tests/testthat/test-mixedmodelslmm.R
In jasp-stats/jaspMixedModels: Mixed Models Module for JASP
context("Linear Mixed Models")
### default, all selected output using Satterwhite testMethod
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "cA", values = c("1", "2", "1", "2",
"1", "2", "1", "2", "1", "2", "1", "2", "1", "2", "1", "2", "1",
"2")), list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "cB", values = c("1", "1", "2", "2",
"3", "3", "1", "1", "2", "2", "3", "3", "1", "1", "2", "2", "3",
"3")), list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "y_beta", values = c("-1", "-1", "-1",
"-1", "-1", "-1", "0", "0", "0", "0", "0", "0", "1", "1", "1",
"1", "1", "1")), list(isContrast = TRUE, levels = c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "16", "17", "18"), name = "Contrast 1", values = c("1",
"-1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), list(isContrast = TRUE, levels = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18"), name = "Contrast 2", values = c("0",
"1", "-1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "Variable4"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "Variable1"),
list(components = "Variable2"),
list(components = c("Variable1", "Variable2")),
list(components = "Variable7"),
list(components = c("Variable1", "Variable7")),
list(components = c("Variable2", "Variable7")),
list(components = c("Variable1", "Variable2", "Variable7")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("Variable1", "Variable2", "Variable7")
options$marginalMeansTerms <- list(list(variable = "Variable1"), list(variable = "Variable2"),
list(variable = "Variable7"))
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$testMethod <- "satterthwaite"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "Variable0"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "jitter"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list(list(variable = "Variable2"))
options$plotHorizontalAxis <- list(list(variable = "Variable1"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(
list(randomSlopes = TRUE, value = "Variable1"),
list(randomSlopes = FALSE, value = "Variable2"),
list(randomSlopes = FALSE, value = c("Variable1", "Variable2" )),
list(randomSlopes = FALSE, value = "Variable7"),
list(randomSlopes = FALSE, value = c("Variable1", "Variable7")),
list(randomSlopes = FALSE, value = c("Variable2", "Variable7")),
list(randomSlopes = FALSE, value = c("Variable1", "Variable2", "Variable7"))), value = "Variable0"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- TRUE
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$randomEffectEstimate
options$interceptTest <- FALSE
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6"), name = "cB", values = c("1", "2", "3", "1", "2", "3"
)), list(isContrast = FALSE, levels = c("1", "2", "3", "4", "5",
"6"), name = "cA", values = c("1", "1", "1", "2", "2", "2")),
list(isContrast = TRUE, levels = c("1", "2", "3", "4", "5",
"6"), name = "Contrast 1", values = c("1", "-1", "0", "0",
"0", "0")), list(isContrast = TRUE, levels = c("1", "2",
"3", "4", "5", "6"), name = "Contrast 2", values = c("0",
"1", "0", "0", "0", "0")))
options$trendsTrendVariable <- list(list(variable = "Variable7"))
options$trendsVariables <- list(list(variable = "Variable2"), list(variable = "Variable1"))
options$type <- "3"
set.seed(1)
dataset <- structure(list(Variable0 = c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), Variable1 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Variable2 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), Variable3 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), Variable4 = c(-0.653989689,
0.597847379, 0.53124944, -0.919283666, 1.549214002, -0.964337181,
0.758624407, -0.633353539, 0.093434858, 0.081944247, 1.251310302,
1.399815493, -0.942716455, -0.495601118, 0.917930091, 0.344838602,
-1.226474961, -1.405114801, 0.686194991, 0.017571144, -0.337816215,
0.384985065, 1.430491376, 1.866825203, 0.752814251, 1.31909783,
0.447452196, -1.346206879, 0.479402493, -0.848513454, 0.850545592,
-3.037579176, 0.545769791, -1.182557897, -0.128004891, 1.11267647,
0.616535768, -0.669227302, 0.076114909, 0.816454623, 0.422781459,
-0.094856662, -0.374461304, 0.691431944, -1.528712893, 1.05380469,
-0.545337415, -0.026770503, -2.189233221, -0.616004017, 1.150339483,
-0.133211268, 0.252652295, 1.399980471, -0.513151105, 1.117392323,
-0.610869543, 0.331300534, -0.992903801, -0.895568118, 0.623585941,
1.05882918, -1.58627026, 0.947877674, 2.033833295, 0.179956552,
1.573438425, -1.694333909, 1.079726669, 1.508240792, 0.90600351,
-0.290763172, -0.496024515, 1.946237186, -0.893948592, 0.830769682,
0.440062416, -0.57837005, 1.790515054, 0.137924932, 0.055424081,
0.547806104, -0.748529992, 1.71335853, 1.808028443, 0.017313744,
0.988861738, -1.43975293, 0.338108584, -0.365015598, 1.86933575,
1.699421632, 0.308476418, 1.556020356, -0.952816041, 1.874185874,
1.104225239, -1.318714635, 1.431532182, 0.756206118, 1.892566353,
-0.933657521, 1.277498726, 0.407372551, 1.021542579, 0.74476069,
0.571588797, -0.762850791, -2.287992665, -0.596915582, 2.920177191,
-0.853565433, -0.771818751, 0.492465518, -0.455610621, 2.667902824,
1.953870427, 0.14233637, -1.188999386, -0.185194402, 2.751932451,
0.1714291, 0.495442662, 0.007490023, -1.381723611, -0.360288418,
1.228175718, 1.270669023, -0.317481349, -1.121300988, 0.248833912,
-0.936079972, -0.019929997, -0.752375481, 1.745747293, 0.005492604,
0.407922866, 0.061474844, 0.69299688, 0.597159811, 2.949895836,
-0.24811046, 0.034494308, -0.78621074, 0.614844377, 1.095323201,
0.672793259, 0.057114702, 0.072950494, 0.346984663, -0.452874548,
-0.114694466, 0.536167379, 2.672375374, 0.618138653, 2.749195306,
2.199564155, -1.821705402, 0.662389551, -0.086448818, 2.350030519,
1.42969294, -0.082903446, 1.526255915, -0.77415644, 1.646198365,
0.550819959, -1.912875322, -0.170004512, -0.153966373, 3.216473665,
-2.384187974, 0.730941972, -0.065087507, 1.330153598, 1.27618167,
1.956183459, 0.436215424, -1.232486611, -0.455381093, 0.83128861,
0.152153259, -0.45491991, -0.256058166, -0.193076508, -0.334064589,
0.215860632, -1.749746886, 0.358765965, 0.211328495, -0.112055855,
0.945593904, 0.532860661, 0.01631963, -0.695297425, -0.182978288,
1.940983578, -1.052570114, 1.265312559, -0.21744826, 2.122842478,
1.291844321, -0.694666126, 2.001880096, 0.977066134, 1.383692522,
-0.085431624, -1.152918968, -1.621837649, 0.647353218, 1.079628054,
-0.220121984, -0.562039994, 2.441868908, 0.688842095, 0.572532136,
1.049670153, -1.439036257, 0.673783789, 0.810812932, -0.557921732,
-0.055039468, 1.065618622, -0.653057442, -0.537812988, 0.818735764,
0.874036767, -0.264722867, -1.083081897, 0.132684797, 1.282776406,
0.980202012, 0.912757975, 0.395195197, -1.294487302, -0.149088612,
1.042843997, -1.213788746, -1.842337004, -0.087241521, 1.759125287,
-0.65217472, -0.468828649, -1.128895132, 0.355130761, -1.13143679,
-0.231067871, -1.353450121, -1.710583197, 0.186715205, -0.543962675,
0.292958499, -0.4283386, 2.670479768, 2.379591267, 1.278406268,
2.298737024, -0.737706867, 1.468454399, 0.055981228, 0.149251786,
-0.332347905, -0.191862331, -0.012294677, 0.139243256, -1.123574851,
-0.034383926, -0.512343287, 0.812126437, 0.486944352, 0.595358492,
1.224605923, 0.863959031, -1.789032311, 0.489475508, 2.019401428,
2.492383813, 0.177655849, -0.587024392, 0.299497534, 1.602179556,
-1.502343948, -1.37596223, 0.74894869, 0.664588217, 1.321486377,
1.888462109, -0.903168893, -3.201437624, -0.535609031, 0.554010178,
-0.547718747, 1.542488798, 1.851156869, 0.154379085, 0.617288371,
1.273637679, -1.466949312, -0.150368723, -0.256217966), Variable5 = c(1L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L), Variable6 = c(2L,
2L, 4L, 3L, 8L, 0L, 2L, 1L, 1L, 1L, 6L, 4L, 0L, 1L, 2L, 1L, 0L,
0L, 4L, 1L, 0L, 1L, 3L, 10L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 0L,
2L, 0L, 3L, 4L, 2L, 1L, 0L, 2L, 1L, 1L, 0L, 2L, 0L, 1L, 0L, 1L,
0L, 0L, 5L, 1L, 2L, 3L, 1L, 3L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 4L,
4L, 2L, 6L, 0L, 4L, 6L, 7L, 1L, 2L, 12L, 0L, 2L, 1L, 1L, 6L,
2L, 1L, 0L, 0L, 12L, 2L, 0L, 2L, 1L, 1L, 2L, 8L, 5L, 2L, 3L,
0L, 7L, 1L, 0L, 2L, 3L, 7L, 0L, 4L, 2L, 4L, 3L, 3L, 0L, 0L, 2L,
11L, 1L, 0L, 3L, 0L, 12L, 5L, 2L, 1L, 0L, 19L, 2L, 1L, 1L, 0L,
0L, 4L, 1L, 0L, 0L, 5L, 1L, 0L, 1L, 4L, 0L, 1L, 2L, 1L, 0L, 16L,
1L, 0L, 0L, 1L, 2L, 3L, 4L, 1L, 0L, 2L, 0L, 1L, 12L, 0L, 18L,
7L, 0L, 2L, 0L, 11L, 5L, 1L, 5L, 0L, 7L, 4L, 0L, 1L, 0L, 23L,
0L, 2L, 1L, 1L, 6L, 11L, 1L, 0L, 0L, 1L, 0L, 1L, 2L, 1L, 0L,
2L, 0L, 1L, 1L, 1L, 3L, 2L, 0L, 0L, 2L, 5L, 0L, 5L, 2L, 8L, 4L,
1L, 10L, 3L, 4L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 7L, 2L, 1L, 3L,
0L, 1L, 2L, 0L, 1L, 6L, 0L, 1L, 3L, 2L, 0L, 0L, 1L, 2L, 3L, 4L,
2L, 1L, 1L, 2L, 1L, 0L, 1L, 4L, 0L, 0L, 0L, 3L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 18L, 10L, 4L, 12L, 0L, 9L, 1L, 1L, 1L, 0L, 3L,
3L, 0L, 0L, 0L, 4L, 1L, 1L, 4L, 1L, 0L, 4L, 5L, 14L, 2L, 1L,
