Merge pull request #112 from tlverse/reduce-knots

reduce defaults for knot points

DESCRIPTION

@@ -68,5 +68,5 @@ LinkingTo:
   Rcpp,
   RcppEigen
 VignetteBuilder: knitr
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)

NEWS.md

@@ -1,19 +1,23 @@
 # hal9001 0.4.6
 * Fixed predict method to address changes required by Matrix 1.6.2
+* Reduced default number of knots used for zero-order and first-order basis
+  expansions as follows:
+  * `base_num_knots_0` set to 20, used to be 200
+  * `base_num_knots_1` set to 10, used to be 50
 
 # hal9001 0.4.5
 * Added multivariate outcome prediction
 
 # hal9001 0.4.4
-* Fixed bug with `prediction_bounds` (a `fit_hal` argument in `fit_control` 
-  list), which would error when it was specified as a numeric vector. Also, 
+* Fixed bug with `prediction_bounds` (a `fit_hal` argument in `fit_control`
+  list), which would error when it was specified as a numeric vector. Also,
   added a check to assert this argument is correctly specified, and tests
   to ensure a numeric vector of bounds is provided.
 * Simplified `fit_control` list arguments in `fit_hal`. Users can still specify
   additional arguments to `cv.glmnet` and `glmnet` in this list.
 * Defined `weights` as a formal argument in `fit_hal`, opposed to an optional
-  argument in `fit_control`, to facilitate specification and avoid confusion. 
-  This increases flexibility with SuperLearner wrapper `SL.hal9001` as well; 
+  argument in `fit_control`, to facilitate specification and avoid confusion.
+  This increases flexibility with SuperLearner wrapper `SL.hal9001` as well;
   `fit_control` can now be customized with `SL.hal9001`.
 
 # hal9001 0.4.3

R/formula_hal9001.R

@@ -251,6 +251,7 @@ print.formula_hal9001 <- function(x, ...) {
   cat(paste0("A hal9001 formula object of the form: ~ ", x$formula_term))
 }
 
+#' Formula Helpers
 #'
 #' @param var_names A \code{character} vector of variable names representing a single type of interaction
 # " (e.g. var_names = c("W1", "W2", "W3") encodes three way interactions between W1, W2 and W3.

R/hal.R

@@ -179,11 +179,12 @@ fit_hal <- function(X,
                     num_knots = num_knots_generator(
                       max_degree = max_degree,
                       smoothness_orders = smoothness_orders,
-                      base_num_knots_0 = 200,
-                      base_num_knots_1 = 50
+                      base_num_knots_0 = 20,
+                      base_num_knots_1 = 10
                     ),
                     reduce_basis = NULL,
-                    family = c("gaussian", "binomial", "poisson", "cox", "mgaussian"),
+                    family = c("gaussian", "binomial", "poisson", "cox",
+                               "mgaussian"),
                     lambda = NULL,
                     id = NULL,
                     weights = NULL,