tests/testthat/test-mixedmodelsglmm.R
In jasp-stats/jaspMixedModels: Mixed Models Module for JASP
context("Generalized Linear Mixed Models")
### bernoulli + logit, default, all selected output using LRT testMethod
{
skip_on_os("mac") # problems with precision outside of windows
skip_on_os("linux") # problems with precision outside of windows
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6"), name = "cA", values = c("1", "2", "1", "2", "1", "2"
)), list(isContrast = FALSE, levels = c("1", "2", "3", "4", "5",
"6"), name = "y_beta", values = c("-1", "-1", "0", "0", "1",
"1")), list(isContrast = TRUE, levels = c("1", "2", "3", "4",
"5", "6"), name = "Contrast 1", values = c("0", "1", "-1", "0",
"0", "0")), list(isContrast = TRUE, levels = c("1", "2", "3",
"4", "5", "6"), name = "Contrast 2", values = c("1", "-1", "0",
"0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "Variable5"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "Variable1"),
list(components = "Variable7"),
list(components = c("Variable1", "Variable7")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("Variable1", "Variable7")
options$marginalMeansTerms <- list(list(variable = "Variable1"), list(variable = "Variable7"))
options$marginalMeansContrast <- TRUE
options$testMethod <- "likelihoodRatioTest"
options$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()
options$plotHorizontalAxis <- list(list(variable = "Variable1"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE,
randomComponents = list(list(randomSlopes = TRUE, value = "Variable1")),
value = "Variable0"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsComparison <- TRUE
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("1", "2"), name = "cA",
values = c("1", "2")), list(isContrast = TRUE, levels = c("1",
"2"), name = "Contrast 1", values = c("-1", "1")))
options$trendsTrendVariable <- list(list(variable = "Variable7"))
options$trendsVariables <- list(list(variable = "Variable1"))
options$type <- "3"
options$link <- "logit"
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))
results <- jaspTools::runAnalysis("MixedModelsGLMM", dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Variable1", 0.991431100095283, 0.0001153417971409, 1, "Variable7",
0.0187024090194103, 5.52912939823165, 1, "Variable1<unicode><unicode><unicode>Variable7",
0.528941241000858, 0.396425181363099))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.134346556802761, 0.67652441946339, 0.526200705374444, 1,
0.0706713772323912, 0.797507910077546, 2, 0.134346556802761,
0.690097553733996, 0.554821377821813, 2, 0.0633312981191573,
0.799148378891918, 1, 0.527784183943333, 0.480762130522145,
0.361876774141483, 3, 0.0624389577258609, 0.601866597179011,
2, 0.527784183943333, 0.538669559336265, 0.423592363725772,
4, 0.0587079310508572, 0.649767869856607, 1, 0.921221811083906,
0.290733584783489, 0.183383579119308, 5, 0.0633116820952347,
0.427989593112089, 2, 0.921221811083906, 0.379752250861563,
0.249903562970398, 6, 0.0731302644993081, 0.529447590192747
))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.361876774141483, 0.480762130522145, 0.601866597179011, 2,
0.423592363725772, 0.538669559336265, 0.649767869856607))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01114713477693, 0.188751395675504, 0.769353382613811, 1.31428178211377,
"Intercept", 0.0049380649488768, 0.991565197089033, 0.46710426273995,
0.010571654644963, "Variable1", -2.50123798054357, 0.0208966035659629,
1.08285853494975, -2.30984740833173, "Variable7", 0.430174244274536,
0.528653207583655, 0.682747723082131, 0.630063242587757, "Variable1<unicode><unicode><unicode>Variable7"
))
})
test_that("Variable0: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -0.860597398092285, 1, "Variable1"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.889188913907964, 0.790656924616825, "Intercept", 0.470404903303557,
0.221280773052028, "Variable1"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.209335423211852, 0.0542554560233471,
0.0163060254993088, 0.0791238861590089, 2.64566660428138, 0.364415390400356,
"Contrast 2", "<unicode>", -0.0135731342706064, -0.183581119886834,
0.875654660762392, 0.0867403620460521, -0.156480027872147, 0.156434851345621
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.430174244274538, -0.907986703493173,
0.528653207583655, 0.682747723082136, 0.630063242587757, 1.76833519204225
))
})
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(385.21748045921, 411.143957781804, 351.86379518625, 7, -185.608740229605
