iic | R Documentation |
Calculate the index of ideality of correlation. This metric has been studied in QSPR/QSAR models as a good criterion for the predictive potential of these models. It is highly dependent on the correlation coefficient as well as the mean absolute error.
Note the application of IIC is useless under two conditions:
When the negative mean absolute error and positive mean absolute error are both zero.
When the outliers are symmetric. Since outliers are context dependent, please use your own checks to validate whether this restriction holds and whether the resulting IIC has interpretative value.
The IIC is seen as an alternative to the traditional correlation coefficient and is in the same units as the original data.
iic(data, ...)
## S3 method for class 'data.frame'
iic(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
iic_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
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 |
na_rm |
A |
case_weights |
The optional column identifier for case weights. This
should be an unquoted column name that evaluates to a numeric column in
|
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 iic_vec()
, a single numeric
value (or NA
).
Joyce Cahoon
Toropova, A. and Toropov, A. (2017). "The index of ideality of correlation. A criterion of predictability of QSAR models for skin permeability?" Science of the Total Environment. 586: 466-472.
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rmse()
,
rpd()
,
rpiq()
,
rsq_trad()
,
rsq()
,
smape()
Other accuracy metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rmse()
,
smape()
# Supply truth and predictions as bare column names
iic(solubility_test, solubility, prediction)
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) %>%
iic(solubility, prediction)
metric_results
# Resampled mean estimate
metric_results %>%
summarise(avg_estimate = mean(.estimate))
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