Calculate the concordance correlation coefficient.

ccc(data, ...)
# S3 method for data.frame
ccc(data, truth, estimate, bias = FALSE, na_rm = TRUE, ...)
ccc_vec(truth, estimate, bias = FALSE, na_rm = TRUE, ...)

## Arguments

data |
A `data.frame` containing the `truth` and `estimate`
columns. |

... |
Not currently used. |

truth |
The column identifier for the true results
(that is `numeric` ). This should be an unquoted column name although
this argument is passed by expression and supports
quasiquotation (you can unquote column
names). For `_vec()` functions, a `numeric` vector. |

estimate |
The column identifier for the predicted
results (that is also `numeric` ). As with `truth` this can be
specified different ways but the primary method is to use an
unquoted variable name. For `_vec()` functions, a `numeric` vector. |

bias |
A `logical` ; should the biased estimate of variance
be used (as is Lin (1989))? |

na_rm |
A `logical` value indicating whether `NA`
values should be stripped before the computation proceeds. |

## Value

A `tibble`

with columns `.metric`

, `.estimator`

,
and `.estimate`

and 1 row of values.

For grouped data frames, the number of rows returned will be the same as
the number of groups.

For `ccc_vec()`

, a single `numeric`

value (or `NA`

).

## Details

`ccc()`

is a metric of both consistency/correlation and accuracy,
while metrics such as `rmse()`

are strictly for accuracy and metrics
such as `rsq()`

are strictly for consistency/correlation

## References

Lin, L. (1989). A concordance correlation
coefficient to evaluate reproducibility. *Biometrics*, 45 (1),
255-268.

Nickerson, C. (1997). A note on "A concordance correlation
coefficient to evaluate reproducibility". *Biometrics*, 53(4),
1503-1507.

## See also

Other numeric metrics:
`huber_loss_pseudo()`

,
`huber_loss()`

,
`iic()`

,
`mae()`

,
`mape()`

,
`mase()`

,
`mpe()`

,
`rmse()`

,
`rpd()`

,
`rpiq()`

,
`rsq_trad()`

,
`rsq()`

,
`smape()`

Other consistency metrics:
`rpd()`

,
`rpiq()`

,
`rsq_trad()`

,
`rsq()`

Other accuracy metrics:
`huber_loss_pseudo()`

,
`huber_loss()`

,
`iic()`

,
`mae()`

,
`mape()`

,
`mase()`

,
`mpe()`

,
`rmse()`

,
`smape()`

## Author

Max Kuhn

## Examples

# Supply truth and predictions as bare column names
ccc(solubility_test, solubility, prediction)

#> # A tibble: 1 x 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 ccc standard 0.934

#> # A tibble: 10 x 4
#> resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 1 ccc standard 0.926
#> 2 10 ccc standard 0.928
#> 3 2 ccc standard 0.934
#> 4 3 ccc standard 0.947
#> 5 4 ccc standard 0.934
#> 6 5 ccc standard 0.916
#> 7 6 ccc standard 0.924
#> 8 7 ccc standard 0.913
#> 9 8 ccc standard 0.945
#> 10 9 ccc standard 0.930

#> # A tibble: 1 x 1
#> avg_estimate
#> <dbl>
#> 1 0.930