Update BFF (#6)

* Update description and README

* add documentation

* disable prior and posterior distribution (still not implemented in the BFF package)

* update renv

* fixes from the package update

* add proper sidedness labels

DESCRIPTION

@@ -16,6 +16,7 @@ Imports:
 Suggests:
   testthat
 Remotes:
+  rshudde/BFF@revisions,
   jasp-stats/jaspBase,
   jasp-stats/jaspGraphs
 RoxygenNote: 7.3.2

R/bffCommon.R

@@ -18,8 +18,6 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
   if (.bffReady(options))
     .bffFitBFF(jaspResults, dataset, options)
 
-  # saveRDS(options, file = "C:/JASP/options.RDS")
-  # saveRDS(dataset, file = "C:/JASP/dataset.RDS")
   # default summary table
   .bffSummaryTable(jaspResults, dataset, options)
 
@@ -34,9 +32,9 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
   if (options[["plotBayesFactorFunction"]])
     .bffBayesFactorFunctionPlot(jaspResults, dataset, options)
 
-  # prior and posterior plot
-  if (FALSE && options[["plotPriorAndPosterior"]])
-    .bffPriorAndPosteriorPlot(jaspResults, dataset, options)
+  # # prior and posterior plot
+  # if (FALSE && options[["plotPriorAndPosterior"]])
+  #   .bffPriorAndPosteriorPlot(jaspResults, dataset, options)
 }
 
 
@@ -51,10 +49,10 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
     "ISTT"        = c("tStatistic", "sampleSizeGroup1", "sampleSizeGroup2"),
     # "correlation" = c("tStatistic", "sampleSize"),
     "regression"  = c("tStatistic", "sampleSize", "predictors"),
-    "ANOVA"       = c("fStatistic", "degreesOfFreedom1", "degreesOfFreedom2"),
+    "ANOVA"       = c("fStatistic", "sampleSize", "degreesOfFreedom1", "degreesOfFreedom2"),
     #"binomial"    = c("zStatistic", "sampleSize"),
     #"AB"          = c("chiqsrStatistic", "degreesOfFreedom"),
-    "Chi2"        = c("chi2Statistic", "sampleSize")
+    "Chi2"        = c("chi2Statistic", "sampleSize", "degreesOfFreedom")
   )
 
   return(c(dependenciesGlobal, dependenciesTest))
@@ -131,6 +129,7 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
   else if (options[["test"]] == "ANOVA")
     fit <- try(BFF::f_test_BFF(
       f_stat      = options[["fStatistic"]],
+      n           = , options[["sampleSize"]],
       df1         = options[["degreesOfFreedom1"]],
       df2         = options[["degreesOfFreedom2"]],
       r           = options[["priorDispersionR"]],
@@ -152,6 +151,7 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
     fit <- try(BFF::chi2_test_BFF(
       chi2_stat   = options[["chi2Statistic"]],
       n           = options[["sampleSize"]],
+      df          = options[["degreesOfFreedom"]],
       r           = options[["priorDispersionR"]],
       omega       = if (fixedOmega) options[["bayesFactorWithPriorModeValue"]] else NULL))
 
@@ -190,10 +190,10 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
     "ISTT"        = options[["sampleSizeGroup1"]] != 0  && options[["sampleSizeGroup2"]] != 0,
     # "correlation" = options[["sampleSize"]] != 0,
     "regression"  = options[["sampleSize"]] != 0 && options[["predictors"]] != 0,
-    "ANOVA"       = options[["degreesOfFreedom1"]] != 0 && options[["degreesOfFreedom2"]] != 0,
+    "ANOVA"       = options[["sampleSize"]] != 0 && options[["degreesOfFreedom1"]] != 0 && options[["degreesOfFreedom2"]] != 0,
     # "binomial"    = options[["sampleSize"]] != 0,
     # "AB"          = options[["degreesOfFreedom"]] != 0,
-    "Chi2"        = options[["sampleSize"]] != 0
+    "Chi2"        = options[["sampleSize"]] != 0 && options[["degreesOfFreedom"]] != 0
   )
 }
 .bffReadDataset              <- function(dataset, options) {
@@ -211,10 +211,10 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
       "ISTT"        = c(options[["tStatistic"]], options[["sampleSizeGroup1"]] , options[["sampleSizeGroup2"]]),
       # "correlation" = c(options[["tStatistic"]], options[["sampleSize"]]),
       "regression"  = c(options[["tStatistic"]], options[["sampleSize"]], options[["predictors"]]),
-      "ANOVA"       = c(options[["fStatistic"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
+      "ANOVA"       = c(options[["fStatistic"]], options[["sampleSize"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
       # "binomial"    = c(options[["zStatistic"]], options[["sampleSize"]]),
       # "AB"          = c(options[["chi2Statistic"]], options[["degreesOfFreedom"]]),
-      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]])
+      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]], options[["degreesOfFreedom"]])
     )
   )
 