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-1")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, -3.02559865497385, 1, 2.11347174630263e-05, 0.487016560627671,
-2.07106373626905, -4.2525530006615, -1.11652881756424, 2, -2.58914867004793,
2, 0.000694942309736545, 0.483814593295167, -1.64088949199451,
-3.39156675870136, -0.692630313941086))
})
}
### bernoulli + probit, type II with LRT, no random slopes, custom options
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5",
"6", "7"), name = "contNormal", values = c("-1.11", "0", "1.11",
"-1.11", "0", "1.11")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5", "6", "7"), name = "facGender", values = c("f",
"f", "f", "m", "m", "m")), 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",
"1", "-1", "0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contBinom"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "contNormal"),
list(components = "facGender"),
list(components = c("contNormal", "facGender")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("contNormal", "facGender")
options$link <- "probit"
options$marginalMeansTerms <- list(list(variable = "contNormal"), list(variable = "facGender"))
options$marginalMeansPAdjustment <- "mvt"
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSd <- 1.11
options$testMethod <- "likelihoodRatioTest"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "violin"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(
list(randomSlopes = FALSE, value = "contNormal"),
list(randomSlopes = FALSE, value = "facGender"),
list(randomSlopes = FALSE, value = c("contNormal", "facGender"))),
value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$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("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "contNormal", 0.609594107923892, 0.260766411860686, 1, "facGender",
0.284327625868667, 1.14628764586072, 1, "contNormal<unicode><unicode><unicode>facGender",
0.334577761935427, 0.931098042304626))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1.36358769374441, 0.149244029378276, "f", -0.359295948223524,
1, 0.56515507556465, 0.259463939956601, 0.575201430315285, 0.657784006980076,
-0.18874858754, -0.102631617037894, "f", -0.467214151818054,
2, 0.58112743017407, 0.186014915404539, -0.551738643187265,
0.261950917742266, 0.98609051866441, -0.354507263454064, "f",
-1.02655222283236, 3, 0.301186960451724, 0.342886381933189,
-1.03389134749346, 0.317537695924233, -1.36358769374441, -0.407989734747062,
"m", -0.980583088901367, 4, 0.162554222969888, 0.292144834635151,
-1.39653242631034, 0.164603619407244, -0.18874858754, -0.366884673848823,
"m", -0.730935585846312, 5, 0.0482431545502667, 0.185743674306812,
-1.97522028794801, -0.00283376185133349, 0.98609051866441, -0.325779612950584,
"m", -0.787241225976078, 6, 0.166455727599482, 0.235443924819764,
-1.38368239146528, 0.13568200007491))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 0.320173334817175, 0.459127674282166, 0.603320358452321,
"m", 0.232409249286845, 0.356852521999701, 0.498869494097947
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.251689251201327, 0.0627285923928148, 0.135236069581482, -1.86111036782004,
"Intercept", -0.0897018938188372, 0.491183326978422, 0.130299610338678,
-0.68842795143962, "contNormal", 0.108591520764834, 0.421987737774077,
0.135236068709804, 0.80297750297559, "facGender (1)", -0.124689715284057,
0.338594416327209, 0.130299610114261, -0.956946188670218, "contNormal<unicode><unicode><unicode>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, 1))
})
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("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.25187564641617, -0.219514047420873,
0.294980046207553, 0.240509365251252, 1.04725920403575, 0.723265340253213,
"Contrast 2", "<unicode>", 0.25187564641617, -0.219514047420873,
0.294980046207553, 0.240509365251252, 1.04725920403575, 0.723265340253213
))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(5, 100))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(143.384651734201, 156.410502664141, 133.384651734201, 5, -66.6923258671005
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-2")
})
}
### gamma + log, parametric bootsrap, no correlation
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
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("1", "0")), list(isContrast = TRUE,
levels = c("2", "3"), name = "Contrast 2", values = c("0",
"0")))
