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$fixedEffects <- list(list(components = "Variable1"), list(components = "Variable2"),
list(components = c("Variable1", "Variable2"
)), list(components = "Variable7"), list(components = c("Variable1",
"Variable7")), list(components = c("Variable2",
"Variable7")), list(components = c("Variable1",
"Variable2", "Variable7")))
options$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", 0.213945078016837, 1.60174616961151,
"2, 277.59", "Variable2", 0.309343042509779, 1.17827777538587,
"1, 282.17", "Variable7", 1.05836297933907e-37, 224.177295420027,
"2, 277.57", "Variable1<unicode><unicode><unicode>Variable2",
0.945697296659691, 0.0558439755031103, "1, 262.96", "Variable1<unicode><unicode><unicode>Variable7",
0.363781953806304, 0.827664139907147, "2, 280.56", "Variable2<unicode><unicode><unicode>Variable7",
0.400279660115262, 0.918586340156144, "2, 280.51", "Variable1<unicode><unicode><unicode>Variable2<unicode><unicode><unicode>Variable7",
0.885178421714029, 0.122019094989307))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, 0.134346556802761, 0.736155006205685, 0.342393423715154,
1, 0.000248073210449379, 0.200902458206616, 3.66424090962886,
1.12991658869622, 2, 1, 0.134346556802761, 0.970099302533339,
0.635342206192299, 2, 1.34837403596711e-08, 0.170797575354221,
5.67981893490833, 1.30485639887438, 1, 2, 0.134346556802761,
1.00178427381052, 0.616871475076595, 3, 3.37766585746435e-07,
0.196387689656578, 5.10105432556557, 1.38669707254444, 2, 2,
0.134346556802761, 1.19068283055538, 0.864944840137335, 4, 7.8166136126896e-13,
0.166195906142881, 7.16433309453324, 1.51642082097343, 1, 3,
0.134346556802761, 0.844388701359286, 0.450412814060802, 5,
2.66100137120876e-05, 0.201011799403517, 4.20069221739682, 1.23836458865777,
2, 3, 0.134346556802761, 0.969527995376535, 0.647253028629337,
6, 3.71672516181958e-09, 0.164429024864366, 5.89633123577957,
1.29180296212373, 1, 1, 0.527784183943333, 0.150291764934604,
-0.157327577391117, 7, 0.338280628553641, 0.156951528064895,
0.957568026177248, 0.457911107260325, 2, 1, 0.527784183943333,
0.325419304660539, 0.0636346562358604, 8, 0.0148345838894603,
0.13356605044256, 2.43639235855435, 0.587203953085217, 1, 2,
0.527784183943333, 0.349095318512178, 0.0429110530483292, 9,
0.0254404251111329, 0.156219332538246, 2.23464863688821, 0.655279583976026,
2, 2, 0.527784183943333, 0.390440750717288, 0.126863453717262,
10, 0.00369226595191063, 0.13448068386924, 2.90332216853483,
0.654018047717313, 1, 3, 0.527784183943333, 0.10816813473798,
-0.199035981730889, 11, 0.49012293629441, 0.156739674245065,
0.690113305766203, 0.415372251206848, 2, 3, 0.527784183943333,
0.188766091926413, -0.0741969071889336, 12, 0.159443399689348,
0.134167260821916, 1.40694600731968, 0.45172909104176, 1, 1,
0.921221811083906, -0.435571476336476, -0.793523489756698, 13,
0.0170805669800602, 0.182631934180271, -2.38496886260064, -0.0776194629162547,
2, 1, 0.921221811083906, -0.319260693212261, -0.665199694092707,
14, 0.0704798360723975, 0.176502733524273, -1.80881444064635,
0.0266783076681851, 1, 2, 0.921221811083906, -0.303593636786162,
-0.67770172338685, 15, 0.111713908529221, 0.190874980128005,
-1.59053657311485, 0.0705144498145267, 2, 2, 0.921221811083906,
-0.409801329120805, -0.777150910368988, 16, 0.0287818326814656,
0.187426699748459, -2.18646185239771, -0.0424517478726227, 1,
3, 0.921221811083906, -0.628052431883326, -0.990856730830479,
17, 0.00069155877137438, 0.185107635552952, -3.39290397182813,
-0.265248132936174, 2, 3, 0.921221811083906, -0.591995811523709,
-0.947636646698645, 18, 0.00110420758715042, 0.181452739938175,
-3.2625344303173, -0.236354976348772))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -0.177418257078525, 0.150291764934604, 0.478001786947733,
2, 1, 0.0517368457711342, 0.325419304660539, 0.599101763549943,
1, 2, 0.0225417565187235, 0.349095318512178, 0.675648880505632,
2, 2, 0.115142857436004, 0.390440750717288, 0.665738643998572,
