tests/testthat/test-unidimensionalReliabilityBayesian.R
In jasp-stats/jaspReliability: Reliability Module for JASP
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$scaleMean <- TRUE
options$scaleSd <- TRUE
options$meanSdScoresMethod <- "meanScores"
options$itemCiLevel <- 0.9
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$itemDeletedPlot <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemRestCorrelation <- TRUE
options$itemMean <- TRUE
options$itemSd <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotFixedRange <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.1
options$probabilityTableUpperBound <- 0.3
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$effectiveSampleSize <- TRUE
options$tracePlot <- TRUE
options$setSeed <- TRUE
options$reverseScaledItems <- "debMiss30"
options$itemDeletedPlotOrderedType <- "kullbackLeibler"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(4.9262740258841e-06, -0.0302604620110933, -0.00730189174965356,
0.381529087630189, 0.00468437245991029, 0.0462164102560101,
0.0322661722934692, 0.0212125066975763, 0.0325973854277802,
0.494218519800714, 0.0831973460090512, 0.198566040293231, -0.18874858754,
1.05841360919316, 0.0908764111289075, 0.0716108498281969, 0.0783885886016254,
0.606959485289979, 0.158707927788854, 0.344714988273422, "contNormal",
9.40024640626554e-07, -0.0157617947104415, -0.00111243856322425,
0.0307771184754301, 0.00866952978122558, 0.0257664363297778,
0.0372377719573929, 0.0263018982813707, 0.0385176798582972,
0.199614543969512, 0.0901500218057822, 0.181149360032541, 0.05254867287,
1.01183864387684, 0.101904192043363, 0.0684025775131465, 0.0730991379330716,
0.391472022288837, 0.162670671684959, 0.312075610949283, "contcor1",
3.87289002980396e-08, -0.00463647527725275, 0.00875019171674774,
0.129610929479583, 0.0225485982857244, -0.0794077068206055,
0.0472986075910507, 0.038667431651618, 0.0486615947429129, 0.293085737284156,
0.102499450157703, 0.0587002325413924, 0.06968807084, 1.0041493380131,
0.109551905583045, 0.0807291934771201, 0.0876232547094318, 0.440233848836187,
0.194147501919935, 0.228903646815888, "contcor2", 3.06076329375632e-08,
-0.0148054356280015, 0.0021066241823039, 0.420553558691172,
0.0159597213180006, -0.0777476757406256, 0.0315765590294507,
0.0308430722235896, 0.0408228091905911, 0.520482384062624, 0.102023432380012,
0.0707644153939082, 3, 1.4213381090374, 0.0878435386563505,
0.0718948260426243, 0.0786323358702756, 0.628165183999919, 0.190816990258425,
0.205341132931993, "facFive", 0.358369568041053, 0.285237073953381,
0.380102391628541, 0.423372619911139, 0.47791304140055, 0.0164539097394997,
0.479869257412775, 0.434004318276764, 0.491435548543508, 0.519513315848713,
0.585673590137766, 0.184196159161922, 15.9882068024571, 24.0657052758223,
0.60016655999309, 0.570258196480784, 0.610285976634783, 0.629418255267226,
0.701113938910745, 0.343529872561356, "debMiss30"))
})
test_that("Cronbach's alpha scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-scale")
})
test_that("Greatest Lower Bound scale plot matches", {
if (jaspBase::getOS() == "linux") skip("Skipped as ubuntu produces a different plot")
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-scale")
})
test_that("Guttman's lambda2 scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-scale")
})
test_that("Guttman's lambda6 scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-scale")
})
test_that("McDonald's omega scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-scale")
})
test_that("Cronbach's alpha item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-item")
})
test_that("Greatest Lower Bound item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-item")
})
test_that("Guttman's lambda2 item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-item")
})
test_that("Guttman's lambda6 item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-item")
})
test_that("McDonald's omega item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-item")
})
test_that("Cronbach's alpha traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-tp")
})
test_that("Greatest Lower Bound traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-tp")
})
test_that("Guttman's lambda2 traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-tp")
})
test_that("Guttman's lambda6 traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-tp")
