Model Browser User's Guide

MLE Settings

Algorithm

The algorithm drop-down menu offers a choice between two covariance estimation algorithms, Quasi-Newton (the default) and Expectation Maximization. These are algorithms for estimating the covariance matrix for the global models.

Quasi-Newton is recommended for smaller problems (< 5 response features and < 100 tests). Quasi-Newton usually produces better answers (smaller values of -logL) and hence is the default for small problems.

Expectation Maximization is an iterative method for calculating the global covariance (as described in Davidian and Giltinan (1995); see References in Two-Stage Models for Engines). This algorithm has slow convergence, so you might want to use the Stop button.

Tolerance

You can edit the tolerance value. Tolerance is used to specify a stopping condition for the algorithm. The default values are usually appropriate, and if calculation is taking too long you can always click Stop.

Initialize with previous estimate

When you recalculate MLE (that is, perform more iterations), there is a check box you can use to initialize with the previous estimate.

Predict missing values

The other check box (selected by default) predicts missing values. When it is selected, response features that are outliers for the univariate global model are replaced by the predicted value. This allows tests to be used for MLE even if one of the response features is missing. If all the response features for a particular test are missing or the check box is unselected, the whole test is removed from MLE calculation.

MLE

Response Level