@@ -233,10 +233,10 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
       "ISTT"        = c(options[["sampleSizeGroup1"]] , options[["sampleSizeGroup2"]]),
       # "correlation" = c(options[["sampleSize"]]),
       "regression"  = c(options[["sampleSize"]], options[["predictors"]]),
-      "ANOVA"       = c(options[["fStatistic"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
+      "ANOVA"       = c(options[["fStatistic"]], options[["sampleSize"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
       # "binomial"    = c(options[["sampleSize"]]),
       # "AB"          = c(options[["chi2Statistic"]], options[["degreesOfFreedom"]])
-      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]])
+      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]], options[["degreesOfFreedom"]])
     ),
     exitAnalysisIfErrors         = TRUE
   )
@@ -251,10 +251,10 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
       "ISTT"        = c(options[["tStatistic"]], options[["sampleSizeGroup1"]] , options[["sampleSizeGroup2"]]),
       # "correlation" = c(options[["tStatistic"]], options[["sampleSize"]]),
       "regression"  = c(options[["tStatistic"]], options[["degreesOfFreedom"]]),
-      "ANOVA"       = c(options[["fStatistic"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
+      "ANOVA"       = c(options[["fStatistic"]], options[["sampleSize"]], options[["degreesOfFreedom1"]], options[["degreesOfFreedom2"]]),
       # "binomial"    = c(options[["zStatistic"]], options[["sampleSize"]]),
       # "AB"          = c(options[["chi2Statistic"]], options[["degreesOfFreedom"]]),
-      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]])
+      "Chi2"        = c(options[["chi2Statistic"]], options[["sampleSize"]], options[["degreesOfFreedom"]])
     ),
     exitAnalysisIfErrors         = TRUE
   )
@@ -378,8 +378,8 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
 
   summaryTable$addRows(list(
     # n       = nrow(dataset),
-    bf        = .recodeBFtype(bfOld = fit[["log_bf"]], newBFtype = options[["bayesFactorType"]], oldBFtype = "LogBF10"),
-    priorMode = fit[["omega"]]
+    bf        = .recodeBFtype(bfOld = fit[["log_bf_h1"]], newBFtype = options[["bayesFactorType"]], oldBFtype = "LogBF10"),
+    priorMode = fit[["omega_h1"]]
   ))
 
   summaryTable$addFootnote(gettextf("Prior mode corresponds to %1$s.", .bffEffectSizeInformation(options)))
@@ -431,7 +431,7 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
 
   omegaTable$addRows(list(
     # n       = nrow(dataset),
-    bf        = .recodeBFtype(bfOld = fit[["log_bf"]], newBFtype = options[["bayesFactorType"]], oldBFtype = "LogBF10"),
+    bf        = .recodeBFtype(bfOld = fit[["log_bf_h1"]], newBFtype = options[["bayesFactorType"]], oldBFtype = "LogBF10"),
     priorMode = options[["bayesFactorWithPriorModeValue"]]
   ))
 
@@ -490,7 +490,7 @@ bffAnalysis <- function(jaspResults, dataset, options, test) {
 
   maxBF10  <- exp(max(fit$BFF$log_bf))
   if (options[["bayesFactorWithPriorMode"]])
-    BF10user <- exp(fitOmega$log_bf)
+    BF10user <- exp(fitOmega$log_bf_h1)
 
   if (options[["bayesFactorType"]] == "BF01") {
     dfLines$y <- -dfLines$y

README.md

@@ -1 +1,9 @@
-# jaspModuleTemplate
+# The Bayes Factor Functions module 
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+## References 
+Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+## R Packages
+- BFF

