tests/testthat/test-anova.R
In jasp-stats/jaspAnova: ANOVA Module for JASP
context("ANOVA")
# does not test
# - if analysis handles too few observations
# Main results ----
test_that("Main table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$contrasts <- list(
list(contrast="none", variable="facFive"),
list(contrast="none", variable="contBinom")
)
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$vovkSellke <- TRUE
refTables <- list(
type1 = list("facFive", 181.151987151139, 4, 45.2879967877848, 1.82424792091397,
0.131040166930968, "TRUE", 0.0733818170125722, 0.0749970945389635,
0.0328259131508147, 1.3814133611929, "contBinom", 24.5000310074981,
1, 24.5000310074981, 0.98688689714382, 0.323167856084615, "FALSE",
0.00992457670748362, 0.0108464739348588, 0, 1.00776449783097,
"facFive <unicode> contBinom", 28.6688295533663, 4, 7.16720738834158,
0.288702616682483, 0.884618241388975, "TRUE", 0.011613291343553,
0.0126686727866262, 0, 1, "Residuals", 2234.30141494065, 90,
24.8255712771184, "", "", "TRUE", "", "", "", ""),
type2 = list("facFive", 174.090039449647, 4, 43.5225098624118, 1.75313225933803,
0.145323959824188, "TRUE", 0.0707234506472374, 0.0722847770997921,
0.0300789007673393, 1.31245187936232, "contBinom", 24.5000310074979,
1, 24.5000310074979, 0.986886897143812, 0.323167856084617, "FALSE",
0.00995304923413341, 0.0108464739348588, 0, 1.00776449783097,
"facFive <unicode> contBinom", 28.6688295533663, 4, 7.16720738834158,
0.288702616682483, 0.884618241388975, "TRUE", 0.0116466086080589,
0.0126686727866262, 0, 1, "Residuals", 2234.30141494066, 90,
24.8255712771184, "", "", "TRUE", "", "", "", ""),
type3 = list("TRUE", 4, 1.66677534088084, 41.3786500279805, 0.164641950089634,
165.514600111922, "facFive", 0.0675231783920824, 0.0267410840853991,
0.0689697039580327, 1.23860485419559, "FALSE", 1, 0.9160552247026,
22.7415942746311, 0.341076850898726, 22.7415942746311, "contBinom",
0.00927763910910521, 0, 0.0100758355874387, 1.00272840654715,
"TRUE", 4, 0.288702616682483, 7.16720738834158, 0.884618241388975,
28.6688295533663, "facFive <unicode> contBinom", 0.0116957083599583,
0, 0.0126686727866262, 1, "TRUE", 90, "", 24.8255712771184,
"", 2234.30141494065, "Residuals", "", "", "", "")
)
for (type in c("type1", "type2", "type3")) {
options$sumOfSquares <- type
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[type]], label=paste("Table with SS", type))
}
})
# Additional results ----
test_that("Homogeneity of Variances table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$modelTerms <- list(list(components="facExperim"))
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_assumptionsContainer"]][["collection"]][["anovaContainer_assumptionsContainer_leveneTable"]][["data"]]
jaspTools::expect_equal_tables(table, list(3.1459013381035, 1, 98, 0.0792241296904395, 1.83142365040653))
})
# Contrasts verified with SPSS
# should we put this in verification project???
test_that("Contrasts table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$contrastCi <- TRUE
options$modelTerms <- list(list(components="facFive"))
refTables <- list(
deviation = list("2 - 1, 2, 3, 4, 5", -0.19513913461, 0.211517937182489, -0.922565420263351, 0.358570880186821,
95, -0.615055331660948, 0.224777062440949, "TRUE", "3 - 1, 2, 3, 4, 5", 0.331498558639999,
0.211517937182488, 1.56723615526752, 0.120384543718471, 95, -0.0884176384109483, 0.751414755690946,
"FALSE", "4 - 1, 2, 3, 4, 5", -0.16911442746, 0.211517937182488, -0.799527594267789, 0.425979159402386,
95, -0.589030624510948, 0.250801769590948, "FALSE", "5 - 1, 2, 3, 4, 5", 0.18254372644, 0.211517937182488,
0.863017713162119, 0.390301247101766, 95, -0.237372470610948, 0.602459923490948, "FALSE"),
simple = list("2 - 1", -0.0453504116000002, 0.334439223738541, -0.135601354090735,
0.892423350599012, 95, -0.709296216138537, 0.618595392938537, "TRUE", "3 - 1", 0.48128728165, 0.334439223738541,
1.43908742601993, 0.153412521131058, 95, -0.182658522888538, 1.14523308618854, "FALSE", "4 - 1", -0.0193257044500001,
0.334439223738541, -0.0577854003904418, 0.954040939034334, 95, -0.683271508988538, 0.644620100088538, "FALSE",
"5 - 1", 0.33233244945, 0.334439223738541, 0.993700576550232,
0.322892982956191, 95, -0.331613355088537, 0.996278253988537, "FALSE"),
difference = list("2 - 1", -0.0453504116, 0.334439223738541, -0.135601354090734,
0.892423350599013, 95, -0.709296216138538, 0.618595392938538,"TRUE", "3 - 1, 2", 0.50396248745, 0.289632863779524,
1.74000450388679, 0.0850966309951808, 95, -0.0710314460164715, 1.07895642091647, "FALSE", "4 - 1, 2, 3",
-0.164637994466667, 0.273068482710641, -0.602918333278054, 0.547999665617461,
95, -0.706747473793691, 0.377471484860357, "FALSE", "5 - 1, 2, 3, 4", 0.22817965805, 0.26439742147811,
0.86301771316212, 0.390301247101765, 95, -0.296715588263685, 0.753074904363685, "FALSE"),
Helmert = list("1 - 2, 3, 4, 5", -0.1872359037625, 0.26439742147811, -0.708160853898499,
0.480579313597282, 95, -0.712131150076185, 0.337659342551185, "TRUE", "2 - 3, 4, 5", -0.31011508715, 0.273068482710641,
-1.13566781516348, 0.25895260195633, 95, -0.852224566477025, 0.231994392177024, "FALSE", "3 - 4, 5", 0.32478390915,
0.289632863779524, 1.12136414670551, 0.264959410721321, 95, -0.250210024316471, 0.899777842616471, "FALSE",
"4 - 5", -0.3516581539, 0.334439223738541, -1.05148597694067,
0.295702769469608, 95, -1.01560395843854, 0.312287650638537, "FALSE"),
repeated = list("1 - 2", 0.0453504115999998, 0.334439223738541, 0.135601354090733,
0.892423350599014, 95, -0.618595392938537, 0.709296216138537, "TRUE", "2 - 3", -0.52663769325, 0.334439223738541,
-1.57468878011066, 0.118652819171739, 95, -1.19058349778854, 0.137308111288537, "FALSE", "3 - 4", 0.5006129861,
0.334439223738541, 1.49687282641037, 0.137741463128169, 95, -0.163332818438537, 1.16455879063854, "FALSE",
"4 - 5", -0.3516581539, 0.334439223738541, -1.05148597694067,
0.295702769469608, 95, -1.01560395843854, 0.312287650638537, "FALSE"),
polynomial = list("linear", 0.218415231132241, 0.236484243000287, 0.923593167820386,
0.358038105335522, 95, -0.251065349597317, 0.687895811861799, "TRUE", "quadratic", -0.0623342877876619,
0.236484243000287, -0.263587488945664, 0.792668695639493, 95, -0.53181486851722, 0.407146292941896, "FALSE",
"cubic", 0.0886332780579033, 0.236484243000287, 0.374795702806278,
0.70864779281998, 95, -0.380847302671655, 0.558113858787461, "FALSE", "quartic", 0.415791419838834, 0.236484243000287,
1.75822039795831, 0.0819306308915546, 95, -0.0536891608907235, 0.885272000568391, "FALSE")
)
contrasts <- c("deviation", "simple", "difference", "Helmert", "repeated", "polynomial")
for (contrast in contrasts) {
options$contrasts <- list(list(contrast=contrast, variable="facFive"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
# table <- results[["results"]][["contrasts"]][["collection"]][[1]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[contrast]], label=paste("Table with contrast", contrast))
}
})
test_that("Post Hoc table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$postHocTypeStandardEffectSize <- TRUE
options$postHocCorrectionBonferroni <- TRUE
options$postHocCorrectionHolm <- TRUE
options$postHocCorrectionScheffe <- TRUE
options$postHocCorrectionTukey <- TRUE
options$postHocCorrectionSidak <- TRUE
options$postHocCi <- TRUE
options$postHocTerms <- "contBinom"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_contBinom$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.214904085649005, 0.448976320466698, 0.15401876311258,
-0.24864690950018, 0.55668443572534, 0, 1, 0.163364220743842,
0.448976320466698, -0.263105943067512, 0.448976320466698, 0.448976320466698,
0.760172707980336, 0.448976320466698, 0.589834384555196)
)
})
test_that("Marginal Means table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$marginalMeanComparedToZero <- TRUE
options$marginalMeanTerms <- "contBinom"
# added df to contrast table
refTables <- list(
none = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE"),
Bonferroni = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE"),
Sidak = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE")
)
for (adjustment in c("none", "Bonferroni", "Sidak")) {
options$marginalMeanCiCorrection <- adjustment
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[adjustment]], label=paste("Table with CI adjustment", adjustment))
}
})
test_that("Descriptives table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$descriptives <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
# removed new group booleans
jaspTools::expect_equal_tables(table,
list(-0.120135614827586, 58, 1.10575982846952, 0.145193378675912, -9.20426328242848,
0, -0.283499835571429, 42, 0.994612407217046, 0.15347202634745,
-3.50833504087324, 1))
})
test_that("Descriptives table respects factor order", {
# tests https://github.com/jasp-stats/jasp-issues/issues/1741
# test 1: one-way ANOVA
set.seed(42)
dat <- data.frame(
x = rnorm(100),
group = sample(c("Sedentary", "Low", "Medium", "High"), 100 , TRUE)
)
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "x"
options$fixedFactors <- "group"
options$modelTerms <- list(list(components = "group"))
options$descriptives <- TRUE
# change the level order
levels1 <- c("Medium", "Sedentary", "Low", "High")
dat$group <- factor(dat$group, levels = levels1)
result1 <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb1 <- result1$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
rowNms1 <- sapply(tb1, `[[`, "group.")
