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Using the identical perceptron model as above and an identical training/testing procedure, we estimate the error rate (p_{mathrm{err}} = mathbb{P}[ynehat{y}]) of the trained perceptron as a function of the cluster separation (| mu_{1} - mu_{0} |_{2} / sigma ).
After defining the starting model as above, we assessed, by backward elimination, which confounding factors should remain in the model.
The relative risks were calculated using the exact same model as above, except that the categorical version of the EDI (by quintile) was introduced into the model.
It is the estimated effect of the positive controls for treatment level j and assay k using the same model as above, but with vague priors for all parameters.
We therefore evaluated exactly the same model as above, but this time calculating the error as the vector difference between the peak significant voxel and the centre of mass of the target Brodmann area.
A direct test of the relationship between male eyespan and offspring sex ratio was performed, using the same generalised linear model as above, testing the offspring sex ratio against thorax, absolute eyespan and relative eyespan.
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Disability variables were modeled as above to calculate multivariate-adjusted PR.
The same models as above were used except lines were treated as random effects to obtain estimates of genetic variance.
However, the same models as above arose (see Table 2), this statistic being discarded as an explanatory variable of the ϕ.
Potential confounders were entered as covariates using the same models as above.
Pre-specified subgroup analyses were done using models as above, omitting the interaction term.
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