1L, 3L, 0L, 0L, 2L, 2L, 5L, 9L, 0L, 0L, 2L, 1L, 0L, 3L, 6L, 0L,
4L, 6L, 0L, 1L, 1L), Variable7 = c(0.427219425,
0.220688309, 0.570053273, 0.231100824, 0.131067892, 1, 0.076567255,
0.405903343, 0.474725634, 0.990018262, 0.192843674, 0.224774893,
1, 0.740614383, 0.851823829, 0.653432541, 1, 1, 0.624686075,
0.607677759, 1, 0.105184005, 0.178594546, 0.002517108, 0.769499354,
0.543466466, 0.904824708, 0.597340464, 0.102275177, 0.939954609,
0.644957841, 1, 0.092147577, 1, 0.052178635, 0.108955976, 0.103881947,
0.307079922, 1, 0.640638174, 0.998201291, 0.358593487, 1, 0.098677587,
1, 0.625249783, 1, 0.94040242, 1, 1, 0.108139376, 0.01932307,
0.068532055, 0.319257561, 0.317226161, 0.042847799, 1, 0.907828255,
1, 1, 1, 0.817368982, 1, 0.120666731, 0.211511707, 0.157775565,
0.160599299, 1, 0.223432249, 0.071663969, 0.152479944, 0.4318303,
0.064199554, 0.084701885, 1, 0.214182802, 0.250437733, 0.64748267,
0.221051177, 0.8792932, 0.102889819, 1, 1, 0.004530114, 0.056033505,
1, 0.107350229, 0.341537754, 0.560227328, 0.56788941, 0.029397749,
0.128741443, 0.340319883, 0.438422074, 1, 0.363424137, 0.64064305,
1, 0.669024544, 0.097899144, 0.184498989, 1, 0.626521215, 0.646898637,
0.263503489, 0.147692887, 0.131565273, 1, 1, 0.132856326, 0.045526628,
0.585667955, 1, 0.549235367, 1, 0.020705723, 0.029918293, 0.456543362,
0.856084292, 1, 0.033240023, 0.000931051, 0.141747406, 0.691832666,
1, 1, 0.119549448, 0.459920161, 1, 1, 0.63650842, 0.885468904,
1, 0.66929208, 0.086900934, 1, 0.00297873, 0.387909833, 0.16488008,
1, 0.154010162, 0.688411372, 1, 1, 0.374140598, 0.489588998,
0.656373572, 0.253907352, 0.341392293, 1, 0.341498251, 1, 0.039745196,
0.049899045, 1, 0.004291585, 0.064142592, 1, 0.162245865, 1,
9.52e-05, 0.334425874, 0.55930246, 0.023006289, 1, 0.092510628,
0.179388773, 1, 0.298066521, 1, 0.020858126, 1, 0.219817914,
0.381100323, 0.383832334, 0.031687818, 0.021982156, 0.100909454,
1, 1, 0.90886201, 1, 0.133518321, 0.321278177, 0.977561022, 1,
0.377386837, 1, 0.924968583, 0.22384265, 0.001476047, 0.141017063,
0.079471224, 1, 1, 0.003846134, 0.056555377, 1, 0.120304732,
0.289193756, 0.021411919, 0.003588727, 0.815915327, 0.186839249,
0.124749619, 0.215773542, 1, 1, 1, 0.09331221, 0.227188488, 1,
1, 0.304550487, 0.242236769, 0.915177329, 0.038263021, 1, 0.690036211,
0.115890253, 1, 0.377763011, 0.214936317, 1, 0.409689095, 0.016033388,
0.696195914, 1, 1, 0.579652169, 0.111762879, 0.288249519, 0.685298051,
0.029751715, 0.34902306, 0.740039564, 0.160866749, 0.934899752,
1, 0.089958756, 0.101130973, 1, 1, 1, 0.000367582, 1, 1, 0.501846786,
1, 1, 1, 1, 1, 0.012013178, 0.003237151, 0.37217903, 0.009178291,
1, 0.091345794, 0.743160189, 0.983801698, 0.74850986, 1, 0.152319066,
0.47288842, 1, 1, 1, 0.006951365, 0.522907566, 0.477519199, 0.077229532,
0.344051985, 1, 0.268246943, 0.222762224, 0.013367555, 0.157375875,
0.794346784, 0.403181111, 0.097897052, 1, 1, 0.243050875, 0.188878481,
0.067540856, 0.169211418, 1, 1, 0.052260709, 0.052824504, 1,
0.12313927, 0.085084118, 1, 0.018040391, 0.055642594, 1, 0.901317881,
0.859124256)), class = "data.frame", row.names = c(NA, -300L))
dataset$Variable1 <- as.factor(dataset$Variable1)
dataset$Variable2 <- as.factor(dataset$Variable2)
results <- jaspTools::runAnalysis("MixedModelsLMM", dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 35.30", "Variable1", 0.213945078054209, 1.60174616940147,
"2, 277.59", "Variable2", 0.309343042485159, 1.17827777546627,
"1, 282.17", "Variable7", 1.05836297841345e-37, 224.177295418877,
"2, 277.57", "Variable1<unicode><unicode><unicode>Variable2",
0.945697296669635, 0.0558439754925916, "1, 262.96", "Variable1<unicode><unicode><unicode>Variable7",
0.363781953823721, 0.827664139846734, "2, 280.56", "Variable2<unicode><unicode><unicode>Variable7",
0.400279660081596, 0.918586340240702, "2, 280.51", "Variable1<unicode><unicode><unicode>Variable2<unicode><unicode><unicode>Variable7",
0.885178421723061, 0.122019094979098))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, 0.134346556802761, 0.736155006201862, 0.342393423701632,
1, 0.000248073210555289, 0.200902458211565, 3.66424090951958,
1.12991658870209, 2, 1, 0.134346556802761, 0.970099302524613,
0.635342206174057, 2, 1.34837403764265e-08, 0.170797575359076,
5.67981893469579, 1.30485639887517, 1, 2, 0.134346556802761,
1.00178427381358, 0.616871475069543, 3, 3.37766585957781e-07,
0.196387689661736, 5.10105432544716, 1.38669707255761, 2, 2,
0.134346556802761, 1.19068283055357, 0.864944840125712, 4, 7.81661362562319e-13,
0.166195906147886, 7.16433309430655, 1.51642082098142, 1, 3,
0.134346556802761, 0.844388701356924, 0.450412814048416, 5,
2.66100137260325e-05, 0.201011799408632, 4.20069221727819, 1.23836458866543,
2, 3, 0.134346556802761, 0.969527995376983, 0.647253028619123,
6, 3.71672516615009e-09, 0.164429024869806, 5.89633123558722,
1.29180296213484, 1, 1, 0.527784183943333, 0.150291764934123,
-0.157327577404923, 7, 0.33828062857611, 0.156951528071693,
0.957568026132707, 0.457911107273169, 2, 1, 0.527784183943333,
0.325419304660903, 0.0636346562238179, 8, 0.0148345838940844,
0.13356605044889, 2.43639235844161, 0.587203953097988, 1, 2,
0.527784183943333, 0.349095318512296, 0.0429110530350465, 9,
0.0254404251175091, 0.156219332545083, 2.23464863679116, 0.655279583989545,
2, 2, 0.527784183943333, 0.390440750717584, 0.126863453705241,
10, 0.00369226595348432, 0.134480683875524, 2.90332216840136,
0.654018047729927, 1, 3, 0.527784183943333, 0.108168134737725,
-0.199035981744495, 11, 0.490122936314294, 0.156739674251878,
0.690113305734582, 0.415372251219945, 2, 3, 0.527784183943333,
0.188766091926349, -0.0741969072013631, 12, 0.159443399709109,
0.134167260828225, 1.40694600725305, 0.451729091054061, 1, 1,
0.921221811083906, -0.435571476333615, -0.793523489764854, 13,
0.0170805669841959, 0.182631934185893, -2.38496886251156, -0.0776194629023758,
2, 1, 0.921221811083906, -0.319260693202807, -0.66519969409243,
14, 0.0704798360881783, 0.176502733528956, -1.80881444054481,
0.0266783076868157, 1, 2, 0.921221811083906, -0.303593636788984,
-0.677701723400159, 15, 0.111713908535933, 0.190874980133356,
-1.59053657308505, 0.0705144498221917, 2, 2, 0.921221811083906,
-0.4098013291184, -0.777150910375174, 16, 0.0287818326861408,
0.187426699752843, -2.18646185233374, -0.0424517478616264, 1,
3, 0.921221811083906, -0.628052431881474, -0.990856730839675,
17, 0.00069155877166049, 0.185107635558589, -3.3929039717148,
-0.265248132923273, 2, 3, 0.921221811083906, -0.591995811524284,
-0.947636646709221, 18, 0.00110420758749543, 0.181452739943277,
-3.26253443022874, -0.236354976339348))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -0.177418257095192, 0.150291764934123, 0.478001786963439,
2, 1, 0.0517368457565581, 0.325419304660903, 0.599101763565247,
1, 2, 0.0225417565025579, 0.349095318512296, 0.675648880522034,
2, 2, 0.115142857421496, 0.390440750717584, 0.665738644013672,
1, 3, -0.219231561530436, 0.108168134737725, 0.435567831005885,
2, 3, -0.0860428729184415, 0.188766091926349, 0.463575056771139
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(16.2306089536174, 1.19116029635175, 2.18869798960833e-08, 0.118334626707059,
10.0660333285244, "Intercept", 35.3032297366049, -0.105610070763506,
0.213945086124028, 0.0834465708091049, -1.2656010874923, "Variable1 (1)",
277.81260417867, -0.127937264306791, 0.237630444901206, 0.108102548269185,
-1.18348055947964, "Variable2 (1)", 276.610380395872, 0.153138347192313,
0.151182900097123, 0.106394322322049, 1.43934698628723, "Variable2 (2)",
282.166679989969, -1.77938274268387, 1.05836241786697e-37, 0.118842987431374,
-14.9725514407098, "Variable7", 277.437452308716, -0.0214041122540725,
0.84328700844482, 0.108169334244241, -0.197875972923556, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)",
274.504412706236, -0.0140315797377453, 0.895493628531909, 0.106722015925341,
-0.13147783628414, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)",
262.958608266277, 0.106290129469689, 0.363781940974619, 0.116833087810459,
0.90976050930132, "Variable1 (1)<unicode><unicode><unicode>Variable7",
280.768969888669, 0.215547519201563, 0.187445142898525, 0.163121068333652,
1.32139595089383, "Variable2 (1)<unicode><unicode><unicode>Variable7",
278.764702187189, -0.0670738935266586, 0.685011814699131, 0.165183208902999,
-0.406057576748292, "Variable2 (2)<unicode><unicode><unicode>Variable7",
280.578153316786, -0.0315428855120625, 0.846958786302005, 0.163283458128582,
-0.193178695953531, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)<unicode><unicode><unicode>Variable7",
276.271849657447, 0.081227680668467, 0.625068593623528, 0.166032423105515,
0.489227821585461, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)<unicode><unicode><unicode>Variable7"
))
})
test_that("Variable0: Random Effect Estimates table results match", {
