Evaluate residuals

Simple evaluation of residuals (coming from MLR model output).

Description

This diagnostic evaluates residuals created by MLR models.

Author

Manuel Schlund (DLR, Germany)

Project

CRESCENDO

Configuration options in recipe

ignore: list of dict, optional

Ignore specific datasets by specifying multiple dict s of metadata.

mse_plot: dict, optional

Additional options for plotting the mean square errors (MSE). Specify additional keyword arguments for seaborn.boxplot() by plot_kwargs and plot appearance options by pyplot_kwargs (processed as functions of matplotlib.pyplot).

pattern: str, optional

Pattern matched against ancestor file names.

rmse_plot: dict, optional

Additional options for plotting the root mean square errors (RMSE). Specify additional keyword arguments for seaborn.boxplot() by plot_kwargs and plot appearance options by pyplot_kwargs (processed as functions of matplotlib.pyplot).

savefig_kwargs: dict, optional

Keyword arguments for matplotlib.pyplot.savefig().

seaborn_settings: dict, optional

Options for seaborn.set_theme() (affects all plots).

weighted_samples: dict

If specified, use weighted root mean square error. The given keyword arguments are directly passed to esmvaltool.diag_scripts.mlr.get_all_weights() to calculate the sample weights. By default, area weights and time weights are used.