corrMatOrder: Reorder a correlation matrix.

In taiyun/corrplot: Visualization of a Correlation Matrix

Reorder a correlation matrix.

Description

Draw rectangle(s) around the chart of corrrlation matrix based on the number of each cluster's members.

Usage

corrMatOrder(
  corr,
  order = c("AOE", "FPC", "hclust", "alphabet"),
  hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single", "average",
    "mcquitty", "median", "centroid")
)

Arguments

corr	Correlation matrix to reorder.
order	Character, the ordering method for the correlation matrix. 'AOE' for the angular order of the eigenvectors. It is calculated from the order of the angles, a_i: a_i = arctan (e_{i2} / e_{i1}), if e_{i1} > 0 a_i = arctan (e_{i2} / e_{i1}) + π, otherwise. where e_1 and e_2 are the largest two eigenvalues of matrix corr. See Michael Friendly (2002) for details. 'FPC' for the first principal component order. 'hclust' for hierarchical clustering order. 'alphabet' for alphabetical order.
hclust.method	Character, the agglomeration method to be used when order is hclust. This should be one of 'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'.

Value

Returns a single permutation vector.

Author(s)

Taiyun Wei

See Also

Package seriation offers more methods to reorder matrices, such as ARSA, BBURCG, BBWRCG, MDS, TSP, Chen and so forth.

Examples

M = cor(mtcars)

(order.AOE = corrMatOrder(M, order = 'AOE'))
(order.FPC = corrMatOrder(M, order = 'FPC'))
(order.hc = corrMatOrder(M, order = 'hclust'))
(order.hc2 = corrMatOrder(M, order = 'hclust', hclust.method = 'ward.D'))

M.AOE = M[order.AOE, order.AOE]
M.FPC = M[order.FPC, order.FPC]
M.hc  = M[order.hc, order.hc]
M.hc2 = M[order.hc2, order.hc2]



par(ask = TRUE)
corrplot(M)
corrplot(M.AOE)
corrplot(M.FPC)
corrplot(M.hc)

corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 2)

corrplot(M.hc)
corrRect.hclust(corr = M.hc, k = 3)

corrplot(M.hc2)
corrRect.hclust(M.hc2, k = 2, method = 'ward.D')

taiyun/corrplot documentation built on Sept. 3, 2022, 6:51 a.m.