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, ...)
data | A |
---|---|
... | Not currently used. |
truth | The column identifier for the true results
(that is |
estimate | The column identifier for the predicted
results (that is also |
bias | A |
na_rm | A |
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
).
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
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.
Other numeric metrics:
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
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()
,
msd()
,
rmse()
,
smape()
Max Kuhn
# 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 0.934library(dplyr) set.seed(1234) size <- 100 times <- 10 # create 10 resamples solubility_resampled <- bind_rows( replicate( n = times, expr = sample_n(solubility_test, size, replace = TRUE), simplify = FALSE ), .id = "resample" ) # Compute the metric by group metric_results <- solubility_resampled %>% group_by(resample) %>% ccc(solubility, prediction) metric_results#> # 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