tests/testthat/test-mixedmodelsglmm.R
In jasp-stats/jaspMixedModels: Mixed Models Module for JASP
context("Generalized Linear Mixed Models")
### binomial + logit, default, all selected output using LRT method
{
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$dependentVariable <- "Variable5"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "Variable1"), list(components = "Variable7"),
list(components = c("Variable1", "Variable7"
)))
options$fixedVariables <- c("Variable1", "Variable7")
options$marginalMeans <- list(list(variable = "Variable1"), list(variable = "Variable7"))
options$marginalMeansContrast <- TRUE
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "Variable0"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_jitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "Variable1"))
options$pvalVS <- FALSE
options$randomEffects <- list(
list(correlations = TRUE,
randomComponents = list(list(randomSlopes = TRUE, value = "Variable1")),
value = "Variable0"))
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsCompare <- 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$trendsTrend <- list(list(variable = "Variable7"))
options$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", 0.991431110310274, 0.000115341522132439,
1, "Variable7", 0.0187024090268013, 5.5291293975402,
1, "Variable1 * Variable7", 0.528941241360197,
0.396425180671656))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.134346556802761, 0.676524419073886, 0.526199736932535, 1,
0.0706718107932095, 0.797508536795421, 2, 0.134346556802761,
0.690097550186678, 0.554821270385462, 2, 0.0633313441712108,
0.799148443385041, 1, 0.527784183943333, 0.480762130159797,
0.361876751924427, 3, 0.0624389697912703, 0.601866619537568,
2, 0.527784183943333, 0.538669561398906, 0.423592336486101,
4, 0.0587079462108029, 0.649767899022927, 1, 0.921221811083906,
0.290733584551873, 0.183383174615077, 5, 0.0633119661324577,
0.427990253837139, 2, 0.921221811083906, 0.379752258678516,
0.249903469453633, 6, 0.073130329025205, 0.52944773101734))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.361876751924427, 0.480762130159797, 0.601866619537568, 2,
0.423592336486101, 0.538669561398906, 0.649767899022927))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01114715607801, 0.188755680199758, 0.769360854494757, 1.31426904575492,
"Intercept", 0.00493804175582078, 0.991565307672846, 0.467108193062088,
0.0105715160409623, "Variable1", -2.50123804213005,
0.0208986439411648, 1.08287583244124, -2.30981056848527, "Variable7",
0.43017430669552, 0.528658632555254, 0.682756807631676, 0.630054950587302,
"Variable1 * 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", -0.860597215248448, 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(0.889188580238109, 0.790656331225863, "Intercept", 0.470404764148096,
0.221280642133226, "Variable1"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.209335420026882,
0.0163060641315532, 0.0791239089261232, 2.64566580276431, "Contrast 2",
"<unicode><unicode><unicode>", -0.0135731311127926, 0.8756555048687,
0.0867409355278232, -0.156478956910014))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.430174306695523,
0.528658632555254, 0.68275680763168, 0.630054950587302))
})
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_fitStats"]][["data"]]
jaspTools::expect_equal_tables(table,
list(385.217480459902, 411.143957782495, 351.863795191825, 7, -185.608740229951
))
})
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, -3.02561215398824, 1, 2.11403962162228e-05, 0.487023448432242,
-2.07106373543454, -4.25249285655839, -1.11651531688085, 2,
-2.58915015877371, 2, 0.000694956718574256, 0.483815385136893,
-1.64088942873902, -3.39156107711361, -0.692628698704334))
})
}
### binomial + 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$dependentVariable <- "contBinom"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "contNormal"), list(components = "facGender"),
list(components = c("contNormal", "facGender")))
options$fixedVariables <- c("contNormal", "facGender")
options$link <- "probit"
options$marginalMeans <- list(list(variable = "contNormal"), list(variable = "facGender"))
options$marginalMeansAdjustment <- "mvt"
options$marginalMeansCompare <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSD <- 1.11
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_violin"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- TRUE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$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$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- 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 * 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", -0.359295949541209,
1, 0.565155076572514, 0.259463940628901, 0.575201428824872,
