Draw rectangles on the correlation matrix graph.

Draw rectangles on the correlation matrix graph based on hierarchical cluster (hclust).

corrRect.hclust(
  corr,
  k = 2,
  col = "black",
  lwd = 2,
  method = c("complete", "ward", "ward.D", "ward.D2", "single", "average", "mcquitty",
    "median", "centroid")
)

Arguments

corr

Correlation matrix for function corrRect.hclust. It use 1-corr as dist in hierarchical clustering (hclust).

k

Integer, the number of rectangles drawn on the graph according to the hierarchical cluster, for function corrRect.hclust.

col

Color of rectangles.

lwd

Line width of rectangles.

method

Character, the agglomeration method to be used for hierarchical clustering (hclust). This should be (an unambiguous abbreviation of) one of 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.

Author

Taiyun Wei

Examples

data(mtcars)
M = cor(mtcars)
corrplot(M, order = 'FPC') -> p
corrRect(p, index = c(1, 6, 11))


if(getRversion() >= '4.1.0') {
  corrplot(M, order = 'FPC') |> corrRect(index = c(1, 6, 11))
}

(order.hc = corrMatOrder(M, order = 'hclust'))
#>  [1] 11  6  4  2  3  7  8  1  5  9 10
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D2'))
#>  [1]  6  2  3  4 11  7  8  1  5  9 10
M.hc  = M[order.hc, order.hc]
M.hc2 = M[order.hc2, order.hc2]

par(ask = TRUE)

# same as: corrplot(M, order = 'hclust', addrect = 2)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)


# same as: corrplot(M, order = 'hclust', addrect = 3)
corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)


# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 2)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D2')


# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 3, method = 'ward.D2')


# same as: corrplot(M, order = 'hclust', hclust.method = 'ward.D2', addrect = 4)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 4, method = 'ward.D2')