The OpenD Programming Language

LogisticRes

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

Members

Functions

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.

Variables

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).

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