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"))
Correlation matrix for function corrRect.hclust
. It use
1-corr
as dist in hierarchical clustering (hclust
).
Integer, the number of rectangles drawn on the graph according to
the hierarchical cluster, for function corrRect.hclust
.
Color of rectangles.
Line width of rectangles.
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"
.
# NOT RUN {
data(mtcars)
M <- cor(mtcars)
corrplot(M, method = "circle", order = "FPC")
corrRect(c(5,6))
(order.hc <- corrMatOrder(M, order = "hclust"))
(order.hc2 <- corrMatOrder(M, order = "hclust", hclust.method = "ward"))
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", addrect = 2)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = "ward")
# same as: corrplot(M, order = "hclust", hclust.method = "ward", addrect = 3)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 3, method = "ward")
# same as: corrplot(M, order = "hclust", hclust.method = "ward", addrect = 4)
corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 4, method = "ward")
# }
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