0.657784008297761, -0.18874858754, -0.102631617037894, "f",
-0.467214155012724, 2, 0.581127433486909, 0.186014917034502,
-0.551738638352634, 0.261950920936935, 0.98609051866441, -0.354507263454064,
"f", -1.02655222752204, 3, 0.301186963824939, 0.342886384325926,
-1.03389134027874, 0.31753770061391, -1.36358769374441, -0.407989734747062,
"m", -0.980583087733028, 4, 0.162554222112434, 0.292144834039049,
-1.39653242915988, 0.164603618238905, -0.18874858754, -0.366884673848823,
"m", -0.730935587943824, 5, 0.0482431558411855, 0.185743675376991,
-1.9752202765676, -0.00283375975382183, 0.98609051866441, -0.325779612950584,
"m", -0.787241229561193, 6, 0.166455730892536, 0.235443926648938,
-1.3836823807154, 0.135682003660025))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 0.320173333674467, 0.459127674282166, 0.603320359683825,
"m", 0.232409248646226, 0.356852521999701, 0.49886949493473
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.251689251201327, 0.0627285967943076, 0.135236071846752, -1.86111033664552,
"Intercept", -0.0897018938188371, 0.491183326203148, 0.130299610105594,
-0.688427952671103, "contNormal", 0.108591520764834, 0.421987737684679,
0.135236068683755, 0.802977503130257, "facGender (1)", -0.124689715284057,
0.338594418466376, 0.130299610691309, -0.956946184432264, "contNormal * 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><unicode><unicode>", 0.25187564641617,
0.294980047829034, 0.24050936605887, 1.04725920051911, "Contrast 2",
"<unicode><unicode><unicode>", 0.25187564641617, 0.294980047829034,
0.24050936605887, 1.04725920051911))
})
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_fitStats"]][["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$bootstrapSamples <- 10
options$dependentVariable <- "contGamma"
options$family <- "Gamma"
options$fitStats <- FALSE
options$fixedEffects <- list(list(components = "facGender"), list(components = "contBinom"),
list(components = c("facGender", "contBinom")))
options$fixedVariables <- c("facGender", "contBinom")
options$link <- "log"
options$marginalMeans <- list(list(variable = "facGender"))
options$marginalMeansAdjustment <- "mvt"
options$marginalMeansCompare <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSD <- 1.11
options$method <- "PB"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_boxjitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- TRUE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$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$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- 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", 0.447004500865773, 0.272727272727273, 0.578236212658624,
1, "contBinom", 0.37729109301047, 0.636363636363636, 0.779509830347195,
1, "facGender * contBinom", 0.547514203815074, 0.272727272727273,
0.361789256200893))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.587430349612986, "f", 0.374549906678813, 1, 6.35907081922447e-08,
0.108614466701096, 5.408398783834, 0.800310792547159, 0.746196683562162,
"m", 0.413131329216369, 2, 1.12785900324191e-05, 0.169934425822602,
4.39108603186226, 1.07926203790796))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 1.45433668005698, 1.79935874673052, 2.22623271752231, "m",
1.51154352272584, 2.1089636876888, 2.94250729080506))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.728064599434742, 6.69718693318382e-09, 0.125564170667767, 5.79834673826773,
"Intercept", -0.0356589646204568, 0.749571272929608, 0.111711894088311,
-0.319204726689778, "facGender (1)", -0.122502165694335, 0.407153741126262,
0.147786549919433, -0.828912818934593, "contBinom", -0.0874484047082629,
0.547831413604434, 0.145501717060467, -0.601012871015956, "facGender (1) * 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)", 0.820565738951668, 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(0.698321361072845, 0.835656245757097))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0625128879590991, 0.00390786116098688, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0273978028639619, 0.00075063960177252, "facGender (f)", 0.22325212826067,
0.0498415127729185, "facGender (m)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.587430349612986,
6.35907081922447e-08, 0.108614466701096, 5.408398783834))
})
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$bootstrapSamples <- 10
options$dependentVariable <- "facFifty"
options$family <- "poisson"
options$fitStats <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$fixedVariables <- "facGender"
options$link <- "log"
options$marginalMeans <- list(list(variable = "facGender"))
options$method <- "PB"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "top"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- FALSE
