tests/testthat/test-ancova.R
In jasp-stats/jaspAnova: ANOVA Module for JASP
context("ANCOVA")
# does not test
# - descriptives table/plot & Q-Q plot & bar plot & raincloud plot (uses same code as ANOVA)
# - if analysis handles too few observations
# Main results ----
test_that("Main table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$covariates <- "contGamma"
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contGamma")
)
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$vovkSellke <- TRUE
refTables <- list(
type1 = list("facFive", 181.151987151139, 4, 45.2879967877848, 1.86433860843651,
0.123166554163148, "TRUE", 0.0733818170125722, 0.0735023545150279,
0.0336895280828883, 1.42623435171942, "contGamma", 4.04832694150032,
1, 4.04832694150032, 0.166654582934621, 0.684030683889986, "FALSE",
0.00163991348646033, 0.00176978340871019, 0, 1, "Residuals",
2283.42194856002, 94, 24.2917228570215, "", "", "TRUE", "",
"", "", ""),
type2 = list("facFive", 185.196464516179, 4, 46.2991161290447, 1.90596263597919,
0.115857414147638, "TRUE", 0.0748974625326393, 0.0750202880830827,
0.0352547206519188, 1.47317659150752, "contGamma", 4.04832694150036,
1, 4.04832694150036, 0.166654582934623, 0.684030683889984, "FALSE",
0.00163723112216545, 0.00176978340871021, 0, 1, "Residuals",
2283.42194856002, 94, 24.2917228570215, "", "", "TRUE", "",
"", "", ""),
type3 = list("TRUE", 4, 1.9059626359792, 46.2991161290448, 0.115857414147637,
185.196464516179, "facFive", 0.0748974625326395, 0.035254720651919,
0.0750202880830829, 1.47317659150752, "FALSE", 1, 0.166654582934623,
4.04832694150036, 0.684030683889984, 4.04832694150036, "contGamma",
0.00163723112216545, 0, 0.00176978340871021, 1, "TRUE", 94,
"", 24.2917228570215, "", 2283.42194856002, "Residuals", "",
"", "", "")
)
for (type in c("type1", "type2", "type3")) {
options$sumOfSquares <- type
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[type]], label=paste("Table with SS", type))
}
})
test_that("Main table results match for three-way ANOVA", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom", "facGender")
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components="facGender"),
list(components="contGamma"),
list(components=c("facFive", "contBinom")),
list(components=c("facFive", "facGender")),
list(components=c("contBinom", "facGender")),
list(components=c("facFive", "contBinom", "facGender"))
)
options$covariates <- "contGamma"
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$sumOfSquares <- "type3"
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, list("TRUE", 4, 0.751434182416166, 0.849676498436303, 0.560010211401305,
3.39870599374521, "facFive", 0.0319287041344807, 0, 0.036652761739989,
"FALSE", 1, 0.0718225589534213, 0.0812126222473069, 0.789399900467283,
0.0812126222473069, "contBinom", 0.000762941481990983, 0, 0.000908320519612062,
"FALSE", 1, 4.03921343627838, 4.56729918505773, 0.0478686068240796,
4.56729918505773, "facGender", 0.0429069018154959, 0.0319449750880258,
0.0486422410464899, "TRUE", 4, 0.80133099599275, 0.906096808870625,
0.527940897125054, 3.6243872354825, "facFive <unicode> facGender",
0.0340488373879581, 0, 0.038991683611587, "FALSE", 1, 1.08113657469412,
1.22248409855909, 0.301615301439538, 1.22248409855909, "contBinom <unicode> facGender",
0.0114844688430931, 0.000852821268290123, 0.0135005148645175,
"TRUE", 4, 0.702807193128186, 0.79469202879871, 0.592348641591512,
3.17876811519484, "facFive <unicode> contBinom <unicode> facGender",
0.0298625261640639, 0, 0.0343623829478194, "TRUE", 1, 0.0608023312347904,
0.0687516127283772, 0.805871897218356, 0.0687516127283772, "contGamma",
0.000645878138801233, 0, 0.000769057857268532, "TRUE", 4, 0.215937435642924,
0.244169041669679, 0.928844739039309, 0.976676166678715, "facFive <unicode> contBinom",
0.00917525800068401, 0, 0.0108152916104723, "TRUE", 79, "",
1.13073974849567, "", 89.3284401311577, "Residuals", "", "",
""))
})
# Additional results ----
test_that("Homogeneity of Variances table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_assumptionsContainer"]][["collection"]][["anovaContainer_assumptionsContainer_leveneTable"]][["data"]]
jaspTools::expect_equal_tables(table, list(2.72159218177061, 1, 98, 0.102201011380302, 1.57819444559362))
})
