test for DItoErrormetric

tests/testthat/test-DItoErrormetric.R

@@ -0,0 +1,83 @@
+
+test_that("DItoErrormetric works in default settings", {
+  data(splotdata)
+  splotdata <- sf::st_drop_geometry(splotdata)
+  predictors <- terra::rast(system.file("extdata","predictors_chile.tif", package="CAST"))
+
+  set.seed(100)
+  model <- caret::train(splotdata[,6:16], splotdata$Species_richness, ntree = 10,
+                        trControl = caret::trainControl(method = "cv", savePredictions = TRUE))
+
+  AOA <- CAST::aoa(predictors, model)
+
+  # DI ~ error
+  errormodel_DI <- DItoErrormetric(model, AOA, variable = "DI")
+
+  expected_error_DI = terra::predict(AOA$DI, errormodel_DI)
+
+
+
+  #test model fit:
+  expect_equal(round(as.numeric(summary(errormodel_DI$fitted.values)),2),
+               c(14.25, 14.34, 15.21, 17.23, 18.70, 27.46))
+  # test model predictions
+  expect_equal(as.vector( summary(values(expected_error_DI))),
+               c("Min.   :14.26  ", "1st Qu.:27.46  ", "Median :27.46  ",
+                 "Mean   :26.81  ", "3rd Qu.:27.46  ","Max.   :27.47  ",
+                 "NA's   :17678  "))
+})
+
+
+test_that("DItoErrormetric works in with LPD", {
+  data(splotdata)
+  splotdata <- sf::st_drop_geometry(splotdata)
+  predictors <- terra::rast(system.file("extdata","predictors_chile.tif", package="CAST"))
+
+  set.seed(100)
+  model <- caret::train(splotdata[,6:16], splotdata$Species_richness, ntree = 10,
+                        trControl = caret::trainControl(method = "cv", savePredictions = TRUE))
+
+  AOA <- CAST::aoa(predictors, model, LPD = TRUE, maxLPD = 1)
+  errormodel_LPD <- DItoErrormetric(model, AOA, variable = "LPD")
+  expected_error_LPD = terra::predict(AOA$LPD, errormodel_LPD)
+
+
+  #test model fit:
+  expect_equal(round(as.numeric(summary(errormodel_LPD$fitted.values)),2),
+               c(16.36, 16.36, 16.36, 16.36, 16.36, 16.36))
+  # test model predictions
+  expect_equal(as.vector(summary(values(expected_error_LPD))),
+               c("Min.   :16.36  ", "1st Qu.:16.36  ", "Median :16.36  ",
+                 "Mean   :16.36  ", "3rd Qu.:16.36  ",
+                 "Max.   :16.36  ", "NA's   :17678  "))
+
+})
+
+
+
+test_that("DItoErrormetric works for multiCV", {
+  data(splotdata)
+  splotdata <- sf::st_drop_geometry(splotdata)
+  predictors <- terra::rast(system.file("extdata","predictors_chile.tif", package="CAST"))
+
+  set.seed(100)
+  model <- caret::train(splotdata[,6:16], splotdata$Species_richness, ntree = 10,
+                        trControl = caret::trainControl(method = "cv", savePredictions = TRUE))
+
+  AOA <- CAST::aoa(predictors, model)
+  # with multiCV = TRUE (for DI ~ error)
+  set.seed(100)
+  errormodel_DI = suppressWarnings(DItoErrormetric(model, AOA, multiCV = TRUE, length.out = 3))
+  expected_error_DI = terra::predict(AOA$DI, errormodel_DI)
+
+  #test model fit:
+  expect_equal(round(as.numeric(summary(errormodel_DI$fitted.values)),2),
+               c(12.53, 17.21, 26.80, 26.19, 35.28, 35.30))
+  # test model predictions
+  expect_equal(as.vector( summary(values(expected_error_DI))),
+               c("Min.   :13.11  ", "1st Qu.:32.58  ", "Median :35.05  ",
+                 "Mean   :32.54  ", "3rd Qu.:35.30  ",
+                 "Max.   :35.30  ", "NA's   :17678  "))
+
+
+})