options$plotsGeom <- "geom_boxplot"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE,
value = "facGender")), value = "facFive"))
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list()
options$trendsTrend <- list()
options$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", 0.323811589802939, 0.363636363636364, 0.973488924968706
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(26.9632681754783, "f", 23.4528371326651, 1.91888487591472, 30.9991420905817,
23.882309468471, "m", 21.3136490705777, 1.38654373724911, 26.7605375156136
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(3.23380674854069, 0, 0.0282089155369933, 114.637755014009, "Intercept",
0.0606687532825319, 0.299679366705061, 0.0584972634725293, 1.0371212203973,
"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", 0.413199610716051, 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(0.0442623696713281, 0.00195915736892131, "Intercept", 0.122872211562862,
0.0150975803743487, "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$dependentVariable <- "binom_mean"
options$dependentVariableAggregation <- "rep"
options$family <- "binomialAgg"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "cA"), list(components = "cB"), list(components = c("cA",
"cB")))
options$fixedVariables <- c("cA", "cB")
options$marginalMeans <- list(list(variable = "cA"))
options$marginalMeansContrast <- TRUE
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "id"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- FALSE
options$plotsGeom <- "geom_jitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list(list(variable = "cB"))
options$plotsX <- list(list(variable = "cA"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "cA")), value = "id"))
options$randomVariables <- "id"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- list()
options$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", 0.768666971055732, 0.0865059874772953, 1, "cB", 0.479800725027522,
0.499316216168296, 1, "cA<unicode><unicode><unicode><unicode><unicode><unicode><unicode><unicode><unicode>cB",
0.859034817019825, 0.0315428035947605))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.521559047720783, 0.41115145068317, 1, 0.0567190036068068,
0.629899750093515, 2, 0.543534580013736, 0.424298904623603,
2, 0.0607273848025058, 0.657981320465884))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.661160396272512, 0.526032009864107, 1.04271672627264, 0.634074796743634,
"Intercept", 0.194569016770762, 0.768524273472735, 0.661111825925663,
0.294305757574876, "cA", -0.331580696152255, 0.480882470945843,
0.470404674525666, -0.704883931025144, "cB", -0.0531391637884625,
0.858923106746129, 0.298962625751381, -0.177745173514275, "cA<unicode><unicode><unicode><unicode><unicode><unicode><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", -0.717602519023855, 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(0.672274843452683, 0.45195346513933, "Intercept", 0.386000767561186,
0.148996592557825, "cA"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.021975532292953,
0.74419933094708, 0.0673481619405325, 0.326297432027278))
})
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_fitStats"]][["data"]]
jaspTools::expect_equal_tables(table,
list(202.867152034655, 217.52756397021, 64.2564473172443, 7, -94.4335760173276
))
})
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 Oct. 16, 2021, 10:23 p.m.
R Package Documentation
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context("Generalized Linear Mixed Models")
### binomial + logit, default, all selected output using LRT method
{
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$bootstrapSamples <- 500
options$dependentVariable <- "Variable5"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "Variable1"), list(components = "Variable7"),
list(components = c("Variable1", "Variable7"
)))
options$fixedVariables <- c("Variable1", "Variable7")
options$marginalMeans <- list(list(variable = "Variable1"), list(variable = "Variable7"))
options$marginalMeansContrast <- TRUE
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "Variable0"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_jitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "Variable1"))
options$pvalVS <- FALSE
options$randomEffects <- list(
list(correlations = TRUE,
randomComponents = list(list(randomSlopes = TRUE, value = "Variable1")),
value = "Variable0"))
options$randomVariables <- "Variable0"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsCompare <- TRUE
options$trendsContrast <- TRUE
options$trendsContrasts <- list(list(isContrast = FALSE, levels = c("1", "2"), name = "cA",
values = c("1", "2")), list(isContrast = TRUE, levels = c("1",
"2"), name = "Contrast 1", values = c("-1", "1")))