1, 3, -0.219231561513967, 0.10816813473798, 0.435567830989926,
2, 3, -0.0860428729035236, 0.188766091926413, 0.46357505675635
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(16.2306089556136, 1.19116029635471, 2.18869798551818e-08, 0.118334626701469,
10.0660333290249, "Intercept", 35.3032297709018, -0.105610070765132,
0.213945086086657, 0.0834465708049184, -1.26560108757528, "Variable1 (1)",
277.812604192109, -0.127937264301347, 0.237630444922944, 0.108102548269619,
-1.18348055942455, "Variable2 (1)", 276.610380403133, 0.153138347188594,
0.151182900108738, 0.106394322322507, 1.43934698624607, "Variable2 (2)",
282.166679973301, -1.77938274268946, 1.05836241879256e-37, 0.118842987431442,
-14.9725514407482, "Variable7", 277.43745231696, -0.0214041122558793,
0.843287008432409, 0.108169334244695, -0.197875972939429, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)",
274.504412714573, -0.0140315797394166, 0.895493628520068, 0.106722015925894,
-0.131477836299119, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)",
262.958608246161, 0.106290129473153, 0.363781940957201, 0.116833087810002,
0.909760509334523, "Variable1 (1)<unicode><unicode><unicode>Variable7",
280.768969878538, 0.215547519191354, 0.187445142920564, 0.163121068334086,
1.32139595082773, "Variable2 (1)<unicode><unicode><unicode>Variable7",
278.764702192363, -0.0670738935200115, 0.685011814729663, 0.165183208903557,
-0.40605757670668, "Variable2 (2)<unicode><unicode><unicode>Variable7",
280.57815335384, -0.0315428855082222, 0.846958786320837, 0.163283458129067,
-0.193178695929437, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)<unicode><unicode><unicode>Variable7",
276.271849697048, 0.0812276806714192, 0.625068593612573, 0.166032423106318,
0.489227821600878, "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(0.402775358284212, 0.142689540574104, 1, -0.106160422147755, 0.0324230307436308,
2, -0.0831533638317701, 0.0202788224449967, 3, 0.0760706510306593,
-0.0368607785283819, 4, 0.0307796094653224, 0.014164180927077,
5, 0.201686510994336, 0.00302378487178075, 6, 0.247309398950566,
0.0493268239790027, 7, -0.489082764397019, -0.0653495722501099,
8, -0.231455211152086, -0.135657369390901, 9, -0.0487697671964632,
-0.0240384633711978, 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", 0.533641870784529, 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(0.769891689599776, 0.592733213714797))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.284187792107958, 0.080762701183196, "Intercept", 0.108185898408672,
0.0117041886144914, "Variable1 (1)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", -0.233944296327654, -0.696303715343876,
0.642686601481432, 0.235901997517941, -0.991701209778277, 0.228415122688567,
"Contrast 2", "<unicode>", -0.0316849712771782, -0.486520016849529,
0.891397489316508, 0.232062960931952, -0.136536098436102, 0.423150074295172
))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.169850846531725, -0.62094163259618,
0.673775481531984, 0.403472964485866, 0.420972063761847, 0.96064332565963,
"Contrast 2", "<unicode>", -1.65893882606491, -2.22903094691917,
2.34889273464207e-08, 0.29086867174656, -5.70339464921948, -1.08884670521065
))
})
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(769.200410528668, 828.460930123167, 737.200410528668, 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, -2.03987627522259, 1, 0.281019600377329, -1.48908797953318,
-0.938299683843779, 1, 2, -2.22903094691917, 2, 0.29086867174656,
-1.65893882606491, -1.08884670521065, 1, 3, -2.4341533486912,
3, 0.287200335863538, -1.87125103405086, -1.30834871941051,
2, 1, -2.19158075807258, 4, 0.282147169525315, -1.63858246746304,
-1.08558417685351, 2, 2, -2.60826616217462, 5, 0.293011361612,
-2.03397444635405, -1.45968273053349, 2, 3, -2.52972123409439,
6, 0.278198750448761, -1.98446170267077, -1.43920217124715
))
})
}
### 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$fixedVariables <- c("contGamma", "contBinom", "facExperim", "facGender")
options$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", 0.653715812700852, 1, 0.207944253049949,
