tests: change tests to use "id" and "time" columns for catching evalu…

…ation errors (#151)

tests/testthat/setup-data.R

@@ -6,22 +6,22 @@ if (requireNamespace('ggplot2', quietly = TRUE)) {
 
 testLongData = generateLongData(
   sizes = c(20L, 30L),
-  fixed = Value ~ 1 + Assessment,
-  cluster = ~ 1 + Assessment,
+  fixed = Value ~ 1 + time,
+  cluster = ~ 1 + time,
   random = ~ 1,
-  id = 'Traj',
-  data = data.frame(Assessment = seq(0, 1, by = .1)),
+  id = 'id',
+  data = data.frame(time = seq(0, 1, by = .1)),
   fixedCoefs = c(0, 0),
   clusterCoefs = cbind(c(-2, 1), c(2, -1)),
   randomScales = cbind(.1, .1),
   noiseScales = c(.1, .1),
   clusterNames = c('A', 'B'),
   shuffle = TRUE
 ) %>%
-  .[, .(Traj, Assessment, Value, Class)] %>%
+  .[, .(id, time, Value, Class)] %>%
   .[, Constant := 1] %>%
   .[, Cluster := Class] %>%
-  .[, Traj := factor(Traj)] %>%
+  .[, id := factor(id)] %>%
   .[]
 
 # set up capture functions to test for wrong column name handling

tests/testthat/setup-init.R

@@ -1,7 +1,7 @@
 options(deparse.max.lines=5)
 
-options(latrend.id = 'Traj')
-options(latrend.time = 'Assessment')
+options(latrend.id = 'id')
+options(latrend.time = 'time')
 options(latrend.verbose = R.utils::Verbose())
 options(latrend.warnModelDataClusterColumn = FALSE)
 options(latrend.warnNewDataClusterColumn = FALSE)
@@ -87,28 +87,32 @@ expect_valid_lcModel = function(object) {
 
   # Predict
   # cluster-specific prediction
-  pred = predict(object, newdata=data.frame(Cluster='T1', Assessment=time(object)[c(1,3)]))
+  pred = predict(object, newdata = data.frame(Cluster = 'T1', time = time(object)[c(1,3)]))
   expect_is(pred, 'data.frame', info='predictClusterTime')
   expect_true('Fit' %in% names(pred), info='predictClusterTime')
   expect_equal(nrow(pred), 2, info='predictClusterTime')
 
   # prediction for all clusters; list of data.frames
-  pred2 = predict(object, newdata=data.frame(Assessment=time(object)[c(1,3)]))
+  pred2 = predict(object, newdata = data.frame(time = time(object)[c(1,3)]))
   expect_is(pred2, 'list', info='predictTime')
   expect_length(pred2, nClusters(object))
   expect_true('Fit' %in% names(pred2$T1), info='predictTime')
 
   # id-specific prediction for a specific cluster; data.frame
-  pred3 = predict(object, newdata=data.frame(Cluster=rep('T1', 4),
-                                     Traj=c(ids(object)[c(1,1,2)], tail(ids(object), 1)),
-                                     Assessment=c(time(object)[c(1,3,1,1)])))
+  pred3 = predict(object, newdata = data.frame(
+    Cluster = rep('T1', 4),
+    id = c(ids(object)[c(1,1,2)], tail(ids(object), 1)),
+    time = c(time(object)[c(1,3,1,1)])
+  ))
   expect_is(pred3, 'data.frame', info='predictClusterIdTime')
   expect_true('Fit' %in% names(pred3), info='predictClusterIdTime')
   expect_equal(nrow(pred3), 4, info='predictClusterIdTime')
 
