add tests for paired t-test

Author: grlloyd

tests/testthat/test-ttest.R

@@ -8,6 +8,116 @@ test_that('ttest',{
     ttest(factor_names='Species')
   # apply
   M = model_apply(M,D)
-  expect_equal(M[2]$t_statistic[1],-15.386,tolerance=0.0005)
+  expect_equal(M[2]$t_statistic[1,1],-15.386,tolerance=0.0005)
 })
 
+test_that('paired-ttest',{
+  set.seed('57475')
+  # DatasetExperiment
+  D=iris_DatasetExperiment()
+  
+  # reduce to two classes
+  M=filter_smeta(mode='exclude',levels='setosa',factor_name='Species')
+  M=model_apply(M,D)
+  D=predicted(M)
+  
+  # set up paired sampling
+  D$sample_meta$pair_id=c(1:50,1:50)
+  
+  # remove one row so we can check it works for unequal pairs
+  D=D[1:99,]
+  
+  # set one value to NA so we can check the corresponding sample is excluded for that pair
+  D$data[20,1]=NA
+
+  # reorder to check we're not order dependent
+  D2=D[sample(1:99,99),]
+  
+  # method
+  M = ttest(factor_names='Species',paired=TRUE,paired_factor="pair_id")
+  # apply
+  M = model_apply(M,D)
+  M2= model_apply(M,D2)
+  
+  # 50 pairs - 1 without pair - 1 with an NA - 1 for ttest
+  expect_equal(M$dof[1],47) 
+  expect_equal(M$t_statistic[1,1],-5.195,tolerance=0.0005)
+  
+  # 50 pairs - 1 without pair - 1 for ttest
+  expect_equal(M$dof[2],48) 
+  expect_equal(M$t_statistic[2,1],-3.014,tolerance=0.0005)
+  
+  # check order dependence
+  expect_equal(M$t_statistic[1,1],M2$t_statistic[1,1],tolerance=0.00005)
+  
+  # reduce to less than three pairs to check we get NA
+  D=D[c(1:3,51:53),]
+  D$data[2,2]=NA 
+  
+  # should get NA for second feature as not enough pairs
+  M3=model_apply(M,D)
+  expect_true(is.na(M3$t_statistic[2,1]))
+})
+
+# ttest
+test_that('wilcox',{
+  set.seed('57475')
+  # DatasetExperiment
+  D=iris_DatasetExperiment()
+  
+  # need two groups
+  M = filter_smeta(mode='exclude',levels='versicolor',factor_name='Species')
+  M=model_apply(M,D) # need two groups
+  D = predicted(M)
+  
+  # apply
+  M = wilcox_test(factor_names='Species')
+  M = model_apply(M,D)
+  
+  expect_equal(M$statistic[1,1],38.5,tolerance=0.1)
+})
+
+test_that('paired-wilcox',{
+  set.seed('57475')
+  # DatasetExperiment
+  D=iris_DatasetExperiment()
+  
+  # reduce to two classes
+  M=filter_smeta(mode='exclude',levels='setosa',factor_name='Species')
+  M=model_apply(M,D)
+  D=predicted(M)
+  
+  # set up paired sampling
+  D$sample_meta$pair_id=c(1:50,1:50)
+  
+  # remove one row so we can check it works for unequal pairs
+  D=D[1:99,]
+  
+  # set one value to NA so we can check the corresponding sample is excluded for that pair
+  D$data[20,1]=NA
+  
+  # reorder to check we're not order dependent
+  D2=D[sample(1:99,99),]
+  
+  # method
+  M = wilcox_test(factor_names='Species',paired=TRUE,paired_factor="pair_id")
+  # apply
+  M = model_apply(M,D)
+  M2= model_apply(M,D2)
+  
+  # 50 pairs - 1 without pair - 1 with an NA - 1 for ttest
+  expect_equal(M$statistic[1,1],155.5,tolerance=0.0005)
+  
+  # 50 pairs - 1 without pair - 1 for ttest
+  expect_equal(M$statistic[2,1],238,tolerance=0.0005)
+  
+  # check order dependence
+  expect_equal(M$statistic[1,1],M2$statistic[1,1],tolerance=0.00005)
+  
+  # reduce to less than three pairs to check we get NA
+  D$data[53:99,2]=NA 
+  
+  # should get NA for second feature as not enough pairs
+  M3=model_apply(M,D)
+  expect_true(is.na(M3$statistic[2,1]))
+})