move check_penalty()

Author: hfrick

R/linear_reg.R

@@ -208,40 +208,6 @@ organize_glmnet_pred <- function(x, object) {
 }
 
 
-# ------------------------------------------------------------------------------
-
-# For `predict` methods that use `glmnet`, we have specific methods.
-# Only one value of the penalty should be allowed when called by `predict()`:
-
-check_penalty <- function(penalty = NULL, object, multi = FALSE) {
-
-  if (is.null(penalty)) {
-    penalty <- object$fit$lambda
-  }
-
-  # when using `predict()`, allow for a single lambda
-  if (!multi) {
-    if (length(penalty) != 1)
-      rlang::abort(
-        glue::glue(
-          "`penalty` should be a single numeric value. `multi_predict()` ",
-          "can be used to get multiple predictions per row of data.",
-        )
-      )
-  }
-
-  if (length(object$fit$lambda) == 1 && penalty != object$fit$lambda)
-    rlang::abort(
-      glue::glue(
-        "The glmnet model was fit with a single penalty value of ",
-        "{object$fit$lambda}. Predicting with a value of {penalty} ",
-        "will give incorrect results from `glmnet()`."
-      )
-    )
-
-  penalty
-}
-
 # ------------------------------------------------------------------------------
 # glmnet call stack for linear regression using `predict` when object has
 # classes "_elnet" and "model_fit":

R/misc.R

@@ -323,6 +323,40 @@ stan_conf_int <- function(object, newdata) {
   rlang::eval_tidy(fn)
 }
 
+# ------------------------------------------------------------------------------
+
+# For `predict` methods that use `glmnet`, we have specific methods.
+# Only one value of the penalty should be allowed when called by `predict()`:
+
+check_penalty <- function(penalty = NULL, object, multi = FALSE) {
+
+  if (is.null(penalty)) {
+    penalty <- object$fit$lambda
+  }
+
+  # when using `predict()`, allow for a single lambda
+  if (!multi) {
+    if (length(penalty) != 1)
+      rlang::abort(
+        glue::glue(
+          "`penalty` should be a single numeric value. `multi_predict()` ",
+          "can be used to get multiple predictions per row of data.",
+        )
+      )
+  }
+
+  if (length(object$fit$lambda) == 1 && penalty != object$fit$lambda)
+    rlang::abort(
+      glue::glue(
+        "The glmnet model was fit with a single penalty value of ",
+        "{object$fit$lambda}. Predicting with a value of {penalty} ",
+        "will give incorrect results from `glmnet()`."
+      )
+    )
+
+  penalty
+}
+
 check_glmnet_penalty <- function(x) {
   pen <- rlang::eval_tidy(x$args$penalty)