expect_identical(rowNms1, levels1)
# change the level order
levels2 <- c("Sedentary", "Low", "Medium", "High")
dat$group <- factor(dat$group, levels = levels2)
result2 <- runAnalysis(name = "Anova", options = options, dataset = dat, makeTests = FALSE)
tb2 <- result2$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
rowNms2 <- sapply(tb2, `[[`, "group.")
expect_identical(rowNms2, levels2)
# test 2: three-way ANOVA
set.seed(3141593)
dat <- data.frame(
x = rnorm(20),
group1 = factor(sample(letters[10:13], 100 , TRUE)),
group2 = factor(sample(letters[1:3], 100 , TRUE)),
group3 = factor(sample(LETTERS[1:5], 100 , TRUE))
)
# alphabetic level order
set.seed(2718282)
for (group in 1:3) {
colName <- sprintf("group%d", group)
levels(dat[[colName]]) <- sort(levels(dat[[colName]]))
}
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "x"
options$fixedFactors <- paste0("group", 1:3)
options$modelTerms <- list(list(components = "group1"), list(components = "group2"), list(components = "group3"))
options$descriptives <- TRUE
result <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb <- result$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
for (group in 1:3) {
rowNmsGroup <- sapply(tb, `[[`, sprintf("group%d.", group))
rowNmsGroup <- rowNmsGroup[!duplicated(rowNmsGroup)]
levelsGroup <- levels(dat[[sprintf("group%d", group)]])
expect_identical(rowNmsGroup, levelsGroup)
}
# scramble level order
set.seed(2718282)
for (group in 1:3) {
colName <- sprintf("group%d", group)
levels(dat[[colName]]) <- sample(levels(dat[[colName]]))
}
result <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb <- result$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
for (group in 1:3) {
rowNmsGroup <- sapply(tb, `[[`, sprintf("group%d.", group))
rowNmsGroup <- rowNmsGroup[!duplicated(rowNmsGroup)]
levelsGroup <- levels(dat[[sprintf("group%d", group)]])
expect_identical(rowNmsGroup, levelsGroup)
}
})
test_that("Q-Q plot matches", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$qqPlot <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "q-q")
})
test_that("Descriptives plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$descriptivePlotHorizontalAxis <- "contBinom"
options$descriptivePlotSeparateLines <- "facFive"
options$descriptivePlotErrorBar <- TRUE
options$descriptivePlotCiLevel <- 0.90
options$descriptivePlotErrorBarType <- "ci"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "descriptives-ci")
options$descriptivePlotErrorBarType <- "se"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "descriptives-se")
})
test_that("Bar plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$barPlotHorizontalAxis <- "contBinom"
options$barPlotSeparatePlots <- "facFive"
options$barPlotHorizontalZeroFix <- TRUE
options$barPlotErrorBars <- TRUE
options$barPlotCiInterval <- 0.90
options$barPlotErrorBarType <- "confidenceInterval"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "barPlot-ci")
options$barPlotErrorBarType <- "standardError"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "barPlot-se")
})
test_that("Raincloud plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$rainCloudHorizontalAxis <- "contBinom"
options$rainCloudSeparatePlots <- "facFive"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "raincloud-plots-vertical")
options$rainCloudHorizontalDisplay <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "raincloud-plots-horizontal")
})
test_that("Simple Main Effects table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive")
)
options$simpleMainEffectFactor <- "facExperim"
options$simpleMainEffectModeratorFactorOne <- "facFive"
options$simpleMainEffectModeratorFactorTwo <- ""
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Anova", "debug.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_simpleEffectsContainer$collection$anovaContainer_simpleEffectsContainer_simpleEffectsTable$data
jaspTools::expect_equal_tables(table, list(1, 0.350864897951646, 1, 0.350864897951646, 0.310783783968887,
0.578524772558188, "TRUE", 2, 2.72259751707838, 1, 2.72259751707838,
2.41158110578085, 0.123801175704108, "FALSE", 3, 0.300954391532799,
1, 0.300954391532799, 0.266574813122249, 0.606851206017453,
"FALSE", 4, 3.47907983036715, 1, 3.47907983036715, 3.08164652754846,
0.0824380354608798, "FALSE", 5, 0.313611321775938, 1, 0.313611321775938,
0.27778587668933, 0.599397784945329, "FALSE"))
})
test_that("Nonparametric table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$kruskalWallisFactors <- c( "facFive", "facExperim")
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive")
)
options$kruskalEffectSizeEstimates <- TRUE
options$kruskalEpsilon <- TRUE
options$kruskalEta <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_kruskalContainer$collection$anovaContainer_kruskalContainer_kruskalTable$data
jaspTools::expect_equal_tables(table,
list(4, 0.034303030303031, 0, "facFive", 0.011115645383478, 0, 0.493866894607854,
3.39600000000007, 0.193550362611566, 0.14617504991631, 1, 0.0103733573357337,
0.000275126288139123, "facExperim", 1.21480398103053e-05, 0,
0.310873187457312, 1.02696237623763, 0.0878974178624286, 0.0792208198923551)
)
})
# Error handling ----
test_that("Analysis handles errors", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "debInf"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Infinity found in debInf</li></ul>",
label="Inf dependent check")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$wlsWeights <- "debInf"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Infinity found in debInf</li></ul>",
label="Inf WLS weights check")
options$wlsWeights <- ""
options$dependent <- "contNormal"
options$fixedFactors <- "debSame"
options$modelTerms <- list(list(components="debSame"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Number of factor levels is < 2 in debSame</li></ul>",
label="1-level factor check")
options$dependent <- "contGamma"
options$fixedFactors <- "facFive"
options$wlsWeights <- "contNormal"
options$modelTerms <- list(list(components="facFive"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>The WLS weights contain negative and/or zero values.<br><br>(only positive WLS weights allowed).</li></ul>",
label="Negative WLS weights check")
})
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Species"))
options$customContrasts <- list()
options$dependent <- "Sepal.Length"
options$fixedFactors <- "Species"
options$modelTerms <- list(list(components = "Species"))
options$postHocTerms <- list(list(variable = "Species"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
# dataset is created from:
# dd <- read.csv("~/github/jasp-desktop/Resources/Data Sets/Data Library/10. Machine Learning/iris.csv")
# newLabels <- c("First species", "Second species", "Third species")
# oldLabels <- unique(dd$Species)
# dd$Species <- newLabels[match(dd$Species, oldLabels)]
# dataset <- dd[c(1:5, 51:55, 101:105), ]
dataset <- data.frame(
Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 7, 6.4, 6.9, 5.5, 6.5, 6.3, 5.8, 7.1, 6.3, 6.5),
Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.2, 3.2, 3.1, 2.3, 2.8, 3.3, 2.7, 3, 2.9, 3),
Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 6, 5.1, 5.9, 5.6, 5.8),
Petal.Width = c(0.2, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 2.5, 1.9, 2.1, 1.8, 2.2),
Species = rep(c("First species", "Second species", "Third species"), each = 5)
)
set.seed(1)
results <- jaspTools::runAnalysis("Anova", dataset, options)
test_that("Post Hoc Comparisons - Species table results match and contrast names do not contain commas", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_postHocContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer_Species1"]][["data"]]
jaspTools::expect_equal_tables(table,
list("TRUE", 0.286589136802729, "First species", "Second species",
-1.6, -5.58290526239083, 0.000324812090923943, "FALSE", 0.286589136802729,
"First species", "Third species", -1.54, -5.37354631505117,
0.000453440993214982, "FALSE", 0.286589136802729, "Second species",
"Third species", 0.0599999999999998, 0.209358947339655, 0.976174158311121
))