# Contrasts verified with SPSS
# should we put this in verification project???
test_that("Contrasts table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facFive"),
list(components="contGamma")
)
options$contrastCi <- TRUE
refTables <- list(
deviation = list("2 - 1, 2, 3, 4, 5", -0.200720248979633, 0.213572066533121, -0.939824445386938,
0.349716429527485, 94, -0.6247726, 0.2233321, "TRUE", "3 - 1, 2, 3, 4, 5", 0.326355030521638,
0.213419672724688, 1.52917032603005, 0.129580520593874, 94, -0.09739473, 0.7501048, "FALSE",
"4 - 1, 2, 3, 4, 5", -0.163006179525351, 0.213771994762986,
-0.762523546202018, 0.447656831363019, 94, -0.5874555, 0.2614431, "FALSE", "5 - 1, 2, 3, 4, 5",
0.17396802464162, 0.214943819683978, 0.809365093155025, 0.42034917204134, 94, -0.252808, 0.600744,
"FALSE"),
simple = list("2 - 1", -0.0641236223213591, 0.343277644425165, -0.186798130792167,
0.852221484436515, 94, -0.7457094, 0.6174622, "TRUE", "3 - 1", 0.462951657179913, 0.342949539026206,
1.34991188060625, 0.180285987179755, 94, -0.2179827, 1.1438860, "FALSE", "4 - 1", -0.0264095528670768,
0.337118098977225, -0.0783391723766838, 0.937724778659558, 94, -0.6957654, 0.6429463, "FALSE",
"5 - 1", 0.310564651299894, 0.345720813950643, 0.898310540667163,
0.371315247360673, 94, -0.3758721, 0.9970014, "FALSE"),
difference = list("2 - 1", -0.0641236223213588, 0.343277644425165, -0.186798130792167,
0.852221484436515, 94, -0.7457094, 0.6174622, "TRUE", "3 - 1, 2", 0.495013468340592, 0.292959120347178,
1.68970151109808, 0.0943992176806134, 94, -0.08666373, 1.07669067, "FALSE", "4 - 1, 2, 3",
-0.159352231153261, 0.275116843578239, -0.579216557883866, 0.563828556165238, 94, -0.7056032, 0.3868987,
"FALSE", "5 - 1, 2, 3, 4", 0.217460030802025, 0.268679774604972,
0.809365093155025, 0.42034917204134, 94, -0.31601, 0.75093, "FALSE"),
Helmert = list("1 - 2, 3, 4, 5", -0.170745783322843, 0.272701151705157, -0.626127840880747,
0.532748563022963, 94, -0.7122003, 0.3707087, "TRUE", "2 - 3, 4, 5", -0.313159207525603,
0.274645898379152, -1.14022896163293, 0.257087871753455, 94, -0.8584751, 0.2321567, "FALSE",
"3 - 4, 5", 0.320874107963505, 0.291421643235482, 1.10106478160314,
0.273679968781506, 94, -0.2577504, 0.8994986, "FALSE", "4 - 5", -0.336974204166971, 0.340503320254199,
-0.98963558979515, 0.324892868709605, 94, -1.0130515, 0.3391031, "FALSE"),
repeated = list("1 - 2", 0.0641236223213587, 0.343277644425165, 0.186798130792166,
0.852221484436516, 94, -0.6174622, 0.7457094, "TRUE", "2 - 3", -0.527075279501272, 0.336088495268646,
-1.56826337979812, 0.120179976824791, 94, -1.1943869, 0.1402363, "FALSE", "3 - 4", 0.489361210046989,
0.338686060204421, 1.444881462649, 0.151816532688129, 94, -0.1831079, 1.1618303, "FALSE",
"4 - 5", -0.336974204166971, 0.340503320254199, -0.98963558979515,
0.324892868709605, 94, -1.0130515, 0.3391031, "FALSE"),
polynomial = list("linear", 0.208344567699659, 0.240588115184784, 0.865980298069335,
0.388705894709638, 94, -0.2693488, 0.6860379, "TRUE", "quadratic", -0.0572582720504826,
0.238398138250912, -0.240179191291413, 0.810714351679878, 94, -0.5306034, 0.4160868, "FALSE",
"cubic", 0.0743566940225005, 0.243521087108506, 0.305339857444745,
0.760782433052004, 94, -0.4091601, 0.5578735, "FALSE", "quartic", 0.412402551299919, 0.237982457186854,
1.73291156068751, 0.0863908131438485, 94, -0.0601172, 0.8849223, "FALSE")
)
contrasts <- c("deviation", "simple", "difference", "Helmert", "repeated", "polynomial")
for (contrast in contrasts) {
options$contrasts <- list(list(contrast=contrast, variable="facFive"))
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
# table <- results[["results"]][["contrasts"]][["collection"]][[1]][["data"]]
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[contrast]], label=paste("Table with contrast", contrast))
}
})
test_that("Post Hoc table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$postHocTerms <- list("facExperim")
options$postHocTypeStandard <- TRUE
options$postHocTypeStandardEffectSize <- TRUE
options$postHocCorrectionBonferroni <- TRUE
options$postHocCorrectionHolm <- TRUE
options$postHocCorrectionScheffe <- TRUE
options$postHocCorrectionTukey <- TRUE
options$postHocCorrectionSidak <- TRUE
options$postHocCi <- TRUE
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.21391801479091, 0.698555762823947, -0.0779045273305165,
-0.476129796984023, 0.32032074232299, "control", "experimental",
-0.0830902357515325, 0.698555762823947, -0.507658279613134,
0.698555762823947, 0.698555762823947, -0.388420937024623, 0.698555762823947,
0.341477808110069)
)
})
test_that("Marginal Means table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$marginalMeanComparedToZero <- TRUE
options$marginalMeanTerms <- "facExperim"
refTables <- list(
none = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618, 97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE"),
Bonferroni = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618,97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE"),
Sidak = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618, 97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE")
)
for (adjustment in c("none", "Bonferroni", "Sidak")) {
options$marginalMeanCiCorrection <- adjustment
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[adjustment]], label=paste("Table with CI adjustment", adjustment))
}
})
test_that("Simple Main Effects table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive"),
list(components="contGamma")
)
options$simpleMainEffectFactor <- "facExperim"
options$simpleMainEffectModeratorFactorOne <- "facFive"
options$simpleMainEffectModeratorFactorTwo <- ""
options$homogeneityTests <- TRUE
options$sumOfSquares <- "type1"
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Ancova", "debug.csv", options)
# table <- results[["results"]][["simpleEffects"]][["data"]]
table <- results$results$anovaContainer$collection$anovaContainer_simpleEffectsContainer$collection$anovaContainer_simpleEffectsContainer_simpleEffectsTable$data
jaspTools::expect_equal_tables(table, list(1, 0.350864897951646, 1, 0.350864897951646, 0.307765411627339,
0.580386465552355, "TRUE", 2, 2.72259751707838, 1, 2.72259751707838,
2.38815951789705, 0.125653693703876, "FALSE", 3, 0.300954391532799,
1, 0.300954391532799, 0.263985804028512, 0.608613599742434,
"FALSE", 4, 3.47907983036715, 1, 3.47907983036715, 3.05171717754702,
0.0839531695276169, "FALSE", 5, 0.313611321775938, 1, 0.313611321775938,
0.275087984294933, 0.601186887502708, "FALSE"))
})
# Error handling ----
test_that("Analysis handles errors", {
# Same as ANOVA
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$covariates <- "debInf"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Ancova", "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 covariate check")
})
