How to Convert to Matthew's Correlation Coeff.?

[matt.curcio.ri]

I have been using R and caret to produce many machine learning models. I have been producing many Confusion Matrices and results tables.

R does a great job calculating all sorts of measures for outcomes:

• Sensitivity, Specificity, Pos. Pred. Value, Neg. Pred. Value, Prevalence, Detection Rate, Detection Prevalence, Balanced Accuracy

I know the formula for MCC and could calculate it from the original confusion matrix BUT is there another method using the results above?

Is there a conversion or trick given (any one of the results above) that would allow me to get the value of MCC?

My alternative is to go through the confusion matrices by hand.
Thanks

[technocrat]

Haven't used it myself but there's a single function package for that

mccr.pdf

[Max]

yardstick can do that

library(tidymodels)
#> Registered S3 methods overwritten by 'ggplot2':
#>   method         from 
#>   [.quosures     rlang
#>   c.quosures     rlang
#>   print.quosures rlang
#> Registered S3 method overwritten by 'xts':
#>   method     from
#>   as.zoo.xts zoo
#> ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.2 ──
#> ✔ broom     0.5.1          ✔ purrr     0.3.2     
#> ✔ dials     0.0.2          ✔ recipes   0.1.6     
#> ✔ dplyr     0.8.0.1        ✔ rsample   0.0.4.9000
#> ✔ ggplot2   3.1.1          ✔ tibble    2.1.3     
#> ✔ infer     0.4.0          ✔ yardstick 0.0.2     
#> ✔ parsnip   0.0.2.9000
#> ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ recipes::step()  masks stats::step()

# Two class
data("two_class_example")
mcc(two_class_example, truth, predicted)
#> # A tibble: 1 x 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mcc     binary         0.677

table(two_class_example$truth, two_class_example$predicted) %>% 
  mcc()
#> # A tibble: 1 x 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mcc     binary         0.677

# mutliclass example
data(hpc_cv)

table(hpc_cv$obs, hpc_cv$pred) %>% 
  mcc()
#> # A tibble: 1 x 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 mcc     multiclass     0.515

^{Created on 2019-07-01 by the reprex package (v0.2.1)}