Reorder the correlation matrix according to the correlation coefficient by using hclust for hierarchical clustering order. This is useful to identify the hidden pattern in the matrix.
Author
Tiago Olivoto tiagoolivoto@gmail.com
Examples
# \donttest{
library(metan)
cor_mat <- corr_coef(data_ge2, PH, EH, CD, CL, ED, NKR)
cor_mat$cor
#> PH EH CD CL ED NKR
#> PH 1.0000000 0.9318282 0.3153910 0.3251648 0.6613148 0.3530495
#> EH 0.9318282 1.0000000 0.2805118 0.3971935 0.6302561 0.3310529
#> CD 0.3153910 0.2805118 1.0000000 0.3003636 0.3897128 0.5933206
#> CL 0.3251648 0.3971935 0.3003636 1.0000000 0.6974629 -0.1149405
#> ED 0.6613148 0.6302561 0.3897128 0.6974629 1.0000000 0.2220727
#> NKR 0.3530495 0.3310529 0.5933206 -0.1149405 0.2220727 1.0000000
reorder_cormat(cor_mat$cor)
#> CD NKR PH EH CL ED
#> CD 1.0000000 0.5933206 0.3153910 0.2805118 0.3003636 0.3897128
#> NKR 0.5933206 1.0000000 0.3530495 0.3310529 -0.1149405 0.2220727
#> PH 0.3153910 0.3530495 1.0000000 0.9318282 0.3251648 0.6613148
#> EH 0.2805118 0.3310529 0.9318282 1.0000000 0.3971935 0.6302561
#> CL 0.3003636 -0.1149405 0.3251648 0.3971935 1.0000000 0.6974629
#> ED 0.3897128 0.2220727 0.6613148 0.6302561 0.6974629 1.0000000
# }