dstats ~master (2018-01-23T10:54:54Z)

- aic
`double`aic`()` Akaike Information Criterion, which is a complexity-penalized goodness- of-fit score, equal to 2 * k - 2 log(L) where L is the log likelihood and k is the number of parameters.

- toString
`string`toString`()` Print out the results in the default format.

- betas
`double[]`betas; The coefficients, one for each range in X. These will be in the order * that the X ranges were passed in.

- logLikelihood
`double`logLikelihood; The log likelihood for the model fit.

- lowerBound
`double[]`lowerBound; The Wald lower confidence bounds of the beta terms, at the confidence level specificied. (Default 0.95).

- nullLogLikelihood
`double`nullLogLikelihood; The log likelihood for the null model.

- overallP
`double`overallP; The P-value for the model as a whole, based on the likelihood ratio test. The null here is that the model has no predictive value, the alternative is that it does have predictive value.

- p
`double[]`p; The P-value for the alternative that the corresponding beta value is different from zero against the null that it is equal to zero. These are calculated using the Wald Test.

- stdErr
`double[]`stdErr; The standard error terms of the X ranges passed in.

- upperBound
`double[]`upperBound; The Wald upper confidence bounds of the beta terms, at the confidence level specificied. (Default 0.95).

Plain old data struct to hold the results of a logistic regression.