remaining multi_predict changes

Author: topepo

NEWS.md

@@ -11,6 +11,7 @@
  * `add_rowindex()` can create a column called `.row` to a data frame. 
 
  * If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.  
+ * `nearest_neighbor` gained a `multi_predict` method. The `multi_predict()` documentation is a little better organized.  
 
 
 # parsnip 0.0.2

R/aaa.R

@@ -23,5 +23,6 @@ convert_stan_interval <- function(x, level = 0.95, lower = TRUE) {
 #' @importFrom utils globalVariables
 utils::globalVariables(
   c('.', '.label', '.pred', '.row', 'data', 'engine', 'engine2', 'group',
-    'lab', 'original', 'predicted_label', 'prediction', 'value', 'type')
+    'lab', 'original', 'predicted_label', 'prediction', 'value', 'type',
+    "neighbors")
   )

R/aaa_multi_predict.R

@@ -4,6 +4,11 @@
 #'
 #' For some models, predictions can be made on sub-models in the model object.
 #' @param object A `model_fit` object.
+#' @param new_data A rectangular data object, such as a data frame.
+#' @param type A single character value or `NULL`. Possible values
+#'  are "numeric", "class", "prob", "conf_int", "pred_int", "quantile",
+#'  or "raw". When `NULL`, `predict()` will choose an appropriate value
+#'  based on the model's mode.
 #' @param ... Optional arguments to pass to `predict.model_fit(type = "raw")`
 #'  such as `type`.
 #' @return A tibble with the same number of rows as the data being predicted.

R/linear_reg.R

@@ -302,6 +302,8 @@ predict_raw._elnet <- function(object, new_data, opts = list(), ...)  {
 #' @importFrom dplyr full_join as_tibble arrange
 #' @importFrom tidyr gather
 #' @export
+#'@rdname multi_predict
+#' @param penalty An numeric vector of penalty values.
 multi_predict._elnet <-
   function(object, new_data, type = NULL, penalty = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))

R/logistic_reg.R

@@ -276,6 +276,7 @@ predict._lognet <- function (object, new_data, type = NULL, opts = list(), penal
 #' @importFrom dplyr full_join as_tibble arrange
 #' @importFrom tidyr gather
 #' @export
+#' @rdname multi_predict
 multi_predict._lognet <-
   function(object, new_data, type = NULL, penalty = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))

R/multinom_reg.R

@@ -232,6 +232,7 @@ predict._multnet <-
 #' @importFrom dplyr full_join as_tibble arrange
 #' @importFrom tidyr gather
 #' @export
+#' @rdname multi_predict
 multi_predict._multnet <-
   function(object, new_data, type = NULL, penalty = NULL, ...) {
     if (any(names(enquos(...)) == "newdata"))

R/nearest_neighbor.R

@@ -193,7 +193,7 @@ multi_predict._train.kknn <-
       stop("Did you mean to use `new_data` instead of `newdata`?", call. = FALSE)
 
     if (is.null(neighbors))
-      neighbors <- rlang::eval_tidy(tt$fit$call$ks)
+      neighbors <- rlang::eval_tidy(object$fit$call$ks)
     neighbors <- sort(neighbors)
 
     if (is.null(type)) {