table <- results[["results"]][["REEstimatesSummary"]][["collection"]][["REEstimatesSummary_REEstimates1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.402775358291333, 0.142689540606, 1, -0.106160422159156, 0.0324230307761484,
2, -0.0831533638398433, 0.0202788224676554, 3, 0.0760706510403953,
-0.036860778559647, 4, 0.0307796094651438, 0.0141641809314601,
5, 0.201686511007182, 0.00302378484776545, 6, 0.247309398961227,
0.0493268239762933, 7, -0.48908276442082, -0.0653495722257102,
8, -0.231455211148659, -0.135657369441085, 9, -0.0487697671967913,
-0.0240384633788782, 10))
})
test_that("Variable0: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", 0.533641870616633, 1, "Variable1 (1)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.769891689595146, 0.592733213707669))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.284187792131844, 0.0807627011967724, "Intercept", 0.108185898440959,
0.0117041886214776, "Variable1 (1)"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", -0.233944296322751, -0.696303715348727,
0.642686601522133, 0.235901997522918, -0.991701209736571, 0.228415122703225,
"Contrast 2", "<unicode>", -0.0316849712889628, -0.4865200168714,
0.891397489278759, 0.232062960937098, -0.136536098483856, 0.423150074293474
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.169850846547693, -0.620941632576639,
0.673775481501695, 0.403472964484043, 0.420972063803325, 0.960643325672026,
"Contrast 2", "<unicode>", -1.65893882607238, -2.22903094692442,
2.34889273398185e-08, 0.290868671745428, -5.70339464926737,
-1.08884670522034))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10, 300))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(769.200410528667, 828.460930123167, 737.200410528667, 16, -368.600205264334
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -2.03987627521154, 1, 0.281019600376027, -1.48908797952469,
-0.938299683837836, 1, 2, -2.22903094692442, 2, 0.290868671745428,
-1.65893882607238, -1.08884670522034, 1, 3, -2.43415334868443,
3, 0.287200335862819, -1.8712510340455, -1.30834871940657, 2,
1, -2.19158075804869, 4, 0.282147169524912, -1.63858246743994,
-1.0855841768312, 2, 2, -2.60826616216846, 5, 0.293011361611596,
-2.03397444634869, -1.45968273052893, 2, 3, -2.52972123409858,
6, 0.27819875045024, -1.98446170267207, -1.43920217124555))
})
}
### no correlations between random effects, Kernwald Roggers testMethod, custom values
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5",
"6", "7"), name = "contGamma", values = c("-3.2", "0", "3.2",
"-3.2", "0", "3.2")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5", "6", "7"), name = "contBinom", values = c("0",
"0", "0", "1", "1", "1")), list(isContrast = TRUE, levels = c("2",
"3", "4", "5", "6", "7"), name = "Contrast 1", values = c("-1",
"1", "0", "0", "0", "0")), list(isContrast = TRUE, levels = c("2",
"3", "4", "5", "6", "7"), name = "Contrast 2", values = c("0",
"0", "1", "-1", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "contGamma"), list(components = "contBinom"),
list(components = "facExperim"), list(components = "facGender"),
list(components = c("contGamma", "contBinom")),
list(components = c("contGamma", "facExperim")),
list(components = c("contGamma", "facGender")),
list(components = c("contBinom", "facExperim")),
list(components = c("contBinom", "facGender")),
list(components = c("facExperim", "facGender")),
list(components = c("contGamma", "contBinom", "facExperim")),
list(components = c("contGamma", "contBinom", "facGender")),
list(components = c("contGamma", "facExperim", "facGender")),
list(components = c("contBinom", "facExperim", "facGender")),
list(components = c("contGamma", "contBinom", "facExperim", "facGender")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("contGamma", "contBinom", "facExperim", "facGender")
options$marginalMeansTerms <- list(list(variable = "contGamma"), list(variable = "contBinom"))
options$marginalMeansPAdjustment <- "none"
options$marginalMeansComparison <- TRUE
options$marginalMeansComparisonWith <- 1
options$marginalMeansContrast <- TRUE
options$marginalMeansDf <- "satterthwaite"
options$marginalMeansSd <- 3.2
options$testMethod <- "kenwardRoger"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "bottom"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "violin"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "whiteBackground"
options$plotSeparateLines <- list(list(variable = "facExperim"))
options$plotHorizontalAxis <- list(list(variable = "contBinom"))
options$vovkSellke <- TRUE
options$randomEffects <- list(list(correlations = FALSE, randomComponents = list(
list(randomSlopes = TRUE, value = "contGamma"),
list(randomSlopes = TRUE, value = "contBinom"),
list(randomSlopes = TRUE, value = "facExperim"),
list(randomSlopes = TRUE, value = "facGender"),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom")),
list(randomSlopes = FALSE, value = c("contGamma", "facExperim")),
list(randomSlopes = FALSE, value = c("contGamma", "facGender")),
list(randomSlopes = FALSE, value = c("contBinom", "facExperim")),
list(randomSlopes = FALSE, value = c("contBinom", "facGender")),
list(randomSlopes = FALSE, value = c("facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facExperim")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contBinom", "facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facExperim", "facGender"))),
value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsPAdjustment <- "mvt"
options$trendsComparison <- TRUE
options$trendsComparisonWith <- 9
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5"),
name = "facExperim", values = c("control", "experimental",
"control", "experimental")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5"), name = "facGender", values = c("f", "f", "m",
"m")), list(isContrast = TRUE, levels = c("2", "3", "4", "5"),
name = "Contrast 1", values = c("-1", "0", "1", "0")))
options$trendsDf <- "Kenward-Roger"
options$trendsTrendVariable <- list(list(variable = "contGamma"))
options$trendsVariables <- list(list(variable = "facExperim"), list(variable = "facGender"))
options$type <- "3"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 18.41", "contGamma", 0.653715765378179, 0.207944313682265,
1, "1, 41.32", "contBinom", 0.980966063782961, 0.00057612629834211,
1, "1, 31.67", "facExperim", 0.150390029707028, 2.17227239617178,
1.29117929695413, "1, 35.58", "facGender", 0.089368481449152,
3.04996399298381, 1.70453649302062, "1, 70.43", "contGamma<unicode><unicode><unicode>contBinom",
0.810447815369331, 0.0579608717477727, 1, "1, 33.07", "contGamma<unicode><unicode><unicode>facExperim",
0.164381775352359, 2.02216685079563, 1.23947863936158, "1, 69.88",
"contBinom<unicode><unicode><unicode>facExperim", 0.280529623544102,
1.18277127807505, 1.03170441188499, "1, 72.17", "contGamma<unicode><unicode><unicode>facGender",
0.248688856348377, 1.3524058214999, 1.0630397961954, "1, 76.01",
"contBinom<unicode><unicode><unicode>facGender", 0.584571227409116,
0.301470719596351, 1, "1, 76.13", "facExperim<unicode><unicode><unicode>facGender",
0.322904328936314, 0.989958425613626, 1.0078590764186, "1, 67.54",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim",
0.293532980431837, 1.12073031564216, 1.02244816376139, "1, 77.86",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facGender",
0.596009824311473, 0.283380689688555, 1, "1, 61.91", "contGamma<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.600511416846301, 0.277066284972791, 1, "1, 76.47", "contBinom<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.277160395116976, 1.19798395374794, 1.03441284987856, "1, 75.20",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.148676153260924, 2.1292478607151, 1.2982094549363))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, -2.8707548076634, 71.9898361962622, 0.162066195536834, -0.776927722559118,
1, 0.0794744801115672, 0.471035271902854, -1.77891944498793,
1.10106011363279, 1.8279291354919, 0, 2.03296079621, 6.83523396133517,
-0.0692441915834111, -0.43046197643751, 2, 0.000229220010703137,
0.152013072230043, -7.03389633468703, 0.291973593270688, 191.498796051958,
0, 6.9366764000834, 70.4743822432709, -0.300554578703656, -1.2266981075545,
3, 0.00658189247926922, 0.464418319361195, -2.80039465388136,
0.625588950147191, 11.1263860247066, 1, -2.8707548076634, 76.0088345803666,
0.342195527150194, -1.56362299082157, 4, 0.4939002034027, 0.956895270365842,
-0.687436225490291, 2.24801404512196, 1, 1, 2.03296079621, 13.3949701680173,
-0.174532794650854, -0.587159803413546, 5, 3.15559700076104e-05,
0.191571055802108, -6.13105560092617, 0.238094214111838, 1124.88247055608,
1, 6.9366764000834, 75.9650072523505, -0.691261116451903, -2.63820535779172,
6, 0.0876682720171331, 0.97753502670731, -1.73012840486001,
1.25568312488792, 1.72388251881864))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(29.7312174731474, 0.0266514489386809, 0.912642610565845, 0.240876851241359,
0.110643462837266, "Intercept", 1, 82.2103618363466, -0.0471704327505321,
0.602439548730056, 0.0902066013537085, -0.522915529935248, "contGamma",
