Nothing
# Tests for datasets comprising trajectories of different number of observations, but at aligned moments in time
####
# Prepare data ####
dataset2 = dataset[Cluster %in% unique(Cluster)[1:2]]
refClusters = dataset2[, first(Cluster), keyby = Id]$V1
S = uniqueN(dataset2$Id)
trajNames = unique(dataset2$Id)
moments = unique(dataset2$Time)
clusSizes = dataset2[, uniqueN(Id), keyby = Cluster]$V1
M = uniqueN(dataset2$Time)
# remove an observation from the first trajectory
ns = M - 0:2
testData = dataset2[, head(.SD, ns[.GRP %% length(ns) + 1L]), by = Id]
m = make.lcMethod(id = 'Id', time = 'Time', response = 'Value', nClusters = 2L)
model = latrend(m, data = testData)
refModel = lcModelPartition(
testData,
response = 'Value',
time = 'Time',
id = 'Id',
trajectoryAssignments = refClusters
)
# Tests ####
test('converged', !isFALSE(converged(model) > 0))
test('nClusters', nClusters(model), 2L)
test('nobs', nobs(model), nrow(testData))
test(
'trajectoryAssignments.uniqueN',
uniqueN(trajectoryAssignments(model)),
2L,
text = 'expecting each cluster to have at least some assigned trajectories'
)
if (rlang::is_installed('mclustcomp')) {
test(
'trajectoryAssignments.recovery',
externalMetric(model, refModel, 'adjustedRand') > .99,
check.attributes = FALSE,
text = 'does trajectory assignment match the reference?',
onFail = .Options$latrend.test.checkClusterRecovery
)
}
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