -36,12 +36,26 @@ Section
 	Group
 	{
 
-		MA.ClassicalMetaAnalysisMethod
+		DropDown
 		{
-			visible:	analysisType == "classical"
-			id:			method
-		}
+			name:			"method"
+			label:			qsTr("Method")
+			currentIndex:	2
+			visible:		analysisType == "classical"
 
+			values: [
+				{ label: qsTr("Fixed Effects")		, value: "fixedEffects"			},
+				{ label: qsTr("Maximum Likelihood")	, value: "maximumLikelihood"	},
+				{ label: qsTr("Restricted ML")		, value: "restrictedML"			},
+				{ label: qsTr("DerSimonian-Laird")	, value: "derSimonianLaird"		},
+				{ label: qsTr("Hedges")				, value: "hedges"				},
+				{ label: qsTr("Hunter-Schmidt")		, value: "hunterSchmidt"		},
+				{ label: qsTr("Sidik-Jonkman")		, value: "sidikJonkman"			},
+				{ label: qsTr("Empirical Bayes")	, value: "empiricalBayes"		},
+				{ label: qsTr("Paule-Mandel")		, value: "pauleMandel"			}
+			]
+		}
+
 		DropDown
 		{
 			name:		"withinStudyVariation"

inst/qmldir

@@ -3,6 +3,3 @@ BayesianMetaAnalysisAdvanced		1.0		qml/qml_components/BayesianMetaAnalysisAdvanc
 BayesianMetaAnalysisInference		1.0		qml/qml_components/BayesianMetaAnalysisInference.qml
 BayesianMetaAnalysisPlots			1.0 	qml/qml_components/BayesianMetaAnalysisPlots.qml
 BayesianMetaAnalysisPriors			1.0 	qml/qml_components/BayesianMetaAnalysisPriors.qml
-ClassicalMetaAnalysisDiagnostics	1.0		qml/qml_components/ClassicalMetaAnalysisDiagnostics.qml
-ClassicalMetaAnalysisMethod 		1.0		qml/qml_components/ClassicalMetaAnalysisMethod.qml
-ClassicalMetaAnalysisStatistics 	1.0		qml/qml_components/ClassicalMetaAnalysisStatistics.qml