1, 68.0384465118784, 0.0130387059802043, 0.978448467503813,
0.480891049307753, 0.0271136383157342, "contBinom", 1, 19.5259348450334,
0.399936990556204, 0.10901383932159, 0.23814895148755, 1.67935650381023,
"facExperim (1)", 1.52264678427084, 58.4361635440209, -0.441265819197678,
0.0625340688970968, 0.232394139811786, -1.89878204138476, "facGender (1)",
2.12221190827889, 63.28703130969, -0.0582044224700458, 0.784703506863833,
0.212148133259262, -0.274357457573833, "contGamma<unicode><unicode><unicode>contBinom",
1, 75.4726378753563, -0.158068067249669, 0.0836342854726297,
0.0901615961307854, -1.75316403028604, "contGamma<unicode><unicode><unicode>facExperim (1)",
1.77272589607131, 80.6824154084057, -0.587981162045971, 0.224751802890269,
0.480625001295575, -1.22336782410612, "contBinom<unicode><unicode><unicode>facExperim (1)",
1.09651040386447, 73.8703508425204, 0.116963613634538, 0.192697096404718,
0.0889699005862049, 1.31464251239901, "contGamma<unicode><unicode><unicode>facGender (1)",
1.15939868004665, 69.3237258722911, 0.283197495987284, 0.554210257032175,
0.476479580074023, 0.594353898530737, "contBinom<unicode><unicode><unicode>facGender (1)",
1, 75.8541592439566, -0.254977450313277, 0.270957915134734,
0.229928161321096, -1.10894397992945, "facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.03975196726751, 82.0641701210413, 0.257010724315896, 0.229537327443633,
0.212306463976466, 1.21056476332433, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim (1)",
1.08902029113177, 77.0365414637238, -0.123446358396532, 0.561480465341566,
0.211680642961662, -0.583172635293301, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facGender (1)",
1, 74.0759308391968, 0.0545163941951843, 0.542267974063237,
0.0890472905758949, 0.612218449799099, "contGamma<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1, 68.7528371974279, -0.550625272539332, 0.251264936239254,
0.475896067200395, -1.15702841542388, "contBinom<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.05999096955482, 67.3974411694416, 0.341027875735906, 0.111512345586784,
0.211481681008806, 1.61256461604211, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.50390767486954))
})
test_that("facFive.3: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE4"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facExperim (control)", "NaN", 1, "facExperim (experimental)"
))
})
test_that("facFive.4: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender (f)", "NaN", 1, "facGender (m)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01552954353862, 1.03130025379975))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "contGamma"))
})
test_that("facFive.2: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE3"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "contBinom"))
})
test_that("facFive.3: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE4"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "facExperim (control)", 0.237531480979006, 0.0564212044560797,
"facExperim (experimental)"))
})
test_that("facFive.4: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "facGender (f)", 0.15615057277101, 0.0243830013767146, "facGender (m)"
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 82.2103618363466, -0.231310387120245, -1.11124680402139,
0.602439548730056, 0.442347518630568, -0.522915529935248, 0.648626029780903,
1, "Contrast 2", 79.9025526585763, -0.64275010585385, -2.76369826829121,
0.548152276343399, 1.06574917691935, -0.60309697607441, 1.4781980565835,
1))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113566872652, -0.56054153299882, -1.17501678728625,
0.072982114977134, 0.306860677282278, -1.82669717724433, 0.0539337212886054,
1.9257309136753))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-2")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(54.9443296730237, "control", "f", -0.231674953584259, 1, 2.7550207793005e-44,
0.202640742165015, 0.174435417422133, -43.5527648008266, 0.580545788428525,
1.33130733424273e+41, 71.3758006741828, "experimental", "f",
-0.942035927288307, 2, 2.12976112316655e-37, 0.363903158502749,
-0.216499836520699, -25.3267926402213, 0.509036254246909, 2.04563552441025e+34,
13.0257184752929, "control", "m", -0.919049771040912, 3, 9.75602859583303e-15,
0.246740583572739, -0.386106115576687, -38.0403822495203, 0.146837539887537,
1168842772937.33, 27.2196799853434, "experimental", "m", -0.193181958473471,
4, 5.62936598795852e-30, 0.154194680135938, 0.123079958733032,
-57.5695609825261, 0.439341875939536, 9.70311763362218e+26
))
})
}
### type II, LRT + intercept
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "facGender",
values = c("f", "m")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$testMethod <- "likelihoodRatioTest"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- c("contBinom", "facFive")
options$plotBackgroundColor <- "blue"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "boxplot"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "contBinom"),
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffects[[2]]$randomComponents[[length(options$randomEffects[[2]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- c("contBinom", "facFive")
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- FALSE
options$interceptTest <- TRUE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0892620294750889, 2.88763209614939))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706173425421, "f", -0.704496259899192, 0.144283307603805,
-0.13891608695165, 0.0410342976475168, "m", -0.296581004637054,
0.172255870489273, 0.378649599932087))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", -2.19323331697357, -0.421706173425421, 1.34982097012272,
"m", -0.812059108259057, 0.0410342976475168, 0.894127703554091
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7325465569702, -0.190335937888952, 0.110892631343371, 0.10955506682194,
-1.73735403948323, "Intercept", 7.33390917066214, -0.231370235536469,
0.0824400392092811, 0.115076349521506, -2.01058024953449, "facGender (1)"
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-3")
})
}
### parametric bootstrap
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "facGender",
values = c("f", "m")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("0", "0")))
options$bootstrapSamples <- 100
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$testMethod <- "parametricBootstrap"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "violet"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- FALSE
options$plotLevelsByShape <- FALSE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0585272236145518, 0.129411764705882, 3.57863502661178
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706207520219, "f", -0.704496301095866, 0.144283311227277,
-0.138916113944573, 0.0410341526398586, "m", -0.296585024170459,
0.172257847324448, 0.378653329450176))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7321152909456, -0.19033602744018, 0.110894939742407, 0.109555523338383,
-1.73734761735647, "Intercept", 7.33364788694894, -0.231370180080039,
0.0824437479104128, 0.115077396728607, -2.01056147129998, "facGender (1)"
))
})
test_that("facFive: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -1, 1, "facGender (1)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01581460714797, 1.03187931609519))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0906377552259416, 0.00821520267239771, "Intercept", 0.120437360968589,
0.0145051579170782, "facGender (1)"))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-4")
})
}
### fix plot - S + type II
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$bootstrapSamples <- 100
options$dependent <- "contNormal"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "facGender"),
list(components = "debMiss30"),
list(components = c("facGender", "debMiss30")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("facGender", "debMiss30")
options$testMethod <- "satterthwaite"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- c("facFive")
options$plotBackgroundColor <- "violet"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- FALSE
options$plotLevelsByShape <- FALSE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- c("facFive")
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- FALSE
options$varianceCorrelationEstimate <- FALSE
options$interceptTest <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 64.20", "facGender", 0.252866174258413, 1.33121654854058,
"1, 65.18", "debMiss30", 0.199639389176648, 1.6789088002452,
"1, 62.40", "facGender<unicode><unicode><unicode>debMiss30",
0.304695104142543, 1.07108241646932))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(5, 70))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(245.086327825346, 258.577299277642, 233.086327825346, 6, -116.543163912673
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-5")
})
}
jasp-stats/jaspMixedModels documentation built on April 5, 2025, 3:53 p.m.
