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rsq

R Squared

Description

Regression measure defined as

1 - sum((t - r)^2) / sum((t - mean(t))^2).

Also known as coefficient of determination or explained variation. Subtracts the rse() from 1, hence it compares the squared error of the predictions relative to a naive model predicting the mean.

Usage

rsq(truth, response, na_value = NaN, ...)

Arguments

truth

(numeric())
True (observed) values. Must have the same length as response.
response

(numeric())
Predicted response values. Must have the same length as truth.
na_value

(numeric(1))
Value that should be returned if the measure is not defined for the input (as described in the note). Default is NaN.
...

(any)
Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Meta Information

  • Type: "regr"

  • Range: (-Inf, 1]

  • Minimize: FALSE

  • Required prediction: response

Note

This measure is undefined for constant t.

See Also

Other Regression Measures: bias(), ktau(), mae(), mape(), maxae(), maxse(), medae(), medse(), mse(), msle(), pbias(), rae(), rmse(), rmsle(), rrse(), rse(), sae(), smape(), srho(), sse()

Examples

set.seed(1)
truth = 1:10
response = truth + rnorm(10)
rsq(truth, response)
mlr3measures

Performance Measures for 'mlr3'

v0.3.1
LGPL-3
Authors
Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Martin Binder [ctb]
Initial release

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