   # id-specific prediction for all clusters; list of data.frames
-  pred4 = predict(object, newdata=data.frame(Traj=c(ids(object)[c(1,1,2)], tail(ids(object), 1)),
-                                     Assessment=c(time(object)[c(1,3,1,1)])))
+  pred4 = predict(object, newdata = data.frame(
+    id = c(ids(object)[c(1,1,2)], tail(ids(object), 1)),
+    time = c(time(object)[c(1,3,1,1)])
+  ))
   expect_is(pred4, 'list', info='predictIdTime')
   expect_length(pred4, nClusters(object))
   expect_true('Fit' %in% names(pred4$T1), info='predictIdTime')
@@ -126,7 +130,7 @@ expect_valid_lcModel = function(object) {
   # predictForCluster
   predClus = predictForCluster(
     object,
-    newdata = data.frame(Assessment = time(object)[c(1,3)]),
+    newdata = data.frame(time = time(object)[c(1,3)]),
     cluster = 'T1'
   )
   expect_is(predClus, 'numeric', info='predictForCluster')

tests/testthat/setup-method-functions.R

@@ -22,7 +22,7 @@ lcMethodTestLcmmGBTM = function(..., init = NULL, seed = 1) {
 
 # lme4 ####
 lcMethodTestGCKM = function(...) {
-  lcMethodGCKM(formula = Value ~ (1 | Traj), ...)
+  lcMethodGCKM(formula = Value ~ (1 | id), ...)
 }
 
 
@@ -36,7 +36,7 @@ lcMethodTestStratify = function(...) {
 }
 
 lcMethodTestLMKM = function(...) {
-  lcMethodLMKM(formula = Value ~ Assessment, ..., seed = 1)
+  lcMethodLMKM(formula = Value ~ time, ..., seed = 1)
 }
 
 

tests/testthat/test-assert.R

@@ -113,7 +113,7 @@ test_that('no_trajectories_duplicate_time', {
   )
 
   expect_true(
-    no_trajectories_duplicate_time(testLongData, id = 'Traj', time = 'Assessment')
+    no_trajectories_duplicate_time(testLongData, id = 'id', time = 'time')
   )
 
   expect_false(
@@ -131,7 +131,7 @@ test_that('no_empty_trajectories', {
   )
 
   expect_true(
-    no_empty_trajectories(testLongData, id = 'Traj')
+    no_empty_trajectories(testLongData, id = 'id')
   )
 
   expect_true(
@@ -152,50 +152,50 @@ test_that('no_empty_trajectories', {
 
 test_that('have_trajectories_noNA', {
   expect_true(
-    have_trajectories_noNA(testLongData, id = 'Traj', response = 'Value')
+    have_trajectories_noNA(testLongData, id = 'id', response = 'Value')
   )
 
   expect_false(
-    have_trajectories_noNA(copy(testLongData)[2, Value := NA], id = 'Traj', response = 'Value')
+    have_trajectories_noNA(copy(testLongData)[2, Value := NA], id = 'id', response = 'Value')
   )
 
   expect_true(
-    have_trajectories_noNA(copy(testLongData)[3, Value := Inf], id = 'Traj', response = 'Value')
+    have_trajectories_noNA(copy(testLongData)[3, Value := Inf], id = 'id', response = 'Value')
   )
 })
 
 test_that('are_trajectories_length', {
   expect_true(
-    are_trajectories_length(testLongData, id = 'Traj', time = 'Assessment', min = 1L)
+    are_trajectories_length(testLongData, id = 'id', time = 'time', min = 1L)
   )
 
   expect_true(
-    are_trajectories_length(testLongData, id = 'Traj', time = 'Assessment', min = uniqueN(testLongData$Assessment))
+    are_trajectories_length(testLongData, id = 'id', time = 'time', min = uniqueN(testLongData$time))
   )
 
   expect_false(
-    are_trajectories_length(testLongData, id = 'Traj', time = 'Assessment', min = uniqueN(testLongData$Assessment) + 1L)
+    are_trajectories_length(testLongData, id = 'id', time = 'time', min = uniqueN(testLongData$time) + 1L)
   )
 })
 
 test_that('are_trajectories_equal_length', {
   expect_true(
-    are_trajectories_equal_length(testLongData, id = 'Traj', time = 'Assessment')
+    are_trajectories_equal_length(testLongData, id = 'id', time = 'time')
   )
 
   expect_false(
-    are_trajectories_equal_length(testLongData[2:.N], id = 'Traj', time = 'Assessment')
+    are_trajectories_equal_length(testLongData[2:.N], id = 'id', time = 'time')
   )
 })
 