# Andy Field tests ----
# should we put this into verification project???
## Chapter 6 ----
test_that("Field - Chapter 6 results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$covariates <- "Puppy_love"
options$modelTerms <- list(
list(components = "Dose"),
list(components = "Puppy_love")
)
options$marginalMeanTerms <- list(
list(components = "Dose")
)
options$marginalMeanBootstrap <- TRUE
options$marginalMeanBootstrapSamples <- 500
options$contrasts <- list(
list(contrast = "simple", variable = "Dose")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Dose"
options$postHocCorrectionSidak <- TRUE
options$postHocCorrectionTukey <- FALSE
options$postHocTypeStandardBootstrap <- TRUE
options$postHocTypeStandardBootstrapSamples <- 500
options$postHocCi <- TRUE
set.seed(1) # seed for bootstrapping
results <- jaspTools::runAnalysis("Ancova", "Puppy Love.csv", options)
# main table
table <- results$result$anovaContainer$collection$anovaContainer_anovaTable$data
jaspTools::expect_equal_tables(table,
list("Dose", 25.1851942083821, 2, 12.592597104191, 4.14192880386377,
0.0274465428639959, "TRUE", "Puppy_love", 15.0757477099169,
1, 15.0757477099169, 4.9586811332752, 0.0348333806474409, "FALSE",
"Residuals", 79.0471155379463, 26, 3.0402736745364, "", "", "TRUE"))
# marginal means bootstrapping
table <- results$results$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list(1, 2.89513297007949, 0.00291277082733199, 0.454016672951108, 2.24194236484924,
4.40152013882763, "TRUE", 2, 4.7532347036809, 0.0469177160983509,
0.374655789875328, 3.87665072858526, 5.46307279640562, "FALSE",
3, 5.13975121640456, 0.0363834639636114, 0.660396429197245,
4.00063237957797, 6.57588583114463, "FALSE"))
# contrast
table <- results$results$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("2 - 1", 1.78568011481422, 0.849355306996751, 2.1023947223315,
0.0453535580857103, 26, 0.0398052774148459, 3.5315549522136,
"TRUE", "3 - 1", 2.22488132183556, 0.802810905470398, 2.77136409916095,
0.0101750138508479, 26, 0.574679871977612, 3.8750827716935,
"FALSE"))
# post hoc bootstrap
# adjusted the elements here because the new results also include t ratio and p-value
# The rest of the results matched up
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.538336190074754, -0.0194100366350722, 1, 2, -1.78324124481148,
-2.83630538964887, 0.129983128349044, -2.1023947223315, -0.858078871455782,
"FALSE", 0.763324066789791, -0.0155779657803694, 1, 3, -2.18556663740395,
-3.97107607430337, 0.0302155022603601, -2.77136409916095, -0.844312084720958,
"FALSE", 0.775278163963213, 0.00383207085470289, 2, 3, -0.410252544853603,
-2.12122714048355, 0.932499470707816, -0.541407321622228, 0.972524781215359
))
# interaction with covariate
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$covariates <- "Puppy_love"
options$modelTerms <- list(
list(components = "Dose"),
list(components = "Puppy_love"),
list(components = list("Dose", "Puppy_love"))
)
options$contrasts <- list(
list(contrast = "none", variable = "Dose")
)
options$descriptivePlotHorizontalAxis <- "Puppy_love"
options$descriptivePlotSeparatePlot <- "Dose"
options$descriptivePlotErrorBar <- TRUE
results <- jaspTools::runAnalysis("Ancova", "Puppy Love.csv", options)
table <- results$result$anovaContainer$collection$anovaContainer_anovaTable$data
jaspTools::expect_equal_tables(table,
list("Dose", 36.5575599693692, 2, 18.2787799846846, 7.483568988423,
0.00297956448464929, "TRUE", "Puppy_love", 17.1822242014479,
1, 17.1822242014479, 7.03462486521667, 0.0139474621264158, "FALSE",
"Dose <unicode> Puppy_love", 20.4265936571326, 2, 10.2132968285663,
4.18145584551367, 0.0276671129121842, "TRUE", "Residuals", 58.6205218808138,
24, 2.44252174503391, "", "", "TRUE"))
# descriptive plots
plot1 <- results$state$figures[[1]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot1, "PuppyLove1")
plot2 <- results$state$figures[[2]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot2, "PuppyLove2")
plot3 <- results$state$figures[[3]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot3, "PuppyLove3")
})
# Ordinal Restrictions ----
# this test also tests anova restrictions
options <- initClassicalAnovaOptions("Ancova")
options$contrasts <- list(list(contrast = "none", variable = "contBinom"))
options$covariates <- "contcor1"
options$customContrasts <- list()
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components = "contBinom"), list(components = "contcor1"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
options$restrictedBootstrap <- TRUE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 100
options$restrictedInterceptInclusion <- TRUE
options$restrictedAvailableCoefficients <- TRUE
options$restrictedInformedHypothesisTestForAllModels <- TRUE
options$restrictedMarginalMeanForAllModels <- TRUE
options$restrictedModelSummaryForAllModels <- TRUE
options$restrictedModelComparison <- "unconstrained"
options$restrictedModelComparisonCoefficients <- TRUE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- TRUE
options$restrictedModelComparisonReference <- "Model 2"
options$restrictedModelComparisonWeights <- TRUE
options$restrictedMarginalMeanTerms <- list(list(variable = "contBinom"))
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 1", summary = TRUE, syntax = ".Intercept. == 0"),
list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 2", summary = TRUE, syntax = ".Intercept. > 0"),
list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 3", summary = TRUE, syntax = ".Intercept. == 0\ncontBinom1 > 0"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
## Models with equality restrictions only ----
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(0.280525836689152, 1.17764407696846, "F", "Type classical", 0.277836137015615,
1.17764407696846, "Wald", "Type classical", 0.278271309124288,
