View source: R/corrRect.hclust.R
corrRect.hclust | R Documentation |
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") )
corr |
Correlation matrix for function |
k |
Integer, the number of rectangles drawn on the graph according to
the hierarchical cluster, for function |
col |
Color of rectangles. |
lwd |
Line width of rectangles. |
method |
Character, the agglomeration method to be used for hierarchical
clustering ( |
Taiyun Wei
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')) (order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D2')) 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')
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