"1, 41.32", "contBinom", 0.980965955359944, 1, 0.000576132863050751,
"1, 31.67", "facExperim", 0.150390022121331, 1.29117932770481,
2.17227248698675, "1, 35.58", "facGender", 0.0893684915608006,
1.70453638001982, 3.04996380202582, "1, 70.43", "contGamma * contBinom",
0.810447727524037, 1, 0.0579609265254182, "1, 33.07", "contGamma * facExperim",
0.164381759735133, 1.23947869189993, 2.0221669964754, "1, 69.88",
"contBinom * facExperim", 0.280529553620231, 1.03170446672806,
1.1827716246055, "1, 72.17", "contGamma * facGender", 0.248688864505737,
1.06303978638395, 1.35240577480053, "1, 76.01", "contBinom * facGender",
0.584571198497188, 1, 0.301470766094538, "1, 76.13", "facExperim * facGender",
0.322904314874933, 1.00785908148147, 0.989958483556238, "1, 67.54",
"contGamma * contBinom * facExperim", 0.293532913259059, 1.02244820685648,
1.12073063012002, "1, 77.86", "contGamma * contBinom * facGender",
0.59600976539288, 1, 0.283380780706577, "1, 61.91", "contGamma * facExperim * facGender",
0.600511422182693, 1, 0.277066276890121, "1, 76.47", "contBinom * facExperim * facGender",
0.277160373603747, 1.0344128675967, 1.19798406204581, "1, 75.20",
"contGamma * contBinom * facExperim * facGender", 0.148676132254087,
1.2982095421261, 2.12924808409802))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, -2.8707548076634, 71.9900762735203, 0.162066154069144, -0.776927722113553,
1, 0.079474455095956, 1.82792948364777, 0.471035277718885, -1.77891951105823,
1.10106003025184, 0, 2.03296079621, 6.83551339852499, -0.0692441954233528,
-0.430458935145963, 2, 0.000229176926051888, 191.530501338905,
0.152013085498114, -7.03389574601208, 0.291970544299257, 0,
6.9366764000834, 70.4749790318247, -0.30055454491585, -1.22669794794237,
3, 0.00658188117451307, 11.1264013306953, 0.464418324934558,
-2.8003945475216, 0.625588858110671, 1, -2.8707548076634, 76.0103536416197,
0.342195635115593, -1.56362227186133, 4, 0.493900233473492,
1, 0.956895272816407, -0.687436110900942, 2.24801354209252,
1, 2.03296079621, 13.395648773507, -0.174532777943299, -0.587157752845552,
5, 3.15490120669621e-05, 1125.106616482, 0.191571066548363,
-6.13105516978884, 0.238092196958954, 1, 6.9366764000834, 75.9668749827945,
-0.691261191002192, -2.6382046591406, 6, 0.0876681586952261,
1.72388383172118, 0.977535027346068, -1.73012847999302, 1.25568227713622
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(29.731918444623, 0.0266514294991932, 0.912642659804403, 1, 0.240876860206388,
0.110643378016293, "Intercept", 82.2103671303575, -0.0471704250772184,
0.60243960877149, 1, 0.0902066016520024, -0.522915443142307,
"contGamma", 68.0420477271527, 0.0130387799605219, 0.978448341023034,
1, 0.480891048478861, 0.0271137922025494, "contBinom", 19.5295649109263,
0.399937000042345, 0.109010894179639, 1.52266936011703, 0.238148957337282,
1.67935650239245, "facExperim (1)", 58.4361636099556, -0.441265804273862,
0.0625340789907108, 2.12221168930464, 0.232394141170404, -1.89878196606644,
"facGender (1)", 63.2915011493182, -0.0582044487532978, 0.784703348218709,
1, 0.212148132344444, -0.274357582647944, "contGamma * contBinom",
75.4743641519868, -0.158068070457047, 0.0836341871674355, 1.77272714000759,
0.0901615963468768, -1.75316406165787, "contGamma * facExperim (1)",
80.6828758521473, -0.587981237452524, 0.224751722814565, 1.09651053282451,
0.480625000342396, -1.22336798342502, "contBinom * facExperim (1)",
73.8727112977714, 0.116963610432613, 0.192696976130626, 1.1593989642258,
0.0889699000150756, 1.3146424848493, "contGamma * facGender (1)",
69.3270163125925, 0.283197514348137, 0.554210137009061, 1, 0.476479576698649,
0.594353941275526, "contBinom * facGender (1)", 75.8559631009883,
-0.254977455283747, 0.270957820075519, 1.0397520526904, 0.229928159981303,
-1.10894400800877, "facExperim (1) * facGender (1)", 82.0644948806251,
0.257010756694346, 0.229537254199903, 1.08902040250822, 0.212306463344999,
