added lmerTest

.Rbuildignore

@@ -10,3 +10,6 @@
 ^docs$
 ^pkgdown$
 ^CRAN-SUBMISSION$
+^multilevelmod\.Rcheck$
+^multilevelmod.*\.tar\.gz$
+^multilevelmod.*\.tgz$

.gitignore

@@ -3,3 +3,6 @@
 .Rhistory
 inst/doc
 docs
+multilevelmod.Rcheck/
+multilevelmod*.tar.gz
+multilevelmod*.tgz

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: multilevelmod
 Title: Model Wrappers for Multi-Level Models
-Version: 1.0.0.9000
+Version: 1.0.0.9001
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0003-2402-136X")),
@@ -32,6 +32,7 @@ Suggests:
     gee,
     ggplot2,
     knitr,
+    lmerTest (>= 3.1.3),
     nlme,
     rmarkdown,
     spelling,

R/lmerTest.R

@@ -0,0 +1,67 @@
+make_lmerTest_linear_reg <- function() {
+
+  parsnip::set_model_engine("linear_reg", "regression", "lmerTest")
+  parsnip::set_dependency(model= "linear_reg", eng = "lmerTest", pkg = "lmerTest", mode = "regression")
+  parsnip::set_dependency(model ="linear_reg", eng = "lmerTest", pkg = "multilevelmod", mode = "regression")
+
+  parsnip::set_encoding(
+    model = "linear_reg",
+    eng = "lmerTest",
+    mode = "regression",
+    options = list(
+      predictor_indicators = "none",
+      compute_intercept = FALSE,
+      remove_intercept = FALSE,
+      allow_sparse_x = FALSE
+    )
+  )
+
+  parsnip::set_fit(
+    model = "linear_reg",
+    eng = "lmerTest",
+    mode = "regression",
+    value = list(
+      interface = "formula",
+      protect = c("formula", "data", "weights"),
+      func = c(pkg = "lmerTest", fun = "lmer"),
+      defaults = list()
+    )
+  )
+
+  parsnip::set_pred(
+    model = "linear_reg",
+    eng = "lmerTest",
+    mode = "regression",
+    type = "numeric",
+    value = list(
+      pre = reformat_lme_pred_data,
+      post =  NULL,
+      func = c(fun = "predict"),
+      args = list(
+        object = rlang::expr(object$fit),
+        newdata = rlang::expr(new_data),
+        allow.new.levels = TRUE,
+        re.form = NA,
+        na.action = rlang::expr(na.exclude),
+        type = "response"
+      )
+    )
+  )
+
+  parsnip::set_pred(
+    model = "linear_reg",
+    eng = "lmerTest",
+    mode = "regression",
+    type = "raw",
+    value = list(
+      pre = NULL,
+      post = NULL,
+      func = c(fun = "predict"),
+      args = list(
+        object = rlang::expr(object$fit),
+        newdata = rlang::expr(new_data)
+      )
+    )
+  )
+
+}

R/zzz.R

@@ -17,6 +17,7 @@
   make_stan_poisson_reg()
   make_lme_linear_reg()
   make_gls_linear_reg()
+  make_lmerTest_linear_reg()
 }
 
 

tests/testthat/test-lmerTest.R

@@ -0,0 +1,46 @@
+test_that('lmerTest execution', {
+  skip_if_not_installed("lmerTest")
+  skip_on_cran()
+
+  set.seed(2452)
+  lmer_mod <-
+    lmerTest::lmer(depr_score ~ week + imipramine +  (1 | subject),
+                   data = riesby_tr)
+
+  lmer_spec <-
+    linear_reg() %>%
+    set_engine("lmerTest")
+
+  set.seed(2452)
+  expect_error(
+    ps_mod <-
+      lmer_spec %>%
+      fit(depr_score ~ week + imipramine + (1|subject), data = riesby_tr),
+    regex = NA
+  )
+
+  expect_equal(
+    lme4::fixef(ps_mod$fit),
+    lme4::fixef(lmer_mod)
+  )
+  expect_equal(
+    coef(ps_mod$fit),
+    coef(lmer_mod)
+  )
+
+  lmer_pred <- predict(lmer_mod, riesby_te, allow.new.levels = T) #level = 0,
+
+  ps_pred <- predict(ps_mod, riesby_te)
+
+  expect_equal(as.numeric(lmer_pred), ps_pred$.pred)
+
+})
+
+test_that('mode specific package dependencies', {
+  expect_identical(
+    get_from_env(paste0("linear_reg", "_pkgs")) %>%
+      dplyr::filter(engine == "lmerTest", mode == "regression") %>%
+      dplyr::pull(pkg),
+    list(c("lmerTest", "multilevelmod"))
+  )
+})