# following https://github.com/jasp-stats/jasp-issues/issues/1295, assert that these names do not contain commas.
contrast_A <- vapply(table, `[[`, "contrast_A", FUN.VALUE = character(1L))
contrast_B <- vapply(table, `[[`, "contrast_B", FUN.VALUE = character(1L))
expect_identical(contrast_A, c("First species", "First species", "Second species"))
expect_identical(contrast_B, c("Second species", "Third species", "Third species"))
})
# Andy Field Tests ----
# should we put this in verification project?
## Chapter 4 -----
test_that("Field - Chapter 4 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$modelTerms <- list(list(components = "Dose"))
options$homogeneityCorrections <- TRUE
options$homogeneityCorrectionBrown <- TRUE
options$homogeneityCorrectionWelch <- TRUE
results <- jaspTools::runAnalysis("Anova", "Puppies Dummy.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
# this table is also in the chapter 5
jaspTools::expect_equal_tables(table,
list("TRUE", 2, 5.11864406779661, 10.0666666666667, 0.0246942895382225,
20.1333333333333, "Dose", "None", "TRUE", 12, "", 1.96666666666667,
"", 23.6, "Residuals", "", "TRUE", 2, 5.11864406779661, 10.0666666666667,
0.0255514729692058, 20.1333333333333, "Dose", "Brown-Forsythe",
"TRUE", 11.5743973399834, "", 2.03898305084746, "", 23.6, "Residuals",
"", "TRUE", 2, 4.32045117281357, 10.0666666666667, 0.0537384707116728,
20.1333333333333, "Dose", "Welch", "TRUE", 7.94337535943375,
"", 2.97102918244598, "", 23.6, "Residuals", ""))
})
## Chapter 5 ----
test_that("Field - Chapter 5 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$modelTerms <- list(list(components = "Dose"))
options$contrasts <- list(
list(contrast = "Helmert", variable = "Dose")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Dose"
options$postHocTypeGames <- TRUE
options$postHocTypeDunnet <- TRUE
options$postHocCi <- TRUE
results <- jaspTools::runAnalysis("Anova", "Puppies Dummy.csv", options)
# contrast
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("1 - 2, 3", -1.9, 0.768114574786861, -2.47358930863565, 0.0293002196554282,
12, -3.5735778902, -0.2264221098, "TRUE", "2 - 3", -1.8, 0.886942313043338,
-2.02944427560764, 0.0651922067570462, 12, -3.73248129083355,
0.132481290833552, "FALSE"))
# standard post hoc (tukey)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.886942313043338, 1, 2, -1, -3.36624115850686, -1.12746904200424,
0.516276123508473, 1.36624115850686, "FALSE", 0.886942313043338,
1, 3, -2.8, -5.16624115850687, -3.15691331761188, 0.0209243994922408,
-0.433758841493136, "FALSE", 0.886942313043338, 2, 3, -1.8,
-4.16624115850686, -2.02944427560764, 0.147457622995377, 0.566241158506864
))
# games-howell post hoc
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocGamesContainer$collection$anovaContainer_postHocContainer_postHocGamesContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("1 - 2", -1, -1.21267812518166, 0.824621125123532, -3.3563089273419,
1.3563089273419, 0.47896489393065, 8, "1 - 3", -2.8, -3.05505046330389,
0.916515138991168, -5.43893919399355, -0.161060806006447, 0.0388414107946456,
7.7199124726477, "2 - 3", -1.8, -1.96396101212393, 0.916515138991168,
-4.43893919399355, 0.838939193993553, 0.185393344481167, 7.7199124726477))
# dunnet post hoc
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocDunnettContainer$collection$anovaContainer_postHocContainer_postHocDunnettContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("2 - 1", 1, 1.12746904200424, 0.886942313043338, 0.445888579780104,
-1.21963511399532, 3.21963511399532, "3 - 1", 2.8, 3.15691331761188,
0.886942313043338, 0.0152377020148067, 0.580364886004677, 5.01963511399532))
})
## Chapter 7 ----
test_that("Field - Chapter 7 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Attractiveness"
options$fixedFactors <- list("FaceType", "Alcohol")
options$modelTerms <- list(
list(components = "FaceType"),
list(components = "Alcohol"),
list(components = c("FaceType", "Alcohol"))
)
options$contrasts <- list(
list(contrast = "none", variable = "FaceType"),
list(contrast = "Helmert", variable = "Alcohol")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Alcohol"
options$postHocCorrectionBonferroni <- TRUE
options$postHocCi <- TRUE
options$postHocTypeStandardBootstrap <- TRUE
options$postHocTypeStandardBootstrapSamples <- 500
options$marginalMeanTerms <- list(
list(components = c("FaceType", "Alcohol"))
)
options$marginalMeanBootstrap <- TRUE
options$marginalMeanBootstrapSamples <- 500
options$marginalMeanComparedToZero <- FALSE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "Beer Goggles.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("FaceType", 21.3333333333334, 1, 21.3333333333334, 15.5826086956522,
0.000295223592290028, "TRUE", "Alcohol", 16.5416666666666, 2,
8.27083333333331, 6.04130434782607, 0.00494338949698304, "FALSE",
"FaceType <unicode> Alcohol", 23.2916666666667, 2, 11.6458333333333,
8.50652173913045, 0.000791273868880283, "TRUE", "Residuals",
57.5, 42, 1.36904761904762, "", "", "TRUE"))
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("0 - 1, 2", -1.09375, 0.358257190138194, -3.05297431596026, 0.003921402019941,
42, -1.81674228032104, -0.37075771967896, "TRUE", "1 - 2", -0.6875,
0.413679770330811, -1.66191351211161, 0.103977316507, 42, -1.52233957533075,
0.147339575330745, "FALSE"))
# removed both post hoc table and contrast table because bootstrap results are now in same table
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Alcohol$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.392000159227314, -0.00376914898826786, 0.230950085511107,
0, 1, -0.769579725829724, -1.48733254329573, -1.8129965586672,
0.177726007657148, 0.0548973391001529, "FALSE", 0.435378856593833,
0.0142934574412505, 0.00359956767679779, 0, 2, -1.43536324786325,
-2.31465226049576, -3.47491007077881, 0.00337043014651417, -0.602581004497449,
"FALSE", 0.407611902363181, 0.0180626064295184, 0.311931949521,
1, 2, -0.690674603174603, -1.43846891737073, -1.66191351211161,
0.231712504393661, 0.214248742598683))
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0, 0, 0.609481794897967, -0.000576109837875816, 2.2, 3.50000000000001,
4.74689776796074, "FALSE", 0, 1, 0.319660323889613, -0.0167382728382766,
5.88388102664993, 6.33333333333334, 7.17856492366397, "TRUE",
1, 0, 0.449084420277268, -0.0233288832082961, 4.00000000000001,
4.8397435897436, 6.00000000000001, "FALSE", 1, 1, 0.320922728447884,
-0.0138867382617391, 5.85714285714286, 6.5, 7.125,
"TRUE", 2, 0, 0.388994937578588, -0.0390126855170978, 6, 6.57142857142858,
7.5, "FALSE", 2, 1, 0.395829403830131, 0.0118089133089141, 5.4,
6.14285714285715, 7))
})
test_that("ANOVA - factor level with zero variance works and Welch homogeneity correction shows a footnote", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "value"
