Nothing
#' Within-class Covariance Matrix
#'
#' Calculates the estimated within-class covariance matrix
#'
#' The obtained matrix is the estimated within-class covariance matrix (i.e.
#' within-class covariance matrix divided by its degrees of freedom \code{n-k},
#' where \code{n} is the number of observations and \code{k} is the number of
#' groups)
#'
#' @param variables matrix or data frame with explanatory variables (No missing
#' values are allowed)
#' @param group vector or factor with group memberships (No missing values are
#' allowed)
#' @author Gaston Sanchez
#' @seealso \code{\link{withinCov}}
#' @export
#' @examples
#'
#' \dontrun{
#' # load iris dataset
#' data(iris)
#'
#' # estimated within-class covariance matrix (dividing by n-k)
#' getWithin(iris[,1:4], iris[,5])
#'
#' # compared to the within-class covariance matrix (dividing by n-1)
#' withinCov(iris[,1:4], iris[,5])
#' }
#'
getWithin <-
function(variables, group)
{
# within-class pooled covariance matrix
# variables: matrix or data frame with explanatory variables
# group: vector or factor with group memberships
# check inputs
verify_Xy = my_verify(variables, group, na.rm=FALSE)
X = verify_Xy$X
y = verify_Xy$y
Within = my_withinCov(X, y)
Within
}
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.