pastas.stats.metrics#
The following methods may be used to describe the fit between the model simulation and the observations.
Examples
These methods may be used as follows:
>>> ps.stats.rmse(sim, obs)
or directly from a Pastas model:
>>> ml.stats.rmse()
Functions
Compute the Akaike Information Criterium (AIC). |
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Compute the Bayesian Information Criterium (BIC). |
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Compute the (weighted) Explained Variance Percentage (EVP). |
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Compute the (weighted) Kling-Gupta Efficiency (KGE). |
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Compute the (weighted) Mean Absolute Error (MAE). |
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Compute the (weighted) Nash-Sutcliffe Efficiency (NSE). |
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Compute the (weighted) Pearson correlation (r). |
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Compute the (weighted) Root Mean Squared Error (RMSE). |
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Compute R-squared, possibly adjusted for the number of free parameters. |
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Compute the Sum of the Squared Errors (SSE). |