R Package Documentation
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context("Linear Mixed Models")
### default, all selected output using Satterwhite testMethod
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "cA", values = c("1", "2", "1", "2",
"1", "2", "1", "2", "1", "2", "1", "2", "1", "2", "1", "2", "1",
"2")), list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "cB", values = c("1", "1", "2", "2",
"3", "3", "1", "1", "2", "2", "3", "3", "1", "1", "2", "2", "3",
"3")), list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18"), name = "y_beta", values = c("-1", "-1", "-1",
"-1", "-1", "-1", "0", "0", "0", "0", "0", "0", "1", "1", "1",
"1", "1", "1")), list(isContrast = TRUE, levels = c("1", "2",
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14",
"15", "16", "17", "18"), name = "Contrast 1", values = c("1",
"-1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")), list(isContrast = TRUE, levels = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18"), name = "Contrast 2", values = c("0",
"1", "-1", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"0", "0", "0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "Variable4"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "Variable1"),
list(components = "Variable2"),
list(components = c("Variable1", "Variable2")),
list(components = "Variable7"),
list(components = c("Variable1", "Variable7")),
list(components = c("Variable2", "Variable7")),
list(components = c("Variable1", "Variable2", "Variable7")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("Variable1", "Variable2", "Variable7")
options$marginalMeansTerms <- list(list(variable = "Variable1"), list(variable = "Variable2"),
list(variable = "Variable7"))
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$testMethod <- "satterthwaite"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "Variable0"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "jitter"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list(list(variable = "Variable2"))
options$plotHorizontalAxis <- list(list(variable = "Variable1"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(
list(randomSlopes = TRUE, value = "Variable1"),
list(randomSlopes = FALSE, value = "Variable2"),
list(randomSlopes = FALSE, value = c("Variable1", "Variable2" )),
list(randomSlopes = FALSE, value = "Variable7"),
list(randomSlopes = FALSE, value = c("Variable1", "Variable7")),
list(randomSlopes = FALSE, value = c("Variable2", "Variable7")),
list(randomSlopes = FALSE, value = c("Variable1", "Variable2", "Variable7"))), value = "Variable0"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- TRUE
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$randomEffectEstimate
options$interceptTest <- FALSE
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6"), name = "cB", values = c("1", "2", "3", "1", "2", "3"
)), list(isContrast = FALSE, levels = c("1", "2", "3", "4", "5",
"6"), name = "cA", values = c("1", "1", "1", "2", "2", "2")),
list(isContrast = TRUE, levels = c("1", "2", "3", "4", "5",
"6"), name = "Contrast 1", values = c("1", "-1", "0", "0",
"0", "0")), list(isContrast = TRUE, levels = c("1", "2",
"3", "4", "5", "6"), name = "Contrast 2", values = c("0",
"1", "0", "0", "0", "0")))
options$trendsTrendVariable <- list(list(variable = "Variable7"))
options$trendsVariables <- list(list(variable = "Variable2"), list(variable = "Variable1"))
options$type <- "3"
set.seed(1)
dataset <- structure(list(Variable0 = c(1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), Variable1 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Variable2 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), Variable3 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), Variable4 = c(-0.653989689,
0.597847379, 0.53124944, -0.919283666, 1.549214002, -0.964337181,
0.758624407, -0.633353539, 0.093434858, 0.081944247, 1.251310302,
1.399815493, -0.942716455, -0.495601118, 0.917930091, 0.344838602,
-1.226474961, -1.405114801, 0.686194991, 0.017571144, -0.337816215,
0.384985065, 1.430491376, 1.866825203, 0.752814251, 1.31909783,
0.447452196, -1.346206879, 0.479402493, -0.848513454, 0.850545592,
-3.037579176, 0.545769791, -1.182557897, -0.128004891, 1.11267647,
0.616535768, -0.669227302, 0.076114909, 0.816454623, 0.422781459,
-0.094856662, -0.374461304, 0.691431944, -1.528712893, 1.05380469,
-0.545337415, -0.026770503, -2.189233221, -0.616004017, 1.150339483,
-0.133211268, 0.252652295, 1.399980471, -0.513151105, 1.117392323,
-0.610869543, 0.331300534, -0.992903801, -0.895568118, 0.623585941,
1.05882918, -1.58627026, 0.947877674, 2.033833295, 0.179956552,
1.573438425, -1.694333909, 1.079726669, 1.508240792, 0.90600351,
-0.290763172, -0.496024515, 1.946237186, -0.893948592, 0.830769682,
0.440062416, -0.57837005, 1.790515054, 0.137924932, 0.055424081,
0.547806104, -0.748529992, 1.71335853, 1.808028443, 0.017313744,
0.988861738, -1.43975293, 0.338108584, -0.365015598, 1.86933575,
1.699421632, 0.308476418, 1.556020356, -0.952816041, 1.874185874,
1.104225239, -1.318714635, 1.431532182, 0.756206118, 1.892566353,
-0.933657521, 1.277498726, 0.407372551, 1.021542579, 0.74476069,
0.571588797, -0.762850791, -2.287992665, -0.596915582, 2.920177191,
-0.853565433, -0.771818751, 0.492465518, -0.455610621, 2.667902824,
1.953870427, 0.14233637, -1.188999386, -0.185194402, 2.751932451,
0.1714291, 0.495442662, 0.007490023, -1.381723611, -0.360288418,
1.228175718, 1.270669023, -0.317481349, -1.121300988, 0.248833912,
-0.936079972, -0.019929997, -0.752375481, 1.745747293, 0.005492604,
0.407922866, 0.061474844, 0.69299688, 0.597159811, 2.949895836,
-0.24811046, 0.034494308, -0.78621074, 0.614844377, 1.095323201,
0.672793259, 0.057114702, 0.072950494, 0.346984663, -0.452874548,
-0.114694466, 0.536167379, 2.672375374, 0.618138653, 2.749195306,
2.199564155, -1.821705402, 0.662389551, -0.086448818, 2.350030519,
1.42969294, -0.082903446, 1.526255915, -0.77415644, 1.646198365,
0.550819959, -1.912875322, -0.170004512, -0.153966373, 3.216473665,
-2.384187974, 0.730941972, -0.065087507, 1.330153598, 1.27618167,
1.956183459, 0.436215424, -1.232486611, -0.455381093, 0.83128861,
0.152153259, -0.45491991, -0.256058166, -0.193076508, -0.334064589,
0.215860632, -1.749746886, 0.358765965, 0.211328495, -0.112055855,
0.945593904, 0.532860661, 0.01631963, -0.695297425, -0.182978288,
1.940983578, -1.052570114, 1.265312559, -0.21744826, 2.122842478,
1.291844321, -0.694666126, 2.001880096, 0.977066134, 1.383692522,
-0.085431624, -1.152918968, -1.621837649, 0.647353218, 1.079628054,
-0.220121984, -0.562039994, 2.441868908, 0.688842095, 0.572532136,
1.049670153, -1.439036257, 0.673783789, 0.810812932, -0.557921732,
-0.055039468, 1.065618622, -0.653057442, -0.537812988, 0.818735764,
0.874036767, -0.264722867, -1.083081897, 0.132684797, 1.282776406,
0.980202012, 0.912757975, 0.395195197, -1.294487302, -0.149088612,
1.042843997, -1.213788746, -1.842337004, -0.087241521, 1.759125287,
-0.65217472, -0.468828649, -1.128895132, 0.355130761, -1.13143679,
-0.231067871, -1.353450121, -1.710583197, 0.186715205, -0.543962675,
0.292958499, -0.4283386, 2.670479768, 2.379591267, 1.278406268,
2.298737024, -0.737706867, 1.468454399, 0.055981228, 0.149251786,
-0.332347905, -0.191862331, -0.012294677, 0.139243256, -1.123574851,
-0.034383926, -0.512343287, 0.812126437, 0.486944352, 0.595358492,
1.224605923, 0.863959031, -1.789032311, 0.489475508, 2.019401428,
2.492383813, 0.177655849, -0.587024392, 0.299497534, 1.602179556,
-1.502343948, -1.37596223, 0.74894869, 0.664588217, 1.321486377,
1.888462109, -0.903168893, -3.201437624, -0.535609031, 0.554010178,
-0.547718747, 1.542488798, 1.851156869, 0.154379085, 0.617288371,
1.273637679, -1.466949312, -0.150368723, -0.256217966), Variable5 = c(1L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L,
0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L,
0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L), Variable6 = c(2L,
2L, 4L, 3L, 8L, 0L, 2L, 1L, 1L, 1L, 6L, 4L, 0L, 1L, 2L, 1L, 0L,
0L, 4L, 1L, 0L, 1L, 3L, 10L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 0L,
2L, 0L, 3L, 4L, 2L, 1L, 0L, 2L, 1L, 1L, 0L, 2L, 0L, 1L, 0L, 1L,
0L, 0L, 5L, 1L, 2L, 3L, 1L, 3L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 4L,
4L, 2L, 6L, 0L, 4L, 6L, 7L, 1L, 2L, 12L, 0L, 2L, 1L, 1L, 6L,
2L, 1L, 0L, 0L, 12L, 2L, 0L, 2L, 1L, 1L, 2L, 8L, 5L, 2L, 3L,
0L, 7L, 1L, 0L, 2L, 3L, 7L, 0L, 4L, 2L, 4L, 3L, 3L, 0L, 0L, 2L,
11L, 1L, 0L, 3L, 0L, 12L, 5L, 2L, 1L, 0L, 19L, 2L, 1L, 1L, 0L,
0L, 4L, 1L, 0L, 0L, 5L, 1L, 0L, 1L, 4L, 0L, 1L, 2L, 1L, 0L, 16L,
1L, 0L, 0L, 1L, 2L, 3L, 4L, 1L, 0L, 2L, 0L, 1L, 12L, 0L, 18L,
7L, 0L, 2L, 0L, 11L, 5L, 1L, 5L, 0L, 7L, 4L, 0L, 1L, 0L, 23L,
0L, 2L, 1L, 1L, 6L, 11L, 1L, 0L, 0L, 1L, 0L, 1L, 2L, 1L, 0L,
2L, 0L, 1L, 1L, 1L, 3L, 2L, 0L, 0L, 2L, 5L, 0L, 5L, 2L, 8L, 4L,
1L, 10L, 3L, 4L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 7L, 2L, 1L, 3L,
0L, 1L, 2L, 0L, 1L, 6L, 0L, 1L, 3L, 2L, 0L, 0L, 1L, 2L, 3L, 4L,
2L, 1L, 1L, 2L, 1L, 0L, 1L, 4L, 0L, 0L, 0L, 3L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 18L, 10L, 4L, 12L, 0L, 9L, 1L, 1L, 1L, 0L, 3L,
