Plain old data struct to hold the results of a logistic regression.
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.
Print out the results in the default format.
The coefficients, one for each range in X. These will be in the order * that the X ranges were passed in.
The log likelihood for the model fit.
The Wald lower confidence bounds of the beta terms, at the confidence level specificied. (Default 0.95).
The log likelihood for the null model.
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.
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.
The standard error terms of the X ranges passed in.
The Wald upper confidence bounds of the beta terms, at the confidence level specificied. (Default 0.95).