})
test_that("McDonald's omega traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-tp")
})
test_that("Probability that Reliability Statistic is Larger than 0.10 and Smaller than 0.30 table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0577777777777778, 0.257612007091332, "McDonald's <unicode>",
0.02, 0.2157426951971, "Cronbach's <unicode>", 0.0466666666666667,
0.117489153634043, "Guttman's <unicode>2", 0, 0.0666585500834108,
"Guttman's <unicode>6", 0.6, 0.00548548628617385, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0335378719395441, 0.0379993015242684, 0.0509632083185619, 0.507213118664045,
0.125134713254144, 0.142232495514273, 2.8581975155295, 4.39188432578635,
"Posterior mean", 4.88588161069134e-07, -0.0247593284030748,
0.00211400244586846, 0.358233671861917, 0.0351173024406283,
0.0612110191516504, "", "", "95% CI lower bound", 0.107247682851598,
0.0925517739486723, 0.109789052574058, 0.618692688177314, 0.26368113845796,
0.221618551302267, "", "", "95% CI upper bound", 1.03110267965238,
1.02512927209581, 1.04150159018568, 1.02543279542889, 1.07445147891652,
1.01625374834437, "", "", "R-hat", 261.905719143696, 278.717853339024,
271.566230782654, 450, 380.420178581031, 414.536145329943, "",
"", "ESS"))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$scaleMean <- TRUE
options$scaleSd <- TRUE
options$meanSdScoresMethod <- "meanScores"
options$itemCiLevel <- 0.9
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemRestCorrelation <- TRUE
options$itemMean <- TRUE
options$itemSd <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.1
options$probabilityTableUpperBound <- 0.3
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$samplesSavingDisabled <- TRUE
options$setSeed <- TRUE
options$reverseScaledItems <- "debMiss30"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(4.9262740258841e-06, -0.0302604620110933, -0.00730189174965356,
0.381529087630189, 0.00468437245991029, 0.0462164102560101,
0.0322661722934692, 0.0212125066975763, 0.0325973854277802,
0.494218519800714, 0.0831973460090512, 0.198566040293231, -0.18874858754,
1.05841360919316, 0.0908764111289075, 0.0716108498281969, 0.0783885886016254,
0.606959485289979, 0.158707927788854, 0.344714988273422, "contNormal",
9.40024640626554e-07, -0.0157617947104415, -0.00111243856322425,
0.0307771184754301, 0.00866952978122558, 0.0257664363297778,
0.0372377719573929, 0.0263018982813707, 0.0385176798582972,
0.199614543969512, 0.0901500218057822, 0.181149360032541, 0.05254867287,
1.01183864387684, 0.101904192043363, 0.0684025775131465, 0.0730991379330716,
0.391472022288837, 0.162670671684959, 0.312075610949283, "contcor1",
3.87289002980396e-08, -0.00463647527725275, 0.00875019171674774,
0.129610929479583, 0.0225485982857244, -0.0794077068206055,
0.0472986075910507, 0.038667431651618, 0.0486615947429129, 0.293085737284156,
0.102499450157703, 0.0587002325413924, 0.06968807084, 1.0041493380131,
0.109551905583045, 0.0807291934771201, 0.0876232547094318, 0.440233848836187,
0.194147501919935, 0.228903646815888, "contcor2", 3.06076329375632e-08,
-0.0148054356280015, 0.0021066241823039, 0.420553558691172,
0.0159597213180006, -0.0777476757406256, 0.0315765590294507,
0.0308430722235896, 0.0408228091905911, 0.520482384062624, 0.102023432380012,
0.0707644153939082, 3, 1.4213381090374, 0.0878435386563505,
0.0718948260426243, 0.0786323358702756, 0.628165183999919, 0.190816990258425,
0.205341132931993, "facFive", 0.358369568041053, 0.285237073953381,
0.380102391628541, 0.423372619911139, 0.47791304140055, 0.0164539097394997,
0.479869257412775, 0.434004318276764, 0.491435548543508, 0.519513315848713,
0.585673590137766, 0.184196159161922, 15.9882068024571, 24.0657052758223,
0.60016655999309, 0.570258196480784, 0.610285976634783, 0.629418255267226,
0.701113938910745, 0.343529872561356, "debMiss30"))
})
test_that("Probability that Reliability Statistic is Larger than 0.10 and Smaller than 0.30 table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0577777777777778, 0.257612007091332, "McDonald's <unicode>",
0.02, 0.2157426951971, "Cronbach's <unicode>", 0.0466666666666667,
0.117489153634043, "Guttman's <unicode>2", 0, 0.0666585500834108,
"Guttman's <unicode>6", 0.6, 0.00548548628617385, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0335378719395441, 0.0379993015242684, 0.0509632083185619, 0.507213118664045,
