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Version 6.0 (R2007a) Statistics Toolbox Software

This table summarizes what's new in Version 6.0 (R2007a):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems	Related Documentation at Web Site
Yes Details below	Yes Summary	Bug Reports Includes fixes	No

New features and changes introduced in this version are organized by these topics:

• Data Organization

• Hypothesis Testing

• Multivariate Statistics

• Probability Distributions

• Regression Analysis

• Statistical Visualization

• Other Improvements

Data Organization

New categorical and dataset arrays are available for organizing and processing statistical data.

• Categorical arrays facilitate the use of nominal and ordinal categorical data.

• Dataset arrays provide a natural way to encapsulate heterogeneous statistical data and metadata, so that it can be accessed and manipulated using familiar methods analogous to those for numerical matrices.

• Categorical and dataset arrays are supported by a variety of new functions for manipulating the encapsulated data.

• Categorical arrays are now accepted as input arguments in all Statistics Toolbox functions that make use of grouping variables.

Hypothesis Testing

Expanded options are available for linear hypothesis testing.

• The new linhyptest function performs linear hypothesis tests on parameters such as regression coefficients. These tests have the form H*b = c for specified values of H and c, where b is a vector of unknown parameters.

• The covb output from regstats and the SIGMA output from nlinfit are suitable for use as the covariance matrix input argument required by linhyptest. The following functions have been modified to return a covb output for use with linhyptest: coxphfit, glmfit, mnrfit, robustfit.

Multivariate Statistics

The new cholcov function computes a Cholesky-like decomposition of a covariance matrix, even if the matrix is not positive definite. Factors are useful in many of the same ways as Cholesky factors, such as imposing correlation on random number generators.

The classify function for discriminant analysis has been improved.

• The function now computes the coefficients of the discriminant functions that define boundaries between classification regions.

• The output of the function is now of the same type as the input grouping variable group.

Compatibility Considerations

The classify function now returns outputs of different type than it did in the past. If the input argument group is a logical vector, output is now converted to a logical vector. In the past, output was returned as a cell array of 0s and 1s. If group is numeric, the output is now converted to the same type. For example, if group is of type uint8, the output will be of type uint8.

Probability Distributions

New paretotails objects are available for modeling distributions with an empirical cdf or similar distribution in the center and generalized Pareto distributions in the tails.

• The paretotails function converts a data sample to a paretotails object. The objects are useful for generating random samples from a distribution similar to the data, but with tail behavior that is less discrete than the empirical distribution.

• Objects from the paretotails class are supported by a variety of new methods for working with the piecewise distribution.

• The paretotails class provides function-like behavior, so that p(x) evaluates the cdf of p at values x.

Regression Analysis

The new mvregresslike function is a utility related to the mvregress function for fitting regression models to multivariate data with missing values. The new function computes the objective (log likelihood) function, and can also compute the estimated covariance matrix for the parameter estimates.

New classregtree objects are available for creating and analyzing classification and regression trees.

• The classregtree function fits a classification or regression tree to training data. The objects are useful for predicting response values from new predictors.

• Objects from the classregtree class are supported by a variety of new methods for accessing information about the tree.

• The classregtree class provides function-like behavior, so that t(X) evaluates the tree t at predictor values in X.

• The following functions now create or operate on objects from the new classregtree class: treefit, treedisp, treeval, treefit, treeprune, treetest.

Compatibility Considerations

Objects from the classregtree class are intended to be compatible with the structure arrays that were produced in previous versions by the classification and regression tree functions listed above. In particular, classregtree supports dot indexing of the form t.property to obtain properties of the object t. The class also provides function-like behavior through parenthesis indexing, so that t(x) uses the tree t to classify or compute fitted values for predictors x, rather than index into t as a structure array as it did in the past. As a result, cell arrays should now be used to aggregate classregtree objects.

Statistical Visualization

The new scatterhist function produces a scatterplot of 2D data and illustrates the marginal distributions of the variables by drawing histograms along the two axes. The function is also useful for viewing properties of random samples produced by functions such as copularnd, mvnrnd, and lhsdesign.

Other Improvements

• The mvtrnd function now produces a single random sample from the multivariate t distribution if the cases input argument is absent.

• The zscore function, which centers and scales input data by mean and standard deviation, now returns the means and standard deviations as additional outputs.

Version 6.1 (R2007b) Statistics Toolbox Software

Version 5.3 (R2006b) Statistics Toolbox Software

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