options$bootstrapSamples <- 10
options$dependent <- "contGamma"
options$family <- "gamma"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"),
list(components = "contBinom"),
list(components = c("facGender", "contBinom")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("facGender", "contBinom")
options$link <- "log"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$marginalMeansPAdjustment <- "mvt"
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSd <- 1.11
options$testMethod <- "parametricBootstrap"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
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 = FALSE, randomComponents = list(list(
randomSlopes = TRUE, value = "facGender"),
list(randomSlopes = FALSE, value = "contBinom"),
list(randomSlopes = FALSE, value = c("facGender", "contBinom"))),
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 <- FALSE
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("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.447004500743224, 0.272727272727273, 0.578236212970523,
1, "contBinom", 0.377291093565023, 0.636363636363636, 0.77950982853497,
1, "facGender<unicode><unicode><unicode>contBinom", 0.547514203844492,
0.272727272727273, 0.361789256147745))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.587430344676266, "f", 0.37458694201089, 1, 6.32574573837542e-08,
0.108595568257508, 5.40933993994414, 0.800273747341642, 0.746196682539556,
"m", 0.413133378648097, 2, 1.12771879979508e-05, 0.169933379653208,
4.39111305890791, 1.07925998643101))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 1.45439054289628, 1.79935873784759, 2.22615024780132, "m",
1.51154662053427, 2.10896368553216, 2.9425012543254))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.728064594347703, 6.66648936521263e-09, 0.125547485528779, 5.79911729240334,
"Intercept", -0.0356589672364708, 0.749565019225593, 0.111709015978617,
-0.319212974208782, "facGender (1)", -0.122502161479584, 0.407130511821325,
0.14777922655215, -0.828953868129454, "contBinom", -0.0874484033903483,
0.547831173835027, 0.145501627716119, -0.601013231006354, "facGender (1)<unicode><unicode><unicode>contBinom"
))
})
test_that("facFive.1: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender (f)", 0.820565921340386, 1, "facGender (m)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.69832135980351, 0.487652721557823))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.06251298006749, 0.0039078726769184, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0273977073255754, 0.000750634366697887, "facGender (f)", 0.223252099074577,
0.0498414997412049, "facGender (m)"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.587430344676266, 0.37458694201089,
6.32574573837542e-08, 0.108595568257508, 5.40933993994414, 0.800273747341642
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-3")
})
}
### poisson + log, type II parametric bootsrap
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
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 <- 10
options$dependent <- "facFifty"
options$family <- "poisson"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$link <- "log"
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 <- "top"
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 <- "boxplot"
options$plotLevelsByColor <- FALSE
options$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 = "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 <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsContrasts <- list()
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.323811589816613, 0.363636363636364, 0.973488924913681
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(26.9632681863974, "f", 23.4528367885457, 1.91888508411733, 30.9991425705339,
23.8823095639431, "m", 21.3136484431691, 1.38654415019449, 26.7605385173184
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(3.23380675074198, 0, 0.0282088980895584, 114.637825996436, "Intercept",
0.0606687514862075, 0.299679559121684, 0.0584972850308832, 1.037120807473,
"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", 0.413199633101234, 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, 1))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0442623073281687, 0.00195915185001325, "Intercept", 0.12287221532645,
0.0150975812992296, "facGender (1)"))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-4")
})
}
### aggregated binomial
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "cA",