0.125134713254144, 0.142232495514273, 2.8581975155295, 4.39188432578635,
"Posterior mean", 4.88588161069134e-07, -0.0247593284030748,
0.00211400244586846, 0.358233671861917, 0.0351173024406283,
0.0612110191516504, "", "", "95% CI lower bound", 0.107247682851598,
0.0925517739486723, 0.109789052574058, 0.618692688177314, 0.26368113845796,
0.221618551302267, "", "", "95% CI upper bound", 1.03110267965238,
1.02512927209581, 1.04150159018568, 1.02543279542889, 1.07445147891652,
1.01625374834437, "", "", "R-hat"))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("asrm_1", "asrm_2", "asrm_3", "asrm_4", "asrm_5")
options$scaleOmega <- TRUE
options$scaleLambda2 <- TRUE
options$averageInterItemCorrelation <- TRUE
options$itemCiLevel <- 0.95
options$itemDeletedOmega <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemRestCorrelation <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotFixedRange <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.7
options$probabilityTableUpperBound <- 1
options$samples <- 100
options$chains <- 2
options$rHat <- TRUE
options$setSeed <- TRUE
options$naAction <- "listwise"
options$inverseWishartPriorScale <- 1
options$inverseWishartPriorDf <- 10
options$inverseGammaPriorShape <- 6
options$inverseGammaPriorScale <- 10
options$normalPriorMean <- 1
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "asrm_mis.csv", options)
# results <- runAnalysis("unidimensionalReliabilityBayesian", Bayesrel::asrm_mis, options, makeTests = T)
test_that("Bayesian Individual Item Reliability Statistics table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.365317706663268, 0.629664746841126, 0.555850598549084, 0.547833261943788,
0.713418617285292, 0.705145889748414, 0.719358502963356, 0.800896152983193,
0.801208767853036, "asrm_1", 0.425180238104353, 0.647546515358842,
0.495115177374957, 0.590585184223742, 0.737906643927936, 0.617975659434176,
0.720617721441406, 0.821130476744635, 0.74859948958759, "asrm_2",
0.511558346241041, 0.710291628124685, 0.314038783355194, 0.647299797106082,
0.784623165070472, 0.486841175278608, 0.800023243314042, 0.850735328744899,
0.636968845397171, "asrm_3", 0.419641220251401, 0.687663875830053,
0.408137589364324, 0.615730018474419, 0.767034290352854, 0.573438744579436,
0.72516307294578, 0.849444065720693, 0.696156888092013, "asrm_4",
0.380908166253114, 0.624933985182541, 0.542234275314821, 0.559120671647617,
0.733553360969622, 0.648244546313451, 0.717678239451181, 0.81305056069536,
0.772643236466087, "asrm_5"))
})
test_that("Guttman's lambda2 plot matches with adjusted priors", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-prior-adjusted")
})
test_that("McDonald's omega plot matches with adjusted priors", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-prior-adjusted")
})
test_that("Probability that Reliability Statistic is Larger than 0.70 and Smaller than 1.00 table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.3, 0.521665798540962, "McDonald's <unicode>", 0.99, 0.103854227264579,
"Guttman's <unicode>2"))
})
test_that("Bayesian Scale Reliability Statistics table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.658799634973158, 0.789270505536385, 0.419218581777162, "Posterior mean",
0.526011816625903, 0.715215558587289, 0.316743509774531, "95% CI lower bound",
0.795894536283031, 0.859660650371794, 0.507230342509293, "95% CI upper bound",
1.02778087840674, 0.990343839523617, 0.997596520647971, "R-hat"
))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.2
options$probabilityTableUpperBound <- 0.5
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$setSeed <- TRUE
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
options$standardizedLoadings <- TRUE
options$coefficientType <- "standardized"
options$pointEstimate <- "median"
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.499864243624906, 0.371987972410805, 0.519686705794261, 0.444919635513272,
0.57942106464377, 0.620670635416665, 0.553348700422337, 0.620992269207978,
0.567369697542575, 0.675482992764378, 0.714612686739471, 0.674415156445437,
0.710080098725572, 0.669197440622959, 0.748861253746742, "contNormal",
5.73312727298402e-06, -0.107114290877759, -0.0031654793906686,
-0.0482001841128878, -6.39634897513984e-09, 0.161564148144394,
0.194804130030221, 0.220807457791194, 0.149500575659385, 0.263953470015245,
