Nothing
#' Food data
#'
#' 38 subjects places 45 food items in categories based on similarity.
#' The dissimilarities are the proportions of combinations NOT placed in the same category.
#'
#' @name food
#' @docType data
#'
#' @keywords dataset
#'
#' @format 45 x 45 dissimilarity matrix
#' \itemize{
#' \item V1: dissimilarities for V1.
#' \item V2: dissimilarities for V2.
#' \item V3: dissimilarities for V3.
#' \item V4: dissimilarities for V4.
#' \item V5: dissimilarities for V5.
#' \item V6: dissimilarities for V6.
#' \item V7: dissimilarities for V7.
#' \item V8: dissimilarities for V8.
#' \item V9: dissimilarities for V9.
#' \item V10: dissimilarities for V10.
#' \item V11: dissimilarities for V11.
#' \item V12: dissimilarities for V12.
#' \item V13: dissimilarities for V13.
#' \item V14: dissimilarities for V14.
#' \item V15: dissimilarities for V15.
#' \item V16: dissimilarities for V16.
#' \item V17: dissimilarities for V17.
#' \item V18: dissimilarities for V18.
#' \item V19: dissimilarities for V19.
#' \item V20: dissimilarities for V20.
#' \item V21: dissimilarities for V21.
#' \item V22: dissimilarities for V22.
#' \item V23: dissimilarities for V23.
#' \item V24: dissimilarities for V24.
#' \item V25: dissimilarities for V25.
#' \item V26: dissimilarities for V26.
#' \item V27: dissimilarities for V27.
#' \item V28: dissimilarities for V28.
#' \item V29: dissimilarities for V29.
#' \item V30: dissimilarities for V30.
#' \item V31: dissimilarities for V31.
#' \item V32: dissimilarities for V32.
#' \item V33: dissimilarities for V33.
#' \item V34: dissimilarities for V34.
#' \item V35: dissimilarities for V35.
#' \item V36: dissimilarities for V36.
#' \item V37: dissimilarities for V37.
#' \item V38: dissimilarities for V38.
#' \item V39: dissimilarities for V39.
#' \item V40: dissimilarities for V40.
#' \item V41: dissimilarities for V41.
#' \item V42: dissimilarities for V42.
#' \item V43: dissimilarities for V43.
#' \item V44: dissimilarities for V44.
#' \item V45: dissimilarities for V45.
#' }
#'
#' @references Ross and Murphy (1999). Food for thought: Cross-classification and category organization in a complex real-world domain. Cognitive psychology, 38(4), 495-553.
#' Brusco and Stahl (2000). Using Quadratic Assignment Methods to Generate Initial Permutations for Least-Squares Unidimensional Scaling of Symmetric Proximity Matrices. Journal of Classification, 17(2).
#'
"food"
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.