values = c("1", "2")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("-1", "1")))
options$bootstrapSamples <- 500
options$dependent <- "binom_mean"
options$dependentAggregation <- "rep"
options$family <- "binomial"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "cA"),
list(components = "cB"),
list(components = c("cA", "cB")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("cA", "cB")
options$marginalMeansTerms <- list(list(variable = "cA"))
options$marginalMeansContrast <- TRUE
options$testMethod <- "likelihoodRatioTest"
options$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 <- "id"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
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 = "cB"))
options$plotHorizontalAxis <- list(list(variable = "cA"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "cA")), value = "id"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "id"
options$seed <- 1
options$setSeed <- FALSE
options$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$type <- "3"
options$link <- "logit"
set.seed(1)
dataset <- structure(list(id = 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), cA = 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), cB = 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), binom_mean = c(0.6, 1, 0.6, 0.8, 0.8, 0.6, 0.2, 0.2,
0.4, 0.6, 0.6, 0.6, 0, 0.6, 0.8, 1, 1, 0.4, 0.8, 0.6, 0.6, 0.6,
0.2, 0.8, 0.6, 1, 0.8, 0.2, 0.4, 0.6, 0.2, 0.8, 0.2, 0.6, 0.4,
0.8, 0.6, 0.6, 0.8, 0.4, 0, 0.4, 0.4, 0, 0.8, 0.8, 0.4, 0.4,
0.2, 0.6, 0.4, 0.4, 0.4, 0.4, 0.8, 0.2, 0.8, 0.2, 0.6, 0.2),
rep = c(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)), class = "data.frame", row.names = c(NA, -60L))
results <- jaspTools::runAnalysis("MixedModelsGLMM", dataset = dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "cA", 0.768666936589639, 0.0865060140104958, 1, "cB", 0.479800713357095,
0.499316242701497, 1, "cA<unicode><unicode><unicode>cB", 0.859034758351994,
0.031542830127961))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.521557923880247, 0.411147513780645, 1, 0.0567205115241829,
0.629901441101518, 2, 0.543531352735364, 0.424297968083058,
2, 0.0607262923153063, 0.657976328706945))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.661227504395588, 0.525989206215448, 1.04271469794619, 0.634140389214797,
"Intercept", 0.194514831494138, 0.768585189821104, 0.661106803176433,
0.294226032101847, "cA", -0.331612250089613, 0.480839156002498,
0.470402992765527, -0.704953529610952, "cB", -0.053116323141788,
0.858982330488374, 0.298960955867488, -0.177669766233057, "cA<unicode><unicode><unicode>cB"
))
})
test_that("id: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -0.717633169548817, 1, "cA"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1))
})
test_that("id: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.672328111728483, 0.452025089820388, "Intercept", 0.385999853393285,
0.148995886819638, "cA"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.021973428855117, -0.110026385038528,
0.744222663598295, 0.0673480813600878, 0.326266590099759, 0.153973242748762
))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10, 60))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(202.867152008122, 217.527563943677, 64.2562363767916, 7, -94.433576004061
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-5")
})
}
jasp-stats/jaspMixedModels documentation built on April 5, 2025, 3:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("Generalized Linear Mixed Models")
### bernoulli + logit, default, all selected output using LRT testMethod
{
skip_on_os("mac") # problems with precision outside of windows
skip_on_os("linux") # problems with precision outside of windows
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("1", "2", "3", "4",
"5", "6"), name = "cA", values = c("1", "2", "1", "2", "1", "2"
)), list(isContrast = FALSE, levels = c("1", "2", "3", "4", "5",
"6"), name = "y_beta", values = c("-1", "-1", "0", "0", "1",
"1")), list(isContrast = TRUE, levels = c("1", "2", "3", "4",
"5", "6"), name = "Contrast 1", values = c("0", "1", "-1", "0",
"0", "0")), list(isContrast = TRUE, levels = c("1", "2", "3",
"4", "5", "6"), name = "Contrast 2", values = c("1", "-1", "0",
"0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "Variable5"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "Variable1"),
list(components = "Variable7"),
list(components = c("Variable1", "Variable7")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("Variable1", "Variable7")