1.21056491943301, "contGamma * contBinom * facExperim (1)",
77.0382448937387, -0.123446376566059, 0.561480368434535, 1,
0.211680641963219, -0.583172723878592, "contGamma * contBinom * facGender (1)",
74.0782282496845, 0.0545163929311743, 0.542267923379811, 1,
0.0890472901294798, 0.612218438673478, "contGamma * facExperim (1) * facGender (1)",
68.7562034137428, -0.550625289473946, 0.251264722541625, 1.05999121838667,
0.475896063157734, -1.15702846083735, "contBinom * facExperim (1) * facGender (1)",
67.4013086728085, 0.341027887810233, 0.111512063347184, 1.50390974606896,
0.211481679694976, 1.61256468315413, "contGamma * contBinom * facExperim (1) * facGender (1)"
))
})
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(1.01552953184787, 1.03130023005516))
})
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)", 0.237531600307891, 0.0564212611448276,
"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)", 0.156150662299331, 0.0243830293365197,
"facGender (m)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113581457788, -0.560541517971749, -1.17501676812672,
0.0729821205951162, 1.92573082206986, 0.306860675388687, -1.82669713954626,
0.0539337321832206))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113581457788, -0.560541517971749, -1.17501676812672,
0.0729821205951162, 1.92573082206986, 0.306860675388687, -1.82669713954626,
0.0539337321832206))
})
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(54.9443259882744, "control", "f", -0.23167495146059, 1, 2.7550330650536e-44,
1.33130145662102e+41, 0.202640740799574, 0.174435417422133,
-43.5527650942956, 0.580545786304856, 71.3758039065814, "experimental",
"f", -0.942035950037188, 2, 2.12975342580749e-37, 2.04564283019237e+34,
0.363903152386286, -0.216499872030701, -25.3267931634935, 0.509036205975787,
13.0257190149006, "control", "m", -0.919049752737067, 3, 9.75601854618026e-15,
1168843939638.2, 0.246740583092494, -0.386106100549617, -38.040382262658,
0.146837551637834, 27.2196843463106, "experimental", "m", -0.193181947767217,
4, 5.62931397120266e-30, 9.70320596242461e+26, 0.154194675384417,
0.123079957342715, -57.5695627655531, 0.439341862452647))
})
}
### 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$fixedVariables <- "facGender"
options$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$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0892620294750889, 2.88763209614939))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706173425421, "f", -0.704496259899192, 0.144283307603805,
-0.13891608695165, 0.0410342976475168, "m", -0.296581004637054,
0.172255870489273, 0.378649599932087))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", -2.19323331697357, -0.421706173425421, 1.34982097012273,
"m", -0.812059108259059, 0.0410342976475168, 0.894127703554092
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7325465569702, -0.190335937888952, 0.110892631343371, 0.10955506682194,
-1.73735403948323, "Intercept", 7.33390917066216, -0.231370235536469,
0.082440039209281, 0.115076349521506, -2.01058024953449, "facGender (1)"
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-3")
})
}
### parametric bootstrap
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$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$fixedVariables <- "facGender"
options$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(10.7321152909457, -0.19033602744018, 0.110894939742407, 0.109555523338383,
-1.73734761735647, "Intercept", 7.33364788694893, -0.231370180080039,
0.0824437479104129, 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$fixedVariables <- c("facGender", "debMiss30")
options$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", 0.252866174262488, 1.33121654851917,
"1, 65.18", "debMiss30", 0.199639389175723, 1.67890880025083,
"1, 62.40", "facGender * debMiss30", 0.304695104140844, 1.07108241647693
))
})
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 20, 2024, 4:15 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$fixedEffects <- list(list(components = "Variable1"), list(components = "Variable2"),
