Add prediction.ridgeLinear without se.fit

DESCRIPTION

@@ -3,8 +3,8 @@ Type: Package
 Title: Tidy, Type-Safe 'prediction()' Methods
 Description: A one-function package containing 'prediction()', a type-safe alternative to 'predict()' that always returns a data frame. The 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
 License: MIT + file LICENSE
-Version: 0.3.15
-Date: 2019-12-24
+Version: 0.3.16
+Date: 2020-03-18
 Authors@R: c(person("Thomas J.", "Leeper",
                     role = c("aut", "cre"), 
                     email = "[email protected]",
@@ -13,7 +13,8 @@ Authors@R: c(person("Thomas J.", "Leeper",
                     email = "[email protected]"),
              person("Vincent", "Arel-Bundock", role = "ctb",
                     email = "[email protected]",
-                    comment = c(ORCID = "0000-0003-2042-7063"))
+                    comment = c(ORCID = "0000-0003-2042-7063")),
+             person("Dan", "Frankowski", role = "ctb")
             )
 URL: https://github.com/leeper/prediction
 BugReports: https://github.com/leeper/prediction/issues
@@ -55,6 +56,7 @@ Enhances:
     plm,
     pscl,
     quantreg,
+    ridge (>= 2.5),
     rpart,
     sampleSelection,
     speedglm,

NAMESPACE

@@ -48,6 +48,7 @@ S3method(prediction,ivreg)
 S3method(prediction,knnreg)
 S3method(prediction,kqr)
 S3method(prediction,ksvm)
+S3method(prediction,linearRidge)
 S3method(prediction,lm)
 S3method(prediction,lme)
 S3method(prediction,loess)

NEWS.md

@@ -1,3 +1,7 @@
+## prediction 0.3.16
+
+* Add `prediction.ridgeLinear`
+
 ## prediction 0.3.15
 
 * `build_datalist()` now works correctly with data.table datasets. (#34, #35, h/t Dan Schrage)

R/prediction_ridgeLinear.R

@@ -0,0 +1,48 @@
+## Like prediction.default, but without calculate_se
+
+#' @rdname prediction
+#' @export
+prediction.ridgeLinear <- 
+  function(model, 
+           data = find_data(model, parent.frame()), 
+           at = NULL, 
+           type = "response", 
+           vcov = stats::vcov(model),
+           calculate_se = FALSE,
+           ...) {
+    if (calculate_se) {
+      stop(paste0("calculate_se not implemented"))
+    }
+    # extract predicted values
+    data <- data
+    if (missing(data) || is.null(data)) {
+      pred <- predict(model, type = type, ...)
+      pred <- make_data_frame(fitted = pred, se.fitted = rep(NA_real_, length(pred)))
+    } else {
+      # setup data
+      if (!is.null(at)) {
+        data <- build_datalist(data, at = at, as.data.frame = TRUE)
+        at_specification <- attr(data, "at_specification")
+      }
+      # calculate predictions
+      tmp <- predict(model, newdata = data, type = type, ...)
+      # cbind back together
+      pred <- make_data_frame(data, fitted = tmp, se.fitted = rep(NA_real_, nrow(data)))
+    }
+
+    # variance(s) of average predictions
+    J <- NULL
+    vc <- NA_real_
+
+    # output
+    structure(pred, 
+              class = c("prediction", "data.frame"),
+              at = if (is.null(at)) at else at_specification,
+              type = type,
+              call = if ("call" %in% names(model)) model[["call"]] else NULL,
+              model_class = class(model),
+              row.names = seq_len(nrow(pred)),
+              vcov = vc,
+              jacobian = J,
+              weighted = FALSE)
+  }

tests/testthat/tests-methods.R

@@ -248,6 +248,22 @@ if (require("kernlab", quietly = TRUE)) {
     })
 }
 
+if (require("ridge", quietly = TRUE)) {
+  test_that("Test prediction() for 'ridge'", {
+    data("mtcars", package = "datasets")
+
+    model1 <- lm(mpg ~ wt + cyl, data = mtcars)
+    model2 <- linearRidge(mpg ~ wt + cyl, data = mtcars, lambda = 0)
+
+    preds1 <- prediction(model1)
+    preds2 <- prediction(model2)
+    expect_equal(preds1$fitted, preds2$fitted, tolerance = 0.0001, label = "predictions")
+    expect_true(inherits(preds2, "prediction"), label = "'prediction' class is correct")
+    # expect_true(all(c("fitted", "se.fitted") %in% names(p)), label = "'fitted' and 'se.fitted' columns returned")
+    expect_true(all(c("fitted") %in% names(preds2)), label = "'fitted' column returned")
+  })
+}
+
 if (require("lme4", quietly = TRUE)) {
     test_that("Test prediction() for 'merMod'", {
         data("cbpp", package = "lme4")