## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(butcher) ## ----eval = FALSE------------------------------------------------------------- # > new_model_butcher(model_class = "blob", package_name = "blobber") ## ----eval = FALSE------------------------------------------------------------- # ✔ Setting active project to 'path_to_butcher_package' # ✔ Adding 'blobber' to Suggests field in DESCRIPTION # ◠Use `requireNamespace("blobber", quietly = TRUE)` to test if package is installed # ◠Then directly refer to functons like `blobber::fun()` (replacing `fun()`). # # ℹ Writing skeleton files # ✔ Writing 'R/blob.R' # ✔ Writing 'tests/testthat/test-blob.R' # ◠Modify 'R/blob.R' # ◠Modify 'tests/testthat/test-blob.R' ## ----eval = FALSE------------------------------------------------------------- # > weigh(fitted_blob_object) # # A tibble: 25 x 2 # object size # <chr> <dbl> # 1 terms 4.01 # 2 qr.qr 0.00666 # 3 residuals 0.00286 # 4 fitted.values 0.00286 # 5 effects 0.0014 # 6 coefficients 0.00109 # 7 call 0.000728 # 8 model.mpg 0.000304 # 9 model.cyl 0.000304 # 10 model.disp 0.000304 # # … with 15 more rows ## ----eval = FALSE------------------------------------------------------------- # #' Axing a blob. # #' # #' blob model objects are created from the blobber package. They are # #' generally leveraged for classification ... insert anything relevant # #' ... This is where all the blob specific documentation lies. # #' # #' @param x Model object. # #' @param verbose Print information each time an axe method is executed # #' that notes how much memory is released and what functions are # #' disabled. Default is \code{TRUE}. # #' @param ... Any additional arguments related to axing. # #' # #' @return Axed model object. # #' # #' @name axe-blob # NULL # # #' Remove the call. # #' # #' @rdname axe-blob # #' @export # axe_call.blob <- function(x, verbose = TRUE, ...) { # old <- x # x <- exchange(x, "call", call("dummy_call")) # if (verbose) { # assess_object( # old, # x, # disabled = c("print", "summary") # ) # } # add_butcher_class(x) # }