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test for variable importance function

tests/testthat/test-var_imp.R

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+test_that("var_imp", {
+  require(dplyr)
+
+  data(abies)
+  data(backg)
+  set.seed(0)
+  abies <- abies %>%
+    dplyr::group_by(pr_ab) %>%
+    dplyr::slice_sample(prop = .2)
+  set.seed(0)
+  backg <- backg %>%
+    dplyr::group_by(pr_ab) %>%
+    dplyr::slice_sample(prop = .2)
+
+  abies2 <- part_random(
+    data = abies,
+    pr_ab = "pr_ab",
+    method = c(method = "kfold", folds = 2)
+  )
+
+  backg2 <- part_random(
+    data = backg,
+    pr_ab = "pr_ab",
+    method = c(method = "kfold", folds = 2)
+  )
+
+  max_t1 <- fit_max(
+    data = abies2,
+    response = "pr_ab",
+    predictors = c("aet", "ppt_jja", "pH", "awc", "depth", "cwd", "tmin"),
+    predictors_f = c("landform"),
+    partition = ".part",
+    background = backg2,
+    thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
+    clamp = TRUE,
+    classes = "default",
+    pred_type = "cloglog",
+    regmult = 1
+  )
+
+
+  net_t1 <- fit_net(
+    data = abies2,
+    response = "pr_ab",
+    predictors = c("aet", "ppt_jja", "pH", "awc", "depth", "cwd", "tmin"),
+    predictors_f = c("landform"),
+    partition = ".part",
+    thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen")
+  )
+
+  svm_f1 <- fit_svm(
+    data = abies2,
+    response = "pr_ab",
+    predictors = c("aet", "ppt_jja", "pH", "awc", "depth", "cwd", "tmin"),
+    predictors_f = c("landform"),
+    partition = ".part",
+    thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen")
+  )
+
+  expect_message(
+    v_ip <-
+      var_imp(
+        data = abies2,
+        response = "pr_ab",
+        predictors = c("aet", "ppt_jja", "pH", "awc", "depth", "cwd", "tmin", "landform"),
+        models = list(max_t1, net_t1, svm_f1),
+        clamp = TRUE,
+        pred_type = "cloglog",
+        thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen"),
+        n_sim = 50,
+        n_cores = 5
+      )
+  )
+  expect_length(v_ip, 13)
+})