note_example_data.R

#' @section Example Data:
#'
#' This package provides some resampling objects and datasets for use in examples
#' and vignettes derived from a study on 1212 red-eyed tree frog embryos!
#'
#' Red-eyed tree frog (RETF) embryos can hatch earlier than their normal
#' 7ish days if they detect potential predator threat. Researchers wanted
#' to determine how, and when, these tree frog embryos were able to detect
#' stimulus from their environment. To do so, they subjected the embryos
#' at varying developmental stages to "predator stimulus" by jiggling
#' the embryos with a blunt probe. Beforehand, though some of the embryos
#' were treated with gentamicin, a compound that knocks out their lateral
#' line (a sensory organ.) Researcher Julie Jung and her crew found that
#' these factors inform whether an embryo hatches prematurely or not!
#'
#' Note that the data included with the stacks package is not necessarily
#' a representative or unbiased subset of the complete dataset, and is
#' only for demonstrative purposes.
#'
#' `reg_folds` and `class_folds` are `rset` cross-fold validation objects
#' from `rsample`, splitting the training data into for the regression
#' and classification model objects, respectively. `tree_frogs_reg_test` and
#' `tree_frogs_class_test` are the analogous testing sets.
#'
#' `reg_res_lr`, `reg_res_svm`, and `reg_res_sp` contain regression tuning results
#' for a linear regression, support vector machine, and spline model, respectively,
#' fitting \code{latency} (i.e. how long the embryos took to hatch in response
#' to the jiggle) in the \code{tree_frogs} data, using most all of the other
#' variables as predictors. Note that the data underlying these models is
#' filtered to include data only from embryos that hatched in response to
#' the stimulus.
#'
#' `class_res_rf` and `class_res_nn` contain multiclass classification tuning
#' results for a random forest and neural network classification model,
#' respectively, fitting \code{reflex} (a measure of ear function) in the
#' data using most all of the other variables as predictors.
#'
#' `log_res_rf` and `log_res_nn`, contain binary classification tuning results
#' for a random forest and neural network classification model, respectively,
#' fitting \code{hatched} (whether or not the embryos hatched in response
#' to the stimulus) using most all of the other variables as predictors.
#'
#' See \code{?example_data} to learn more about these objects, as well as browse
#' the source code that generated them.