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

See also

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 x 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 x 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