list(components = c("Variable1", "Variable2"
)), list(components = "Variable7"), list(components = c("Variable1",
"Variable7")), list(components = c("Variable2",
"Variable7")), list(components = c("Variable1",
"Variable2", "Variable7")))
options$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", 0.213945078016837, 1.60174616961151,
"2, 277.59", "Variable2", 0.309343042509779, 1.17827777538587,
"1, 282.17", "Variable7", 1.05836297933907e-37, 224.177295420027,
"2, 277.57", "Variable1<unicode><unicode><unicode>Variable2",
0.945697296659691, 0.0558439755031103, "1, 262.96", "Variable1<unicode><unicode><unicode>Variable7",
0.363781953806304, 0.827664139907147, "2, 280.56", "Variable2<unicode><unicode><unicode>Variable7",
0.400279660115262, 0.918586340156144, "2, 280.51", "Variable1<unicode><unicode><unicode>Variable2<unicode><unicode><unicode>Variable7",
0.885178421714029, 0.122019094989307))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, 0.134346556802761, 0.736155006205685, 0.342393423715154,
1, 0.000248073210449379, 0.200902458206616, 3.66424090962886,
1.12991658869622, 2, 1, 0.134346556802761, 0.970099302533339,
0.635342206192299, 2, 1.34837403596711e-08, 0.170797575354221,
5.67981893490833, 1.30485639887438, 1, 2, 0.134346556802761,
1.00178427381052, 0.616871475076595, 3, 3.37766585746435e-07,
0.196387689656578, 5.10105432556557, 1.38669707254444, 2, 2,
0.134346556802761, 1.19068283055538, 0.864944840137335, 4, 7.8166136126896e-13,
0.166195906142881, 7.16433309453324, 1.51642082097343, 1, 3,
0.134346556802761, 0.844388701359286, 0.450412814060802, 5,
2.66100137120876e-05, 0.201011799403517, 4.20069221739682, 1.23836458865777,
2, 3, 0.134346556802761, 0.969527995376535, 0.647253028629337,
6, 3.71672516181958e-09, 0.164429024864366, 5.89633123577957,
1.29180296212373, 1, 1, 0.527784183943333, 0.150291764934604,
-0.157327577391117, 7, 0.338280628553641, 0.156951528064895,
0.957568026177248, 0.457911107260325, 2, 1, 0.527784183943333,
0.325419304660539, 0.0636346562358604, 8, 0.0148345838894603,
0.13356605044256, 2.43639235855435, 0.587203953085217, 1, 2,
0.527784183943333, 0.349095318512178, 0.0429110530483292, 9,
0.0254404251111329, 0.156219332538246, 2.23464863688821, 0.655279583976026,
2, 2, 0.527784183943333, 0.390440750717288, 0.126863453717262,
10, 0.00369226595191063, 0.13448068386924, 2.90332216853483,
0.654018047717313, 1, 3, 0.527784183943333, 0.10816813473798,
-0.199035981730889, 11, 0.49012293629441, 0.156739674245065,
0.690113305766203, 0.415372251206848, 2, 3, 0.527784183943333,
0.188766091926413, -0.0741969071889336, 12, 0.159443399689348,
0.134167260821916, 1.40694600731968, 0.45172909104176, 1, 1,
0.921221811083906, -0.435571476336476, -0.793523489756698, 13,
0.0170805669800602, 0.182631934180271, -2.38496886260064, -0.0776194629162547,
2, 1, 0.921221811083906, -0.319260693212261, -0.665199694092707,
14, 0.0704798360723975, 0.176502733524273, -1.80881444064635,
0.0266783076681851, 1, 2, 0.921221811083906, -0.303593636786162,
-0.67770172338685, 15, 0.111713908529221, 0.190874980128005,
-1.59053657311485, 0.0705144498145267, 2, 2, 0.921221811083906,
-0.409801329120805, -0.777150910368988, 16, 0.0287818326814656,
0.187426699748459, -2.18646185239771, -0.0424517478726227, 1,
3, 0.921221811083906, -0.628052431883326, -0.990856730830479,
17, 0.00069155877137438, 0.185107635552952, -3.39290397182813,
-0.265248132936174, 2, 3, 0.921221811083906, -0.591995811523709,
-0.947636646698645, 18, 0.00110420758715042, 0.181452739938175,
-3.2625344303173, -0.236354976348772))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1, -0.177418257078525, 0.150291764934604, 0.478001786947733,
2, 1, 0.0517368457711342, 0.325419304660539, 0.599101763549943,
1, 2, 0.0225417565187235, 0.349095318512178, 0.675648880505632,
2, 2, 0.115142857436004, 0.390440750717288, 0.665738643998572,
1, 3, -0.219231561513967, 0.10816813473798, 0.435567830989926,
