climpred.metrics._spearman_r#
- climpred.metrics._spearman_r(forecast: Dataset, verif: Dataset, dim: str | List[str] | None = None, **metric_kwargs: Any) Dataset [source]#
Spearman’s rank correlation coefficient.
This correlation coefficient is nonparametric and assesses how well the relationship between the forecast and verification data can be described using a monotonic function. It is computed by first ranking the forecasts and verification data, and then correlating those ranks using the
_pearson_r()
correlation.This is also known as the anomaly correlation coefficient (ACC) when comparing anomalies, although the Pearson product-moment correlation coefficient
_pearson_r()
is typically used when computing the ACC.Note
Use metric
_spearman_r_p_value()
or_spearman_r_eff_p_value`()
to get the corresponding p value.- Parameters:
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see
xskillscore.spearman_r()
Notes
minimum
-1.0
maximum
1.0
perfect
1.0
orientation
positive
See also
Example
>>> HindcastEnsemble.verify( ... metric="spearman_r", ... comparison="e2o", ... alignment="same_verifs", ... dim="init", ... ) <xarray.Dataset> Dimensions: (lead: 10) Coordinates: * lead (lead) int32 1 2 3 4 5 6 7 8 9 10 skill <U11 'initialized' Data variables: SST (lead) float64 0.9336 0.9311 0.9293 0.9474 ... 0.9465 0.9346 0.9328 Attributes: prediction_skill_software: climpred https://climpred.readthedocs.io/ skill_calculated_by_function: HindcastEnsemble.verify() number_of_initializations: 64 number_of_members: 10 alignment: same_verifs metric: spearman_r comparison: e2o dim: init reference: []