# rmse

##### Calculate Metrics for Numeric Outcomes

These functions are appropriate for cases where the model
outcome is a number. The root mean squared error (`rmse`

) and
mean absolute error (`mae`

) are error measures that are in the
same units as the original data. 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. The concordance
correlation coefficient (`ccc`

) is a measure of both.

- Keywords
- manip

##### Usage

`rmse(data, ...)`# S3 method for data.frame
rmse(data, truth, estimate, na.rm = TRUE, ...)

rsq(data, ...)

# S3 method for data.frame
rsq(data, truth, estimate, na.rm = TRUE, ...)

rsq_trad(data, ...)

# S3 method for data.frame
rsq_trad(data, truth, estimate, na.rm = TRUE, ...)

mae(data, ...)

# S3 method for data.frame
mae(data, truth, estimate, na.rm = TRUE, ...)

ccc(data, ...)

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

##### Arguments

- data
A data frame

- ...
Not currently used.

- truth
The column identifier for the true results (that is numeric). This should an unquoted column name although this argument is passed by expression and support quasiquotation (you can unquote column names or column positions).

- 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.- na.rm
A logical value indicating whether

`NA`

values should be stripped before the computation proceeds.- bias
A logical; should the biased estimate of variance be used for the concordance correlation coefficient (as is Lin (1989))?

##### Value

A number or `NA`

##### References

Kvalseth. Cautionary note about \(R^2\). American Statistician (1985) vol. 39 (4) pp. 279-285.

Lin, L. (1989). A concordance correlation
coefficient to evaluate reproducibility. *Biometrics*, 45 (1),
255<U+2013>268.

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

##### Examples

```
# NOT RUN {
rmse(solubility_test, truth = solubility, estimate = prediction)
mae(solubility_test, truth = solubility, estimate = prediction)
rsq(solubility_test, solubility, prediction)
set.seed(2291)
solubility_test$randomized <- sample(solubility_test$prediction)
rsq(solubility_test, solubility, randomized)
rsq_trad(solubility_test, solubility, randomized)
ccc(solubility_test, solubility, prediction)
# }
```

*Documentation reproduced from package yardstick, version 0.0.1, License: GPL-2*