Version 7.6 (R2011b) Statistics Toolbox Software

This table summarizes what's new in Version 7.6 (R2011b):

New Features and Changes	Version Compatibility Considerations	Fixed Bugs and Known Problems
Yes Details below	Yes Summary	Bug Reports Includes fixes

New features and changes introduced in this version are:

• Lasso Regularization for Linear Regression

• Discriminant Analysis Classification Object

• Nearest Neighbor Searching for Points Within a Fixed Distance

• datasample Function for Random Sampling

• Fractional Factorial Design Improvements

• nlmefit Returns the Covariance Matrix of Estimated Coefficients

• signrank Change

• Conversion of Error and Warning Message Identifiers

Lasso Regularization for Linear Regression

The lasso function incorporates both the lasso regularization algorithm and the elastic net regularization algorithm. Use lasso to remove redundant or unimportant variables in linear regression. The lassoPlot function helps you visualize lasso results, with a variety of coefficient trace plots and a cross-validation plot.

For details, see Lasso and Elastic Net.

Discriminant Analysis Classification Object

You can now use the ClassificationDiscriminant and CompactClassificationDiscriminant classes for classification via discriminant analysis. The syntax and methods resemble those in the existing ClassificationTree and CompactClassificationTree classes. The ClassificationDiscriminant class includes the functionality of the classify function. ClassificationDiscriminant provides several benefits compared to the classify function:

• After you fit a classifier, you can predict without refitting.

• ClassificationDiscriminant is built on the same framework as ClassificationTree, so you have a variety of options and methods, including:

  • Cross validation

  • Resubstitution statistics

  • A choice of cost functions

  • Weighted classification

• ClassificationDiscriminant can fit several models, including linear, quadratic, and linear or quadratic with pseudoinverse.

For details, see Discriminant Analysis.

Nearest Neighbor Searching for Points Within a Fixed Distance

The rangesearch function finds all members of a data set that are within a specified distance of members of another data set. As with the knnsearch function, you can set a variety of distance metrics, or program your own. rangesearch has counterparts that are methods of the ExhaustiveSearcher and KDTreeSearcher classes.

datasample Function for Random Sampling

The datasample function samples with or without replacement from a data set. It can also perform weighted sampling, with or without replacement.

Fractional Factorial Design Improvements

The fracfactgen function now allows up to 52 factors, instead of the previous limit of 26 factors. Specify factors as case-sensitive strings, using 'a' through 'z' for the first 26 factors, and 'A' through 'Z' for the remaining factors.

fracfact now checks for an arbitrary level of interaction in confounding, instead of the previous limit of confounding up to products of two factors. Set the MaxInt name-value pair to the level of interaction you want. You can also set names for the factors using the FactorNames name-value pair.

nlmefit Returns the Covariance Matrix of Estimated Coefficients

The nlmefit function now returns the covariance matrix of the estimated coefficients as the covb field of the stats structure.

signrank Change

The signrank test now defines ties to be entries that differ by 2*eps or less. Previously, ties were entries that were identical to machine precision.

Conversion of Error and Warning Message Identifiers

For R2011b, error and warning message identifiers have changed in Statistics Toolbox™.

Compatibility Considerations

If you have scripts or functions that use message identifiers that changed, you must update the code to use the new identifiers. Typically, message identifiers are used to turn off specific warning messages, or in code that uses a try/catch statement and performs an action based on a specific error identifier.

For example, if you use the 'resubstitution' method, the 'stats:plsregress:InvalidMCReps' identifier has changed to 'stats:plsregress:InvalidResubMCReps'. If you use the 'resubstitution' method and your code checks for 'stats:plsregress:InvalidMCReps', you must update it to check for 'stats:plsregress:InvalidResubMCReps' instead.

To determine the identifier for a warning, run the following command just after you see the warning:

[MSG,MSGID] = lastwarn;

This command saves the message identifier to the variable MSGID.

To determine the identifier for an error, run the following command just after you see the error:

exception = MException.last;
MSGID = exception.identifier;

Tip   Warning messages indicate a potential issue with your code. While you can turn off a warning, a suggested alternative is to change your code so it runs warning free.

Version 8.0 (R2012a) Statistics Toolbox Software

Version 7.5 (R2011a) Statistics Toolbox Software

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