At this threshold, the McNemar test for improvement was significant (p = 0.012, based on 10 of 11 who improved rather than worsened).
The best model for each fleece trait was chosen after testing for improvement of the log-likelihood values.
These models with increasing constraints were successively tested for improvement of the fit.
Genes encoding seed storage proteins of various plant species have been transgenically expressed to test for improvement of nutritional quality.
We will calculate the increase in AUC with 95% CI, and the p-value from the likelihood ratio test for improvement of goodness of fit.
Several drugs are currently being tested for improvement of fracture repair, but there is still no consensus regarding which outcome variables should be used.
This test (the likelihood ratio test) provides not only a statistical test for improvement in fit, but also a quantitative measure of the extent to which the added term improves risk prediction.
When models are nested (identical in structure, but differing in the number of free parameters) the change in χ, Δχ, across the models provides a significance test for improvement in model fit (the degrees of freedom for the comparison is equal to the difference in degrees of freedom across the models).
Most maximum parsimony and maximum likelihood analysis methods build an initial tree and then iteratively test for improvement by rearranging the tree topology using branch-swapping algorithms such as nearest neighbor interchange (NNI), subtree pruning and regrafting (SPR), tree bisection and reconnection (TBR), or combinations thereof.
As before, the difference in twice the log-likelihood between 2 nested models (which gives a χ statistic with df equal to the difference in the number of variables between models) was used to provide both an assessment of how well the reduced set of factors predicted risk compared with the full set and a formal test for improvement in model fit.
