Nothing
#' Toys data
"toys.data"
#' @description toys.data is a simple simulated dataset of a binary classification problem, introduced by Weston et.al..
#' @format The format is a list of 2 component.
#' @details
#' \itemize{
#' \item $Y: output variable: a factor with 2 levels "-1" and "1";
#' \item $x A data-frame containing input variables: with 30 obs. of 50 variables.
#' }
#' The data-frame x is composed by 2 independant clusters, each cluster contains 25 correlated variables. It is an equiprobable two class problem, Y belongs to -1,1, with 12 true variables (6 true variables in each cluster), the others being noise. The simulation model is defined through the conditional distribution of the X^j for Y=y. In the first cluster, the X^j are simulated in the following way:
#' \itemize{
#' \item with probability 0.7, X^j ~N(y,2) for j=1,2,3, and X^j ~ N(0,2) for j=4,5,6 ;
#' \item with probability 0.3, X^j ~ N(0,2) for j=1,2,3, and X^j ~ N(y(j-3),2) for j=4,5,6 ;
#' \item the other variables are noise, X^j ~ N(0,1) for j=7,. . . ,25.
#' }
#' The second cluster of 25 variables is simulated in a similar way.
#' @docType data
#' @keywords datasets
#' @name toys.data
#' @source Weston, J., Elisseff, A., Schoelkopf, B., Tipping, M. (2003), Use of the zero norm with linear models and Kernel methods, J. Machine Learn. Res. 3, 1439-14611
#' @examples
#' library(ClustOfVar)
#' library(impute)
#' library(FAMT)
#' library(VSURF)
#' library(glmnet)
#' library(anapuce)
#' library(qvalue)
#' X<-toys.data$x
#' Y<-toys.data$Y
#' scoreX<-data.frame(c(rep(8,6),rep(0,19),rep(8,6),rep(0,19)))
#' rownames(scoreX)<-colnames(X)
#' select<-ARMADA.heatmap(X, Y, scoreX, threshold=1)
#' \dontrun{
#' result<-ARMADA(X,Y, nclust=2)
#' select<-ARMADA.heatmap(X, Y, result[[3]], threshold=5)
#' }
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.