0.372255405615577, 0.445366740296484, 0.439504615186215, 0.353753091342017,
0.429398503438359, "contcor1", 0.0393067987346478, 0.0189905734187845,
0.0792132799731724, 0.0283360768101215, 0.133307764798279, 0.280634498409812,
0.299360676376119, 0.314435040862574, 0.230611750413446, 0.349989129538079,
0.456896489326536, 0.49846757129663, 0.494379320017436, 0.408350563197256,
0.544288622938786, "contcor2", 0.518994671551128, 0.381555713653778,
0.523617983089419, 0.44863921584234, 0.605936721967155, 0.627501070070124,
0.542402992030407, 0.614551863581106, 0.567433639851765, 0.700263930105156,
0.714884024479502, 0.692929487440639, 0.707153922148802, 0.671066022395026,
0.776950364200239, "facFive"))
})
test_that("Single-Factor Model for omega table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_loadTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.118960690052957, "contNormal", 0.808474151944778, "contcor1",
0.768612766587744, "contcor2", 0.144763227189446, "facFive"
))
})
test_that("Cronbach's alpha plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-std")
})
test_that("Greatest Lower Bound plot matches", {
if (jaspBase::getOS() == "linux") skip("Skipped as ubuntu produces a different plot")
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-std")
})
test_that("Guttman's lambda2 plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-std")
})
test_that("Guttman's lambda6 plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-std")
})
test_that("McDonald's omega plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-std")
})
test_that("Probability that Reliability Statistic is Larger than 0.20 and Smaller than 0.50 table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.211111111111111, 0.322277450512426, "McDonald's <unicode>",
0.546666666666667, 0.327282806190898, "Cronbach's <unicode>",
0.206666666666667, 0.233873326185613, "Guttman's <unicode>2",
0.328888888888889, 0.20968370679484, "Guttman's <unicode>6",
0.00666666666666671, 0.0557055139950303, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.549475923084279, 0.485978025107501, 0.553414961378168, 0.527639174641981,
0.649735352170137, 0.191174460794261, "Posterior median", 0.435706048978678,
0.322525692701179, 0.443001980608279, 0.391309056840046, 0.530738963961284,
0.106359073588414, "95% CI lower bound", 0.664754350432929,
0.639694091225906, 0.669397876270056, 0.636673491920215, 0.743596201899069,
0.307409602568225, "95% CI upper bound", 1.00879071329335, 1.00688014001917,
1.00846765229577, 1.00873819260755, 1.01603809839108, 1.01104129919821,
"R-hat"))
})
# results were compared to blavFitIndices and lavaan omegaFitMeasures with the same data but 2000 obs
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("asrm_1", "asrm_2", "asrm_3", "asrm_4", "asrm_5")
options$scaleOmega <- TRUE
options$samples <- 200
options$chains <- 3
options$setSeed <- TRUE
options$omegaFitMeasures <- TRUE
options$omegaFitMeasuresCutoffRmsea <- .1
options$omegaFitMeasuresCutoffCfiTli <- .85
options$omegaPosteriorPredictiveCheck <- TRUE
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "asrm.csv", options)
test_that("Fit Measures for the Single-Factor Model table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_fitTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.929544182303969, "Posterior mean", 13.0900880096089, 0.1319747009136,
0.061860112775025, 0.860237253334787, 0.845602177064211, "90% CI lower bound",
"", 0.0233139770705626, "", 0.693722044278387, 1, "90% CI upper bound",
"", 0.222796365776379, "", 1, 0.946666666666667, "Relative to cutoff",
"", 0.215555555555556, "", 0.595555555555556))
})
test_that("Posterior Predictive Check Omega plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_omegaPPC"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "posterior-predictive-check-omega")
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedPlot <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemDeletedPlotOrderedType <- "mean"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
options$samples <- 200
options$chains <- 2
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Cronbach's alpha plot item deleted plot matches ordered by mean", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "cronbach-s-alpha-item-mean-ordered")
})
jasp-stats/jaspReliability documentation built on May 5, 2024, 10:57 p.m.