3L, 0L, 0L, 0L, 4L, 1L, 1L, 4L, 1L, 0L, 4L, 5L, 14L, 2L, 1L,
1L, 3L, 0L, 0L, 2L, 2L, 5L, 9L, 0L, 0L, 2L, 1L, 0L, 3L, 6L, 0L,
4L, 6L, 0L, 1L, 1L), Variable7 = c(0.427219425,
0.220688309, 0.570053273, 0.231100824, 0.131067892, 1, 0.076567255,
0.405903343, 0.474725634, 0.990018262, 0.192843674, 0.224774893,
1, 0.740614383, 0.851823829, 0.653432541, 1, 1, 0.624686075,
0.607677759, 1, 0.105184005, 0.178594546, 0.002517108, 0.769499354,
0.543466466, 0.904824708, 0.597340464, 0.102275177, 0.939954609,
0.644957841, 1, 0.092147577, 1, 0.052178635, 0.108955976, 0.103881947,
0.307079922, 1, 0.640638174, 0.998201291, 0.358593487, 1, 0.098677587,
1, 0.625249783, 1, 0.94040242, 1, 1, 0.108139376, 0.01932307,
0.068532055, 0.319257561, 0.317226161, 0.042847799, 1, 0.907828255,
1, 1, 1, 0.817368982, 1, 0.120666731, 0.211511707, 0.157775565,
0.160599299, 1, 0.223432249, 0.071663969, 0.152479944, 0.4318303,
0.064199554, 0.084701885, 1, 0.214182802, 0.250437733, 0.64748267,
0.221051177, 0.8792932, 0.102889819, 1, 1, 0.004530114, 0.056033505,
1, 0.107350229, 0.341537754, 0.560227328, 0.56788941, 0.029397749,
0.128741443, 0.340319883, 0.438422074, 1, 0.363424137, 0.64064305,
1, 0.669024544, 0.097899144, 0.184498989, 1, 0.626521215, 0.646898637,
0.263503489, 0.147692887, 0.131565273, 1, 1, 0.132856326, 0.045526628,
0.585667955, 1, 0.549235367, 1, 0.020705723, 0.029918293, 0.456543362,
0.856084292, 1, 0.033240023, 0.000931051, 0.141747406, 0.691832666,
1, 1, 0.119549448, 0.459920161, 1, 1, 0.63650842, 0.885468904,
1, 0.66929208, 0.086900934, 1, 0.00297873, 0.387909833, 0.16488008,
1, 0.154010162, 0.688411372, 1, 1, 0.374140598, 0.489588998,
0.656373572, 0.253907352, 0.341392293, 1, 0.341498251, 1, 0.039745196,
0.049899045, 1, 0.004291585, 0.064142592, 1, 0.162245865, 1,
9.52e-05, 0.334425874, 0.55930246, 0.023006289, 1, 0.092510628,
0.179388773, 1, 0.298066521, 1, 0.020858126, 1, 0.219817914,
0.381100323, 0.383832334, 0.031687818, 0.021982156, 0.100909454,
1, 1, 0.90886201, 1, 0.133518321, 0.321278177, 0.977561022, 1,
0.377386837, 1, 0.924968583, 0.22384265, 0.001476047, 0.141017063,
0.079471224, 1, 1, 0.003846134, 0.056555377, 1, 0.120304732,
0.289193756, 0.021411919, 0.003588727, 0.815915327, 0.186839249,
0.124749619, 0.215773542, 1, 1, 1, 0.09331221, 0.227188488, 1,
1, 0.304550487, 0.242236769, 0.915177329, 0.038263021, 1, 0.690036211,
0.115890253, 1, 0.377763011, 0.214936317, 1, 0.409689095, 0.016033388,
0.696195914, 1, 1, 0.579652169, 0.111762879, 0.288249519, 0.685298051,
0.029751715, 0.34902306, 0.740039564, 0.160866749, 0.934899752,
1, 0.089958756, 0.101130973, 1, 1, 1, 0.000367582, 1, 1, 0.501846786,
1, 1, 1, 1, 1, 0.012013178, 0.003237151, 0.37217903, 0.009178291,
1, 0.091345794, 0.743160189, 0.983801698, 0.74850986, 1, 0.152319066,
0.47288842, 1, 1, 1, 0.006951365, 0.522907566, 0.477519199, 0.077229532,
0.344051985, 1, 0.268246943, 0.222762224, 0.013367555, 0.157375875,
0.794346784, 0.403181111, 0.097897052, 1, 1, 0.243050875, 0.188878481,
0.067540856, 0.169211418, 1, 1, 0.052260709, 0.052824504, 1,
0.12313927, 0.085084118, 1, 0.018040391, 0.055642594, 1, 0.901317881,
0.859124256)), class = "data.frame", row.names = c(NA, -300L))
dataset$Variable1 <- as.factor(dataset$Variable1)
dataset$Variable2 <- as.factor(dataset$Variable2)
results <- jaspTools::runAnalysis("MixedModelsLMM", dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 35.30", "Variable1", 0.213945078054209, 1.60174616940147,
"2, 277.59", "Variable2", 0.309343042485159, 1.17827777546627,
"1, 282.17", "Variable7", 1.05836297841345e-37, 224.177295418877,
"2, 277.57", "Variable1<unicode><unicode><unicode>Variable2",
0.945697296669635, 0.0558439754925916, "1, 262.96", "Variable1<unicode><unicode><unicode>Variable7",
0.363781953823721, 0.827664139846734, "2, 280.56", "Variable2<unicode><unicode><unicode>Variable7",
0.400279660081596, 0.918586340240702, "2, 280.51", "Variable1<unicode><unicode><unicode>Variable2<unicode><unicode><unicode>Variable7",
0.885178421723061, 0.122019094979098))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, 0.134346556802761, 0.736155006201862, 0.342393423701632,
1, 0.000248073210555289, 0.200902458211565, 3.66424090951958,
1.12991658870209, 2, 1, 0.134346556802761, 0.970099302524613,
0.635342206174057, 2, 1.34837403764265e-08, 0.170797575359076,
5.67981893469579, 1.30485639887517, 1, 2, 0.134346556802761,
1.00178427381358, 0.616871475069543, 3, 3.37766585957781e-07,
0.196387689661736, 5.10105432544716, 1.38669707255761, 2, 2,
0.134346556802761, 1.19068283055357, 0.864944840125712, 4, 7.81661362562319e-13,
0.166195906147886, 7.16433309430655, 1.51642082098142, 1, 3,
0.134346556802761, 0.844388701356924, 0.450412814048416, 5,
2.66100137260325e-05, 0.201011799408632, 4.20069221727819, 1.23836458866543,
2, 3, 0.134346556802761, 0.969527995376983, 0.647253028619123,
6, 3.71672516615009e-09, 0.164429024869806, 5.89633123558722,
1.29180296213484, 1, 1, 0.527784183943333, 0.150291764934123,
-0.157327577404923, 7, 0.33828062857611, 0.156951528071693,
0.957568026132707, 0.457911107273169, 2, 1, 0.527784183943333,
0.325419304660903, 0.0636346562238179, 8, 0.0148345838940844,
0.13356605044889, 2.43639235844161, 0.587203953097988, 1, 2,
0.527784183943333, 0.349095318512296, 0.0429110530350465, 9,
0.0254404251175091, 0.156219332545083, 2.23464863679116, 0.655279583989545,
2, 2, 0.527784183943333, 0.390440750717584, 0.126863453705241,
10, 0.00369226595348432, 0.134480683875524, 2.90332216840136,
0.654018047729927, 1, 3, 0.527784183943333, 0.108168134737725,
-0.199035981744495, 11, 0.490122936314294, 0.156739674251878,
0.690113305734582, 0.415372251219945, 2, 3, 0.527784183943333,
0.188766091926349, -0.0741969072013631, 12, 0.159443399709109,
0.134167260828225, 1.40694600725305, 0.451729091054061, 1, 1,
0.921221811083906, -0.435571476333615, -0.793523489764854, 13,
0.0170805669841959, 0.182631934185893, -2.38496886251156, -0.0776194629023758,
2, 1, 0.921221811083906, -0.319260693202807, -0.66519969409243,
14, 0.0704798360881783, 0.176502733528956, -1.80881444054481,
0.0266783076868157, 1, 2, 0.921221811083906, -0.303593636788984,
-0.677701723400159, 15, 0.111713908535933, 0.190874980133356,
-1.59053657308505, 0.0705144498221917, 2, 2, 0.921221811083906,
-0.4098013291184, -0.777150910375174, 16, 0.0287818326861408,
0.187426699752843, -2.18646185233374, -0.0424517478616264, 1,
3, 0.921221811083906, -0.628052431881474, -0.990856730839675,
17, 0.00069155877166049, 0.185107635558589, -3.3929039717148,
-0.265248132923273, 2, 3, 0.921221811083906, -0.591995811524284,
-0.947636646709221, 18, 0.00110420758749543, 0.181452739943277,
-3.26253443022874, -0.236354976339348))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -0.177418257095192, 0.150291764934123, 0.478001786963439,
2, 1, 0.0517368457565581, 0.325419304660903, 0.599101763565247,
1, 2, 0.0225417565025579, 0.349095318512296, 0.675648880522034,
2, 2, 0.115142857421496, 0.390440750717584, 0.665738644013672,
1, 3, -0.219231561530436, 0.108168134737725, 0.435567831005885,
2, 3, -0.0860428729184415, 0.188766091926349, 0.463575056771139
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(16.2306089536174, 1.19116029635175, 2.18869798960833e-08, 0.118334626707059,
10.0660333285244, "Intercept", 35.3032297366049, -0.105610070763506,
0.213945086124028, 0.0834465708091049, -1.2656010874923, "Variable1 (1)",
277.81260417867, -0.127937264306791, 0.237630444901206, 0.108102548269185,
-1.18348055947964, "Variable2 (1)", 276.610380395872, 0.153138347192313,
0.151182900097123, 0.106394322322049, 1.43934698628723, "Variable2 (2)",
282.166679989969, -1.77938274268387, 1.05836241786697e-37, 0.118842987431374,
-14.9725514407098, "Variable7", 277.437452308716, -0.0214041122540725,
0.84328700844482, 0.108169334244241, -0.197875972923556, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)",
274.504412706236, -0.0140315797377453, 0.895493628531909, 0.106722015925341,
-0.13147783628414, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)",
262.958608266277, 0.106290129469689, 0.363781940974619, 0.116833087810459,
0.90976050930132, "Variable1 (1)<unicode><unicode><unicode>Variable7",
280.768969888669, 0.215547519201563, 0.187445142898525, 0.163121068333652,
1.32139595089383, "Variable2 (1)<unicode><unicode><unicode>Variable7",
278.764702187189, -0.0670738935266586, 0.685011814699131, 0.165183208902999,
-0.406057576748292, "Variable2 (2)<unicode><unicode><unicode>Variable7",
280.578153316786, -0.0315428855120625, 0.846958786302005, 0.163283458128582,
-0.193178695953531, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)<unicode><unicode><unicode>Variable7",
276.271849657447, 0.081227680668467, 0.625068593623528, 0.166032423105515,
0.489227821585461, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)<unicode><unicode><unicode>Variable7"
))
})
test_that("Variable0: Random Effect Estimates table results match", {
table <- results[["results"]][["REEstimatesSummary"]][["collection"]][["REEstimatesSummary_REEstimates1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.402775358291333, 0.142689540606, 1, -0.106160422159156, 0.0324230307761484,
2, -0.0831533638398433, 0.0202788224676554, 3, 0.0760706510403953,