1.17551323040948, "Score", "Type classical"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.00631082413156547, 0, -0.0052940718699722, 0.00710593238617753,
0.0201427487900576, 0.152623965297405, 1, -0.541673449142385,
-0.283815957286218, 0.0347949556202055))
})
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(".Intercept.", 0, 0, 0, 0, "contBinom1", -0.28901764982443, -0.545890974144477,
0.152410269162387, 0.0216009611708116, "contcor1", 0.135225724991321,
-0.100746062285325, 0.120094833739718, 0.383316032355158))
})
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, 0))
})
## Models with in-equality restrictions only ----
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(0.141270077631025, 4.86463661917856, "F", "Type global", 0.143009738633418,
4.83608638110945, "LRT", "Type global", 0.150214932639365, 4.72138311792181,
"Score", "Type global", 1, 0, "F", "Type A", 1, 0, "LRT", "Type A",
0.495597802688024, 0.000122398295544508, "Score", "Type A",
0.140262918344576, 1.17764407696847, "F", "Type B", 0.13734744589723,
1.20675538818887, "LRT", "Type B", 0.141706057440304, 1.16351819744612,
"Score", "Type B", 0.859737081655424, -1.08519310584267, "t",
"Type C"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0401499620148025, 0, -0.0052940718699722, 0.00762632152811411,
0.147781545335185, 0.15219130722473, 1, -0.541673449142385,
-0.285158551298243, 0.0345874620687721))
})
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(".Intercept.", 0, 0, 0.0390913797847908, 0.135449151526049, "contBinom1",
-0.294040143250604, -0.553042198939247, 0.15095032207897, 0.0149620362407961,
"contcor1", 0.133967448915338, -0.100746062285325, 0.119436094843916,
0.367522848917437))
})
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(1, 0, 0, 0))
})
## Models with in-equality and equality restrictions ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.227906731091751, 2.29055827266306, "F", "Type global", 0.233102480397295,
2.24800995009798, "LRT", "Type global", 0.236222676760002, 2.22294941447971,
"Score", "Type global", 1, 0, "F", "Type A", 1, 0, "LRT", "Type A",
0.492283243845526, 0.00037613437452225, "Score", "Type A", 0.107200891136129,
3.75172242348396, "F", "Type B", 0.104890547890444, 3.79483181920034,
"LRT", "Type B", 0.112908725975451, 3.64925570161498, "Score",
"Type B"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.00615640047820512, 0, -0.0040934464864444, 0.00784355639438009,
0.021670734045724, 0.0126705139715333, 1, -0.0040934464864444,
0.00795127192950033, 0.0347949556202055))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(".Intercept.", 0, 0, 0, 0, "contBinom1", 0, 0, 0.0115253766048944,
0.0216009611708116, "contcor1", 0.149262692395377, -0.077898189675906,
0.117156155274091, 0.41239355557723))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0, 0, 0, 0, 1, 0, 0))
})
## Model comparison ----
test_that("Ordinal restrictions: Coefficients Comparison table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, 0, 0, 0, 0, ".Intercept.", -0.151936095480945, 0, 0, 0,
-0.262153994575154, -0.262153994575154, 0, "contBinom1", -0.107591826354404,
0, 0, 0, 0.141934956659658, 0.141934956659658, 0.158227556203348,
"contcor1", 0.159396509032993))
})
test_that("Ordinal restrictions: Relative GORIC-Weights table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1.64872127070013, 2.21231220567314, 1.48679427057462, "Model 1",
0.606530659712633, 1, 1.34183518159724, 0.901786309788586, "Model 2",
0.45201576768218, 0.745248010869458, 1, 0.672054453599252, "Model 3",
0.672588010184839, 1.10891015880962, 1.48797466432133, 1, "Unconstrained"
))
})
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(298.462647640822, 0.366148215276508, -146.231323820411, "Model 1",
3, 1.64872127070013, 299.462647640822, 0.222080118564264, -146.231323820411,
"Model 2", 3.5, 1, 300.050724071833, 0.165504766613671, -147.525362035916,
"Model 3", 2.5, 0.745248010869458, 299.255892252633, 0.246266899545557,
-145.627946126316, "Unconstrained", 4, 1.10891015880962))
})
## see https://restriktor.org/tutorial/example2.html for comparison ----
options <- initClassicalAnovaOptions("Ancova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$covariates <- "Age"
options$customContrasts <- list()
options$dependent <- "Anger"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"), list(components = "Age"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedAvailableCoefficients <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedMarginalMeanTerms <- list(list(variable = "Group"))
options$restrictedModels <- list(list(informedHypothesisTest = FALSE, marginalMean = TRUE,
name = "Model 1", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical == GroupBehavioral\nGroupBehavioral < GroupBoth"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Ancova", "AngerManagement.csv", options)
test_that("Ordinal restrictions: Adjuested marginal means results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer_Group"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Behavioral", 0.467592714510265, 1.95108158220025, "Both", 0.702820099125852,
4.06863411619283, "No", 0.69741742112576, -0.17079728059332,
"Physical", 0.467592714510269, 1.95108158220025))
})
jasp-stats/jaspAnova documentation built on June 14, 2024, 6:48 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("ANCOVA")
# does not test
# - descriptives table/plot & Q-Q plot & bar plot & raincloud plot (uses same code as ANOVA)
# - if analysis handles too few observations
# Main results ----
test_that("Main table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$covariates <- "contGamma"
options$wlsWeights <- "facFifty"
options$modelTerms <- list(
list(components="facFive"),
list(components="contGamma")
)
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$vovkSellke <- TRUE
refTables <- list(
