RobStatTM: Robust Statistics: Theory and Methods

#' Vehicle data
#'
#' The original data set contains an ensemble of shape feature extractors to the
#' 2D silhouettes of different vehicles. The purpose is to classify a given
#' silhouette as one of four types of vehicle, using a set of 18 features extracted
#' from the silhouette. Here we deal with the "van" type, which has 217 cases.
#' Description; The following features were extracted from the silhouettes.
#' 1. compactness
#' 2. circularity
#' 3. distance circularity
#' 4. radius ratio
#' 5. principal axis aspect ratio
#' 6. maximum length aspect ratio
#' 7. scatter ratio
#' 8. elongatedness
#' 9. principal axis rectangularity
#' 10. maximum length rectangularity
#' 11. scaled variance along major axis
#' 12. scaled variance along minor axis
#' 13. scaled radius of gyration
#' 14. skewness about major axis
#' 15. skewness about minor axis
#' 16. kurtosis about minor axis
#' 17. kurtosis about major axis
#' 18. hollows ratio
#'
#' Format: Numeric, 217 rows and 18 columns.
#'
#' @docType data
#'
#' @usage data(vehicle)
#'
#' @format An object of class \code{"data.frame"}.
#'
#' @source Turing Institute, Glasgow, and are available at https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes).
#'
#' @examples
#' data(vehicle)
"vehicle"