2, 3, -0.0860428729035236, 0.188766091926413, 0.46357505675635
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(16.2306089556136, 1.19116029635471, 2.18869798551818e-08, 0.118334626701469,
10.0660333290249, "Intercept", 35.3032297709018, -0.105610070765132,
0.213945086086657, 0.0834465708049184, -1.26560108757528, "Variable1 (1)",
277.812604192109, -0.127937264301347, 0.237630444922944, 0.108102548269619,
-1.18348055942455, "Variable2 (1)", 276.610380403133, 0.153138347188594,
0.151182900108738, 0.106394322322507, 1.43934698624607, "Variable2 (2)",
282.166679973301, -1.77938274268946, 1.05836241879256e-37, 0.118842987431442,
-14.9725514407482, "Variable7", 277.43745231696, -0.0214041122558793,
0.843287008432409, 0.108169334244695, -0.197875972939429, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)",
274.504412714573, -0.0140315797394166, 0.895493628520068, 0.106722015925894,
-0.131477836299119, "Variable1 (1)<unicode><unicode><unicode>Variable2 (2)",
262.958608246161, 0.106290129473153, 0.363781940957201, 0.116833087810002,
0.909760509334523, "Variable1 (1)<unicode><unicode><unicode>Variable7",
280.768969878538, 0.215547519191354, 0.187445142920564, 0.163121068334086,
1.32139595082773, "Variable2 (1)<unicode><unicode><unicode>Variable7",
278.764702192363, -0.0670738935200115, 0.685011814729663, 0.165183208903557,
-0.40605757670668, "Variable2 (2)<unicode><unicode><unicode>Variable7",
280.57815335384, -0.0315428855082222, 0.846958786320837, 0.163283458129067,
-0.193178695929437, "Variable1 (1)<unicode><unicode><unicode>Variable2 (1)<unicode><unicode><unicode>Variable7",
276.271849697048, 0.0812276806714192, 0.625068593612573, 0.166032423106318,
0.489227821600878, "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(0.402775358284212, 0.142689540574104, 1, -0.106160422147755, 0.0324230307436308,
2, -0.0831533638317701, 0.0202788224449967, 3, 0.0760706510306593,
-0.0368607785283819, 4, 0.0307796094653224, 0.014164180927077,
5, 0.201686510994336, 0.00302378487178075, 6, 0.247309398950566,
0.0493268239790027, 7, -0.489082764397019, -0.0653495722501099,
8, -0.231455211152086, -0.135657369390901, 9, -0.0487697671964632,
-0.0240384633711978, 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", 0.533641870784529, 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(0.769891689599776, 0.592733213714797))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.284187792107958, 0.080762701183196, "Intercept", 0.108185898408672,
0.0117041886144914, "Variable1 (1)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", -0.233944296327654, -0.696303715343876,
0.642686601481432, 0.235901997517941, -0.991701209778277, 0.228415122688567,
"Contrast 2", "<unicode>", -0.0316849712771782, -0.486520016849529,
0.891397489316508, 0.232062960931952, -0.136536098436102, 0.423150074295172
))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.169850846531725, -0.62094163259618,
0.673775481531984, 0.403472964485866, 0.420972063761847, 0.96064332565963,
"Contrast 2", "<unicode>", -1.65893882606491, -2.22903094691917,
2.34889273464207e-08, 0.29086867174656, -5.70339464921948, -1.08884670521065
))
})
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(769.200410528668, 828.460930123167, 737.200410528668, 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, -2.03987627522259, 1, 0.281019600377329, -1.48908797953318,
-0.938299683843779, 1, 2, -2.22903094691917, 2, 0.29086867174656,
-1.65893882606491, -1.08884670521065, 1, 3, -2.4341533486912,
3, 0.287200335863538, -1.87125103405086, -1.30834871941051,
2, 1, -2.19158075807258, 4, 0.282147169525315, -1.63858246746304,
-1.08558417685351, 2, 2, -2.60826616217462, 5, 0.293011361612,
-2.03397444635405, -1.45968273053349, 2, 3, -2.52972123409439,
6, 0.278198750448761, -1.98446170267077, -1.43920217124715
))
})
}
### 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$fixedVariables <- c("contGamma", "contBinom", "facExperim", "facGender")
options$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", 0.653715812700852, 1, 0.207944253049949,
