Evaluate residuals#
Simple evaluation of residuals (coming from MLR model output).
Description#
This diagnostic evaluates residuals created by MLR models.
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()
byplot_kwargs
and plot appearance options bypyplot_kwargs
(processed as functions ofmatplotlib.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()
byplot_kwargs
and plot appearance options bypyplot_kwargs
(processed as functions ofmatplotlib.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.