options$trendsTrend <- 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", 0.991431110310274, 0.000115341522132439,
1, "Variable7", 0.0187024090268013, 5.5291293975402,
1, "Variable1 * Variable7", 0.528941241360197,
0.396425180671656))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.134346556802761, 0.676524419073886, 0.526199736932535, 1,
0.0706718107932095, 0.797508536795421, 2, 0.134346556802761,
0.690097550186678, 0.554821270385462, 2, 0.0633313441712108,
0.799148443385041, 1, 0.527784183943333, 0.480762130159797,
0.361876751924427, 3, 0.0624389697912703, 0.601866619537568,
2, 0.527784183943333, 0.538669561398906, 0.423592336486101,
4, 0.0587079462108029, 0.649767899022927, 1, 0.921221811083906,
0.290733584551873, 0.183383174615077, 5, 0.0633119661324577,
0.427990253837139, 2, 0.921221811083906, 0.379752258678516,
0.249903469453633, 6, 0.073130329025205, 0.52944773101734))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.361876751924427, 0.480762130159797, 0.601866619537568, 2,
0.423592336486101, 0.538669561398906, 0.649767899022927))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1.01114715607801, 0.188755680199758, 0.769360854494757, 1.31426904575492,
"Intercept", 0.00493804175582078, 0.991565307672846, 0.467108193062088,
0.0105715160409623, "Variable1", -2.50123804213005,
0.0208986439411648, 1.08287583244124, -2.30981056848527, "Variable7",
0.43017430669552, 0.528658632555254, 0.682756807631676, 0.630054950587302,
"Variable1 * 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", -0.860597215248448, 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(0.889188580238109, 0.790656331225863, "Intercept", 0.470404764148096,
0.221280642133226, "Variable1"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.209335420026882,
0.0163060641315532, 0.0791239089261232, 2.64566580276431, "Contrast 2",
"<unicode><unicode><unicode>", -0.0135731311127926, 0.8756555048687,
0.0867409355278232, -0.156478956910014))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsTrends"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.430174306695523,
0.528658632555254, 0.68275680763168, 0.630054950587302))
})
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_fitStats"]][["data"]]
jaspTools::expect_equal_tables(table,
list(385.217480459902, 411.143957782495, 351.863795191825, 7, -185.608740229951
))
})
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, -3.02561215398824, 1, 2.11403962162228e-05, 0.487023448432242,
-2.07106373543454, -4.25249285655839, -1.11651531688085, 2,
-2.58915015877371, 2, 0.000694956718574256, 0.483815385136893,
-1.64088942873902, -3.39156107711361, -0.692628698704334))
})
}
### binomial + 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$bootstrapSamples <- 500
options$dependentVariable <- "contBinom"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "contNormal"), list(components = "facGender"),
list(components = c("contNormal", "facGender")))
options$fixedVariables <- c("contNormal", "facGender")
options$link <- "probit"
options$marginalMeans <- list(list(variable = "contNormal"), list(variable = "facGender"))
options$marginalMeansAdjustment <- "mvt"
options$marginalMeansCompare <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSD <- 1.11
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_violin"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- TRUE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = FALSE,
value = "contNormal"), list(randomSlopes = FALSE, value = "facGender"),
list(randomSlopes = FALSE, value = c("contNormal", "facGender"
))), value = "facFive"))
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- list()
options$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 * 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", -0.359295949541209,
1, 0.565155076572514, 0.259463940628901, 0.575201428824872,
0.657784008297761, -0.18874858754, -0.102631617037894, "f",
-0.467214155012724, 2, 0.581127433486909, 0.186014917034502,
-0.551738638352634, 0.261950920936935, 0.98609051866441, -0.354507263454064,
"f", -1.02655222752204, 3, 0.301186963824939, 0.342886384325926,
-1.03389134027874, 0.31753770061391, -1.36358769374441, -0.407989734747062,
"m", -0.980583087733028, 4, 0.162554222112434, 0.292144834039049,
-1.39653242915988, 0.164603618238905, -0.18874858754, -0.366884673848823,
"m", -0.730935587943824, 5, 0.0482431558411855, 0.185743675376991,
-1.9752202765676, -0.00283375975382183, 0.98609051866441, -0.325779612950584,
"m", -0.787241229561193, 6, 0.166455730892536, 0.235443926648938,
-1.3836823807154, 0.135682003660025))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 0.320173333674467, 0.459127674282166, 0.603320359683825,
"m", 0.232409248646226, 0.356852521999701, 0.49886949493473