"1, 41.32", "contBinom", 0.980965955359944, 1, 0.000576132863050751,
"1, 31.67", "facExperim", 0.150390022121331, 1.29117932770481,
2.17227248698675, "1, 35.58", "facGender", 0.0893684915608006,
1.70453638001982, 3.04996380202582, "1, 70.43", "contGamma * contBinom",
0.810447727524037, 1, 0.0579609265254182, "1, 33.07", "contGamma * facExperim",
0.164381759735133, 1.23947869189993, 2.0221669964754, "1, 69.88",
"contBinom * facExperim", 0.280529553620231, 1.03170446672806,
1.1827716246055, "1, 72.17", "contGamma * facGender", 0.248688864505737,
1.06303978638395, 1.35240577480053, "1, 76.01", "contBinom * facGender",
0.584571198497188, 1, 0.301470766094538, "1, 76.13", "facExperim * facGender",
0.322904314874933, 1.00785908148147, 0.989958483556238, "1, 67.54",
"contGamma * contBinom * facExperim", 0.293532913259059, 1.02244820685648,
1.12073063012002, "1, 77.86", "contGamma * contBinom * facGender",
0.59600976539288, 1, 0.283380780706577, "1, 61.91", "contGamma * facExperim * facGender",
0.600511422182693, 1, 0.277066276890121, "1, 76.47", "contBinom * facExperim * facGender",
0.277160373603747, 1.0344128675967, 1.19798406204581, "1, 75.20",
"contGamma * contBinom * facExperim * facGender", 0.148676132254087,
1.2982095421261, 2.12924808409802))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0, -2.8707548076634, 71.9900762735203, 0.162066154069144, -0.776927722113553,
1, 0.079474455095956, 1.82792948364777, 0.471035277718885, -1.77891951105823,
1.10106003025184, 0, 2.03296079621, 6.83551339852499, -0.0692441954233528,
-0.430458935145963, 2, 0.000229176926051888, 191.530501338905,
0.152013085498114, -7.03389574601208, 0.291970544299257, 0,
6.9366764000834, 70.4749790318247, -0.30055454491585, -1.22669794794237,
3, 0.00658188117451307, 11.1264013306953, 0.464418324934558,
-2.8003945475216, 0.625588858110671, 1, -2.8707548076634, 76.0103536416197,
0.342195635115593, -1.56362227186133, 4, 0.493900233473492,
1, 0.956895272816407, -0.687436110900942, 2.24801354209252,
1, 2.03296079621, 13.395648773507, -0.174532777943299, -0.587157752845552,
5, 3.15490120669621e-05, 1125.106616482, 0.191571066548363,
-6.13105516978884, 0.238092196958954, 1, 6.9366764000834, 75.9668749827945,
-0.691261191002192, -2.6382046591406, 6, 0.0876681586952261,
1.72388383172118, 0.977535027346068, -1.73012847999302, 1.25568227713622
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(29.731918444623, 0.0266514294991932, 0.912642659804403, 1, 0.240876860206388,
0.110643378016293, "Intercept", 82.2103671303575, -0.0471704250772184,
0.60243960877149, 1, 0.0902066016520024, -0.522915443142307,
"contGamma", 68.0420477271527, 0.0130387799605219, 0.978448341023034,
1, 0.480891048478861, 0.0271137922025494, "contBinom", 19.5295649109263,
0.399937000042345, 0.109010894179639, 1.52266936011703, 0.238148957337282,
1.67935650239245, "facExperim (1)", 58.4361636099556, -0.441265804273862,
0.0625340789907108, 2.12221168930464, 0.232394141170404, -1.89878196606644,
"facGender (1)", 63.2915011493182, -0.0582044487532978, 0.784703348218709,
1, 0.212148132344444, -0.274357582647944, "contGamma * contBinom",
75.4743641519868, -0.158068070457047, 0.0836341871674355, 1.77272714000759,
0.0901615963468768, -1.75316406165787, "contGamma * facExperim (1)",
80.6828758521473, -0.587981237452524, 0.224751722814565, 1.09651053282451,
0.480625000342396, -1.22336798342502, "contBinom * facExperim (1)",
73.8727112977714, 0.116963610432613, 0.192696976130626, 1.1593989642258,
0.0889699000150756, 1.3146424848493, "contGamma * facGender (1)",
69.3270163125925, 0.283197514348137, 0.554210137009061, 1, 0.476479576698649,
0.594353941275526, "contBinom * facGender (1)", 75.8559631009883,
-0.254977455283747, 0.270957820075519, 1.0397520526904, 0.229928159981303,
-1.10894400800877, "facExperim (1) * facGender (1)", 82.0644948806251,
0.257010756694346, 0.229537254199903, 1.08902040250822, 0.212306463344999,
