R/example_data.R

In stacks: Tidy Model Stacking

#' Example Objects
#'
#' stacks 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.
#' 
#' The source code for generating these objects is given below.
#' 
#' @includeRmd man-roxygen/example_models.Rmd
#' 
#' @source 
#' Julie Jung et al. (2020) Multimodal mechanosensing enables treefrog 
#' embryos to escape egg-predators. \doi{10.1242/jeb.236141}
#'
#' @name example_data
NULL

#' @rdname example_data
"reg_res_svm"
#' @rdname example_data
"reg_res_sp"
#' @rdname example_data
"reg_res_lr"
#' @rdname example_data
"reg_folds"
#' @rdname example_data
"class_res_nn"
#' @rdname example_data
"class_res_rf"
#' @rdname example_data
"class_folds"
#' @rdname example_data
"log_res_nn"
#' @rdname example_data
"log_res_rf"

#' @name tree_frogs_reg_test
#' @docType data
#' @keywords datasets
#' @rdname example_data
NULL

#' @name tree_frogs_class_test
#' @docType data
#' @keywords datasets
#' @rdname example_data
NULL