Update to default reference prior

Author: koenderks

R/auditBayesianEstimation.R

@@ -148,7 +148,7 @@ auditBayesianEstimation <- function(jaspResults, dataset, options, ...) {
     result <- jaspResults[["state"]]$object
 
     pTry <- try({
-      if (options[["priorKappa"]] == 0 || options[["priorNu"]] == 0 || options[["priorSigma2"]] == 0) {
+      if (options[["priorKappa"]] == 0 || options[["priorNu"]] <= 0 || options[["priorSigma2"]] == 0) {
         xseq <- seq(extraDistr::qlst(0.0001, df = result$posterior$nu, mu = result$posterior$mu, sigma = result$posterior$sigma),
           extraDistr::qlst(0.9999, df = result$posterior$nu, mu = result$posterior$mu, sigma = result$posterior$sigma),
           length.out = 1000
@@ -215,7 +215,7 @@ auditBayesianEstimation <- function(jaspResults, dataset, options, ...) {
   }
 }
 
-.jfaDirectBayes <- function(y, n, N, mu0 = 0, kappa0 = 0, nu0 = 0, sigma20 = 0, conf.level = 0.95) {
+.jfaDirectBayes <- function(y, n, N, mu0 = 0, kappa0 = 0, nu0 = -1, sigma20 = 0, conf.level = 0.95) {
   alpha <- (1 - conf.level) / 2
   mu_n <- (kappa0 * mu0 + n * mean(y)) / (kappa0 + n)
   kappa_n <- kappa0 + n
@@ -234,7 +234,7 @@ auditBayesianEstimation <- function(jaspResults, dataset, options, ...) {
   return(list(est = muY, lb = lb, ub = ub, unc = ub - muY, prior = list(nu = nu0, sigma = sigmaYprior, mu = muYprior), posterior = list(nu = nu_n, sigma = sigmaY, mu = muY)))
 }
 
-.jfaDifferenceBayes <- function(y, x, n, X, N, mu0 = 0, kappa0 = 0, nu0 = 0, sigma20 = 0, conf.level = 0.95) {
+.jfaDifferenceBayes <- function(y, x, n, X, N, mu0 = 0, kappa0 = 0, nu0 = -1, sigma20 = 0, conf.level = 0.95) {
   alpha <- (1 - conf.level) / 2
   e <- x - y
   mu_n <- (kappa0 * mu0 + n * mean(e)) / (kappa0 + n)
@@ -254,7 +254,7 @@ auditBayesianEstimation <- function(jaspResults, dataset, options, ...) {
   return(list(est = muY, lb = lb, ub = ub, unc = ub - muY, prior = list(nu = nu0, sigma = sigmaYprior, mu = muYprior), posterior = list(nu = nu_n, sigma = sigmaY, mu = muY)))
 }
 
-.jfaRatioBayes <- function(y, x, n, X, N, mu0 = 0, kappa0 = 0, nu0 = 0, sigma20 = 0, conf.level = 0.95) {
+.jfaRatioBayes <- function(y, x, n, X, N, mu0 = 0, kappa0 = 0, nu0 = -1, sigma20 = 0, conf.level = 0.95) {
   alpha <- (1 - conf.level) / 2
   q <- y / x
   mu_n <- (kappa0 * mu0 + n * mean(q)) / (kappa0 + n)
@@ -274,7 +274,7 @@ auditBayesianEstimation <- function(jaspResults, dataset, options, ...) {
   return(list(est = muY, lb = lb, ub = ub, unc = ub - muY, prior = list(nu = nu0, sigma = sigmaYprior, mu = muYprior), posterior = list(nu = nu_n, sigma = sigmaY, mu = muY)))
 }
 
-.jfaRegressionBayes <- function(y, x, n, X, N, mu0 = c(0, 0), Lambda0 = diag(2) * 0, nu0 = 0, sigma20 = 0, conf.level = 0.95) {
+.jfaRegressionBayes <- function(y, x, n, X, N, mu0 = c(0, 0), Lambda0 = diag(2) * 0, nu0 = -1, sigma20 = 0, conf.level = 0.95) {
   alpha <- (1 - conf.level) / 2
   D <- cbind(1, x)
   DtD <- t(D) %*% D

inst/qml/auditBayesianEstimation.qml

@@ -76,8 +76,9 @@ Form
 			{
 				name: 				"priorNu"
 				text: 				qsTr("Prior variance weight \u03BD")
-				defaultValue: 		0
-				min: 				0
+				defaultValue: 		-1
+				min: 				-1
+				negativeValues:		true
 				decimals: 			2
 				info:				qsTr("The prior parameter for the implicit sample size of the variance.")
 			}

tests/testthat/test-auditbayesianestimation.R

@@ -19,7 +19,7 @@ test_that("Direct Estimator table results match", {
   table <- results[["results"]][["summaryTable"]][["data"]]
   jaspTools::expect_equal_tables(
     table,
-    list(815398.066666667, 699530.415390775, 115867.651275891, 931265.717942558)
+    list(815398.066666667, 699135.90261438, 116262.164052287, 931660.230718954)
   )
 })
 
@@ -75,7 +75,7 @@ test_that("Ratio Estimator table results match", {
   table <- results[["results"]][["summaryTable"]][["data"]]
   jaspTools::expect_equal_tables(
     table,
-    list(760959.912828661, 748177.445523007, 12782.4673056537, 773742.380134315)
+    list(760959.912828661, 748133.92305051, 12825.9897781507, 773785.902606811)
   )
 })
 
@@ -98,6 +98,6 @@ test_that("Regression Estimator table results match", {
   table <- results[["results"]][["summaryTable"]][["data"]]
   jaspTools::expect_equal_tables(
     table,
-    list(757392.591641175, 744560.540015853, 12832.051625322, 770224.643266497)
+    list(757392.591641175, 744516.848715841, 12875.7429253338, 770268.334566509)
   )
 })