# corrMatOrder

0th

Percentile

##### Reorder a correlation matrix.

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

Keywords
hplot
##### 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, $$a_i$$: $$a_i = tan (e_{i2} / e_{i1}), if e_{i1} > 0$$ $$a_i = tan (e_{i2} / e_{i1}) + \pi, otherwise.$$ 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.

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

• corrMatOrder
##### Examples
# NOT RUN {
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"))

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]

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")
# }

Documentation reproduced from package corrplot, version 0.84, License: GPL

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