options$marginalMeansTerms <- list(list(variable = "Variable1"), list(variable = "Variable7"))
options$marginalMeansContrast <- TRUE
options$testMethod <- "likelihoodRatioTest"
options$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()
options$plotHorizontalAxis <- list(list(variable = "Variable1"))
options$vovkSellke <- FALSE
options$randomEffects <- list(
list(correlations = TRUE,
randomComponents = list(list(randomSlopes = TRUE, value = "Variable1")),
value = "Variable0"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsComparison <- TRUE
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("1", "2"), name = "cA",
values = c("1", "2")), list(isContrast = TRUE, levels = c("1",
"2"), name = "Contrast 1", values = c("-1", "1")))
options$trendsTrendVariable <- list(list(variable = "Variable7"))
options$trendsVariables <- list(list(variable = "Variable1"))
options$type <- "3"
options$link <- "logit"
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))
results <- jaspTools::runAnalysis("MixedModelsGLMM", dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Variable1", 0.991431100095283, 0.0001153417971409, 1, "Variable7",
0.0187024090194103, 5.52912939823165, 1, "Variable1<unicode><unicode><unicode>Variable7",
0.528941241000858, 0.396425181363099))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.134346556802761, 0.67652441946339, 0.526200705374444, 1,
0.0706713772323912, 0.797507910077546, 2, 0.134346556802761,
0.690097553733996, 0.554821377821813, 2, 0.0633312981191573,
0.799148378891918, 1, 0.527784183943333, 0.480762130522145,
0.361876774141483, 3, 0.0624389577258609, 0.601866597179011,
2, 0.527784183943333, 0.538669559336265, 0.423592363725772,
4, 0.0587079310508572, 0.649767869856607, 1, 0.921221811083906,
0.290733584783489, 0.183383579119308, 5, 0.0633116820952347,
0.427989593112089, 2, 0.921221811083906, 0.379752250861563,
0.249903562970398, 6, 0.0731302644993081, 0.529447590192747
))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.361876774141483, 0.480762130522145, 0.601866597179011, 2,
0.423592363725772, 0.538669559336265, 0.649767869856607))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01114713477693, 0.188751395675504, 0.769353382613811, 1.31428178211377,
"Intercept", 0.0049380649488768, 0.991565197089033, 0.46710426273995,
0.010571654644963, "Variable1", -2.50123798054357, 0.0208966035659629,
1.08285853494975, -2.30984740833173, "Variable7", 0.430174244274536,
0.528653207583655, 0.682747723082131, 0.630063242587757, "Variable1<unicode><unicode><unicode>Variable7"
))
})
test_that("Variable0: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -0.860597398092285, 1, "Variable1"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1))
})
test_that("Variable0: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.889188913907964, 0.790656924616825, "Intercept", 0.470404903303557,
0.221280773052028, "Variable1"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.209335423211852, 0.0542554560233471,
0.0163060254993088, 0.0791238861590089, 2.64566660428138, 0.364415390400356,
"Contrast 2", "<unicode>", -0.0135731342706064, -0.183581119886834,
0.875654660762392, 0.0867403620460521, -0.156480027872147, 0.156434851345621
))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.430174244274538, -0.907986703493173,
0.528653207583655, 0.682747723082136, 0.630063242587757, 1.76833519204225
))
})
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(385.21748045921, 411.143957781804, 351.86379518625, 7, -185.608740229605
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-1")
})
test_that("Estimated Trends table results match", {
table <- results[["results"]][["trendsSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, -3.02559865497385, 1, 2.11347174630263e-05, 0.487016560627671,
-2.07106373626905, -4.2525530006615, -1.11652881756424, 2, -2.58914867004793,
2, 0.000694942309736545, 0.483814593295167, -1.64088949199451,
-3.39156675870136, -0.692630313941086))
})
}
### bernoulli + probit, type II with LRT, no random slopes, custom options
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3", "4", "5",
"6", "7"), name = "contNormal", values = c("-1.11", "0", "1.11",
"-1.11", "0", "1.11")), list(isContrast = FALSE, levels = c("2",
"3", "4", "5", "6", "7"), name = "facGender", values = c("f",
"f", "f", "m", "m", "m")), 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",
"1", "-1", "0", "0", "0")))
options$bootstrapSamples <- 500