R Package Documentation
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options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$scaleMean <- TRUE
options$scaleSd <- TRUE
options$meanSdScoresMethod <- "meanScores"
options$itemCiLevel <- 0.9
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$itemDeletedPlot <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemRestCorrelation <- TRUE
options$itemMean <- TRUE
options$itemSd <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotFixedRange <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.1
options$probabilityTableUpperBound <- 0.3
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$effectiveSampleSize <- TRUE
options$tracePlot <- TRUE
options$setSeed <- TRUE
options$reverseScaledItems <- "debMiss30"
options$itemDeletedPlotOrderedType <- "kullbackLeibler"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(4.9262740258841e-06, -0.0302604620110933, -0.00730189174965356,
0.381529087630189, 0.00468437245991029, 0.0462164102560101,
0.0322661722934692, 0.0212125066975763, 0.0325973854277802,
0.494218519800714, 0.0831973460090512, 0.198566040293231, -0.18874858754,
1.05841360919316, 0.0908764111289075, 0.0716108498281969, 0.0783885886016254,
0.606959485289979, 0.158707927788854, 0.344714988273422, "contNormal",
9.40024640626554e-07, -0.0157617947104415, -0.00111243856322425,
0.0307771184754301, 0.00866952978122558, 0.0257664363297778,
0.0372377719573929, 0.0263018982813707, 0.0385176798582972,
0.199614543969512, 0.0901500218057822, 0.181149360032541, 0.05254867287,
1.01183864387684, 0.101904192043363, 0.0684025775131465, 0.0730991379330716,
0.391472022288837, 0.162670671684959, 0.312075610949283, "contcor1",
3.87289002980396e-08, -0.00463647527725275, 0.00875019171674774,
0.129610929479583, 0.0225485982857244, -0.0794077068206055,
0.0472986075910507, 0.038667431651618, 0.0486615947429129, 0.293085737284156,
0.102499450157703, 0.0587002325413924, 0.06968807084, 1.0041493380131,
0.109551905583045, 0.0807291934771201, 0.0876232547094318, 0.440233848836187,
0.194147501919935, 0.228903646815888, "contcor2", 3.06076329375632e-08,
-0.0148054356280015, 0.0021066241823039, 0.420553558691172,
0.0159597213180006, -0.0777476757406256, 0.0315765590294507,
0.0308430722235896, 0.0408228091905911, 0.520482384062624, 0.102023432380012,
0.0707644153939082, 3, 1.4213381090374, 0.0878435386563505,
0.0718948260426243, 0.0786323358702756, 0.628165183999919, 0.190816990258425,
0.205341132931993, "facFive", 0.358369568041053, 0.285237073953381,
0.380102391628541, 0.423372619911139, 0.47791304140055, 0.0164539097394997,
0.479869257412775, 0.434004318276764, 0.491435548543508, 0.519513315848713,
0.585673590137766, 0.184196159161922, 15.9882068024571, 24.0657052758223,
0.60016655999309, 0.570258196480784, 0.610285976634783, 0.629418255267226,
0.701113938910745, 0.343529872561356, "debMiss30"))
})
test_that("Cronbach's alpha scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-scale")
})
test_that("Greatest Lower Bound scale plot matches", {
if (jaspBase::getOS() == "linux") skip("Skipped as ubuntu produces a different plot")
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-scale")
})
test_that("Guttman's lambda2 scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-scale")
})
test_that("Guttman's lambda6 scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-scale")
})
test_that("McDonald's omega scale plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-scale")
})
test_that("Cronbach's alpha item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-item")
})
test_that("Greatest Lower Bound item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-item")
})
test_that("Guttman's lambda2 item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-item")
})
test_that("Guttman's lambda6 item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-item")
})
test_that("McDonald's omega item plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-item")
})
test_that("Cronbach's alpha traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-tp")
})
test_that("Greatest Lower Bound traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-tp")
})
test_that("Guttman's lambda2 traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-tp")
})
test_that("Guttman's lambda6 traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-tp")
})
test_that("McDonald's omega traceplot plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerTP"]][["collection"]][["stateContainer_plotContainerTP_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-tp")
})
test_that("Probability that Reliability Statistic is Larger than 0.10 and Smaller than 0.30 table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0577777777777778, 0.257612007091332, "McDonald's <unicode>",
0.02, 0.2157426951971, "Cronbach's <unicode>", 0.0466666666666667,
