rsq | R Documentation |
Calculate the coefficient of determination using correlation. For the
traditional measure of R squared, see rsq_trad()
.
rsq(data, ...)
## S3 method for class 'data.frame'
rsq(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)
rsq_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
|
The two estimates for the
coefficient of determination, rsq()
and rsq_trad()
, differ by
their formula. The former guarantees a value on (0, 1) while the
latter can generate inaccurate values when the model is
non-informative (see the examples). Both are measures of
consistency/correlation and not of accuracy.
rsq()
is simply the squared correlation between truth
and estimate
.
Because rsq()
internally computes a correlation, if either truth
or
estimate
are constant it can result in a divide by zero error. In these
cases, a warning is thrown and NA
is returned. This can occur when a model
predicts a single value for all samples. For example, a regularized model
that eliminates all predictors except for the intercept would do this.
Another example would be a CART model that contains no splits.
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 rsq_vec()
, a single numeric
value (or NA
).
Max Kuhn
Kvalseth. Cautionary note about R^2
.
American Statistician (1985) vol. 39 (4) pp. 279-285.
Other numeric metrics:
ccc()
,
huber_loss_pseudo()
,
huber_loss()
,
iic()
,
mae()
,
mape()
,
mase()
,
mpe()
,
msd()
,
poisson_log_loss()
,
rmse()
,
rpd()
,
rpiq()
,
rsq_trad()
,
smape()
Other consistency metrics:
ccc()
,
rpd()
,
rpiq()
,
rsq_trad()
# Supply truth and predictions as bare column names
rsq(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) %>%
rsq(solubility, prediction)
metric_results
# Resampled mean estimate
metric_results %>%
summarise(avg_estimate = mean(.estimate))
# With uninformitive data, the traditional version of R^2 can return
# negative values.
set.seed(2291)
solubility_test$randomized <- sample(solubility_test$prediction)
rsq(solubility_test, solubility, randomized)
rsq_trad(solubility_test, solubility, randomized)
# A constant `truth` or `estimate` vector results in a warning from
# a divide by zero error in the correlation calculation.
# `NA` will be returned in these cases.
truth <- c(1, 2)
estimate <- c(1, 1)
rsq_vec(truth, estimate)
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