type1 = list("facFive", 181.151987151139, 4, 45.2879967877848, 1.86433860843651,
0.123166554163148, "TRUE", 0.0733818170125722, 0.0735023545150279,
0.0336895280828883, 1.42623435171942, "contGamma", 4.04832694150032,
1, 4.04832694150032, 0.166654582934621, 0.684030683889986, "FALSE",
0.00163991348646033, 0.00176978340871019, 0, 1, "Residuals",
2283.42194856002, 94, 24.2917228570215, "", "", "TRUE", "",
"", "", ""),
type2 = list("facFive", 185.196464516179, 4, 46.2991161290447, 1.90596263597919,
0.115857414147638, "TRUE", 0.0748974625326393, 0.0750202880830827,
0.0352547206519188, 1.47317659150752, "contGamma", 4.04832694150036,
1, 4.04832694150036, 0.166654582934623, 0.684030683889984, "FALSE",
0.00163723112216545, 0.00176978340871021, 0, 1, "Residuals",
2283.42194856002, 94, 24.2917228570215, "", "", "TRUE", "",
"", "", ""),
type3 = list("TRUE", 4, 1.9059626359792, 46.2991161290448, 0.115857414147637,
185.196464516179, "facFive", 0.0748974625326395, 0.035254720651919,
0.0750202880830829, 1.47317659150752, "FALSE", 1, 0.166654582934623,
4.04832694150036, 0.684030683889984, 4.04832694150036, "contGamma",
0.00163723112216545, 0, 0.00176978340871021, 1, "TRUE", 94,
"", 24.2917228570215, "", 2283.42194856002, "Residuals", "",
"", "", "")
)
for (type in c("type1", "type2", "type3")) {
options$sumOfSquares <- type
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, refTables[[type]], label=paste("Table with SS", type))
}
})
test_that("Main table results match for three-way ANOVA", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- c("facFive", "contBinom", "facGender")
options$modelTerms <- list(
list(components="facFive"),
list(components="contBinom"),
list(components="facGender"),
list(components="contGamma"),
list(components=c("facFive", "contBinom")),
list(components=c("facFive", "facGender")),
list(components=c("contBinom", "facGender")),
list(components=c("facFive", "contBinom", "facGender"))
)
options$covariates <- "contGamma"
options$effectSizeEstimates <- TRUE
options$effectSizeEtaSquared <- TRUE
options$effectSizeOmegaSquared <- TRUE
options$effectSizePartialEtaSquared <- TRUE
options$sumOfSquares <- "type3"
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_anovaTable"]][["data"]]
jaspTools::expect_equal_tables(table, list("TRUE", 4, 0.751434182416166, 0.849676498436303, 0.560010211401305,
3.39870599374521, "facFive", 0.0319287041344807, 0, 0.036652761739989,
"FALSE", 1, 0.0718225589534213, 0.0812126222473069, 0.789399900467283,
0.0812126222473069, "contBinom", 0.000762941481990983, 0, 0.000908320519612062,
"FALSE", 1, 4.03921343627838, 4.56729918505773, 0.0478686068240796,
4.56729918505773, "facGender", 0.0429069018154959, 0.0319449750880258,
0.0486422410464899, "TRUE", 4, 0.80133099599275, 0.906096808870625,
0.527940897125054, 3.6243872354825, "facFive <unicode> facGender",
0.0340488373879581, 0, 0.038991683611587, "FALSE", 1, 1.08113657469412,
1.22248409855909, 0.301615301439538, 1.22248409855909, "contBinom <unicode> facGender",
0.0114844688430931, 0.000852821268290123, 0.0135005148645175,
"TRUE", 4, 0.702807193128186, 0.79469202879871, 0.592348641591512,
3.17876811519484, "facFive <unicode> contBinom <unicode> facGender",
0.0298625261640639, 0, 0.0343623829478194, "TRUE", 1, 0.0608023312347904,
0.0687516127283772, 0.805871897218356, 0.0687516127283772, "contGamma",
0.000645878138801233, 0, 0.000769057857268532, "TRUE", 4, 0.215937435642924,
0.244169041669679, 0.928844739039309, 0.976676166678715, "facFive <unicode> contBinom",
0.00917525800068401, 0, 0.0108152916104723, "TRUE", 79, "",
1.13073974849567, "", 89.3284401311577, "Residuals", "", "",
""))
})
# Additional results ----
test_that("Homogeneity of Variances table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$homogeneityTests <- TRUE
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_assumptionsContainer"]][["collection"]][["anovaContainer_assumptionsContainer_leveneTable"]][["data"]]
jaspTools::expect_equal_tables(table, list(2.72159218177061, 1, 98, 0.102201011380302, 1.57819444559362))
})
# Contrasts verified with SPSS
# should we put this in verification project???
test_that("Contrasts table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facFive"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facFive"),
list(components="contGamma")
)
options$contrastCi <- TRUE
refTables <- list(
deviation = list("2 - 1, 2, 3, 4, 5", -0.200720248979633, 0.213572066533121, -0.939824445386938,
0.349716429527485, 94, -0.6247726, 0.2233321, "TRUE", "3 - 1, 2, 3, 4, 5", 0.326355030521638,
0.213419672724688, 1.52917032603005, 0.129580520593874, 94, -0.09739473, 0.7501048, "FALSE",
"4 - 1, 2, 3, 4, 5", -0.163006179525351, 0.213771994762986,
-0.762523546202018, 0.447656831363019, 94, -0.5874555, 0.2614431, "FALSE", "5 - 1, 2, 3, 4, 5",
0.17396802464162, 0.214943819683978, 0.809365093155025, 0.42034917204134, 94, -0.252808, 0.600744,
"FALSE"),
simple = list("2 - 1", -0.0641236223213591, 0.343277644425165, -0.186798130792167,
0.852221484436515, 94, -0.7457094, 0.6174622, "TRUE", "3 - 1", 0.462951657179913, 0.342949539026206,
1.34991188060625, 0.180285987179755, 94, -0.2179827, 1.1438860, "FALSE", "4 - 1", -0.0264095528670768,
0.337118098977225, -0.0783391723766838, 0.937724778659558, 94, -0.6957654, 0.6429463, "FALSE",
"5 - 1", 0.310564651299894, 0.345720813950643, 0.898310540667163,
0.371315247360673, 94, -0.3758721, 0.9970014, "FALSE"),
difference = list("2 - 1", -0.0641236223213588, 0.343277644425165, -0.186798130792167,
0.852221484436515, 94, -0.7457094, 0.6174622, "TRUE", "3 - 1, 2", 0.495013468340592, 0.292959120347178,
1.68970151109808, 0.0943992176806134, 94, -0.08666373, 1.07669067, "FALSE", "4 - 1, 2, 3",
-0.159352231153261, 0.275116843578239, -0.579216557883866, 0.563828556165238, 94, -0.7056032, 0.3868987,
"FALSE", "5 - 1, 2, 3, 4", 0.217460030802025, 0.268679774604972,