0.117489153634043, "Guttman's <unicode>2", 0, 0.0666585500834108,
"Guttman's <unicode>6", 0.6, 0.00548548628617385, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0335378719395441, 0.0379993015242684, 0.0509632083185619, 0.507213118664045,
0.125134713254144, 0.142232495514273, 2.8581975155295, 4.39188432578635,
"Posterior mean", 4.88588161069134e-07, -0.0247593284030748,
0.00211400244586846, 0.358233671861917, 0.0351173024406283,
0.0612110191516504, "", "", "95% CI lower bound", 0.107247682851598,
0.0925517739486723, 0.109789052574058, 0.618692688177314, 0.26368113845796,
0.221618551302267, "", "", "95% CI upper bound", 1.03110267965238,
1.02512927209581, 1.04150159018568, 1.02543279542889, 1.07445147891652,
1.01625374834437, "", "", "R-hat", 261.905719143696, 278.717853339024,
271.566230782654, 450, 380.420178581031, 414.536145329943, "",
"", "ESS"))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$scaleMean <- TRUE
options$scaleSd <- TRUE
options$meanSdScoresMethod <- "meanScores"
options$itemCiLevel <- 0.9
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemRestCorrelation <- TRUE
options$itemMean <- TRUE
options$itemSd <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.1
options$probabilityTableUpperBound <- 0.3
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$samplesSavingDisabled <- TRUE
options$setSeed <- TRUE
options$reverseScaledItems <- "debMiss30"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(4.9262740258841e-06, -0.0302604620110933, -0.00730189174965356,
0.381529087630189, 0.00468437245991029, 0.0462164102560101,
0.0322661722934692, 0.0212125066975763, 0.0325973854277802,
0.494218519800714, 0.0831973460090512, 0.198566040293231, -0.18874858754,
1.05841360919316, 0.0908764111289075, 0.0716108498281969, 0.0783885886016254,
0.606959485289979, 0.158707927788854, 0.344714988273422, "contNormal",
9.40024640626554e-07, -0.0157617947104415, -0.00111243856322425,
0.0307771184754301, 0.00866952978122558, 0.0257664363297778,
0.0372377719573929, 0.0263018982813707, 0.0385176798582972,
0.199614543969512, 0.0901500218057822, 0.181149360032541, 0.05254867287,
1.01183864387684, 0.101904192043363, 0.0684025775131465, 0.0730991379330716,
0.391472022288837, 0.162670671684959, 0.312075610949283, "contcor1",
3.87289002980396e-08, -0.00463647527725275, 0.00875019171674774,
0.129610929479583, 0.0225485982857244, -0.0794077068206055,
0.0472986075910507, 0.038667431651618, 0.0486615947429129, 0.293085737284156,
0.102499450157703, 0.0587002325413924, 0.06968807084, 1.0041493380131,
0.109551905583045, 0.0807291934771201, 0.0876232547094318, 0.440233848836187,
0.194147501919935, 0.228903646815888, "contcor2", 3.06076329375632e-08,
-0.0148054356280015, 0.0021066241823039, 0.420553558691172,
0.0159597213180006, -0.0777476757406256, 0.0315765590294507,
0.0308430722235896, 0.0408228091905911, 0.520482384062624, 0.102023432380012,
0.0707644153939082, 3, 1.4213381090374, 0.0878435386563505,
0.0718948260426243, 0.0786323358702756, 0.628165183999919, 0.190816990258425,
0.205341132931993, "facFive", 0.358369568041053, 0.285237073953381,
0.380102391628541, 0.423372619911139, 0.47791304140055, 0.0164539097394997,
0.479869257412775, 0.434004318276764, 0.491435548543508, 0.519513315848713,
0.585673590137766, 0.184196159161922, 15.9882068024571, 24.0657052758223,
0.60016655999309, 0.570258196480784, 0.610285976634783, 0.629418255267226,
0.701113938910745, 0.343529872561356, "debMiss30"))
})
test_that("Probability that Reliability Statistic is Larger than 0.10 and Smaller than 0.30 table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0577777777777778, 0.257612007091332, "McDonald's <unicode>",
0.02, 0.2157426951971, "Cronbach's <unicode>", 0.0466666666666667,
0.117489153634043, "Guttman's <unicode>2", 0, 0.0666585500834108,
"Guttman's <unicode>6", 0.6, 0.00548548628617385, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match with disabled sample saving", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0335378719395441, 0.0379993015242684, 0.0509632083185619, 0.507213118664045,
0.125134713254144, 0.142232495514273, 2.8581975155295, 4.39188432578635,
"Posterior mean", 4.88588161069134e-07, -0.0247593284030748,
0.00211400244586846, 0.358233671861917, 0.0351173024406283,
0.0612110191516504, "", "", "95% CI lower bound", 0.107247682851598,
0.0925517739486723, 0.109789052574058, 0.618692688177314, 0.26368113845796,
0.221618551302267, "", "", "95% CI upper bound", 1.03110267965238,
1.02512927209581, 1.04150159018568, 1.02543279542889, 1.07445147891652,
1.01625374834437, "", "", "R-hat"))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("asrm_1", "asrm_2", "asrm_3", "asrm_4", "asrm_5")
options$scaleOmega <- TRUE
options$scaleLambda2 <- TRUE
options$averageInterItemCorrelation <- TRUE
options$itemCiLevel <- 0.95