options$dependent <- "contBinom"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "contNormal"),
list(components = "facGender"),
list(components = c("contNormal", "facGender")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("contNormal", "facGender")
options$link <- "probit"
options$marginalMeansTerms <- list(list(variable = "contNormal"), list(variable = "facGender"))
options$marginalMeansPAdjustment <- "mvt"
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSd <- 1.11
options$testMethod <- "likelihoodRatioTest"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "violin"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
options$plotLevelsByLinetype <- TRUE
options$plotLevelsByShape <- TRUE
options$plotSeparatePlots <- list()
options$plotTheme <- "jasp"
options$plotSeparateLines <- list()
options$plotHorizontalAxis <- list(list(variable = "facGender"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(
list(randomSlopes = FALSE, value = "contNormal"),
list(randomSlopes = FALSE, value = "facGender"),
list(randomSlopes = FALSE, value = c("contNormal", "facGender"))),
value = "facFive"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$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("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "contNormal", 0.609594107923892, 0.260766411860686, 1, "facGender",
0.284327625868667, 1.14628764586072, 1, "contNormal<unicode><unicode><unicode>facGender",
0.334577761935427, 0.931098042304626))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-1.36358769374441, 0.149244029378276, "f", -0.359295948223524,
1, 0.56515507556465, 0.259463939956601, 0.575201430315285, 0.657784006980076,
-0.18874858754, -0.102631617037894, "f", -0.467214151818054,
2, 0.58112743017407, 0.186014915404539, -0.551738643187265,
0.261950917742266, 0.98609051866441, -0.354507263454064, "f",
-1.02655222283236, 3, 0.301186960451724, 0.342886381933189,
-1.03389134749346, 0.317537695924233, -1.36358769374441, -0.407989734747062,
"m", -0.980583088901367, 4, 0.162554222969888, 0.292144834635151,
-1.39653242631034, 0.164603619407244, -0.18874858754, -0.366884673848823,
"m", -0.730935585846312, 5, 0.0482431545502667, 0.185743674306812,
-1.97522028794801, -0.00283376185133349, 0.98609051866441, -0.325779612950584,
"m", -0.787241225976078, 6, 0.166455727599482, 0.235443924819764,
-1.38368239146528, 0.13568200007491))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 0.320173334817175, 0.459127674282166, 0.603320358452321,
"m", 0.232409249286845, 0.356852521999701, 0.498869494097947
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.251689251201327, 0.0627285923928148, 0.135236069581482, -1.86111036782004,
"Intercept", -0.0897018938188372, 0.491183326978422, 0.130299610338678,
-0.68842795143962, "contNormal", 0.108591520764834, 0.421987737774077,
0.135236068709804, 0.80297750297559, "facGender (1)", -0.124689715284057,
0.338594416327209, 0.130299610114261, -0.956946188670218, "contNormal<unicode><unicode><unicode>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, 1))
})
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("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.25187564641617, -0.219514047420873,
0.294980046207553, 0.240509365251252, 1.04725920403575, 0.723265340253213,
"Contrast 2", "<unicode>", 0.25187564641617, -0.219514047420873,
0.294980046207553, 0.240509365251252, 1.04725920403575, 0.723265340253213
))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(5, 100))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(143.384651734201, 156.410502664141, 133.384651734201, 5, -66.6923258671005
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-2")
})
}
### gamma + log, parametric bootsrap, no correlation
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
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("1", "0")), list(isContrast = TRUE,
levels = c("2", "3"), name = "Contrast 2", values = c("0",
"0")))
options$bootstrapSamples <- 10
options$dependent <- "contGamma"
options$family <- "gamma"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"),
list(components = "contBinom"),
list(components = c("facGender", "contBinom")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("facGender", "contBinom")
options$link <- "log"
options$marginalMeansTerms <- list(list(variable = "facGender"))
options$marginalMeansPAdjustment <- "mvt"
options$marginalMeansComparison <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSd <- 1.11
options$testMethod <- "parametricBootstrap"