options$fixedFactors <- "group"
options$homogeneityCorrectionWelch <- TRUE
options$modelTerms <- list(list(components = "group"))
dataset <- data.frame(
group = rep(c("A","B","C","D"), c(3, 4, 3, 5)),
value = c(2.3, 2.1, 2.8, rep(3, 4), c(4, 3, 5, 4, 2), 3.6, 4.5, 5.1)
)
set.seed(1)
results <- jaspTools::runAnalysis("Anova", dataset, options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("TRUE", 3, 2.67905056759546, 1.888, 0.0985227092069956, 5.664,
"group", "None", "TRUE", 11, "", 0.704727272727272, "", 7.752,
"Residuals", "", "TRUE", 3, "NaN", 1.888, "NaN", 5.664, "group",
"Welch", "TRUE", "NaN", "", "NaN", "", 7.752, "Residuals",
""))
footnote <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["footnotes"]][[1L]][["text"]]
testthat::expect_identical(
footnote,
"The Welch correction could not be computed because 'value' has zero variance after grouping on the following level(s) of 'group': B"
)
})
# Ordinal restrictions ----
# Tests from https://restriktor.org
## Basic model ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Age"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- TRUE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 1", summary = TRUE, syntax = "GroupActive < GroupPassive\nGroupPassive < GroupNo"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
dataset <- read.csv("ZelazoKolb1972.csv")
dataset <- subset(dataset, Group != "Control")
results <- jaspTools::runAnalysis("Anova", dataset, options)
### see https://restriktor.org/tutorial/restriktor.html for comparison ----
test_that("Ordinal restrictions: Likelihood, penalty and GORIC match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(64.7533269649054, 1, -29.5348420070337, "Model 1", 2.84182147541904
))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.59384623700347e-10, 0.618538022852505,
16.3692442920593, "GroupNo", 12.35, 3.77071859965597e-11, 0.677574455581925,
18.2267792096639, "GroupPassive", 11.375, 3.34358875477653e-11,
0.618538022852504, 18.3901386491037))
})
### see https://restriktor.org/tutorial/contest.html for comparison ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.028341275692807, 5.97803555494542, "F", "Type global", 0.0276478338044095,
6.04479400936707, "LRT", "Type global", 0.044864172884687, 4.78768638768638,
"Score", "Type global", 0.028341275692807, 5.97803555494542,
"F", "Type A", 0.0276478338044095, 6.04479400936707, "LRT",
"Type A", 0.044864172884687, 4.78768638768638, "Score", "Type A",
1, 0, "F", "Type B", 1, -7.105427357601e-15, "LRT", "Type B",
1, 0, "Score", "Type B", 0.152944344084674, 1.0627396241062,
"t", "Type C"))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1, 0, 1, 0, 0, 1, -1, 0))
})
## Ordered means and with effect sizes ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Age"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 1", summary = TRUE, syntax = "GroupActive < GroupPassive\nGroupPassive < GroupControl\nGroupControl < GroupNo"),
list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 2", summary = TRUE, syntax = "(GroupPassive - GroupActive) / 1.516 > 0.2\n(GroupControl - GroupPassive) / 1.516 > 0.2\n(GroupNo - GroupControl) / 1.516 > 0.2"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "ZelazoKolb1972.csv", options)
### see https://restriktor.org/tutorial/example1.html for comparison ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0308475934292769, 6.42666580972066, "F", "Type global", 0.0278151519017313,
6.70126802148546, "LRT", "Type global", 0.0431126887497895,
5.56056578050442, "Score", "Type global", 0.0308475934292769,
6.42666580972066, "F", "Type A", 0.0278151519017313, 6.70126802148546,
"LRT", "Type A", 0.0431126887497895, 5.56056578050442, "Score",
"Type A", 1, 0, "F", "Type B", 1, 0, "LRT", "Type B", 1, 0,
"Score", "Type B", 0.35381027878063, 0.380738874434879, "t",
"Type C"))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.19077449127484e-12, 0.619065391589877,
16.3552996784348, "GroupControl", 11.7083333333333, 8.7743489822692e-14,
0.619065391589877, 18.91291855819, "GroupNo", 12.35, 1.73632870169313e-13,
0.678152159089088, 18.2112522012594, "GroupPassive", 11.375,
1.4783390885313e-13, 0.619065391589877, 18.3744724782415))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1, 0, 0, 1, 0, 0, 1, 0, -1, 0, 0, -1, 1, 0, 0))
})
### see https://restriktor.org/tutorial/example3.html for comparison
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.168856977319199, 2.38400146304897, "F", "Type global", 0.144342629920537,
2.71869356602637, "LRT", "Type global", 0.163454084273826, 2.45267623450673,
"Score", "Type global", 0.168856977319199, 2.38400146304897,
"F", "Type A", 0.144342629920537, 2.71869356602637, "LRT", "Type A",
0.163454084273826, 2.45267623450673, "Score", "Type A", 1, 0,
"F", "Type B", 1, 0, "LRT", "Type B", 1, 0, "Score", "Type B",
0.48645111069787, 0.0344187942489155, "t", "Type C"))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.19077449127485e-12, 0.619065391589877,
16.3552996784348, "GroupControl", 11.7083333333333, 8.77434898226892e-14,
0.619065391589876, 18.91291855819, "GroupNo", 12.35, 1.73632870169313e-13,
0.678152159089088, 18.2112522012594, "GroupPassive", 11.375,
1.47833908853122e-13, 0.619065391589875, 18.3744724782416))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.659630606860158, 0, 0, 0.659630606860158, 0.2, 0, 0.659630606860158,
0, -0.659630606860158, 0.2, 0, -0.659630606860158, 0.659630606860158,
0, 0.2))
})
### see https://restriktor.org/tutorial/example6.html for comparison ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Anger"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "unconstrained"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 2"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 1", summary = FALSE, syntax = "GroupNo = GroupPhysical\nGroupPhysical = GroupBehavioral\nGroupBehavioral = GroupBoth"),
list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 2", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical = GroupBehavioral\nGroupBehavioral < GroupBoth"),
list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 3", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical < GroupBehavioral\nGroupBehavioral < GroupBoth"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "AngerManagement.csv", options)
test_that("Ordinal restrictions: Model Comparison Table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(190.802338005453, 6.15799536281089e-06, -93.4011690027263, "Model 1",
2, 174.107875497759, 0.025977408886342, -84.1621111968489, "Model 2",
2.89182655203061, 167.133864718034, 0.849147292951048, -80.4838987099312,
"Model 3", 3.08303364908579, 170.967797419862, 0.124869140167247,
-80.4838987099312, "Unconstrained", 5))
})
jasp-stats/jaspAnova documentation built on June 14, 2024, 6:48 p.m.