options$itemDeletedOmega <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemRestCorrelation <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotFixedRange <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.7
options$probabilityTableUpperBound <- 1
options$samples <- 100
options$chains <- 2
options$rHat <- TRUE
options$setSeed <- TRUE
options$naAction <- "listwise"
options$inverseWishartPriorScale <- 1
options$inverseWishartPriorDf <- 10
options$inverseGammaPriorShape <- 6
options$inverseGammaPriorScale <- 10
options$normalPriorMean <- 1
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "asrm_mis.csv", options)
# results <- runAnalysis("unidimensionalReliabilityBayesian", Bayesrel::asrm_mis, options, makeTests = T)
test_that("Bayesian Individual Item Reliability Statistics table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.365317706663268, 0.629664746841126, 0.555850598549084, 0.547833261943788,
0.713418617285292, 0.705145889748414, 0.719358502963356, 0.800896152983193,
0.801208767853036, "asrm_1", 0.425180238104353, 0.647546515358842,
0.495115177374957, 0.590585184223742, 0.737906643927936, 0.617975659434176,
0.720617721441406, 0.821130476744635, 0.74859948958759, "asrm_2",
0.511558346241041, 0.710291628124685, 0.314038783355194, 0.647299797106082,
0.784623165070472, 0.486841175278608, 0.800023243314042, 0.850735328744899,
0.636968845397171, "asrm_3", 0.419641220251401, 0.687663875830053,
0.408137589364324, 0.615730018474419, 0.767034290352854, 0.573438744579436,
0.72516307294578, 0.849444065720693, 0.696156888092013, "asrm_4",
0.380908166253114, 0.624933985182541, 0.542234275314821, 0.559120671647617,
0.733553360969622, 0.648244546313451, 0.717678239451181, 0.81305056069536,
0.772643236466087, "asrm_5"))
})
test_that("Guttman's lambda2 plot matches with adjusted priors", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-prior-adjusted")
})
test_that("McDonald's omega plot matches with adjusted priors", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-prior-adjusted")
})
test_that("Probability that Reliability Statistic is Larger than 0.70 and Smaller than 1.00 table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.3, 0.521665798540962, "McDonald's <unicode>", 0.99, 0.103854227264579,
"Guttman's <unicode>2"))
})
test_that("Bayesian Scale Reliability Statistics table results match with adjusted priors", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.658799634973158, 0.789270505536385, 0.419218581777162, "Posterior mean",
0.526011816625903, 0.715215558587289, 0.316743509774531, "95% CI lower bound",
0.795894536283031, 0.859660650371794, 0.507230342509293, "95% CI upper bound",
1.02778087840674, 0.990343839523617, 0.997596520647971, "R-hat"
))
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive")
options$scaleAlpha <- TRUE
options$scaleLambda2 <- TRUE
options$scaleLambda6 <- TRUE
options$scaleGreatestLowerBound <- TRUE
options$averageInterItemCorrelation <- TRUE
options$itemDeletedOmega <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedLambda2 <- TRUE
options$itemDeletedLambda6 <- TRUE
options$itemDeletedGreatestLowerBound <- TRUE
options$posteriorPlot <- TRUE
options$posteriorPlotPriorDisplayed <- TRUE
options$probabilityTable <- TRUE
options$probabilityTableLowerBound <- 0.2
options$probabilityTableUpperBound <- 0.5
options$posteriorPlotShaded <- TRUE
options$samples <- 200
options$rHat <- TRUE
options$setSeed <- TRUE
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
options$standardizedLoadings <- TRUE
options$coefficientType <- "standardized"
options$pointEstimate <- "median"
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Bayesian Individual Item Reliability Statistics table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_itemTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.499864243624906, 0.371987972410805, 0.519686705794261, 0.444919635513272,
0.57942106464377, 0.620670635416665, 0.553348700422337, 0.620992269207978,
0.567369697542575, 0.675482992764378, 0.714612686739471, 0.674415156445437,
0.710080098725572, 0.669197440622959, 0.748861253746742, "contNormal",
5.73312727298402e-06, -0.107114290877759, -0.0031654793906686,
-0.0482001841128878, -6.39634897513984e-09, 0.161564148144394,
0.194804130030221, 0.220807457791194, 0.149500575659385, 0.263953470015245,
0.372255405615577, 0.445366740296484, 0.439504615186215, 0.353753091342017,
0.429398503438359, "contcor1", 0.0393067987346478, 0.0189905734187845,
0.0792132799731724, 0.0283360768101215, 0.133307764798279, 0.280634498409812,
0.299360676376119, 0.314435040862574, 0.230611750413446, 0.349989129538079,
0.456896489326536, 0.49846757129663, 0.494379320017436, 0.408350563197256,
0.544288622938786, "contcor2", 0.518994671551128, 0.381555713653778,
0.523617983089419, 0.44863921584234, 0.605936721967155, 0.627501070070124,
0.542402992030407, 0.614551863581106, 0.567433639851765, 0.700263930105156,