1.21056491943301, "contGamma * contBinom * facExperim (1)",
77.0382448937387, -0.123446376566059, 0.561480368434535, 1,
0.211680641963219, -0.583172723878592, "contGamma * contBinom * facGender (1)",
74.0782282496845, 0.0545163929311743, 0.542267923379811, 1,
0.0890472901294798, 0.612218438673478, "contGamma * facExperim (1) * facGender (1)",
68.7562034137428, -0.550625289473946, 0.251264722541625, 1.05999121838667,
0.475896063157734, -1.15702846083735, "contBinom * facExperim (1) * facGender (1)",
67.4013086728085, 0.341027887810233, 0.111512063347184, 1.50390974606896,
0.211481679694976, 1.61256468315413, "contGamma * contBinom * facExperim (1) * facGender (1)"
))
})
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(1.01552953184787, 1.03130023005516))
})
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)", 0.237531600307891, 0.0564212611448276,
"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)", 0.156150662299331, 0.0243830293365197,
"facGender (m)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113581457788, -0.560541517971749, -1.17501676812672,
0.0729821205951162, 1.92573082206986, 0.306860675388687, -1.82669713954626,
0.0539337321832206))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", 57.0113581457788, -0.560541517971749, -1.17501676812672,
0.0729821205951162, 1.92573082206986, 0.306860675388687, -1.82669713954626,
0.0539337321832206))
})
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(54.9443259882744, "control", "f", -0.23167495146059, 1, 2.7550330650536e-44,
1.33130145662102e+41, 0.202640740799574, 0.174435417422133,
-43.5527650942956, 0.580545786304856, 71.3758039065814, "experimental",
"f", -0.942035950037188, 2, 2.12975342580749e-37, 2.04564283019237e+34,
0.363903152386286, -0.216499872030701, -25.3267931634935, 0.509036205975787,
13.0257190149006, "control", "m", -0.919049752737067, 3, 9.75601854618026e-15,
1168843939638.2, 0.246740583092494, -0.386106100549617, -38.040382262658,
0.146837551637834, 27.2196843463106, "experimental", "m", -0.193181947767217,
4, 5.62931397120266e-30, 9.70320596242461e+26, 0.154194675384417,
0.123079957342715, -57.5695627655531, 0.439341862452647))
})
}
### 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$fixedVariables <- "facGender"
options$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$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.0892620294750889, 2.88763209614939))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.421706173425421, "f", -0.704496259899192, 0.144283307603805,
-0.13891608695165, 0.0410342976475168, "m", -0.296581004637054,
0.172255870489273, 0.378649599932087))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", -2.19323331697357, -0.421706173425421, 1.34982097012273,
"m", -0.812059108259059, 0.0410342976475168, 0.894127703554092
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10.7325465569702, -0.190335937888952, 0.110892631343371, 0.10955506682194,
-1.73735403948323, "Intercept", 7.33390917066216, -0.231370235536469,
0.082440039209281, 0.115076349521506, -2.01058024953449, "facGender (1)"
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-lmm-3")
})
}
### parametric bootstrap
{
options <- jaspTools::analysisOptions("MixedModelsLMM")
options$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$fixedVariables <- "facGender"
options$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(10.7321152909457, -0.19033602744018, 0.110894939742407, 0.109555523338383,
-1.73734761735647, "Intercept", 7.33364788694893, -0.231370180080039,
0.0824437479104129, 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$fixedVariables <- c("facGender", "debMiss30")
options$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", 0.252866174262488, 1.33121654851917,
"1, 65.18", "debMiss30", 0.199639389175723, 1.67890880025083,
"1, 62.40", "facGender * debMiss30", 0.304695104140844, 1.07108241647693
))
})
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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