Merge pull request #61 from tlverse/devel

.Rbuildignore

@@ -18,6 +18,7 @@
 ^Makefile$
 ^LICENSE$
 ^sandbox$
+^paper$
 ^docs$
 ^_pkgdown\.yml$
 ^CRAN-RELEASE$

.travis.yml

@@ -1,6 +1,7 @@
 branches:
   only:
     - master
+    - devel
 
 env:
   global:

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: hal9001
 Title: The Scalable Highly Adaptive Lasso
-Version: 0.2.5
+Version: 0.2.6
 Authors@R: c(
   person("Jeremy", "Coyle", email = "jeremyrcoyle@gmail.com",
          role = c("aut", "cre"),
@@ -43,7 +43,7 @@ Imports:
   utils,
   methods,
   assertthat,
-  origami (>= 0.8.1),
+  origami (>= 1.0.3),
   glmnet
 Suggests:
   testthat,
@@ -62,4 +62,4 @@ LinkingTo:
   Rcpp,
   RcppEigen
 VignetteBuilder: knitr
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.0

R/hal.R

@@ -65,10 +65,6 @@
 #' @param id a vector of ID values, used to generate cross-validation folds for
 #'  cross-validated selection of the regularization parameter lambda.
 #' @param offset a vector of offset values, used in fitting.
-#' @param screen_basis If \code{TRUE}, use a screening procedure to reduce the
-#'  number of basis functions fitted.
-#' @param screen_lambda If \code{TRUE}, use a screening procedure to reduce the
-#'  number of lambda values evaluated.
 #' @param ... Other arguments passed to \code{\link[glmnet]{cv.glmnet}}. Please
 #'  consult its documentation for a full list of options.
 #' @param yolo A \code{logical} indicating whether to print one of a curated
@@ -112,23 +108,37 @@ fit_hal <- function(X,
                     id = NULL,
                     offset = NULL,
                     cv_select = TRUE,
-                    screen_basis = FALSE,
-                    screen_lambda = FALSE,
                     ...,
                     yolo = TRUE) {
-
   # check arguments and catch function call
   call <- match.call(expand.dots = TRUE)
   fit_type <- match.arg(fit_type)
   family <- match.arg(family)
 
+  # catch dot arguments to stop misuse of glmnet's `lambda.min.ratio`
+  dot_args <- list(...)
+  assertthat::assert_that(!("lambda.min.ratio" %in% names(dot_args) &
+    family == "binomial"),
+  msg = paste(
+    "`glmnet` silently ignores",
+    "`lambda.min.ratio` when",
+    "`family = 'binomial'`."
+  )
+  )
+
   # NOTE: NOT supporting binomial outcomes with lassi method currently
-  if (fit_type == "lassi" && family == "binomial") {
-    stop("For binary outcomes, please set argument 'fit_type' to 'glmnet'.")
-  }
-  if (fit_type == "lassi" && family == "cox") {
-    stop("For Cox models, please set argument 'fit_type' to 'glmnet'.")
-  }
+  assertthat::assert_that(!(fit_type == "lassi" && family == "binomial"),
+    msg = paste(
+      "For binary outcomes, please set",
+      "argument 'fit_type' to 'glmnet'."
+    )
+  )
+  assertthat::assert_that(!(fit_type == "lassi" && family == "cox"),
+    msg = paste(
+      "For Cox models, please set argument",
+      "'fit_type' to 'glmnet'."
+    )
+  )
 
   # cast X to matrix -- and don't start the timer until after
   if (!is.matrix(X)) {
@@ -154,21 +164,7 @@ fit_hal <- function(X,
 
   # make design matrix for HAL
   if (is.null(basis_list)) {
-    if (screen_basis) {
-      # NOTE: foldid is never missing since created above if not supplied
-      good_basis <- hal_screen_basis(
-        x = X,
-        y = Y,
-        family = family,
-        offset = offset,
-        foldid = foldid,
-        max_degree = max_degree
-      )
-      basis_lists <- lapply(good_basis, basis_list_cols, X)
-      basis_list <- unlist(basis_lists, recursive = FALSE)
-    } else {
-      basis_list <- enumerate_basis(X, max_degree)
-    }
+    basis_list <- enumerate_basis(X, max_degree)
   }
 
   # generate a vector of col lists corresponding to the bases generated
@@ -240,15 +236,6 @@ fit_hal <- function(X,
       coefs <- hal_lasso$betas_mat[, "lambda_1se"]
     }
   } else if (fit_type == "glmnet") {
-    if ((screen_lambda) && (length(lambda) != 1)) {
-      # reduce the set of lambdas to fit
-      lambda <- hal_screen_lambda(x_basis, Y,
-        family = family,
-        lambda = lambda,
-        foldid = foldid,
-        offset = offset
-      )
-    }
     # just use the standard implementation available in glmnet
     if (!cv_select) {
       hal_lasso <- glmnet::glmnet(

R/predict.R

@@ -5,11 +5,8 @@
 #'  \code{hal9001}.
 #'
 #' @param object An object of class \code{hal9001}, containing the results of
-#'  fitting the Highly Adaptive Lasso, as produced by a call to \code{fit_hal}.
+#'  fitting the Highly Adaptive Lasso, as produced by \code{\link{fit_hal}}.
 #' @param offset A vector of offsets. Must be provided if provided at training
-#' @param lambda A single lambda value or a vector of lambdas to use for
-#'  prediction. If \code{NULL}, a value of lambda will be selected based on
-#'  cross-validation, using \code{\link[glmnet]{cv.glmnet}}.
 #' @param ... Additional arguments passed to \code{predict} as necessary.
 #' @param new_data A \code{matrix} or \code{data.frame} containing new data
 #'  (observations NOT used in fitting the \code{hal9001} object passed in via
@@ -25,11 +22,18 @@
 #'
 #' @export
 #'
-#' @return A \code{numeric} vector of predictions from a fitted \code{hal9001}
-#'  object.
+#' @note This prediction method does not function similarly to the equivalent
+#'  method from \pkg{glmnet}. In particular, this procedure will NOT return a
+#'  subset of lambdas originally specified in callingo \code{\link{fit_hal}}
+#'  nor result in re-fitting. Instead, it will return predictions for all of
+#'  the lambdas specified in the call to \code{\link{fit_hal}} that constructs
+#'  \code{object}, when \code{cv_select = FALSE}. When \code{cv_select = TRUE},
+#'  predictions will only be returned for the value of lambda selected by
+#'  cross-validation.
+#'
+#' @return A \code{numeric} vector of predictions from a \code{hal9001} object.
 predict.hal9001 <- function(object,
                             offset = NULL,
-                            lambda = NULL,
                             ...,
                             new_data,
                             new_X_unpenalized = NULL) {
@@ -89,8 +93,8 @@ predict.hal9001 <- function(object,
       ) + object$coefs[1])
     }
   } else {
-    # Note: there is no intercept in the Cox mode (its built into the baseline
-    #       hazard, and like it, would cancel in the partial likelihood.)
+    # Note: there is no intercept in the Cox model (built into the baseline
+    #       hazard and would cancel in the partial likelihood).
     # message(paste("The Cox Model is not commonly used for prediction,",
     # "proceed with caution."))
     if (ncol(object$coefs) > 1) {