For independent binomial distributions, the log-likelihood, up to known binomial coefficients, isThe saturated statistical model maximizes each term at . Under the logit link, the fitted probabilities are . Subtracting the fitted log-likelihood from the saturated log-likelihood gives the binomial devianceA zero-count summand uses the continuous convention . For the restricted model, replace by to obtain . Since the saturated likelihood cancels, is the likelihood-ratio test statistic. For the nested restriction setting coefficients to zero, Wilks theorem gives the asymptotic chi-squared distribution under regularity and an interior true parameter; reject for a large difference.
For the six anther cells, gives a common success probability, gives a separate probability for each storage condition, gives a common intercept and a log-force slope, and gives storage-specific intercepts with a common log-force slope. Their parameter counts are , so their residual statistical degrees of freedom are . In , storage has a constant effect on log odds; multiplying force by multiplies the odds by in either storage group.
The relevant nested likelihood-ratio tests at the level are:
- Adding storage to gives on one statistical degree of freedom, with .
- Adding log-force to gives on one statistical degree of freedom, with .
- Adding log-force to the storage model gives on one statistical degree of freedom, with .
- Adding storage to the log-force model gives on one statistical degree of freedom, with .
- Adding both terms to gives on two statistical degrees of freedom, with .
The two two-parameter models and are not nested, so their deviance difference has no ordinary nested chi-squared distribution calibration. The preferred parsimonious model is , with a storage effect and no log-force effect. Its deviance goodness-of-fit test is acceptable: on four statistical degrees of freedom. The common-probability model also passes its separate goodness-of-fit comparison, , but this does not contradict the relative evidence for adding storage: the two tests assess different null hypotheses. These are large-sample approximations; sufficiently large expected successes and failures, not merely the number of anthers, underpin their calibration.
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