| Version 6.1 (R2007b) Statistics Toolbox™ Software Release Notes | |
This table summarizes what's new in Version 6.1 (R2007b):
| New Features and Changes | Version Compatibility Considerations | Fixed Bugs and Known Problems | Related Documentation at Web Site |
|---|---|---|---|
Yes | Yes | Bug Reports | No |
New features and changes introduced in this version are organized by these topics:
The new gmdistribution class represents Gaussian mixture distributions, where random points come from different multivariate normal distributions with certain probabilities. The gmdistribution constructor creates mixture models with specified means, covariances, and mixture proportions, or by fitting a mixture model with a specified number of components to data. Methods for the class include:
fit — Distribution fitting function
pdf — Probability density function
cdf — Cumulative distribution function
random — Random number generator
cluster — Data clustering
posterior — Cluster posterior probabilities
mahal — Mahalanobis distance
The cluster function for hierarchical clustering now accepts a vector of cutoff values, and returns a matrix of cluster assignments, with one column per cutoff value.
The kmeans function now returns a vector of cluster indices of length n, where n is the number of rows in the input data matrix X, even when X contains NaN values. In the past, rows of X with NaN values were ignored, and the vector of cluster indices was correspondingly reduced in size. Now the vector of cluster indices contains NaN values where rows have been ignored, consistent with other toolbox functions.
A new option in the D-optimal design function candexch specifies fixed design points in the row-exchange algorithm. A similar feature is already available for the daugment function, which uses the coordinate-exchange algorithm.
The kstest function now uses a more accurate method to calculate the p-value for a single-sample Kolmogorov-Smirnov test.
kstest now compares the computed p-value to the desired cutoff, rather than comparing the test statistic to a table of values. Results may differ from those in previous releases, especially for small samples in two-sided tests where an asymptotic formula was used in the past.
A new fitting function, copulafit, has been added to the family of functions that describe dependencies among variables using copulas. The function fits parametric copulas to data, providing a link between models of marginal distributions and models of data correlations.
A number of probability functions now have improved accuracy, especially for extreme parameter values. The functions are:
betainv — More accurate for probabilities in P near 1.
binocdf — More efficient and less likely to run out of memory for large values in X.
binopdf — More accurate when the probabilities in P are on the order of eps.
fcdf — More accurate when the parameter ratios V2./V1 are much less than the values in X.
ncx2cdf — More accurate in some extreme cases that previously returned 0.
poisscdf — More efficient and less likely to run out of memory for large values in X.
tcdf — More accurate when the squares of the values in X are much less than the parameters in V.
tinv — More accurate when the probabilities in P are very close to 0.5 and the outputs are very small in magnitude.
Function-style syntax for paretotails objects has been removed.
The changes to the probability functions listed above may lead to different, but more accurate, outputs than in previous releases.
In previous releases, syntax of the form obj(x) for a paretotails objects obj invoked the cdf method. This syntax now produces a warning. To evaluate the cumulative distribution function, use the syntax cdf(obj,x).
The new corrcov function converts a covariance matrix to the corresponding correlation matrix.
The mvregress function now supports an option to force the estimated covariance matrix to be diagonal.
In previous releases the mvregress function, when using the 'cwls' algorithm, estimated the covariance of coefficients COVB using the estimated, rather than the initial, covariance of the responses SIGMA. The initial SIGMA is now used, and COVB differs to a degree dependent on the difference between the initial and final estimates of SIGMA.
The boxplot function has a new 'compact' plot style suitable for displaying large numbers of groups.
| Version 6.2 (R2008a) Statistics Toolbox™ Software | Version 6.0 (R2007a) Statistics Toolbox™ Software | |
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