Class RWLogisticLevenbergMarquardt finds logistic regression parameters using a more sophisticated technique than iterative least squares. It implements what is known as the Levenberg-Marquardt method. The extra sophistication of this algorithm often causes a recovery from poor initial estimates for . The starting vector of parameters is the same as for iterative least squares, and at each iteration, the algorithm tries to take a step that is similar to the one taken by iterative least squares. However, it checks to make sure that the step improves the likelihood of the model producing the data. If the step does improve likelihood, the step is taken. If the step does not improve likelihood, the algorithm tries a modified step that falls closer to the gradient. This process of checking and trying a step even closer to the gradient repeats until a step is found that finally improves the likelihood. For further discussion of this and similar optimization algorithms, see Dennis & Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, (1983).