refactor: update arx_forecaster predictor arg #298

Author: dshemetov

NEWS.md

@@ -4,10 +4,11 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 
 # epipredict 0.1
 
+- the `predictor` argument in `arx_forecaster()` now defaults to the value of the `outcome` argument
 - `*_args_list()` functions now warn if `forecast_date + ahead != target_date`
 - `layer_residual_quantiles()` will now error if any of the residual quantiles are NA
-- add `check_enough_train_data` that will error if training data is too small
-- added `check_enough_train_data` to `arx_forecaster`
+- add `check_enough_train_data()` that will error if training data is too small
+- added `check_enough_train_data()` to `arx_forecaster()`
 - simplify `layer_residual_quantiles()` to avoid timesuck in `utils::methods()`
 - rename the `dist_quantiles()` to be more descriptive, breaking change
 - removes previous `pivot_quantiles()` (now `*_wider()`, breaking change)

R/arx_forecaster.R

@@ -8,8 +8,11 @@
 #' @param epi_data An `epi_df` object
 #' @param outcome A character (scalar) specifying the outcome (in the
 #'   `epi_df`).
-#' @param predictors A character vector giving column(s) of predictor
-#'   variables.
+#' @param predictors A character vector giving column(s) of predictor variables.
+#'   This defaults to the `outcome`. However, if manually specified, only those variables
+#'   specifically mentioned will be used. (The `outcome` will not be added.)
+#'   By default, equals the outcome. If manually specified, does not add the
+#'   outcome variable, so make sure to specify it.
 #' @param trainer A `{parsnip}` model describing the type of estimation.
 #'   For now, we enforce `mode = "regression"`.
 #' @param args_list A list of customization arguments to determine
@@ -37,7 +40,7 @@
 #' )
 arx_forecaster <- function(epi_data,
                            outcome,
-                           predictors,
+                           predictors = outcome,
                            trainer = parsnip::linear_reg(),
                            args_list = arx_args_list()) {
   if (!is_regression(trainer)) {
@@ -104,7 +107,7 @@ arx_forecaster <- function(epi_data,
 arx_fcast_epi_workflow <- function(
     epi_data,
     outcome,
-    predictors,
+    predictors = outcome,
     trainer = NULL,
     args_list = arx_args_list()) {
   # --- validation

R/canned-epipred.R

@@ -5,8 +5,8 @@ validate_forecaster_inputs <- function(epi_data, outcome, predictors) {
       "!" = "This one is a {.cls {class(epi_data)}}."
     ))
   }
-  arg_is_chr(predictors)
   arg_is_chr_scalar(outcome)
+  arg_is_chr(predictors)
   if (!outcome %in% names(epi_data)) {
     cli::cli_abort("{.var {outcome}} was not found in the training data.")
   }