-0.036860778559647, 4, 0.0307796094651438, 0.0141641809314601,
5, 0.201686511007182, 0.00302378484776545, 6, 0.247309398961227,
0.0493268239762933, 7, -0.48908276442082, -0.0653495722257102,
8, -0.231455211148659, -0.135657369441085, 9, -0.0487697671967913,
-0.0240384633788782, 10))
})
test_that("Variable0: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", 0.533641870616633, 1, "Variable1 (1)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.769891689595146, 0.592733213707669))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.284187792131844, 0.0807627011967724, "Intercept", 0.108185898440959,
0.0117041886214776, "Variable1 (1)"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", -0.233944296322751, -0.696303715348727,
0.642686601522133, 0.235901997522918, -0.991701209736571, 0.228415122703225,
"Contrast 2", "<unicode>", -0.0316849712889628, -0.4865200168714,
0.891397489278759, 0.232062960937098, -0.136536098483856, 0.423150074293474
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.169850846547693, -0.620941632576639,
0.673775481501695, 0.403472964484043, 0.420972063803325, 0.960643325672026,
"Contrast 2", "<unicode>", -1.65893882607238, -2.22903094692442,
2.34889273398185e-08, 0.290868671745428, -5.70339464926737,
-1.08884670522034))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10, 300))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(769.200410528667, 828.460930123167, 737.200410528667, 16, -368.600205264334
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -2.03987627521154, 1, 0.281019600376027, -1.48908797952469,
-0.938299683837836, 1, 2, -2.22903094692442, 2, 0.290868671745428,
-1.65893882607238, -1.08884670522034, 1, 3, -2.43415334868443,
3, 0.287200335862819, -1.8712510340455, -1.30834871940657, 2,
1, -2.19158075804869, 4, 0.282147169524912, -1.63858246743994,
-1.0855841768312, 2, 2, -2.60826616216846, 5, 0.293011361611596,
-2.03397444634869, -1.45968273052893, 2, 3, -2.52972123409858,
6, 0.27819875045024, -1.98446170267207, -1.43920217124555))
})
}
### no correlations between random effects, Kernwald Roggers testMethod, custom values
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5",
"6", "7"), name = "contGamma", values = c("-3.2", "0", "3.2",
"-3.2", "0", "3.2")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5", "6", "7"), name = "contBinom", values = c("0",
"0", "0", "1", "1", "1")), list(isContrast = TRUE, levels = c("2",
"3", "4", "5", "6", "7"), name = "Contrast 1", values = c("-1",
"1", "0", "0", "0", "0")), list(isContrast = TRUE, levels = c("2",
"3", "4", "5", "6", "7"), name = "Contrast 2", values = c("0",
"0", "1", "-1", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "contGamma"), list(components = "contBinom"),
list(components = "facExperim"), list(components = "facGender"),
list(components = c("contGamma", "contBinom")),
list(components = c("contGamma", "facExperim")),
list(components = c("contGamma", "facGender")),
list(components = c("contBinom", "facExperim")),
list(components = c("contBinom", "facGender")),
list(components = c("facExperim", "facGender")),
list(components = c("contGamma", "contBinom", "facExperim")),
list(components = c("contGamma", "contBinom", "facGender")),
list(components = c("contGamma", "facExperim", "facGender")),
list(components = c("contBinom", "facExperim", "facGender")),
list(components = c("contGamma", "contBinom", "facExperim", "facGender")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("contGamma", "contBinom", "facExperim", "facGender")
options$marginalMeansTerms <- list(list(variable = "contGamma"), list(variable = "contBinom"))
options$marginalMeansPAdjustment <- "none"
options$marginalMeansComparison <- TRUE
options$marginalMeansComparisonWith <- 1
options$marginalMeansContrast <- TRUE
options$marginalMeansDf <- "satterthwaite"
options$marginalMeansSd <- 3.2
options$testMethod <- "kenwardRoger"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "bottom"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "violin"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "whiteBackground"
options$plotSeparateLines <- list(list(variable = "facExperim"))
options$plotHorizontalAxis <- list(list(variable = "contBinom"))
options$vovkSellke <- TRUE
options$randomEffects <- list(list(correlations = FALSE, randomComponents = list(
list(randomSlopes = TRUE, value = "contGamma"),
list(randomSlopes = TRUE, value = "contBinom"),
list(randomSlopes = TRUE, value = "facExperim"),
list(randomSlopes = TRUE, value = "facGender"),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom")),
list(randomSlopes = FALSE, value = c("contGamma", "facExperim")),
list(randomSlopes = FALSE, value = c("contGamma", "facGender")),
list(randomSlopes = FALSE, value = c("contBinom", "facExperim")),
list(randomSlopes = FALSE, value = c("contBinom", "facGender")),
list(randomSlopes = FALSE, value = c("facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facExperim")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contBinom", "facExperim", "facGender")),
list(randomSlopes = FALSE, value = c("contGamma", "contBinom", "facExperim", "facGender"))),
value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsPAdjustment <- "mvt"
options$trendsComparison <- TRUE
options$trendsComparisonWith <- 9
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5"),
name = "facExperim", values = c("control", "experimental",
"control", "experimental")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5"), name = "facGender", values = c("f", "f", "m",
"m")), list(isContrast = TRUE, levels = c("2", "3", "4", "5"),
name = "Contrast 1", values = c("-1", "0", "1", "0")))
options$trendsDf <- "Kenward-Roger"
options$trendsTrendVariable <- list(list(variable = "contGamma"))
options$trendsVariables <- list(list(variable = "facExperim"), list(variable = "facGender"))
options$type <- "3"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 18.41", "contGamma", 0.653715765378179, 0.207944313682265,
1, "1, 41.32", "contBinom", 0.980966063782961, 0.00057612629834211,
1, "1, 31.67", "facExperim", 0.150390029707028, 2.17227239617178,
1.29117929695413, "1, 35.58", "facGender", 0.089368481449152,
3.04996399298381, 1.70453649302062, "1, 70.43", "contGamma<unicode><unicode><unicode>contBinom",
0.810447815369331, 0.0579608717477727, 1, "1, 33.07", "contGamma<unicode><unicode><unicode>facExperim",
0.164381775352359, 2.02216685079563, 1.23947863936158, "1, 69.88",
"contBinom<unicode><unicode><unicode>facExperim", 0.280529623544102,
1.18277127807505, 1.03170441188499, "1, 72.17", "contGamma<unicode><unicode><unicode>facGender",
0.248688856348377, 1.3524058214999, 1.0630397961954, "1, 76.01",
"contBinom<unicode><unicode><unicode>facGender", 0.584571227409116,
0.301470719596351, 1, "1, 76.13", "facExperim<unicode><unicode><unicode>facGender",
0.322904328936314, 0.989958425613626, 1.0078590764186, "1, 67.54",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim",
0.293532980431837, 1.12073031564216, 1.02244816376139, "1, 77.86",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facGender",
0.596009824311473, 0.283380689688555, 1, "1, 61.91", "contGamma<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.600511416846301, 0.277066284972791, 1, "1, 76.47", "contBinom<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.277160395116976, 1.19798395374794, 1.03441284987856, "1, 75.20",
"contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim<unicode><unicode><unicode>facGender",
0.148676153260924, 2.1292478607151, 1.2982094549363))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, -2.8707548076634, 71.9898361962622, 0.162066195536834, -0.776927722559118,
1, 0.0794744801115672, 0.471035271902854, -1.77891944498793,
1.10106011363279, 1.8279291354919, 0, 2.03296079621, 6.83523396133517,
-0.0692441915834111, -0.43046197643751, 2, 0.000229220010703137,
0.152013072230043, -7.03389633468703, 0.291973593270688, 191.498796051958,
0, 6.9366764000834, 70.4743822432709, -0.300554578703656, -1.2266981075545,
3, 0.00658189247926922, 0.464418319361195, -2.80039465388136,
0.625588950147191, 11.1263860247066, 1, -2.8707548076634, 76.0088345803666,
0.342195527150194, -1.56362299082157, 4, 0.4939002034027, 0.956895270365842,
-0.687436225490291, 2.24801404512196, 1, 1, 2.03296079621, 13.3949701680173,
-0.174532794650854, -0.587159803413546, 5, 3.15559700076104e-05,
0.191571055802108, -6.13105560092617, 0.238094214111838, 1124.88247055608,
1, 6.9366764000834, 75.9650072523505, -0.691261116451903, -2.63820535779172,
6, 0.0876682720171331, 0.97753502670731, -1.73012840486001,
1.25568312488792, 1.72388251881864))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(29.7312174731474, 0.0266514489386809, 0.912642610565845, 0.240876851241359,
0.110643462837266, "Intercept", 1, 82.2103618363466, -0.0471704327505321,
0.602439548730056, 0.0902066013537085, -0.522915529935248, "contGamma",
1, 68.0384465118784, 0.0130387059802043, 0.978448467503813,
0.480891049307753, 0.0271136383157342, "contBinom", 1, 19.5259348450334,
0.399936990556204, 0.10901383932159, 0.23814895148755, 1.67935650381023,