 test_that('no_trajectories_allNA', {
   expect_true(
-    no_trajectories_allNA(testLongData, id = 'Traj', response = 'Value')
+    no_trajectories_allNA(testLongData, id = 'id', response = 'Value')
   )
 
   naData = copy(testLongData) %>%
-    .[Traj == unique(Traj)[2], Value := NA]
+    .[id == unique(id)[2], Value := NA]
   expect_false(
-    no_trajectories_allNA(naData, id = 'Traj', response = 'Value')
+    no_trajectories_allNA(naData, id = 'id', response = 'Value')
   )
 })

tests/testthat/test-cluslong.R

@@ -18,8 +18,8 @@ test_that('method var', {
 })
 
 test_that('method name', {
-  refMethod = lcMethodLMKM(formula = Value ~ Assessment, seed = 1)
-  model = latrend('lcMethodLMKM', formula = Value ~ Assessment, seed = 1, data = testLongData)
+  refMethod = lcMethodLMKM(formula = Value ~ time, seed = 1)
+  model = latrend('lcMethodLMKM', formula = Value ~ time, seed = 1, data = testLongData)
 
   newMethod = getLcMethod(model)
   expect_equal(newMethod$seed, refMethod$seed)
@@ -48,10 +48,10 @@ test_that('new method arguments', {
 })
 
 test_that('subset', {
-  model = latrend(mTest, data = testLongData[Assessment < .5])
+  model = latrend(mTest, data = testLongData[time < .5])
 
   expect_is(model, 'lcModel')
-  expect_equal(deparse(getCall(model)$data), 'testLongData[Assessment < 0.5]')
+  expect_equal(deparse(getCall(model)$data), 'testLongData[time < 0.5]')
 })
 
 test_that('data call', {
@@ -72,7 +72,7 @@ test_that('specify id and time with matrix input', {
 })
 
 test_that('envir', {
-  method = lcMethodLMKM(nClusters = a, formula = Value ~ Assessment)
+  method = lcMethodLMKM(nClusters = a, formula = Value ~ time)
   env = list2env(list(a = 1))
 
   model = latrend(method, data = testLongData, envir = env)
@@ -97,7 +97,7 @@ test_that('matrix input', {
 
 test_that('custom id and time', {
   nameData = copy(testLongData) %>%
-    setnames(c('Traj', 'Assessment'), c('Device', 'Observation'))
+    setnames(c('id', 'time'), c('Device', 'Observation'))
   method = lcMethodLMKM(Value ~ Observation, id = 'Device', time = 'Observation')
   model = latrend(method, data = nameData)
 
@@ -108,45 +108,45 @@ test_that('custom id and time', {
 test_that('id with NA', {
   set.seed(1)
   naData = copy(testLongData) %>%
-    .[sample(.N, 10), Traj := NA]
+    .[sample(.N, 10), id := NA]
 
   expect_error(latrend(mTest, data = naData))
 })
 
 test_that('factor id', {
   facData = copy(testLongData) %>%
-    .[, Traj := factor(Traj)]
+    .[, id := factor(id)]
 
   model = latrend(mTest, data = facData)
 
   expect_is(model, 'lcModel')
-  expect_equal(ids(model), levels(facData$Traj))
+  expect_equal(ids(model), levels(facData$id))
 })
 
 test_that('factor id, out of order', {
   facData = copy(testLongData) %>%
-    .[, Traj := factor(Traj, levels = rev(unique(Traj)))]
+    .[, id := factor(id, levels = rev(unique(id)))]
 
   model = latrend(mTest, data = facData)
 
   expect_is(model, 'lcModel')
-  expect_equal(ids(model), levels(facData$Traj))
+  expect_equal(ids(model), levels(facData$id))
 })
 
 test_that('factor id with empty levels', {
   facData = copy(testLongData) %>%
-    .[, Traj := factor(Traj, levels = seq(0, uniqueN(Traj) + 1))]
+    .[, id := factor(id, levels = seq(0, uniqueN(id) + 1))]
 
