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#' Draw rectangles on the correlation matrix graph.
#'
#' Draw rectangles on the correlation matrix graph based on hierarchical cluster
#' (\code{\link{hclust}}).
#'
#' @param corr Correlation matrix for function \code{corrRect.hclust}. It use
#' \code{1-corr} as dist in hierarchical clustering (\code{\link{hclust}}).
#'
#' @param k Integer, the number of rectangles drawn on the graph according to
#' the hierarchical cluster, for function \code{corrRect.hclust}.
#'
#' @param col Color of rectangles.
#' @param lwd Line width of rectangles.
#'
#' @param method Character, the agglomeration method to be used for hierarchical
#' clustering (\code{\link{hclust}}). This should be (an unambiguous
#' abbreviation of) one of \code{'ward'}, \code{'ward.D'}, \code{'ward.D2'},
#' \code{'single'}, \code{'complete'}, \code{'average'}, \code{'mcquitty'},
#' \code{'median'} or \code{'centroid'}.
#'
#' @example vignettes/example-corrRect.hclust.R
#' @keywords hplot
#' @author Taiyun Wei
#' @export
corrRect.hclust = function(
corr,
k = 2,
col = 'black',
lwd = 2,
method = c('complete', 'ward', 'ward.D', 'ward.D2', 'single', 'average',
'mcquitty', 'median', 'centroid'))
{
n = nrow(corr)
method = match.arg(method)
tree = hclust(as.dist(1 - corr), method = method)
hc = cutree(tree, k = k)
clustab = table(hc)[unique(hc[tree$order])]
cu = c(0, cumsum(clustab))
rect(cu[-(k + 1)] + 0.5,
n - cu[-(k + 1)] + 0.5,
cu[-1] + 0.5,
n - cu[-1] + 0.5,
border = col, lwd = lwd)
}
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