0.809365093155025, 0.42034917204134, 94, -0.31601, 0.75093, "FALSE"),
Helmert = list("1 - 2, 3, 4, 5", -0.170745783322843, 0.272701151705157, -0.626127840880747,
0.532748563022963, 94, -0.7122003, 0.3707087, "TRUE", "2 - 3, 4, 5", -0.313159207525603,
0.274645898379152, -1.14022896163293, 0.257087871753455, 94, -0.8584751, 0.2321567, "FALSE",
"3 - 4, 5", 0.320874107963505, 0.291421643235482, 1.10106478160314,
0.273679968781506, 94, -0.2577504, 0.8994986, "FALSE", "4 - 5", -0.336974204166971, 0.340503320254199,
-0.98963558979515, 0.324892868709605, 94, -1.0130515, 0.3391031, "FALSE"),
repeated = list("1 - 2", 0.0641236223213587, 0.343277644425165, 0.186798130792166,
0.852221484436516, 94, -0.6174622, 0.7457094, "TRUE", "2 - 3", -0.527075279501272, 0.336088495268646,
-1.56826337979812, 0.120179976824791, 94, -1.1943869, 0.1402363, "FALSE", "3 - 4", 0.489361210046989,
0.338686060204421, 1.444881462649, 0.151816532688129, 94, -0.1831079, 1.1618303, "FALSE",
"4 - 5", -0.336974204166971, 0.340503320254199, -0.98963558979515,
0.324892868709605, 94, -1.0130515, 0.3391031, "FALSE"),
polynomial = list("linear", 0.208344567699659, 0.240588115184784, 0.865980298069335,
0.388705894709638, 94, -0.2693488, 0.6860379, "TRUE", "quadratic", -0.0572582720504826,
0.238398138250912, -0.240179191291413, 0.810714351679878, 94, -0.5306034, 0.4160868, "FALSE",
"cubic", 0.0743566940225005, 0.243521087108506, 0.305339857444745,
0.760782433052004, 94, -0.4091601, 0.5578735, "FALSE", "quartic", 0.412402551299919, 0.237982457186854,
1.73291156068751, 0.0863908131438485, 94, -0.0601172, 0.8849223, "FALSE")
)
contrasts <- c("deviation", "simple", "difference", "Helmert", "repeated", "polynomial")
for (contrast in contrasts) {
options$contrasts <- list(list(contrast=contrast, variable="facFive"))
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
# table <- results[["results"]][["contrasts"]][["collection"]][[1]][["data"]]
table <- results[["results"]]$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[contrast]], label=paste("Table with contrast", contrast))
}
})
test_that("Post Hoc table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$postHocTerms <- list("facExperim")
options$postHocTypeStandard <- TRUE
options$postHocTypeStandardEffectSize <- TRUE
options$postHocCorrectionBonferroni <- TRUE
options$postHocCorrectionHolm <- TRUE
options$postHocCorrectionScheffe <- TRUE
options$postHocCorrectionTukey <- TRUE
options$postHocCorrectionSidak <- TRUE
options$postHocCi <- TRUE
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.21391801479091, 0.698555762823947, -0.0779045273305165,
-0.476129796984023, 0.32032074232299, "control", "experimental",
-0.0830902357515325, 0.698555762823947, -0.507658279613134,
0.698555762823947, 0.698555762823947, -0.388420937024623, 0.698555762823947,
0.341477808110069)
)
})
test_that("Marginal Means table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- "facExperim"
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="contGamma")
)
options$marginalMeanComparedToZero <- TRUE
options$marginalMeanTerms <- "facExperim"
refTables <- list(
none = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618, 97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE"),
Bonferroni = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618,97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE"),
Sidak = list("control", -0.230293705415766, 0.151049119466849, -0.530084395048618, 97,
0.0694969842170856, -1.52462792387419, 0.13060580966841, "TRUE",
"experimental", -0.147203469664234, 0.151049119466849, -0.446994159297086, 97,
0.152587219968618, -0.974540402379113, 0.332212375969363, "FALSE")
)
for (adjustment in c("none", "Bonferroni", "Sidak")) {
options$marginalMeanCiCorrection <- adjustment
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
table <- results[["results"]]$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table, refTables[[adjustment]], label=paste("Table with CI adjustment", adjustment))
}
})
test_that("Simple Main Effects table results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$fixedFactors <- c( "facFive", "facExperim")
options$covariates <- "contGamma"
options$modelTerms <- list(
list(components="facExperim"),
list(components="facFive"),
list(components="contGamma")
)
options$simpleMainEffectFactor <- "facExperim"
options$simpleMainEffectModeratorFactorOne <- "facFive"
options$simpleMainEffectModeratorFactorTwo <- ""
options$homogeneityTests <- TRUE
options$sumOfSquares <- "type1"
options$vovkSellke <- TRUE
results <- jaspTools::runAnalysis("Ancova", "debug.csv", options)
# table <- results[["results"]][["simpleEffects"]][["data"]]
table <- results$results$anovaContainer$collection$anovaContainer_simpleEffectsContainer$collection$anovaContainer_simpleEffectsContainer_simpleEffectsTable$data
jaspTools::expect_equal_tables(table, list(1, 0.350864897951646, 1, 0.350864897951646, 0.307765411627339,
0.580386465552355, "TRUE", 2, 2.72259751707838, 1, 2.72259751707838,
2.38815951789705, 0.125653693703876, "FALSE", 3, 0.300954391532799,
1, 0.300954391532799, 0.263985804028512, 0.608613599742434,
"FALSE", 4, 3.47907983036715, 1, 3.47907983036715, 3.05171717754702,
0.0839531695276169, "FALSE", 5, 0.313611321775938, 1, 0.313611321775938,
0.275087984294933, 0.601186887502708, "FALSE"))
})
# Error handling ----
test_that("Analysis handles errors", {
# Same as ANOVA
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "contNormal"
options$covariates <- "debInf"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components="contBinom"))
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
expect_identical(results[["results"]][["errorMessage"]],
"The following problem(s) occurred while running the analysis:<ul><li>Infinity found in debInf</li></ul>",
label="Inf covariate check")
})