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(-0.251689251201327, 0.0627285967943076, 0.135236071846752, -1.86111033664552,
"Intercept", -0.0897018938188371, 0.491183326203148, 0.130299610105594,
-0.688427952671103, "contNormal", 0.108591520764834, 0.421987737684679,
0.135236068683755, 0.802977503130257, "facGender (1)", -0.124689715284057,
0.338594418466376, 0.130299610691309, -0.956946184432264, "contNormal * 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><unicode><unicode>", 0.25187564641617,
0.294980047829034, 0.24050936605887, 1.04725920051911, "Contrast 2",
"<unicode><unicode><unicode>", 0.25187564641617, 0.294980047829034,
0.24050936605887, 1.04725920051911))
})
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_fitStats"]][["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$dependentVariable <- "contGamma"
options$family <- "Gamma"
options$fitStats <- FALSE
options$fixedEffects <- list(list(components = "facGender"), list(components = "contBinom"),
list(components = c("facGender", "contBinom")))
options$fixedVariables <- c("facGender", "contBinom")
options$link <- "log"
options$marginalMeans <- list(list(variable = "facGender"))
options$marginalMeansAdjustment <- "mvt"
options$marginalMeansCompare <- TRUE
options$marginalMeansContrast <- TRUE
options$marginalMeansResponse <- FALSE
options$marginalMeansSD <- 1.11
options$method <- "PB"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "right"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- TRUE
options$plotsGeom <- "geom_boxjitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- TRUE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = FALSE, randomComponents = list(list(
randomSlopes = TRUE, value = "facGender"), list(randomSlopes = FALSE,
value = "contBinom"), list(randomSlopes = FALSE, value = c("facGender",
"contBinom"))), value = "facFive"))
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- list()
options$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", 0.447004500865773, 0.272727272727273, 0.578236212658624,
1, "contBinom", 0.37729109301047, 0.636363636363636, 0.779509830347195,
1, "facGender * contBinom", 0.547514203815074, 0.272727272727273,
0.361789256200893))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.587430349612986, "f", 0.374549906678813, 1, 6.35907081922447e-08,
0.108614466701096, 5.408398783834, 0.800310792547159, 0.746196683562162,
"m", 0.413131329216369, 2, 1.12785900324191e-05, 0.169934425822602,
4.39108603186226, 1.07926203790796))
})
test_that("Estimated Means and Confidence Intervals table results match", {
table <- results[["results"]][["EstimatesTable"]][["data"]]
jaspTools::expect_equal_tables(table,
list("f", 1.45433668005698, 1.79935874673052, 2.22623271752231, "m",
1.51154352272584, 2.1089636876888, 2.94250729080506))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.728064599434742, 6.69718693318382e-09, 0.125564170667767, 5.79834673826773,
"Intercept", -0.0356589646204568, 0.749571272929608, 0.111711894088311,
-0.319204726689778, "facGender (1)", -0.122502165694335, 0.407153741126262,
0.147786549919433, -0.828912818934593, "contBinom", -0.0874484047082629,
0.547831413604434, 0.145501717060467, -0.601012871015956, "facGender (1) * 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)", 0.820565738951668, 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(0.698321361072845, 0.835656245757097))
})
test_that("facFive: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE1"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0625128879590991, 0.00390786116098688, "Intercept"))
})
test_that("facFive.1: Variance Estimates table results match", {
table <- results[["results"]][["REsummary"]][["collection"]][["REsummary_VE2"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.0273978028639619, 0.00075063960177252, "facGender (f)", 0.22325212826067,
0.0498415127729185, "facGender (m)"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.587430349612986,
6.35907081922447e-08, 0.108614466701096, 5.408398783834))
})
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$dependentVariable <- "facFifty"
options$family <- "poisson"
options$fitStats <- FALSE
options$fixedEffects <- list(list(components = "facGender"))
options$fixedVariables <- "facGender"
options$link <- "log"
options$marginalMeans <- list(list(variable = "facGender"))
options$method <- "PB"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "top"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "facFive"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- FALSE
options$plotsGeom <- "geom_boxplot"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list()
options$plotsX <- list(list(variable = "facGender"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE,
