R yardstick ccc 一致性相關係數

ccc(data, ...)

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

ccc_vec(truth, estimate, bias = FALSE, na_rm = TRUE, case_weights = NULL, ...)

其他數字指標：huber_loss_pseudo() , huber_loss() , iic() , mae() , mape() , mase() , mpe() , msd() , poisson_log_loss() , rmse() , rpd() , rpiq() , rsq_trad() , rsq() , smape()

其他一致性指標：rpd()、rpiq()、rsq_trad()、rsq()

其他準確度指標：huber_loss_pseudo() , huber_loss() , iic() , mae() , mape() , mase() , mpe() , msd() , poisson_log_loss() , rmse() , smape()

# Supply truth and predictions as bare column names
ccc(solubility_test, solubility, prediction)
#> # A tibble: 1 × 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 ccc     standard       0.937

library(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 × 4
#>    resample .metric .estimator .estimate
#>    <chr>    <chr>   <chr>          <dbl>
#>  1 1        ccc     standard       0.935
#>  2 10       ccc     standard       0.937
#>  3 2        ccc     standard       0.943
#>  4 3        ccc     standard       0.956
#>  5 4        ccc     standard       0.944
#>  6 5        ccc     standard       0.925
#>  7 6        ccc     standard       0.933
#>  8 7        ccc     standard       0.922
#>  9 8        ccc     standard       0.955
#> 10 9        ccc     standard       0.940

# Resampled mean estimate
metric_results %>%
  summarise(avg_estimate = mean(.estimate))
#> # A tibble: 1 × 1
#>   avg_estimate
#>          <dbl>
#> 1        0.939