Regression measure defined as $$ \frac{1}{n} \sum_{i=1}^n \left( \ln (1 + t_i) - \ln (1 + r_i) \right)^2. $$

msle(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).

Note

This measure is undefined if any element of \(t\) or \(r\) is less than or equal to \(-1\).

Meta Information

  • Type: "regr"

  • Range: \([0, \infty)\)

  • Minimize: TRUE

  • Required prediction: response

See also

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

Examples

set.seed(1) truth = 1:10 response = truth + rnorm(10) msle(truth, response)
#> [1] 0.03083585