climpred.metrics._pearson_r_p_value#
- climpred.metrics._pearson_r_p_value(forecast: Dataset, verif: Dataset, dim: Optional[Union[str, List[str]]] = None, **metric_kwargs: Any) Dataset [source]#
Probability that forecast and verification data are linearly uncorrelated.
Two-tailed p value associated with the Pearson product-moment correlation coefficient
_pearson_r()
, assuming that all samples are independent. Use_pearson_r_eff_p_value()
to account for autocorrelation in the forecast and verification data.- Parameters:
forecast – Forecast.
verif – Verification data.
dim – Dimension(s) to perform metric over.
metric_kwargs – see
xskillscore.pearson_r_p_value()
Notes
minimum
0.0
maximum
1.0
perfect
1.0
orientation
negative
See also
Example
>>> HindcastEnsemble.verify( ... metric="pearson_r_p_value", ... 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 5.779e-23 2.753e-21 4.477e-21 ... 8.7e-22 6.781e-21 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: pearson_r_p_value comparison: e2o dim: init reference: []