   expect_warning({
     model = latrend(mTest, data = facData)
-  }, regexp = 'mpty traj')
+  }, regexp = 'mpty')
 
   expect_is(model, 'lcModel')
 })
 
 test_that('id with NA', {
   naData = copy(testLongData) %>%
-    .[Traj == 1, Traj := NA]
+    .[id == 1, id := NA]
 
   expect_error(latrend(mTest, data = naData))
 })
@@ -226,7 +226,7 @@ test_that('trajectory length warning', {
 
 test_that('"time" column', {
   timeData = copy(testLongData)
-  setnames(timeData, 'Assessment', 'time')
+  setnames(timeData, 'time', 'time')
   method = lcMethodTestKML(time = 'time')
   model = latrend(method, data = timeData)
   expect_is(model, 'lcModel')

tests/testthat/test-cluslongBatch.R

@@ -26,16 +26,16 @@ test_that('multiple datasets', {
   models = latrendBatch(
     methods,
     data = .(testLongData,
-      testLongData[Assessment < .5],
-      testLongData[Assessment >= .5]),
+      testLongData[time < .5],
+      testLongData[time >= .5]),
     cartesian = TRUE)
 
   expect_is(models, 'lcModels')
   expect_length(models, 2*3)
 
   expect_equal(deparse(getCall(models[[1]])$data), 'testLongData')
-  expect_equal(deparse(getCall(models[[2]])$data), 'testLongData[Assessment < 0.5]')
-  expect_equal(deparse(getCall(models[[3]])$data), 'testLongData[Assessment >= 0.5]')
+  expect_equal(deparse(getCall(models[[2]])$data), 'testLongData[time < 0.5]')
+  expect_equal(deparse(getCall(models[[3]])$data), 'testLongData[time >= 0.5]')
 })
 
 
@@ -60,8 +60,8 @@ test_that('datasets list', {
   methods = lcMethods(mTest, nClusters = 1:2)
   dataList = list(
     testLongData,
-    testLongData[Assessment < .5],
-    testLongData[Assessment >= .5]
+    testLongData[time < .5],
+    testLongData[time >= .5]
   )
 
   models = latrendBatch(methods, data = dataList, cartesian = TRUE)
@@ -78,8 +78,8 @@ test_that('datasets list, cartesian=FALSE', {
   methods = lcMethods(mTest, nClusters = 1:3)
   dataList = list(
     testLongData,
-    testLongData[Assessment < .5],
-    testLongData[Assessment >= .5]
+    testLongData[time < .5],
+    testLongData[time >= .5]
   )
   models = latrendBatch(methods, data = dataList, cartesian = FALSE)
 
@@ -95,8 +95,8 @@ test_that('datasets list, cartesian=FALSE', {
 test_that('single method, multiple datasets', {
   dataList = list(
     testLongData,
-    testLongData[Assessment < .5],
-    testLongData[Assessment >= .5]
+    testLongData[time < .5],
+    testLongData[time >= .5]
   )
 
   models = latrendBatch(mTest, data = dataList, cartesian = TRUE)

tests/testthat/test-cluslongBoot.R

@@ -10,9 +10,9 @@ test_that('latrendBoot', {
   expect_length(models, 3)
 
   # test if data bootstrap sample calls are correct
-  expect_equal(deparse(getCall(models[[1]])$data), 'bootSample(testLongData, "Traj", 1140350788L)')
-  expect_equal(deparse(getCall(models[[2]])$data), 'bootSample(testLongData, "Traj", 312928385L)')
-  expect_equal(deparse(getCall(models[[3]])$data), 'bootSample(testLongData, "Traj", 866248189L)')
+  expect_equal(deparse(getCall(models[[1]])$data), 'bootSample(testLongData, "id", 1140350788L)')
+  expect_equal(deparse(getCall(models[[2]])$data), 'bootSample(testLongData, "id", 312928385L)')
+  expect_equal(deparse(getCall(models[[3]])$data), 'bootSample(testLongData, "id", 866248189L)')
 })
 
 test_that('latrendBoot without seed', {