value = "facGender")), value = "facFive"))
options$randomVariables <- "facFive"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list()
options$trendsTrend <- 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", 0.323811589802939, 0.363636363636364, 0.973488924968706
))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(26.9632681754783, "f", 23.4528371326651, 1.91888487591472, 30.9991420905817,
23.882309468471, "m", 21.3136490705777, 1.38654373724911, 26.7605375156136
))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(3.23380674854069, 0, 0.0282089155369933, 114.637755014009, "Intercept",
0.0606687532825319, 0.299679366705061, 0.0584972634725293, 1.0371212203973,
"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", 0.413199610716051, 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(0.0442623696713281, 0.00195915736892131, "Intercept", 0.122872211562862,
0.0150975803743487, "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$bootstrapSamples <- 500
options$dependentVariable <- "binom_mean"
options$dependentVariableAggregation <- "rep"
options$family <- "binomialAgg"
options$fitStats <- TRUE
options$fixedEffects <- list(list(components = "cA"), list(components = "cB"), list(components = c("cA",
"cB")))
options$fixedVariables <- c("cA", "cB")
options$marginalMeans <- list(list(variable = "cA"))
options$marginalMeansContrast <- TRUE
options$method <- "LRT"
options$plotAlpha <- 0.7
options$plotDodge <- 0.3
options$plotGeomWidth <- 1
options$plotJitterHeight <- 0
options$plotJitterWidth <- 0.1
options$plotLegendPosition <- "none"
options$plotRelativeSize <- 1
options$plotRelativeSizeText <- 1.5
options$plotsAgregatedOver <- "id"
options$plotsBackgroundColor <- "darkgrey"
options$plotsCImethod <- "model"
options$plotsCIwidth <- 0.95
options$plotsEstimatesTable <- FALSE
options$plotsGeom <- "geom_jitter"
options$plotsMappingColor <- FALSE
options$plotsMappingFill <- FALSE
options$plotsMappingLineType <- TRUE
options$plotsMappingShape <- TRUE
options$plotsPanel <- list()
options$plotsTheme <- "JASP"
options$plotsTrace <- list(list(variable = "cB"))
options$plotsX <- list(list(variable = "cA"))
options$pvalVS <- FALSE
options$randomEffects <- list(list(correlations = TRUE, randomComponents = list(list(randomSlopes = TRUE, value = "cA")), value = "id"))
options$randomVariables <- "id"
options$seed <- 1
options$setSeed <- FALSE
options$showFE <- TRUE
options$showRE <- TRUE
options$test_intercept <- FALSE
options$trendsContrasts <- list(list(isContrast = TRUE, levels = list(), name = "Contrast 1",
values = list()))
options$trendsTrend <- 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", 0.768666971055732, 0.0865059874772953, 1, "cB", 0.479800725027522,
0.499316216168296, 1, "cA<unicode><unicode><unicode><unicode><unicode><unicode><unicode><unicode><unicode>cB",
0.859034817019825, 0.0315428035947605))
})
test_that("Estimated Marginal Means table results match", {
table <- results[["results"]][["EMMsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(1, 0.521559047720783, 0.41115145068317, 1, 0.0567190036068068,
0.629899750093515, 2, 0.543534580013736, 0.424298904623603,
2, 0.0607273848025058, 0.657981320465884))
})
test_that("Fixed Effects Estimates table results match", {
table <- results[["results"]][["FEsummary"]][["data"]]
jaspTools::expect_equal_tables(table,
list(0.661160396272512, 0.526032009864107, 1.04271672627264, 0.634074796743634,
"Intercept", 0.194569016770762, 0.768524273472735, 0.661111825925663,
0.294305757574876, "cA", -0.331580696152255, 0.480882470945843,
0.470404674525666, -0.704883931025144, "cB", -0.0531391637884625,
0.858923106746129, 0.298962625751381, -0.177745173514275, "cA<unicode><unicode><unicode><unicode><unicode><unicode><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", -0.717602519023855, 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(0.672274843452683, 0.45195346513933, "Intercept", 0.386000767561186,
0.148996592557825, "cA"))
})
test_that("Contrasts table results match", {
table <- results[["results"]][["contrastsMeans"]][["data"]]
jaspTools::expect_equal_tables(table,
list("Contrast 1", "<unicode><unicode><unicode>", 0.021975532292953,
0.74419933094708, 0.0673481619405325, 0.326297432027278))
})
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_fitStats"]][["data"]]
jaspTools::expect_equal_tables(table,
list(202.867152034655, 217.52756397021, 64.2564473172443, 7, -94.4335760173276
))
})
test_that("Plot matches", {
plotName <- results[["results"]][["plots"]][["data"]]
testPlot <- results[["state"]][["figures"]][[plotName]][["obj"]]
jaspTools::expect_equal_plots(testPlot, "plot-glmm-5")
})
}
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