options$plotTransparency <- 0.7
options$plotDodge <- 0.3
options$plotElementWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSizeData <- 1
options$plotRelativeSizeText <- 1.5
options$plotBackgroundData <- "facFive"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- TRUE
options$plotBackgroundElement <- "boxjitter"
options$plotLevelsByColor <- FALSE
options$plotLevelsByFill <- TRUE
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 = FALSE, randomComponents = list(list(
randomSlopes = TRUE, value = "facGender"),
list(randomSlopes = FALSE, value = "contBinom"),
list(randomSlopes = FALSE, value = c("facGender", "contBinom"))),
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 <- FALSE
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("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.447004500743224, 0.272727272727273, 0.578236212970523,
1, "contBinom", 0.377291093565023, 0.636363636363636, 0.77950982853497,
1, "facGender<unicode><unicode><unicode>contBinom", 0.547514203844492,
0.272727272727273, 0.361789256147745))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.587430344676266, "f", 0.37458694201089, 1, 6.32574573837542e-08,
0.108595568257508, 5.40933993994414, 0.800273747341642, 0.746196682539556,
"m", 0.413133378648097, 2, 1.12771879979508e-05, 0.169933379653208,
4.39111305890791, 1.07925998643101))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 1.45439054289628, 1.79935873784759, 2.22615024780132, "m",
1.51154662053427, 2.10896368553216, 2.9425012543254))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.728064594347703, 6.66648936521263e-09, 0.125547485528779, 5.79911729240334,
"Intercept", -0.0356589672364708, 0.749565019225593, 0.111709015978617,
-0.319212974208782, "facGender (1)", -0.122502161479584, 0.407130511821325,
0.14777922655215, -0.828953868129454, "contBinom", -0.0874484033903483,
0.547831173835027, 0.145501627716119, -0.601013231006354, "facGender (1)<unicode><unicode><unicode>contBinom"
))
})
test_that("facFive.1: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender (f)", 0.820565921340386, 1, "facGender (m)"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.69832135980351, 0.487652721557823))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.06251298006749, 0.0039078726769184, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0273977073255754, 0.000750634366697887, "facGender (f)", 0.223252099074577,
0.0498414997412049, "facGender (m)"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.587430344676266, 0.37458694201089,
6.32574573837542e-08, 0.108595568257508, 5.40933993994414, 0.800273747341642
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-3")
})
}
### poisson + log, type II parametric bootsrap
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
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 <- 10
options$dependent <- "facFifty"
options$family <- "poisson"
options$modelSummary <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- "facGender"
options$link <- "log"
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 <- "top"
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 <- "boxplot"
options$plotLevelsByColor <- FALSE
options$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 = "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 <- FALSE
options$fixedEffectEstimate <- TRUE
options$varianceCorrelationEstimate <- TRUE
options$interceptTest <- FALSE
options$trendsContrasts <- list()
options$trendsTrendVariable <- list()
options$type <- "2"
set.seed(1)
results <- jaspTools::runAnalysis("MixedModelsGLMM", "debug", options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "facGender", 0.323811589816613, 0.363636363636364, 0.973488924913681
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(26.9632681863974, "f", 23.4528367885457, 1.91888508411733, 30.9991425705339,
23.8823095639431, "m", 21.3136484431691, 1.38654415019449, 26.7605385173184
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(3.23380675074198, 0, 0.0282088980895584, 114.637825996436, "Intercept",
0.0606687514862075, 0.299679559121684, 0.0584972850308832, 1.037120807473,
"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", 0.413199633101234, 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, 1))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0442623073281687, 0.00195915185001325, "Intercept", 0.12287221532645,
0.0150975812992296, "facGender (1)"))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-4")