# Andy Field tests ----
# should we put this into verification project???
## Chapter 6 ----
test_that("Field - Chapter 6 results match", {
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$covariates <- "Puppy_love"
options$modelTerms <- list(
list(components = "Dose"),
list(components = "Puppy_love")
)
options$marginalMeanTerms <- list(
list(components = "Dose")
)
options$marginalMeanBootstrap <- TRUE
options$marginalMeanBootstrapSamples <- 500
options$contrasts <- list(
list(contrast = "simple", variable = "Dose")
)
options$contrastCi <- TRUE
options$postHocTerms <- "Dose"
options$postHocCorrectionSidak <- TRUE
options$postHocCorrectionTukey <- FALSE
options$postHocTypeStandardBootstrap <- TRUE
options$postHocTypeStandardBootstrapSamples <- 500
options$postHocCi <- TRUE
set.seed(1) # seed for bootstrapping
results <- jaspTools::runAnalysis("Ancova", "Puppy Love.csv", options)
# main table
table <- results$result$anovaContainer$collection$anovaContainer_anovaTable$data
jaspTools::expect_equal_tables(table,
list("Dose", 25.1851942083821, 2, 12.592597104191, 4.14192880386377,
0.0274465428639959, "TRUE", "Puppy_love", 15.0757477099169,
1, 15.0757477099169, 4.9586811332752, 0.0348333806474409, "FALSE",
"Residuals", 79.0471155379463, 26, 3.0402736745364, "", "", "TRUE"))
# marginal means bootstrapping
table <- results$results$anovaContainer$collection$anovaContainer_marginalMeansContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list(1, 2.89513297007949, 0.00291277082733199, 0.454016672951108, 2.24194236484924,
4.40152013882763, "TRUE", 2, 4.7532347036809, 0.0469177160983509,
0.374655789875328, 3.87665072858526, 5.46307279640562, "FALSE",
3, 5.13975121640456, 0.0363834639636114, 0.660396429197245,
4.00063237957797, 6.57588583114463, "FALSE"))
# contrast
table <- results$results$anovaContainer$collection$anovaContainer_contrastContainer$collection[[1]]$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("2 - 1", 1.78568011481422, 0.849355306996751, 2.1023947223315,
0.0453535580857103, 26, 0.0398052774148459, 3.5315549522136,
"TRUE", "3 - 1", 2.22488132183556, 0.802810905470398, 2.77136409916095,
0.0101750138508479, 26, 0.574679871977612, 3.8750827716935,
"FALSE"))
# post hoc bootstrap
# adjusted the elements here because the new results also include t ratio and p-value
# The rest of the results matched up
table <- results$results$anovaContainer$collection$anovaContainer_postHocContainer$collection$anovaContainer_postHocContainer_postHocStandardContainer$collection[[1]]$data
jaspTools::expect_equal_tables(table,
list("TRUE", 0.538336190074754, -0.0194100366350722, 1, 2, -1.78324124481148,
-2.83630538964887, 0.129983128349044, -2.1023947223315, -0.858078871455782,
"FALSE", 0.763324066789791, -0.0155779657803694, 1, 3, -2.18556663740395,
-3.97107607430337, 0.0302155022603601, -2.77136409916095, -0.844312084720958,
"FALSE", 0.775278163963213, 0.00383207085470289, 2, 3, -0.410252544853603,
-2.12122714048355, 0.932499470707816, -0.541407321622228, 0.972524781215359
))
# interaction with covariate
options <- initClassicalAnovaOptions("Ancova")
options$dependent <- "Happiness"
options$fixedFactors <- "Dose"
options$covariates <- "Puppy_love"
options$modelTerms <- list(
list(components = "Dose"),
list(components = "Puppy_love"),
list(components = list("Dose", "Puppy_love"))
)
options$contrasts <- list(
list(contrast = "none", variable = "Dose")
)
options$descriptivePlotHorizontalAxis <- "Puppy_love"
options$descriptivePlotSeparatePlot <- "Dose"
options$descriptivePlotErrorBar <- TRUE
results <- jaspTools::runAnalysis("Ancova", "Puppy Love.csv", options)
table <- results$result$anovaContainer$collection$anovaContainer_anovaTable$data
jaspTools::expect_equal_tables(table,
list("Dose", 36.5575599693692, 2, 18.2787799846846, 7.483568988423,
0.00297956448464929, "TRUE", "Puppy_love", 17.1822242014479,
1, 17.1822242014479, 7.03462486521667, 0.0139474621264158, "FALSE",
"Dose <unicode> Puppy_love", 20.4265936571326, 2, 10.2132968285663,
4.18145584551367, 0.0276671129121842, "TRUE", "Residuals", 58.6205218808138,
24, 2.44252174503391, "", "", "TRUE"))
# descriptive plots
plot1 <- results$state$figures[[1]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot1, "PuppyLove1")
plot2 <- results$state$figures[[2]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot2, "PuppyLove2")
plot3 <- results$state$figures[[3]]$obj$subplots$mainPlot
jaspTools::expect_equal_plots(plot3, "PuppyLove3")
})
# Ordinal Restrictions ----
# this test also tests anova restrictions
options <- initClassicalAnovaOptions("Ancova")
options$contrasts <- list(list(contrast = "none", variable = "contBinom"))
options$covariates <- "contcor1"
options$customContrasts <- list()
options$dependent <- "contNormal"
options$fixedFactors <- "contBinom"
options$modelTerms <- list(list(components = "contBinom"), list(components = "contcor1"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
options$restrictedBootstrap <- TRUE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 100
options$restrictedInterceptInclusion <- TRUE
options$restrictedAvailableCoefficients <- TRUE
options$restrictedInformedHypothesisTestForAllModels <- TRUE
options$restrictedMarginalMeanForAllModels <- TRUE
options$restrictedModelSummaryForAllModels <- TRUE
options$restrictedModelComparison <- "unconstrained"
options$restrictedModelComparisonCoefficients <- TRUE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- TRUE
options$restrictedModelComparisonReference <- "Model 2"
options$restrictedModelComparisonWeights <- TRUE
options$restrictedMarginalMeanTerms <- list(list(variable = "contBinom"))
options$restrictedModels <- list(list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 1", summary = TRUE, syntax = ".Intercept. == 0"),
list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 2", summary = TRUE, syntax = ".Intercept. > 0"),
list(informedHypothesisTest = TRUE, marginalMean = TRUE,
name = "Model 3", summary = TRUE, syntax = ".Intercept. == 0\ncontBinom1 > 0"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Ancova", "test.csv", options)
## Models with equality restrictions only ----
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(0.280525836689152, 1.17764407696846, "F", "Type classical", 0.277836137015615,
1.17764407696846, "Wald", "Type classical", 0.278271309124288,
1.17551323040948, "Score", "Type classical"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.00631082413156547, 0, -0.0052940718699722, 0.00710593238617753,
0.0201427487900576, 0.152623965297405, 1, -0.541673449142385,
-0.283815957286218, 0.0347949556202055))
})
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(".Intercept.", 0, 0, 0, 0, "contBinom1", -0.28901764982443, -0.545890974144477,