inst/Description.qml

@@ -4,16 +4,17 @@ import JASP.Module	1.0
 Description
 {
 	name		: "jaspBFF"
-	title		: qsTr("BFF (beta)")
+	title		: qsTr("Bayes Factor Functions")
 	description	: qsTr("This module offers Bayes factor functions.")
-	version			: "0.19.2"
+	version		: "0.19.2"
 	author		: "JASP Team"
 	maintainer	: "JASP Team <[email protected]>"
 	website		: "https://jasp-stats.org"
 	license		: "GPL (>= 2)"
 	icon		: "analysis-bayes-factor-functions.svg"
+	preloadData:  true // no data actually needed for this module
 	requiresData: false
-
+/*
 	GroupTitle
 	{
 		title:	qsTr("Z-Tests")
@@ -31,7 +32,7 @@ Description
 		title:	qsTr("Independent Samples Z-Test")
 		func:	"bffIndependentSamplesZTest"
 	}
-
+*/
 	GroupTitle
 	{
 		title:	qsTr("T-Tests")

inst/help/bffANOVA.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffChi2.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffIndependentSamplesTTest.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffIndependentSamplesZTest.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffOneSampleTTest.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffOneSampleZTest.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/help/bffRegression.md

@@ -0,0 +1,12 @@
+Bayes Factor Functions 
+===
+
+The module employs Bayes factor functions (BFFs; Johnson, Pramanik and Shudde, 2023) to streamline hypothesis testing by directly comparing competing hypotheses using standardized effect sizes. This approach simplifies the calculation of Bayes factors from common test statistics, provides clear and intuitive summaries of experimental results without relying on arbitrary significance thresholds.
+
+### References 
+--- 
+-	Johnson, V. E., Pramanik, S., & Shudde, R. (2023). Bayes factor functions for reporting outcomes of hypothesis tests. *Proceedings of the National Academy of Sciences, 120*(8), e2217331120.
+
+### R Packages
+---
+- BFF

inst/qml/bffANOVA.qml

@@ -39,6 +39,12 @@ Form {
 			negativeValues:		true
 		}
 
+		IntegerField
+		{
+			name:				"sampleSize"
+			label:				qsTr("Sample size")
+		}
+
 		IntegerField
 		{
 			name:				"degreesOfFreedom1"

inst/qml/bffChi2.qml

@@ -44,6 +44,12 @@ Form {
 			name:				"sampleSize"
 			label:				qsTr("Sample size")
 		}
+
+		IntegerField
+		{
+			name:				"degreesOfFreedom"
+			label:				qsTr("Degrees of freedom")
+		}
 	}
 
 	/*VariablesForm

inst/qml/bffOneSampleTTest.qml

@@ -71,6 +71,6 @@ Form {
 		}
 	}*/
 
-	BFF.Analysis{}
+	BFF.Analysis{oneSampleTest: true}
 	BFF.Priors{}
 }

inst/qml/bffOneSampleZTest.qml

@@ -71,6 +71,6 @@ Form {
 		}
 	}*/
 
-	BFF.Analysis{}
+	BFF.Analysis{oneSampleTest: true}
 	BFF.Priors{}
 }

inst/qml/qml_components/Analysis.qml

@@ -23,7 +23,8 @@ import JASP				1.0
 
 Section
 {
-	property bool directionalTest:		true	
+	property bool directionalTest:		true
+	property bool oneSampleTest:		false
 
 	expanded:	true
 	title:		qsTr("Analysis")
@@ -35,9 +36,9 @@ Section
 			name:		"alternativeHypothesis"
 			title:		qsTr("Alternative Hypothesis")
 			visible:	directionalTest
-			RadioButton { value: "equal";		label: qsTr("Group 1 ≠ Group 2"); checked: true	}
-			RadioButton { value: "greater";		label: qsTr("Group 1 > Group 2")}
-			RadioButton { value: "less";		label: qsTr("Group 1 < Group 2")}
+			RadioButton { value: "equal";		label: oneSampleTest ?  qsTr("Group 1 ≠ 0") : qsTr("Group 1 ≠ Group 2"); checked: true	}
+			RadioButton { value: "greater";		label: oneSampleTest ?  qsTr("Group 1 > 0") : qsTr("Group 1 > Group 2")}
+			RadioButton { value: "less";		label: oneSampleTest ?  qsTr("Group 1 < 0") : qsTr("Group 1 < Group 2")}
 		}
 
 		BayesFactorType { }
@@ -75,7 +76,7 @@ Section
 				checked:	true
 			}
 		}
-
+		/* Disabled till implemented in the BFF package
 		CheckBox
 		{
 			name:		"plotPriorAndPosterior"
@@ -88,5 +89,6 @@ Section
 				checked:	true
 			}
 		}
+		*/
 	}
 }