R/spam.R

In MAVE: Methods for Dimension Reduction

#' 4601 email record
#'
#' A dataset containing 4601 record of email with 57 features. These features are the
#' relative frequency of most commonly used phrases and punctions. The data of these
#' features are recorded 1 to 57 columns of the spam data. The outcome is spam or email which is
#' denoted as 1 or 0, recorded in the 58th column of the data.
#'
#'
#' @docType data
#' @keywords datasets
#' @format A data frame with 4601 rows and 57 variables
#' @name spam
#'
#' @examples
#' data(spam)
#' train = sample(1:4601)[1:1000]
#' x.train <- as.matrix(spam[train,1:57])
#' y.train <- as.matrix(spam[train,58])
#' x.test <- as.matrix(spam[-train,1:57])
#' y.test <- as.matrix(spam[-train,58])
#' x.train <- sqrt(x.train)
#' x.test <- sqrt(x.test)
#
# dr <- mave(y.train~x.train,method='meanopg',max.dim=5)
# dr.dim <- mave.dim(dr)
# y.pred <- ifelse(predict(dr.dim,x.test)>.5,1,0)
# #classification rate
# mean(y.pred==y.test)
NULL