reorder correlation matrix, according to the coefficients, using the hierarchical clustering method.

cor_reorder(x)

## Arguments

x

a correlation matrix. Particularly, an object of class cor_mat.

## Value

a data frame

cor_mat(), cor_gather(), cor_spread()

## Examples

# Compute correlation matrix
#::::::::::::::::::::::::::::::::::::::::::
cor.mat <- mtcars %>%
select(mpg, disp, hp, drat, wt, qsec) %>%
cor_mat()

# Reorder by correlation and get p-values
#::::::::::::::::::::::::::::::::::::::::::
# Reorder
cor.mat %>%
cor_reorder()
#> # A tibble: 6 × 7
#>   rowname    hp  disp    wt   qsec   mpg   drat
#> * <chr>   <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
#> 1 hp       1     0.79  0.66 -0.71  -0.78 -0.45
#> 2 disp     0.79  1     0.89 -0.43  -0.85 -0.71
#> 3 wt       0.66  0.89  1    -0.17  -0.87 -0.71
#> 4 qsec    -0.71 -0.43 -0.17  1      0.42  0.091
#> 5 mpg     -0.78 -0.85 -0.87  0.42   1     0.68
#> 6 drat    -0.45 -0.71 -0.71  0.091  0.68  1
# Get p-values of the reordered cor_mat
cor.mat %>%
cor_reorder() %>%
cor_get_pval()
#> # A tibble: 6 × 7
#>   rowname           hp     disp        wt       qsec      mpg       drat
#>   <chr>          <dbl>    <dbl>     <dbl>      <dbl>    <dbl>      <dbl>
#> 1 hp      0            7.14e- 8 4.15e-  5 0.00000577 1.79e- 7 0.00999
#> 2 disp    0.0000000714 0        1.22e- 11 0.0131     9.38e-10 0.00000528
#> 3 wt      0.0000415    1.22e-11 2.27e-236 0.339      1.29e-10 0.00000478
#> 4 qsec    0.00000577   1.31e- 2 3.39e-  1 0          1.71e- 2 0.62
#> 5 mpg     0.000000179  9.38e-10 1.29e- 10 0.0171     0        0.0000178
#> 6 drat    0.00999      5.28e- 6 4.78e-  6 0.62       1.78e- 5 0