correlation_method

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Axis correlation_method on sub-layer L1_5_E_correlation_pre_cleaning (layer l1_5).

Sub-layer

L1_5_E_correlation_pre_cleaning

Axis metadata

  • Default: 'pearson'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 3 option(s)

  • Future: 0 option(s)

Options

kendall – operational

Kendall tau rank correlation.

L1.5.E correlation method kendall.

This option configures the correlation_method axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Conservative rank measure; smaller variance than Spearman in small samples (n < 30).

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: pearson, spearman

Last reviewed 2026-05-05 by macroforecast author.

pearson – operational

Pearson product-moment correlation – linear association measure.

L1.5.E correlation method pearson.

This option configures the correlation_method axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Default; assumes approximate normality of pairs and linear association.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: spearman, kendall

Last reviewed 2026-05-05 by macroforecast author.

spearman – operational

Spearman rank correlation – monotonic-association measure.

L1.5.E correlation method spearman.

This option configures the correlation_method axis on the L1_5_E_correlation_pre_cleaning sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_E_correlation_pre_cleaning/ alongside the other selected views.

When to use

Robust to outliers and non-normal marginals; preferred when pairs have heavy tails.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: pearson, kendall

Last reviewed 2026-05-05 by macroforecast author.