Added tests for detectSingleOut (#79)

R/detectSingleOut.R

@@ -54,11 +54,11 @@
 #' ## First select a subset of plants, for example here 9 plants
 #' plantSel <- phenoTP[[1]]$plotId[1:9]
 #' # Then run on the subset
-#' resuVatorHTP <- detectSingleOut (TP = phenoTP,
-#'                                  trait = "EffpsII",
-#'                                  plotIds = plantSel,
-#'                                  confIntSize = 3,
-#'                                  nnLocfit = 0.1)
+#' resuVatorHTP <- detectSingleOut(TP = phenoTP,
+#'                                 trait = "EffpsII",
+#'                                 plotIds = plantSel,
+#'                                 confIntSize = 3,
+#'                                 nnLocfit = 0.1)
 #'
 #' @family functions for detecting outliers for single observations
 #'
@@ -151,6 +151,9 @@ detectSingleOut <- function(TP,
   })
   ## Bind everything together in a single data.frame.
   plotPred <- do.call(rbind, plotPreds)
+  if (is.null(plotPred)) {
+    stop("Not enough data points (<= 6) for any of the plots.\n")
+  }
   ## Rownames are confusing and redundant.
   rownames(plotPred) <- NULL
   ## Add class and trait as attribute.

inst/tinytest/test_detectSingleOut.R

@@ -0,0 +1,141 @@
+### Test detectSingleOut.
+
+## Read test data from .csv
+testDat <- read.csv("testDat.csv", stringsAsFactors = FALSE)
+
+## Create TP object.
+testTP <- createTimePoints(dat = testDat, experimentName = "testExp",
+                           genotype = "Genotype", timePoint = "timepoints",
+                           plotId = "pos")
+
+## Check that general checks in detectSingleOut function correctly.
+expect_error(detectSingleOut(1),
+             "TP should be an object of class TP")
+expect_error(detectSingleOut(testTP, trait = 1),
+             "trait should be a character string of length 1")
+expect_error(detectSingleOut(testTP, trait = c("t1", "t2")),
+             "trait should be a character string of length 1")
+expect_error(detectSingleOut(testTP, trait = "t2"),
+             "TP should contain a column t2")
+expect_error(detectSingleOut(testTP, trait = "t1", plotIds = "c1r1"),
+             "All plotIds should be in TP")
+
+## Check that check for minimal number of time points functions correctly.
+expect_warning(detectSingleOut(testTP, trait = "t1"),
+               "to fit a model for: c10r2")
+expect_error(detectSingleOut(testTP, trait = "t1"),
+             "for any of the plots")
+
+## testDat only contains 5 timepoints so cannot be used for actual testing.
+## Using the data in the package for that instead.
+phenoTP <- createTimePoints(dat = PhenovatorDat1,
+                            experimentName = "Phenovator",
+                            genotype = "Genotype",
+                            timePoint = "timepoints",
+                            plotId = "pos")
+
+## Select some plots.
+plantSel <- c("c14r32", "c13r17")
+
+singleOut <- detectSingleOut(phenoTP, trait = "EffpsII",
+                             plotIds = plantSel)
+
+## Check that general structure of the output is correct.
+expect_inherits(singleOut, c("singleOut", "data.frame"))
+expect_equal(dim(singleOut), c(132, 8))
+expect_equal(colnames(singleOut),
+             c("plotId", "timePoint", "EffpsII", "yPred", "sd_yPred",
+               "lwr", "upr", "outlier"))
+
+## Check that full output contenct is correct.
+expect_equal_to_reference(singleOut, file = "singleOut")
+
+## Check that parameter confIntSize functions correctly.
+
+# Setting a high value should lead to no outliers.
+singleOut2 <- detectSingleOut(phenoTP, trait = "EffpsII",
+                              plotIds = plantSel,
+                              confIntSize = 15)
+expect_equal(sum(singleOut2[["outlier"]]), 0)
+
+# Setting confIntSize to 0 should cause (almost) all observations to be outliers.
+singleOut3 <- detectSingleOut(phenoTP, trait = "EffpsII",
+                              plotIds = plantSel,
+                              confIntSize = 0)
+expect_equal(sum(singleOut3[["outlier"]]), 132)
+
+## Check that parameter nnLocFit functions correctly.
+
+# A low value of nnLocFit should lead to a decrease of number of outliers.
+singleOut4 <- detectSingleOut(phenoTP, trait = "EffpsII",
+                              plotIds = plantSel,
+                              nnLocfit = 0.1)
+expect_equal(sum(singleOut4[["outlier"]]), 1)
+
+## Check that parameter checkEdges functions correctly.
+
+# Add an edge outlier.
+phenoTP2 <- phenoTP
+phenoTP2[[1]][phenoTP2[[1]][["plotId"]] == "c14r32", "EffpsII"] <- 1
+
+# Not checking edges influences outliers detected elsewhere.
+singleOut5 <- detectSingleOut(phenoTP2, trait = "EffpsII",
+                              plotIds = "c14r32")
+singleOut6 <- detectSingleOut(phenoTP2, trait = "EffpsII",
+                              plotIds = "c14r32",
+                              checkEdges = FALSE)
+expect_equal(setdiff(singleOut5[singleOut5[["outlier"]] == 1, "timePoint"],
+                     singleOut6[singleOut6[["outlier"]] == 1, "timePoint"]),
+             1528448820)
+expect_equal(setdiff(singleOut6[singleOut6[["outlier"]] == 1, "timePoint"],
+                     singleOut5[singleOut5[["outlier"]] == 1, "timePoint"]),
+             1527871020)
+
+### Check plotting of detectSingleOut results.
+
+## Check that general checks in plot function correctly.
+expect_error(plot(singleOut, plotIds = "a"),
+             "All plotIds should be in x")
+
+## Check that general output structure is correct.
+expect_silent(p <- plot(singleOut))
+expect_inherits(p, "list")
+expect_equal(length(p), 1)
+expect_inherits(p[[1]], "ggplot")
+
+## Check that parameter outOnly functions correctly.
+# singleOut2 has no ouliers.
+
+expect_error(plot(singleOut2),
+             "No outliers present for selected plotIds")
+expect_silent(plot(singleOut2, outOnly = FALSE))
+
+### Check removal of outliers detected by detectSSinleOut
+
+## Check that general checks in plot function correctly.
+expect_error(removeSingleOut(1),
+             "TP should be an object of class TP")
+expect_error(removeSingleOut(phenoTP, singleOut = 1),
+             "singleOut should be a data.frame")
+expect_error(removeSingleOut(phenoTP, singleOut = data.frame()),
+             "singleOut should at least contain the columns plotId and timePoint")
+expect_error(removeSingleOut(phenoTP[1], singleOut = singleOut),
+             "All time points in singleOut should be in TP")
+expect_error(removeSingleOut(phenoTP, singleOut = singleOut2),
+             "There are no outlying points in TP")
+
+## Check that outliers are removed.
+phenoTPOut <- removeSingleOut(phenoTP, singleOut = singleOut)
+
+expect_inherits(phenoTPOut, "TP")
+
+expect_true(is.na(phenoTPOut[[11]][phenoTPOut[[11]][["plotId"]] == "c13r17",
+                                   "EffpsII"]))
+
+## Check that paramater trait functions correctly.
+# Data contains only one trait, so set Sowing_Position to NA.
+phenoTPOut2 <- removeSingleOut(phenoTP, singleOut = singleOut,
+                               trait = "Sowing_Position")
+expect_true(is.na(phenoTPOut2[[11]][phenoTPOut2[[11]][["plotId"]] == "c13r17",
+                                    "Sowing_Position"]))
+