})
}
### aggregated binomial
{
options <- jaspTools::analysisOptions("MixedModelsGLMM")
options$contrasts <- list(list(isContrast = FALSE, levels = c("2", "3"), name = "cA",
values = c("1", "2")), list(isContrast = TRUE, levels = c("2",
"3"), name = "Contrast 1", values = c("-1", "1")))
options$bootstrapSamples <- 500
options$dependent <- "binom_mean"
options$dependentAggregation <- "rep"
options$family <- "binomial"
options$modelSummary <- TRUE
options$fixedEffects <- list(list(components = "cA"),
list(components = "cB"),
list(components = c("cA", "cB")))
options$includeIntercept <- TRUE
options$factorContrast <- "sum"
options$fixedVariables <- c("cA", "cB")
options$marginalMeansTerms <- list(list(variable = "cA"))
options$marginalMeansContrast <- TRUE
options$testMethod <- "likelihoodRatioTest"
options$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 <- "id"
options$plotBackgroundColor <- "darkgrey"
options$plotCiType <- "model"
options$plotCiLevel <- 0.95
options$plotEstimatesTable <- FALSE
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 = "cB"))
options$plotHorizontalAxis <- list(list(variable = "cA"))
options$vovkSellke <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "cA")), value = "id"))
options$randomEffects[[1]]$randomComponents[[length(options$randomEffects[[1]]$randomComponents) + 1]] <- list(randomSlopes = TRUE, value = "Intercept")
options$randomEffectEstimate <- FALSE
options$randomVariables <- "id"
options$seed <- 1
options$setSeed <- FALSE
options$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$type <- "3"
options$link <- "logit"
set.seed(1)
dataset <- structure(list(id = 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), cA = 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), cB = 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), binom_mean = c(0.6, 1, 0.6, 0.8, 0.8, 0.6, 0.2, 0.2,
0.4, 0.6, 0.6, 0.6, 0, 0.6, 0.8, 1, 1, 0.4, 0.8, 0.6, 0.6, 0.6,
0.2, 0.8, 0.6, 1, 0.8, 0.2, 0.4, 0.6, 0.2, 0.8, 0.2, 0.6, 0.4,
0.8, 0.6, 0.6, 0.8, 0.4, 0, 0.4, 0.4, 0, 0.8, 0.8, 0.4, 0.4,
0.2, 0.6, 0.4, 0.4, 0.4, 0.4, 0.8, 0.2, 0.8, 0.2, 0.6, 0.2),
rep = c(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)), class = "data.frame", row.names = c(NA, -60L))
results <- jaspTools::runAnalysis("MixedModelsGLMM", dataset = dataset, options)
test_that("ANOVA Summary table results match", {
table <- results[["results"]][["ANOVAsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "cA", 0.768666936589639, 0.0865060140104958, 1, "cB", 0.479800713357095,
0.499316242701497, 1, "cA<unicode><unicode><unicode>cB", 0.859034758351994,
0.031542830127961))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.521557923880247, 0.411147513780645, 1, 0.0567205115241829,
0.629901441101518, 2, 0.543531352735364, 0.424297968083058,
2, 0.0607262923153063, 0.657976328706945))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.661227504395588, 0.525989206215448, 1.04271469794619, 0.634140389214797,
"Intercept", 0.194514831494138, 0.768585189821104, 0.661106803176433,
0.294226032101847, "cA", -0.331612250089613, 0.480839156002498,
0.470402992765527, -0.704953529610952, "cB", -0.053116323141788,
0.858982330488374, 0.298960955867488, -0.177669766233057, "cA<unicode><unicode><unicode>cB"
))
})
test_that("id: Correlation Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_CE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, "Intercept", -0.717633169548817, 1, "cA"))
})
test_that("Residual Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_RES1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 1))
})
test_that("id: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.672328111728483, 0.452025089820388, "Intercept", 0.385999853393285,
0.148995886819638, "cA"))
})
test_that("contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode>", 0.021973428855117, -0.110026385038528,
0.744222663598295, 0.0673480813600878, 0.326266590099759, 0.153973242748762
))
})
test_that("Sample sizes table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_fitSizes"]][["data"]]
jaspTools::expect_equal_tables(table,
list(10, 60))
})
test_that("Fit statistics table results match", {
table <- results[["results"]][["fitSummary"]][["collection"]][["fitSummary_modelSummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(202.867152008122, 217.527563943677, 64.2562363767916, 7, -94.433576004061
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-5")
})
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.