0.714884024479502, 0.692929487440639, 0.707153922148802, 0.671066022395026,
0.776950364200239, "facFive"))
})
test_that("Single-Factor Model for omega table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_loadTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.118960690052957, "contNormal", 0.808474151944778, "contcor1",
0.768612766587744, "contcor2", 0.144763227189446, "facFive"
))
})
test_that("Cronbach's alpha plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "alpha-std")
})
test_that("Greatest Lower Bound plot matches", {
if (jaspBase::getOS() == "linux") skip("Skipped as ubuntu produces a different plot")
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_glb"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "greatest-lower-bound-std")
})
test_that("Guttman's lambda2 plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda2"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda2-std")
})
test_that("Guttman's lambda6 plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_lambda6"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "lambda6-std")
})
test_that("McDonald's omega plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainer"]][["collection"]][["stateContainer_plotContainer_omega"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "omega-std")
})
test_that("Probability that Reliability Statistic is Larger than 0.20 and Smaller than 0.50 table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_probabilityTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.211111111111111, 0.322277450512426, "McDonald's <unicode>",
0.546666666666667, 0.327282806190898, "Cronbach's <unicode>",
0.206666666666667, 0.233873326185613, "Guttman's <unicode>2",
0.328888888888889, 0.20968370679484, "Guttman's <unicode>6",
0.00666666666666671, 0.0557055139950303, "Greatest Lower Bound"
))
})
test_that("Bayesian Scale Reliability Statistics table results match with standardization and median", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_scaleTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.549475923084279, 0.485978025107501, 0.553414961378168, 0.527639174641981,
0.649735352170137, 0.191174460794261, "Posterior median", 0.435706048978678,
0.322525692701179, 0.443001980608279, 0.391309056840046, 0.530738963961284,
0.106359073588414, "95% CI lower bound", 0.664754350432929,
0.639694091225906, 0.669397876270056, 0.636673491920215, 0.743596201899069,
0.307409602568225, "95% CI upper bound", 1.00879071329335, 1.00688014001917,
1.00846765229577, 1.00873819260755, 1.01603809839108, 1.01104129919821,
"R-hat"))
})
# results were compared to blavFitIndices and lavaan omegaFitMeasures with the same data but 2000 obs
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("asrm_1", "asrm_2", "asrm_3", "asrm_4", "asrm_5")
options$scaleOmega <- TRUE
options$samples <- 200
options$chains <- 3
options$setSeed <- TRUE
options$omegaFitMeasures <- TRUE
options$omegaFitMeasuresCutoffRmsea <- .1
options$omegaFitMeasuresCutoffCfiTli <- .85
options$omegaPosteriorPredictiveCheck <- TRUE
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "asrm.csv", options)
test_that("Fit Measures for the Single-Factor Model table results match", {
table <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_fitTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.929544182303969, "Posterior mean", 13.0900880096089, 0.1319747009136,
0.061860112775025, 0.860237253334787, 0.845602177064211, "90% CI lower bound",
"", 0.0233139770705626, "", 0.693722044278387, 1, "90% CI upper bound",
"", 0.222796365776379, "", 1, 0.946666666666667, "Relative to cutoff",
"", 0.215555555555556, "", 0.595555555555556))
})
test_that("Posterior Predictive Check Omega plot matches", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_omegaPPC"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "posterior-predictive-check-omega")
})
options <- analysisOptions("unidimensionalReliabilityBayesian")
options$variables <- c("contNormal", "contcor1", "contcor2", "facFive", "debMiss30")
options$scaleAlpha <- TRUE
options$itemDeletedAlpha <- TRUE
options$itemDeletedPlot <- TRUE
options$itemDeletedPlotOrdered <- TRUE
options$itemDeletedPlotOrderedType <- "mean"
options$inverseWishartPriorDf <- length(options$variables)
options$inverseWishartPriorScale <- 0.0000000001
options$samples <- 200
options$chains <- 2
set.seed(1)
results <- runAnalysis("unidimensionalReliabilityBayesian", "test.csv", options)
test_that("Cronbach's alpha plot item deleted plot matches ordered by mean", {
plotName <- results[["results"]][["stateContainer"]][["collection"]][["stateContainer_plotContainerItem"]][["collection"]][["stateContainer_plotContainerItem_alpha"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "cronbach-s-alpha-item-mean-ordered")
})
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