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context("ANOVA")
# does not test
# - if analysis handles too few observations
# Main results ----
test_that("Main table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$contrasts <- list(
list(contrast="none", variable="facFive"),
list(contrast="none", variable="contBinom")
)
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$vovkSellke <- TRUE
refTables <- list(
type1 = list("facFive", 181.151987151139, 4, 45.2879967877848, 1.82424792091397,
0.131040166930968, "TRUE", 0.0733818170125722, 0.0749970945389635,
0.0328259131508147, 1.3814133611929, "contBinom", 24.5000310074981,
1, 24.5000310074981, 0.98688689714382, 0.323167856084615, "FALSE",
0.00992457670748362, 0.0108464739348588, 0, 1.00776449783097,
"facFive <unicode> contBinom", 28.6688295533663, 4, 7.16720738834158,
0.288702616682483, 0.884618241388975, "TRUE", 0.011613291343553,
0.0126686727866262, 0, 1, "Residuals", 2234.30141494065, 90,
24.8255712771184, "", "", "TRUE", "", "", "", ""),
type2 = list("facFive", 174.090039449647, 4, 43.5225098624118, 1.75313225933803,
0.145323959824188, "TRUE", 0.0707234506472374, 0.0722847770997921,
0.0300789007673393, 1.31245187936232, "contBinom", 24.5000310074979,
1, 24.5000310074979, 0.986886897143812, 0.323167856084617, "FALSE",
0.00995304923413341, 0.0108464739348588, 0, 1.00776449783097,
"facFive <unicode> contBinom", 28.6688295533663, 4, 7.16720738834158,
0.288702616682483, 0.884618241388975, "TRUE", 0.0116466086080589,
0.0126686727866262, 0, 1, "Residuals", 2234.30141494066, 90,
24.8255712771184, "", "", "TRUE", "", "", "", ""),
type3 = list("TRUE", 4, 1.66677534088084, 41.3786500279805, 0.164641950089634,
165.514600111922, "facFive", 0.0675231783920824, 0.0267410840853991,
0.0689697039580327, 1.23860485419559, "FALSE", 1, 0.9160552247026,
22.7415942746311, 0.341076850898726, 22.7415942746311, "contBinom",
0.00927763910910521, 0, 0.0100758355874387, 1.00272840654715,
"TRUE", 4, 0.288702616682483, 7.16720738834158, 0.884618241388975,
28.6688295533663, "facFive <unicode> contBinom", 0.0116957083599583,
0, 0.0126686727866262, 1, "TRUE", 90, "", 24.8255712771184,
"", 2234.30141494065, "Residuals", "", "", "", "")
)
for (type in c("type1", "type2", "type3")) {
options$sumOfSquares <- type
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[type]], label=paste("Table with SS", type))
}
})
# Additional results ----
test_that("Homogeneity of Variances table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$modelTerms <- list(list(components="facExperim"))
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_assumptionsContainer"]][["collection"]][["anovaContainer_assumptionsContainer_leveneTable"]][["data"]]
jaspTools::expect_equal_tables(table, list(3.1459013381035, 1, 98, 0.0792241296904395, 1.83142365040653))
})
# Contrasts verified with SPSS
# should we put this in verification project???
test_that("Contrasts table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$contrastCi <- TRUE
options$modelTerms <- list(list(components="facFive"))
refTables <- list(
deviation = list("2 - 1, 2, 3, 4, 5", -0.19513913461, 0.211517937182489, -0.922565420263351, 0.358570880186821,
95, -0.615055331660948, 0.224777062440949, "TRUE", "3 - 1, 2, 3, 4, 5", 0.331498558639999,
0.211517937182488, 1.56723615526752, 0.120384543718471, 95, -0.0884176384109483, 0.751414755690946,
"FALSE", "4 - 1, 2, 3, 4, 5", -0.16911442746, 0.211517937182488, -0.799527594267789, 0.425979159402386,
95, -0.589030624510948, 0.250801769590948, "FALSE", "5 - 1, 2, 3, 4, 5", 0.18254372644, 0.211517937182488,
0.863017713162119, 0.390301247101766, 95, -0.237372470610948, 0.602459923490948, "FALSE"),
simple = list("2 - 1", -0.0453504116000002, 0.334439223738541, -0.135601354090735,
0.892423350599012, 95, -0.709296216138537, 0.618595392938537, "TRUE", "3 - 1", 0.48128728165, 0.334439223738541,
1.43908742601993, 0.153412521131058, 95, -0.182658522888538, 1.14523308618854, "FALSE", "4 - 1", -0.0193257044500001,
0.334439223738541, -0.0577854003904418, 0.954040939034334, 95, -0.683271508988538, 0.644620100088538, "FALSE",
"5 - 1", 0.33233244945, 0.334439223738541, 0.993700576550232,
0.322892982956191, 95, -0.331613355088537, 0.996278253988537, "FALSE"),
difference = list("2 - 1", -0.0453504116, 0.334439223738541, -0.135601354090734,
0.892423350599013, 95, -0.709296216138538, 0.618595392938538,"TRUE", "3 - 1, 2", 0.50396248745, 0.289632863779524,
1.74000450388679, 0.0850966309951808, 95, -0.0710314460164715, 1.07895642091647, "FALSE", "4 - 1, 2, 3",
-0.164637994466667, 0.273068482710641, -0.602918333278054, 0.547999665617461,
95, -0.706747473793691, 0.377471484860357, "FALSE", "5 - 1, 2, 3, 4", 0.22817965805, 0.26439742147811,
0.86301771316212, 0.390301247101765, 95, -0.296715588263685, 0.753074904363685, "FALSE"),
Helmert = list("1 - 2, 3, 4, 5", -0.1872359037625, 0.26439742147811, -0.708160853898499,
0.480579313597282, 95, -0.712131150076185, 0.337659342551185, "TRUE", "2 - 3, 4, 5", -0.31011508715, 0.273068482710641,
-1.13566781516348, 0.25895260195633, 95, -0.852224566477025, 0.231994392177024, "FALSE", "3 - 4, 5", 0.32478390915,
0.289632863779524, 1.12136414670551, 0.264959410721321, 95, -0.250210024316471, 0.899777842616471, "FALSE",
"4 - 5", -0.3516581539, 0.334439223738541, -1.05148597694067,
0.295702769469608, 95, -1.01560395843854, 0.312287650638537, "FALSE"),
repeated = list("1 - 2", 0.0453504115999998, 0.334439223738541, 0.135601354090733,
0.892423350599014, 95, -0.618595392938537, 0.709296216138537, "TRUE", "2 - 3", -0.52663769325, 0.334439223738541,
-1.57468878011066, 0.118652819171739, 95, -1.19058349778854, 0.137308111288537, "FALSE", "3 - 4", 0.5006129861,
0.334439223738541, 1.49687282641037, 0.137741463128169, 95, -0.163332818438537, 1.16455879063854, "FALSE",
"4 - 5", -0.3516581539, 0.334439223738541, -1.05148597694067,
0.295702769469608, 95, -1.01560395843854, 0.312287650638537, "FALSE"),
polynomial = list("linear", 0.218415231132241, 0.236484243000287, 0.923593167820386,
0.358038105335522, 95, -0.251065349597317, 0.687895811861799, "TRUE", "quadratic", -0.0623342877876619,
0.236484243000287, -0.263587488945664, 0.792668695639493, 95, -0.53181486851722, 0.407146292941896, "FALSE",
"cubic", 0.0886332780579033, 0.236484243000287, 0.374795702806278,
0.70864779281998, 95, -0.380847302671655, 0.558113858787461, "FALSE", "quartic", 0.415791419838834, 0.236484243000287,
1.75822039795831, 0.0819306308915546, 95, -0.0536891608907235, 0.885272000568391, "FALSE")
)
contrasts <- c("deviation", "simple", "difference", "Helmert", "repeated", "polynomial")
for (contrast in contrasts) {
options$contrasts <- list(list(contrast=contrast, variable="facFive"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
# table <- results[["results"]][["contrasts"]][["collection"]][[1]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[contrast]], label=paste("Table with contrast", contrast))
}
})
test_that("Post Hoc table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$postHocTypeStandardEffectSize <- TRUE
options$postHocCorrectionBonferroni <- TRUE
options$postHocCorrectionHolm <- TRUE
options$postHocCorrectionScheffe <- TRUE
options$postHocCorrectionTukey <- TRUE
options$postHocCorrectionSidak <- TRUE
options$postHocCi <- TRUE
options$postHocTerms <- "contBinom"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_contBinom$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.214904085649005, 0.448976320466698, 0.15401876311258,
-0.24864690950018, 0.55668443572534, 0, 1, 0.163364220743842,
0.448976320466698, -0.263105943067512, 0.448976320466698, 0.448976320466698,
0.760172707980336, 0.448976320466698, 0.589834384555196)
)
})
test_that("Marginal Means table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$marginalMeanComparedToZero <- TRUE
options$marginalMeanTerms <- "contBinom"
# added df to contrast table
refTables <- list(
none = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE"),
Bonferroni = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE"),
Sidak = list(0, -0.120135614827586, 0.139273765411964, -0.396519869554477, 98,
0.156248639899304, -0.862586104943973, 0.390471041862811, "TRUE",
1, -0.283499835571429, 0.163666075582597, -0.608289835972217, 98,
0.0412901648293597, -1.73218447721841, 0.0863869418751253, "FALSE")
)
for (adjustment in c("none", "Bonferroni", "Sidak")) {
options$marginalMeanCiCorrection <- adjustment
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[adjustment]], label=paste("Table with CI adjustment", adjustment))
}
})
test_that("Descriptives table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$descriptives <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
# removed new group booleans
jaspTools::expect_equal_tables(table,
list(-0.120135614827586, 58, 1.10575982846952, 0.145193378675912, -9.20426328242848,
0, -0.283499835571429, 42, 0.994612407217046, 0.15347202634745,
-3.50833504087324, 1))
})
test_that("Descriptives table respects factor order", {
# tests https://github.com/jasp-stats/jasp-issues/issues/1741
# test 1: one-way ANOVA
set.seed(42)
dat <- data.frame(
x = rnorm(100),
group = sample(c("Sedentary", "Low", "Medium", "High"), 100 , TRUE)
)
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "x"
options$fixedFactors <- "group"
options$modelTerms <- list(list(components = "group"))
options$descriptives <- TRUE
# change the level order
levels1 <- c("Medium", "Sedentary", "Low", "High")
dat$group <- factor(dat$group, levels = levels1)
result1 <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb1 <- result1$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
rowNms1 <- sapply(tb1, `[[`, "group.")
