Distribution with Best Error Intervals

Description

best_distribution computes the distribution assumption that gives error intervals with the lower accuracy error for a given set of residuals.

Usage

1
best_distribution(phi, errors, dists = c("n", "nm", "l", "lm", "w", "b"), ...)

Arguments

phi

residual values used to compute the error interval.

errors

set of real errors corresponding to the predictions of a particular model.

dists

character vector with the distribution assumptions to test. See also 'Details'.

...

additional arguments to be passed to functions error_interval and acc_intervals.

Details

Allowed distribution assumptions are:

  • "n": Zero-mu Gaussian

  • "nm": General Gaussian

  • "l": Zero-mu Laplace

  • "lm": General Laplace

  • "b": Beta

  • "w": Weibull

Value

Returns an object of class c("df_intervals", "data.frame") with information of the distribution assumption with lower accuracy error.

Author(s)

Jesus Prada, jesus.prada@estudiante.uam.es

References

Link to the scientific paper

Prada, Jesus, and Jose Ramon Dorronsoro. "SVRs and Uncertainty Estimates in Wind Energy Prediction." Advances in Computational Intelligence. Springer International Publishing, 2015. 564-577,

with theoretical background for this package is provided below.

http://link.springer.com/chapter/10.1007/978-3-319-19222-2_47

See Also

df_intervals error_interval acc_intervals

Examples

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2
best_distribution(rnorm(10000),rnorm(10000),dists=c("n","b"))
best_distribution(rnorm(10000),rnorm(10000))