"facExperim (1)", 1.52264678427084, 58.4361635440209, -0.441265819197678,
0.0625340688970968, 0.232394139811786, -1.89878204138476, "facGender (1)",
2.12221190827889, 63.28703130969, -0.0582044224700458, 0.784703506863833,
0.212148133259262, -0.274357457573833, "contGamma<unicode><unicode><unicode>contBinom",
1, 75.4726378753563, -0.158068067249669, 0.0836342854726297,
0.0901615961307854, -1.75316403028604, "contGamma<unicode><unicode><unicode>facExperim (1)",
1.77272589607131, 80.6824154084057, -0.587981162045971, 0.224751802890269,
0.480625001295575, -1.22336782410612, "contBinom<unicode><unicode><unicode>facExperim (1)",
1.09651040386447, 73.8703508425204, 0.116963613634538, 0.192697096404718,
0.0889699005862049, 1.31464251239901, "contGamma<unicode><unicode><unicode>facGender (1)",
1.15939868004665, 69.3237258722911, 0.283197495987284, 0.554210257032175,
0.476479580074023, 0.594353898530737, "contBinom<unicode><unicode><unicode>facGender (1)",
1, 75.8541592439566, -0.254977450313277, 0.270957915134734,
0.229928161321096, -1.10894397992945, "facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.03975196726751, 82.0641701210413, 0.257010724315896, 0.229537327443633,
0.212306463976466, 1.21056476332433, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim (1)",
1.08902029113177, 77.0365414637238, -0.123446358396532, 0.561480465341566,
0.211680642961662, -0.583172635293301, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facGender (1)",
1, 74.0759308391968, 0.0545163941951843, 0.542267974063237,
0.0890472905758949, 0.612218449799099, "contGamma<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1, 68.7528371974279, -0.550625272539332, 0.251264936239254,
0.475896067200395, -1.15702841542388, "contBinom<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.05999096955482, 67.3974411694416, 0.341027875735906, 0.111512345586784,
0.211481681008806, 1.61256461604211, "contGamma<unicode><unicode><unicode>contBinom<unicode><unicode><unicode>facExperim (1)<unicode><unicode><unicode>facGender (1)",
1.50390767486954))
})
test_that("facFive.3: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE4"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facExperim (control)", "NaN", 1, "facExperim (experimental)"
))
})
test_that("facFive.4: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender (f)", "NaN", 1, "facGender (m)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01552954353862, 1.03130025379975))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "contGamma"))
})
test_that("facFive.2: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE3"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "contBinom"))
})
test_that("facFive.3: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE4"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "facExperim (control)", 0.237531480979006, 0.0564212044560797,
"facExperim (experimental)"))
})
test_that("facFive.4: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE5"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, 0, "facGender (f)", 0.15615057277101, 0.0243830013767146, "facGender (m)"
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 82.2103618363466, -0.231310387120245, -1.11124680402139,
0.602439548730056, 0.442347518630568, -0.522915529935248, 0.648626029780903,
1, "Contrast 2", 79.9025526585763, -0.64275010585385, -2.76369826829121,
0.548152276343399, 1.06574917691935, -0.60309697607441, 1.4781980565835,
1))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113566872652, -0.56054153299882, -1.17501678728625,
0.072982114977134, 0.306860677282278, -1.82669717724433, 0.0539337212886054,
1.9257309136753))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-2")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(54.9443296730237, "control", "f", -0.231674953584259, 1, 2.7550207793005e-44,
0.202640742165015, 0.174435417422133, -43.5527648008266, 0.580545788428525,
1.33130733424273e+41, 71.3758006741828, "experimental", "f",
-0.942035927288307, 2, 2.12976112316655e-37, 0.363903158502749,
-0.216499836520699, -25.3267926402213, 0.509036254246909, 2.04563552441025e+34,
13.0257184752929, "control", "m", -0.919049771040912, 3, 9.75602859583303e-15,
0.246740583572739, -0.386106115576687, -38.0403822495203, 0.146837539887537,
1168842772937.33, 27.2196799853434, "experimental", "m", -0.193181958473471,
4, 5.62936598795852e-30, 0.154194680135938, 0.123079958733032,
-57.5695609825261, 0.439341875939536, 9.70311763362218e+26
))
})
}
### type II, LRT + intercept
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "facGender",
values = c("f", "m")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$testMethod <- "likelihoodRatioTest"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- c("contBinom", "facFive")
options$plotBackgroundColor <- "blue"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "boxplot"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "contBinom"),
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffects[[2]]$randomComponents[[length(options$randomEffects[[2]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- c("contBinom", "facFive")
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- FALSE
options$interceptTest <- TRUE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0892620294750889, 2.88763209614939))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706173425421, "f", -0.704496259899192, 0.144283307603805,
-0.13891608695165, 0.0410342976475168, "m", -0.296581004637054,
0.172255870489273, 0.378649599932087))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", -2.19323331697357, -0.421706173425421, 1.34982097012272,
"m", -0.812059108259057, 0.0410342976475168, 0.894127703554091
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7325465569702, -0.190335937888952, 0.110892631343371, 0.10955506682194,
-1.73735403948323, "Intercept", 7.33390917066214, -0.231370235536469,
0.0824400392092811, 0.115076349521506, -2.01058024953449, "facGender (1)"
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-3")
})
}
### parametric bootstrap
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "facGender",
values = c("f", "m")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("0", "0")))
options$bootstrapSamples <- 100
options$dependent <- "contNormal"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$testMethod <- "parametricBootstrap"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "violet"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- FALSE
options$plotLevelsByShape <- FALSE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "facGender")), value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0585272236145518, 0.129411764705882, 3.57863502661178
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706207520219, "f", -0.704496301095866, 0.144283311227277,
-0.138916113944573, 0.0410341526398586, "m", -0.296585024170459,
0.172257847324448, 0.378653329450176))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7321152909456, -0.19033602744018, 0.110894939742407, 0.109555523338383,
-1.73734761735647, "Intercept", 7.33364788694894, -0.231370180080039,
0.0824437479104128, 0.115077396728607, -2.01056147129998, "facGender (1)"
))
})
test_that("facFive: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -1, 1, "facGender (1)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01581460714797, 1.03187931609519))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0906377552259416, 0.00821520267239771, "Intercept", 0.120437360968589,
0.0145051579170782, "facGender (1)"))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-4")
})
}
### fix plot - S + type II
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$contrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$bootstrapSamples <- 100
options$dependent <- "contNormal"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "facGender"),
list(components = "debMiss30"),
list(components = c("facGender", "debMiss30")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("facGender", "debMiss30")
options$testMethod <- "satterthwaite"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "left"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- c("facFive")
options$plotBackgroundColor <- "violet"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- TRUE
options$plotLevelsByFill <- FALSE
options$plotLevelsByLinetype <- FALSE
options$plotLevelsByShape <- FALSE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- c("facFive")
options$seed <- 1
options$setSeed <- TRUE
options$fixedEffectEstimate <- FALSE
options$varianceCorrelationEstimate <- FALSE
options$interceptTest <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list("1, 64.20", "facGender", 0.252866174258413, 1.33121654854058,
"1, 65.18", "debMiss30", 0.199639389176648, 1.6789088002452,
"1, 62.40", "facGender<unicode><unicode><unicode>debMiss30",
0.304695104142543, 1.07108241646932))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(5, 70))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(245.086327825346, 258.577299277642, 233.086327825346, 6, -116.543163912673
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-5")
})
}
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