expect_identical(rowNms1, levels1)
# change the level order
levels2 <- c("Sedentary", "Low", "Medium", "High")
dat$group <- factor(dat$group, levels = levels2)
result2 <- runAnalysis(name = "Anova", options = options, dataset = dat, makeTests = FALSE)
tb2 <- result2$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
rowNms2 <- sapply(tb2, `[[`, "group.")
expect_identical(rowNms2, levels2)
# test 2: three-way ANOVA
set.seed(3141593)
dat <- data.frame(
x = rnorm(20),
group1 = factor(sample(letters[10:13], 100 , TRUE)),
group2 = factor(sample(letters[1:3], 100 , TRUE)),
group3 = factor(sample(LETTERS[1:5], 100 , TRUE))
)
# alphabetic level order
set.seed(2718282)
for (group in 1:3) {
colName <- sprintf("group%d", group)
levels(dat[[colName]]) <- sort(levels(dat[[colName]]))
}
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "x"
options$fixedFactors <- paste0("group", 1:3)
options$modelTerms <- list(list(components = "group1"), list(components = "group2"), list(components = "group3"))
options$descriptives <- TRUE
result <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb <- result$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
for (group in 1:3) {
rowNmsGroup <- sapply(tb, `[[`, sprintf("group%d.", group))
rowNmsGroup <- rowNmsGroup[!duplicated(rowNmsGroup)]
levelsGroup <- levels(dat[[sprintf("group%d", group)]])
expect_identical(rowNmsGroup, levelsGroup)
}
# scramble level order
set.seed(2718282)
for (group in 1:3) {
colName <- sprintf("group%d", group)
levels(dat[[colName]]) <- sample(levels(dat[[colName]]))
}
result <- runAnalysis(name = "Anova", options = options, dataset = dat)
tb <- result$results$anovaContainer$collection$anovaContainer_descriptivesContainer$collection$anovaContainer_descriptivesContainer_tableDescriptives$data
for (group in 1:3) {
rowNmsGroup <- sapply(tb, `[[`, sprintf("group%d.", group))
rowNmsGroup <- rowNmsGroup[!duplicated(rowNmsGroup)]
levelsGroup <- levels(dat[[sprintf("group%d", group)]])
expect_identical(rowNmsGroup, levelsGroup)
}
})
test_that("Q-Q plot matches", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
options$qqPlot <- TRUE
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "q-q")
})
test_that("Descriptives plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$descriptivePlotHorizontalAxis <- "contBinom"
options$descriptivePlotSeparateLines <- "facFive"
options$descriptivePlotErrorBar <- TRUE
options$descriptivePlotCiLevel <- 0.90
options$descriptivePlotErrorBarType <- "ci"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "descriptives-ci")
options$descriptivePlotErrorBarType <- "se"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "descriptives-se")
})
test_that("Bar plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$barPlotHorizontalAxis <- "contBinom"
options$barPlotSeparatePlots <- "facFive"
options$barPlotHorizontalZeroFix <- TRUE
options$barPlotErrorBars <- TRUE
options$barPlotCiInterval <- 0.90
options$barPlotErrorBarType <- "confidenceInterval"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "barPlot-ci")
options$barPlotErrorBarType <- "standardError"
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "barPlot-se")
})
test_that("Raincloud plots match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom")
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components=c("facFive", "contBinom"))
)
options$rainCloudHorizontalAxis <- "contBinom"
options$rainCloudSeparatePlots <- "facFive"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "raincloud-plots-vertical")
options$rainCloudHorizontalDisplay <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
testPlot <- results$state$figures[[1]]$obj
jaspTools::expect_equal_plots(testPlot, "raincloud-plots-horizontal")
})
test_that("Simple Main Effects table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive")
)
options$simpleMainEffectFactor <- "facExperim"
options$simpleMainEffectModeratorFactorOne <- "facFive"
options$simpleMainEffectModeratorFactorTwo <- ""
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Anova", "debug.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_simpleEffectsContainer$collection$anovaContainer_simpleEffectsContainer_simpleEffectsTable$data
jaspTools::expect_equal_tables(table, list(1, 0.350864897951646, 1, 0.350864897951646, 0.310783783968887,
0.578524772558188, "TRUE", 2, 2.72259751707838, 1, 2.72259751707838,
2.41158110578085, 0.123801175704108, "FALSE", 3, 0.300954391532799,
1, 0.300954391532799, 0.266574813122249, 0.606851206017453,
"FALSE", 4, 3.47907983036715, 1, 3.47907983036715, 3.08164652754846,
0.0824380354608798, "FALSE", 5, 0.313611321775938, 1, 0.313611321775938,
0.27778587668933, 0.599397784945329, "FALSE"))
})
test_that("Nonparametric table results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$kruskalWallisFactors <- c( "facFive", "facExperim")
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive")
)
options$kruskalEffectSizeEstimates <- TRUE
options$kruskalEpsilon <- TRUE
options$kruskalEta <- TRUE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_kruskalContainer$collection$anovaContainer_kruskalContainer_kruskalTable$data
jaspTools::expect_equal_tables(table,
list(4, 0.034303030303031, 0, "facFive", 0.011115645383478, 0, 0.493866894607854,
3.39600000000007, 0.193550362611566, 0.14617504991631, 1, 0.0103733573357337,
0.000275126288139123, "facExperim", 1.21480398103053e-05, 0,
0.310873187457312, 1.02696237623763, 0.0878974178624286, 0.0792208198923551)
)
})
# Error handling ----
test_that("Analysis handles errors", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "debInf"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Infinity found in debInf</li></ul>",
label="Inf dependent check")
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$wlsWeights <- "debInf"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Infinity found in debInf</li></ul>",
label="Inf WLS weights check")
options$wlsWeights <- ""
options$dependent <- "contNormal"
options$fixedFactors <- "debSame"
options$modelTerms <- list(list(components="debSame"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Number of factor levels is < 2 in debSame</li></ul>",
label="1-level factor check")
options$dependent <- "contGamma"
options$fixedFactors <- "facFive"
options$wlsWeights <- "contNormal"
options$modelTerms <- list(list(components="facFive"))
results <- jaspTools::runAnalysis("Anova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>The WLS weights contain negative and/or zero values.<br><br>(only positive WLS weights allowed).</li></ul>",
label="Negative WLS weights check")
})
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Species"))
options$customContrasts <- list()
options$dependent <- "Sepal.Length"
options$fixedFactors <- "Species"
options$modelTerms <- list(list(components = "Species"))
options$postHocTerms <- list(list(variable = "Species"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
# dataset is created from:
# dd <- read.csv("~/github/jasp-desktop/Resources/Data Sets/Data Library/10. Machine Learning/iris.csv")
# newLabels <- c("First species", "Second species", "Third species")
# oldLabels <- unique(dd$Species)
# dd$Species <- newLabels[match(dd$Species, oldLabels)]
# dataset <- dd[c(1:5, 51:55, 101:105), ]
dataset <- data.frame(
Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 7, 6.4, 6.9, 5.5, 6.5, 6.3, 5.8, 7.1, 6.3, 6.5),
Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.2, 3.2, 3.1, 2.3, 2.8, 3.3, 2.7, 3, 2.9, 3),
Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 4.7, 4.5, 4.9, 4, 4.6, 6, 5.1, 5.9, 5.6, 5.8),
Petal.Width = c(0.2, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 2.5, 1.9, 2.1, 1.8, 2.2),
Species = rep(c("First species", "Second species", "Third species"), each = 5)
)
set.seed(1)
results <- jaspTools::runAnalysis("Anova", dataset, options)
test_that("Post Hoc Comparisons - Species table results match and contrast names do not contain commas", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_postHocContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer"]][["collection"]][["anovaContainer_postHocContainer_postHocStandardContainer_Species1"]][["data"]]
jaspTools::expect_equal_tables(table,
list("TRUE", 0.286589136802729, "First species", "Second species",
-1.6, -5.58290526239083, 0.000324812090923943, "FALSE", 0.286589136802729,
"First species", "Third species", -1.54, -5.37354631505117,
0.000453440993214982, "FALSE", 0.286589136802729, "Second species",
"Third species", 0.0599999999999998, 0.209358947339655, 0.976174158311121
))
