Inferential statistics for one GLM coefficient, on the coefficient (linear-predictor) scale rather than the response scale. Use one object per fitted coefficient, in the same order as beta.
This type is a container: components have no default values and must be populated by an inference calculation before use. IRLS can populate it through its optional stats output after convergence; on nonconvergence that output contains NaNs. Standard errors require a GLM covariance estimate; robust fits generally require an estimating-equation sandwich covariance, not simply the inverse robust-weighted information.
| Type | Visibility | Attributes | Name | Initial | |||
|---|---|---|---|---|---|---|---|
| real(kind=real64), | public | :: | confidence_interval_lower |
Lower confidence bound for the coefficient, not an interval half-width. The producing calculation specifies the confidence level and interval method (for example, Wald or profile likelihood). |
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| real(kind=real64), | public | :: | confidence_interval_upper |
Upper confidence bound at the same level and using the same method as confidence_interval_lower. Separate bounds allow asymmetric intervals; no inverse-link transformation is implied. |
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| real(kind=real64), | public | :: | p_value |
Two-sided tail probability under the null hypothesis and the chosen reference distribution, in [0,1]. This is not the probability that the coefficient is unimportant or zero. |
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| real(kind=real64), | public | :: | standard_error |
Square root of the coefficient covariance diagonal entry. |
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| real(kind=real64), | public | :: | wald_statistic |
Signed coefficient divided by its standard error, testing the null hypothesis that the coefficient is zero. Ordinarily an asymptotic z statistic; approximate t inference may be used when dispersion is estimated. The reference distribution must be specified by the calculation producing this object. |