0.152410269162387, 0.0216009611708116, "contcor1", 0.135225724991321,
-0.100746062285325, 0.120094833739718, 0.383316032355158))
})
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, 0))
})
## Models with in-equality restrictions only ----
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(0.141270077631025, 4.86463661917856, "F", "Type global", 0.143009738633418,
4.83608638110945, "LRT", "Type global", 0.150214932639365, 4.72138311792181,
"Score", "Type global", 1, 0, "F", "Type A", 1, 0, "LRT", "Type A",
0.495597802688024, 0.000122398295544508, "Score", "Type A",
0.140262918344576, 1.17764407696847, "F", "Type B", 0.13734744589723,
1.20675538818887, "LRT", "Type B", 0.141706057440304, 1.16351819744612,
"Score", "Type B", 0.859737081655424, -1.08519310584267, "t",
"Type C"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 2_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0401499620148025, 0, -0.0052940718699722, 0.00762632152811411,
0.147781545335185, 0.15219130722473, 1, -0.541673449142385,
-0.285158551298243, 0.0345874620687721))
})
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(".Intercept.", 0, 0, 0.0390913797847908, 0.135449151526049, "contBinom1",
-0.294040143250604, -0.553042198939247, 0.15095032207897, 0.0149620362407961,
"contcor1", 0.133967448915338, -0.100746062285325, 0.119436094843916,
0.367522848917437))
})
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(1, 0, 0, 0))
})
## Models with in-equality and equality restrictions ----
test_that("Ordinal restrictions: Informative Hypothesis Tests table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_ihtTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.227906731091751, 2.29055827266306, "F", "Type global", 0.233102480397295,
2.24800995009798, "LRT", "Type global", 0.236222676760002, 2.22294941447971,
"Score", "Type global", 1, 0, "F", "Type A", 1, 0, "LRT", "Type A",
0.492283243845526, 0.00037613437452225, "Score", "Type A", 0.107200891136129,
3.75172242348396, "F", "Type B", 0.104890547890444, 3.79483181920034,
"LRT", "Type B", 0.112908725975451, 3.64925570161498, "Score",
"Type B"))
})
test_that("Ordinal restrictions: Marginal means table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_marginalMeansContainer_contBinom"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.00615640047820512, 0, -0.0040934464864444, 0.00784355639438009,
0.021670734045724, 0.0126705139715333, 1, -0.0040934464864444,
0.00795127192950033, 0.0347949556202055))
})
test_that("Ordinal restrictions: Coefficients table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(".Intercept.", 0, 0, 0, 0, "contBinom1", 0, 0, 0.0115253766048944,
0.0216009611708116, "contcor1", 0.149262692395377, -0.077898189675906,
0.117156155274091, 0.41239355557723))
})
test_that("Ordinal restrictions: Restriction Matrix table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 3_modelSummaryContainer_restrictionMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0, 0, 0, 0, 1, 0, 0))
})
## Model comparison ----
test_that("Ordinal restrictions: Coefficients Comparison table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_coefficientsTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, 0, 0, 0, 0, ".Intercept.", -0.151936095480945, 0, 0, 0,
-0.262153994575154, -0.262153994575154, 0, "contBinom1", -0.107591826354404,
0, 0, 0, 0.141934956659658, 0.141934956659658, 0.158227556203348,
"contcor1", 0.159396509032993))
})
test_that("Ordinal restrictions: Relative GORIC-Weights table results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison"]][["collection"]][["anovaContainer_ordinalRestrictions_modelComparison_comparisonMatrix"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1.64872127070013, 2.21231220567314, 1.48679427057462, "Model 1",
0.606530659712633, 1, 1.34183518159724, 0.901786309788586, "Model 2",
0.45201576768218, 0.745248010869458, 1, 0.672054453599252, "Model 3",
0.672588010184839, 1.10891015880962, 1.48797466432133, 1, "Unconstrained"
))
})
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(298.462647640822, 0.366148215276508, -146.231323820411, "Model 1",
3, 1.64872127070013, 299.462647640822, 0.222080118564264, -146.231323820411,
"Model 2", 3.5, 1, 300.050724071833, 0.165504766613671, -147.525362035916,
"Model 3", 2.5, 0.745248010869458, 299.255892252633, 0.246266899545557,
-145.627946126316, "Unconstrained", 4, 1.10891015880962))
})
## see https://restriktor.org/tutorial/example2.html for comparison ----
options <- initClassicalAnovaOptions("Ancova")
options$contrasts <- list(list(contrast = "none", variable = "Group"))
options$covariates <- "Age"
options$customContrasts <- list()
options$dependent <- "Anger"
options$fixedFactors <- "Group"
options$modelTerms <- list(list(components = "Group"), list(components = "Age"))
options$rainCloudHorizontalAxis <- ""
options$rainCloudHorizontalDisplay <- FALSE
options$rainCloudYAxisLabel <- ""
options$rainCloudSeparatePlots <- ""
options$restrictedBootstrap <- FALSE
options$restrictedBootstrapCiLevel <- 0.95
options$restrictedBootstrapSamples <- 1000
options$restrictedInterceptInclusion <- FALSE
options$restrictedAvailableCoefficients <- FALSE
options$restrictedInformedHypothesisTestForAllModels <- FALSE
options$restrictedMarginalMeanForAllModels <- FALSE
options$restrictedModelSummaryForAllModels <- FALSE
options$restrictedModelComparison <- "none"
options$restrictedModelComparisonCoefficients <- FALSE
options$restrictedModelComparisonCoefficientsHighlight <- TRUE
options$restrictedModelComparisonMatrix <- FALSE
options$restrictedModelComparisonReference <- "Model 1"
options$restrictedModelComparisonWeights <- FALSE
options$restrictedMarginalMeanTerms <- list(list(variable = "Group"))
options$restrictedModels <- list(list(informedHypothesisTest = FALSE, marginalMean = TRUE,
name = "Model 1", summary = FALSE, syntax = "GroupNo < GroupPhysical\nGroupPhysical == GroupBehavioral\nGroupBehavioral < GroupBoth"))
options$restrictedHeterogeneityCorrection <- "none"
set.seed(1)
results <- jaspTools::runAnalysis("Ancova", "AngerManagement.csv", options)
test_that("Ordinal restrictions: Adjuested marginal means results match", {
table <- results[["results"]][["anovaContainer"]][["collection"]][["anovaContainer_ordinalRestrictions"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer"]][["collection"]][["anovaContainer_ordinalRestrictions_Model 1_marginalMeansContainer_Group"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Behavioral", 0.467592714510265, 1.95108158220025, "Both", 0.702820099125852,
4.06863411619283, "No", 0.69741742112576, -0.17079728059332,
"Physical", 0.467592714510269, 1.95108158220025))
})
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.