# following https://github.com/jasp-stats/jasp-issues/issues/1295, assert that these names do not contain commas.
contrast_A <- vapply(table, `[[`, "contrast_A", FUN.VALUE = character(1L))
contrast_B <- vapply(table, `[[`, "contrast_B", FUN.VALUE = character(1L))
expect_identical(contrast_A, c("First species", "First species", "Second species"))
expect_identical(contrast_B, c("Second species", "Third species", "Third species"))
})
# Andy Field Tests ----
# should we put this in verification project?
## Chapter 4 -----
test_that("Field - Chapter 4 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$modelTerms <- list(list(components = "Dose"))
options$homogeneityCorrections <- TRUE
options$homogeneityCorrectionBrown <- TRUE
options$homogeneityCorrectionWelch <- TRUE
results <- jaspTools::runAnalysis("Anova", "Puppies Dummy.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
# this table is also in the chapter 5
jaspTools::expect_equal_tables(table,
list("TRUE", 2, 5.11864406779661, 10.0666666666667, 0.0246942895382225,
20.1333333333333, "Dose", "None", "TRUE", 12, "", 1.96666666666667,
"", 23.6, "Residuals", "", "TRUE", 2, 5.11864406779661, 10.0666666666667,
0.0255514729692058, 20.1333333333333, "Dose", "Brown-Forsythe",
"TRUE", 11.5743973399834, "", 2.03898305084746, "", 23.6, "Residuals",
"", "TRUE", 2, 4.32045117281357, 10.0666666666667, 0.0537384707116728,
20.1333333333333, "Dose", "Welch", "TRUE", 7.94337535943375,
"", 2.97102918244598, "", 23.6, "Residuals", ""))
})
## Chapter 5 ----
test_that("Field - Chapter 5 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$modelTerms <- list(list(components = "Dose"))
options$contrasts <- list(
list(contrast = "Helmert", variable = "Dose")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Dose"
options$postHocTypeGames <- TRUE
options$postHocTypeDunnet <- TRUE
options$postHocCi <- TRUE
results <- jaspTools::runAnalysis("Anova", "Puppies Dummy.csv", options)
# contrast
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("1 - 2, 3", -1.9, 0.768114574786861, -2.47358930863565, 0.0293002196554282,
12, -3.5735778902, -0.2264221098, "TRUE", "2 - 3", -1.8, 0.886942313043338,
-2.02944427560764, 0.0651922067570462, 12, -3.73248129083355,
0.132481290833552, "FALSE"))
# standard post hoc (tukey)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.886942313043338, 1, 2, -1, -3.36624115850686, -1.12746904200424,
0.516276123508473, 1.36624115850686, "FALSE", 0.886942313043338,
1, 3, -2.8, -5.16624115850687, -3.15691331761188, 0.0209243994922408,
-0.433758841493136, "FALSE", 0.886942313043338, 2, 3, -1.8,
-4.16624115850686, -2.02944427560764, 0.147457622995377, 0.566241158506864
))
# games-howell post hoc
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocGamesContainer$collection$anovaContainer_postHocContainer_postHocGamesContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("1 - 2", -1, -1.21267812518166, 0.824621125123532, -3.3563089273419,
1.3563089273419, 0.47896489393065, 8, "1 - 3", -2.8, -3.05505046330389,
0.916515138991168, -5.43893919399355, -0.161060806006447, 0.0388414107946456,
7.7199124726477, "2 - 3", -1.8, -1.96396101212393, 0.916515138991168,
-4.43893919399355, 0.838939193993553, 0.185393344481167, 7.7199124726477))
# dunnet post hoc
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocDunnettContainer$collection$anovaContainer_postHocContainer_postHocDunnettContainer_Dose$data
jaspTools::expect_equal_tables(table,
list("2 - 1", 1, 1.12746904200424, 0.886942313043338, 0.445888579780104,
-1.21963511399532, 3.21963511399532, "3 - 1", 2.8, 3.15691331761188,
0.886942313043338, 0.0152377020148067, 0.580364886004677, 5.01963511399532))
})
## Chapter 7 ----
test_that("Field - Chapter 7 results match", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "Attractiveness"
options$fixedFactors <- list("FaceType", "Alcohol")
options$modelTerms <- list(
list(components = "FaceType"),
list(components = "Alcohol"),
list(components = c("FaceType", "Alcohol"))
)
options$contrasts <- list(
list(contrast = "none", variable = "FaceType"),
list(contrast = "Helmert", variable = "Alcohol")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Alcohol"
options$postHocCorrectionBonferroni <- TRUE
options$postHocCi <- TRUE
options$postHocTypeStandardBootstrap <- TRUE
options$postHocTypeStandardBootstrapSamples <- 500
options$marginalMeanTerms <- list(
list(components = c("FaceType", "Alcohol"))
)
options$marginalMeanBootstrap <- TRUE
options$marginalMeanBootstrapSamples <- 500
options$marginalMeanComparedToZero <- FALSE
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "Beer Goggles.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("FaceType", 21.3333333333334, 1, 21.3333333333334, 15.5826086956522,
0.000295223592290028, "TRUE", "Alcohol", 16.5416666666666, 2,
8.27083333333331, 6.04130434782607, 0.00494338949698304, "FALSE",
"FaceType <unicode> Alcohol", 23.2916666666667, 2, 11.6458333333333,
8.50652173913045, 0.000791273868880283, "TRUE", "Residuals",
57.5, 42, 1.36904761904762, "", "", "TRUE"))
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("0 - 1, 2", -1.09375, 0.358257190138194, -3.05297431596026, 0.003921402019941,
42, -1.81674228032104, -0.37075771967896, "TRUE", "1 - 2", -0.6875,
0.413679770330811, -1.66191351211161, 0.103977316507, 42, -1.52233957533075,
0.147339575330745, "FALSE"))
# removed both post hoc table and contrast table because bootstrap results are now in same table
table <- results[["results"]]$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer_Alcohol$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.392000159227314, -0.00376914898826786, 0.230950085511107,
0, 1, -0.769579725829724, -1.48733254329573, -1.8129965586672,
0.177726007657148, 0.0548973391001529, "FALSE", 0.435378856593833,
0.0142934574412505, 0.00359956767679779, 0, 2, -1.43536324786325,
-2.31465226049576, -3.47491007077881, 0.00337043014651417, -0.602581004497449,
"FALSE", 0.407611902363181, 0.0180626064295184, 0.311931949521,
1, 2, -0.690674603174603, -1.43846891737073, -1.66191351211161,
0.231712504393661, 0.214248742598683))
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0, 0, 0.609481794897967, -0.000576109837875816, 2.2, 3.50000000000001,
4.74689776796074, "FALSE", 0, 1, 0.319660323889613, -0.0167382728382766,
5.88388102664993, 6.33333333333334, 7.17856492366397, "TRUE",
1, 0, 0.449084420277268, -0.0233288832082961, 4.00000000000001,
4.8397435897436, 6.00000000000001, "FALSE", 1, 1, 0.320922728447884,
-0.0138867382617391, 5.85714285714286, 6.5, 7.125,
"TRUE", 2, 0, 0.388994937578588, -0.0390126855170978, 6, 6.57142857142858,
7.5, "FALSE", 2, 1, 0.395829403830131, 0.0118089133089141, 5.4,
6.14285714285715, 7))
})
test_that("ANOVA - factor level with zero variance works and Welch homogeneity correction shows a footnote", {
options <- initClassicalAnovaOptions("Anova")
options$dependent <- "value"
options$fixedFactors <- "group"
options$homogeneityCorrectionWelch <- TRUE
options$modelTerms <- list(list(components = "group"))
dataset <- data.frame(
