Nothing
#' Predicting hepatic injury from chemical information
#'
#' @description
#' A quantitative structure-activity relationship (QSAR) data set to predict
#' when a molecule has risk associated with liver function.
#'
#' @name hepatic_injury_qsar
#' @aliases hepatic_injury_qsar
#' @docType data
#' @return \item{hepatic_injury_qsar}{a tibble}
#'
#' @details
#' This data set was used to develop a model for predicting compounds'
#' probability of causing hepatic injury (i.e. liver damage). This data set
#' consisted of 281 unique compounds; 376 predictors were measured or computed
#' for each. The response was categorical (either "none", "mild", or "severe"),
#' and was highly unbalanced.
#'
#' This kind of response often occurs in pharmaceutical data because companies
#' steer away from creating molecules that have undesirable characteristics.
#' Therefore, well-behaved molecules often greatly outnumber undesirable
#' molecules. The predictors consisted of measurements from 184 biological
#' screens and 192 chemical feature predictors. The biological predictors
#' represent activity for each screen and take values between 0 and 10 with a
#' mode of 4. The chemical feature predictors represent counts of important
#' sub-structures as well as measures of physical properties that are thought to
#' be associated with hepatic injury.
#'
#' Columns:
#' \itemize{
#' \item \code{class}: ordered and factor (levels: 'none', 'mild', and 'severe')
#' \item \code{bio_assay_001} - \code{bio_assay_184}: numeric
#' \item \code{chem_fp_001} - \code{chem_fp_192}: numeric
#' }
#' @source
#' Kuhn, Max, and Kjell Johnson. _Applied predictive modeling_. New York:
#' Springer, 2013.
#'
#' @examples
#' data(hepatic_injury_qsar)
#' str(hepatic_injury_qsar)
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.