group = rep(c("A","B","C","D"), c(3, 4, 3, 5)),
value = c(2.3, 2.1, 2.8, rep(3, 4), c(4, 3, 5, 4, 2), 3.6, 4.5, 5.1)
)
set.seed(1)
results <- jaspTools::runAnalysis("Anova", dataset, options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("TRUE", 3, 2.67905056759546, 1.888, 0.0985227092069956, 5.664,
"group", "None", "TRUE", 11, "", 0.704727272727272, "", 7.752,
"Residuals", "", "TRUE", 3, "NaN", 1.888, "NaN", 5.664, "group",
"Welch", "TRUE", "NaN", "", "NaN", "", 7.752, "Residuals",
""))
footnote <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["footnotes"]][[1L]][["text"]]
testthat::expect_identical(
footnote,
"The Welch correction could not be computed because 'value' has zero variance after grouping on the following level(s) of 'group': B"
)
})
# Ordinal restrictions ----
# Tests from https://restriktor.org
## Basic model ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Age"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- TRUE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 1", summary = TRUE, syntax = "GroupActive < GroupPassive\nGroupPassive < GroupNo"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
dataset <- read.csv("ZelazoKolb1972.csv")
dataset <- subset(dataset, Group != "Control")
results <- jaspTools::runAnalysis("Anova", dataset, options)
### see https://restriktor.org/tutorial/restriktor.html for comparison ----
test_that("Ordinal restrictions: Likelihood, penalty and GORIC match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(64.7533269649054, 1, -29.5348420070337, "Model 1", 2.84182147541904
))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.59384623700347e-10, 0.618538022852505,
16.3692442920593, "GroupNo", 12.35, 3.77071859965597e-11, 0.677574455581925,
18.2267792096639, "GroupPassive", 11.375, 3.34358875477653e-11,
0.618538022852504, 18.3901386491037))
})
### see https://restriktor.org/tutorial/contest.html for comparison ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.028341275692807, 5.97803555494542, "F", "Type global", 0.0276478338044095,
6.04479400936707, "LRT", "Type global", 0.044864172884687, 4.78768638768638,
"Score", "Type global", 0.028341275692807, 5.97803555494542,
"F", "Type A", 0.0276478338044095, 6.04479400936707, "LRT",
"Type A", 0.044864172884687, 4.78768638768638, "Score", "Type A",
1, 0, "F", "Type B", 1, -7.105427357601e-15, "LRT", "Type B",
1, 0, "Score", "Type B", 0.152944344084674, 1.0627396241062,
"t", "Type C"))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1, 0, 1, 0, 0, 1, -1, 0))
})
## Ordered means and with effect sizes ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Age"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 1", summary = TRUE, syntax = "GroupActive < GroupPassive\nGroupPassive < GroupControl\nGroupControl < GroupNo"),
list(informedHypothesisTest = TRUE, marginalMean = FALSE,
name = "Model 2", summary = TRUE, syntax = "(GroupPassive - GroupActive) / 1.516 > 0.2\n(GroupControl - GroupPassive) / 1.516 > 0.2\n(GroupNo - GroupControl) / 1.516 > 0.2"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "ZelazoKolb1972.csv", options)
### see https://restriktor.org/tutorial/example1.html for comparison ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0308475934292769, 6.42666580972066, "F", "Type global", 0.0278151519017313,
6.70126802148546, "LRT", "Type global", 0.0431126887497895,
5.56056578050442, "Score", "Type global", 0.0308475934292769,
6.42666580972066, "F", "Type A", 0.0278151519017313, 6.70126802148546,
"LRT", "Type A", 0.0431126887497895, 5.56056578050442, "Score",
"Type A", 1, 0, "F", "Type B", 1, 0, "LRT", "Type B", 1, 0,
"Score", "Type B", 0.35381027878063, 0.380738874434879, "t",
"Type C"))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.19077449127484e-12, 0.619065391589877,
16.3552996784348, "GroupControl", 11.7083333333333, 8.7743489822692e-14,
0.619065391589877, 18.91291855819, "GroupNo", 12.35, 1.73632870169313e-13,
0.678152159089088, 18.2112522012594, "GroupPassive", 11.375,
1.4783390885313e-13, 0.619065391589877, 18.3744724782415))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1, 0, 0, 1, 0, 0, 1, 0, -1, 0, 0, -1, 1, 0, 0))
})
### see https://restriktor.org/tutorial/example3.html for comparison
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.168856977319199, 2.38400146304897, "F", "Type global", 0.144342629920537,
2.71869356602637, "LRT", "Type global", 0.163454084273826, 2.45267623450673,
"Score", "Type global", 0.168856977319199, 2.38400146304897,
"F", "Type A", 0.144342629920537, 2.71869356602637, "LRT", "Type A",
0.163454084273826, 2.45267623450673, "Score", "Type A", 1, 0,
"F", "Type B", 1, 0, "LRT", "Type B", 1, 0, "Score", "Type B",
0.48645111069787, 0.0344187942489155, "t", "Type C"))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("GroupActive", 10.125, 1.19077449127485e-12, 0.619065391589877,
16.3552996784348, "GroupControl", 11.7083333333333, 8.77434898226892e-14,
0.619065391589876, 18.91291855819, "GroupNo", 12.35, 1.73632870169313e-13,
0.678152159089088, 18.2112522012594, "GroupPassive", 11.375,
1.47833908853122e-13, 0.619065391589875, 18.3744724782416))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.659630606860158, 0, 0, 0.659630606860158, 0.2, 0, 0.659630606860158,
0, -0.659630606860158, 0.2, 0, -0.659630606860158, 0.659630606860158,
0, 0.2))
})
### see https://restriktor.org/tutorial/example6.html for comparison ----
options <- initClassicalAnovaOptions("Anova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$customContrasts <- list()
options$dependent <- "Anger"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"))
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelComparison <- "unconstrained"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 2"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedModelMarginalMeanTerms <- list()
options$restrictedAvailableCoefficients <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModels <- list(list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 1", summary = FALSE, syntax = "GroupNo = GroupPhysical\nGroupPhysical = GroupBehavioral\nGroupBehavioral = GroupBoth"),
list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 2", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical = GroupBehavioral\nGroupBehavioral < GroupBoth"),
list(informedHypothesisTest = FALSE, marginalMean = FALSE,
name = "Model 3", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical < GroupBehavioral\nGroupBehavioral < GroupBoth"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Anova", "AngerManagement.csv", options)
test_that("Ordinal restrictions: Model Comparison Table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(190.802338005453, 6.15799536281089e-06, -93.4011690027263, "Model 1",
2, 174.107875497759, 0.025977408886342, -84.1621111968489, "Model 2",
2.89182655203061, 167.133864718034, 0.849147292951048, -80.4838987099312,
"Model 3", 3.08303364908579, 170.967797419862, 0.124869140167247,
-80.4838987099312, "Unconstrained", 5))
})
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