From: Liaw, Andy <andy_liaw_at_merck.com>

Date: Fri 09 Jul 2004 - 00:06:45 EST

Changes in 4.3-0:

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Date: Fri 09 Jul 2004 - 00:06:45 EST

Dear all,

Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last public release) is shown below.

As many changes were made to the package, it's very likely that new bugs have crept in. I'd very much appreciate bug reports or even patches!

The plan is still to add features to the package so that it matches the features in Breiman and Cutler's latest Fortran version. There is also plan to add some functions so that the package will work with Adele Cutler's Java visualization program (RAFT).

Best,

Andy

Changes in 4.3-0:

- Thanks to Adele Cutler, there's now casewise variable importance measures in classification. Similar feature is also added for regression. Use the new localImp option in randomForest().
- The `importance' component of randomForest object has been changed: The permutation-based measures are not divided by their `standard errors'. Instead, the `standard errors' are stored in the `importanceSD' component. One should use the importance() extractor function rather than something like rf.obj$importance for extracting the importance measures.
- The importance() extractor function has been updated: If the permutation-based measures are available, calling importance() with only a randomForest object returns the matrix of variable importance measures. There is the `scale' argument, which defaults to TRUE.
- In predict.randomForest, there is a new argument `nodes' (default to FALSE). For classification, if nodes=TRUE, the returned object has an attribute `nodes', which is an n by ntree matrix of terminal node indicators. This is ignored for regression.

Changes in 4.2-1:

- There is now a package name space. Only generics are exported.
- Some function names have been changed: partial.plot -> partialPlot var.imp.plot -> varImpPlot var.used -> varUsed
- There is a new option `replace' in randomForest() (default to TRUE) indicating whether the sampling of cases is with or without replacement.
- In randomForest(), the `sampsize' option now works for both classification and regression, and indicate the number of cases to be drawn to grow each tree. For classification, if sampsize is a vector of length the number of classes, then sampling is stratified by class.
- With the formula interface for randomForest(), the default na.action, na.fail, is effective. I.e., an error is given if there are NAs present in the data. If na.omit is desired, it must be given explicitly.
- For classification, the err.rate component of the randomForest object (and the corresponding one for test set) now is a ntree by (nclass + 1) matrix, the first column of which contains the overall error rate, and the remaining columns the class error rates. The running output now also prints class error rates. The plot method for randomForest will plot the class error rates as well.
- The predict() method now checks whether the variable names in newdata match those from the training data (if the randomForest object is not created from the formula interface).
- partialPlot() and varImpPlot() now have optional arguments xlab, ylab and main for more flexible labelling. Also, if a factor is given as the variable, a real bar plot is produced.
- partialPlot() will now remove rows with NAs from the data frame given.
- For regression, if proximity=FALSE, an n by n array of integers is erroneously allocated but not used (it's only used for proximity calculation, so not needed otherwise).
- Updated combine() to conform to the new randomForest object.
- na.roughfix() was not working correctly for matrices, which in turns causes problem in rfImpute().

Changes in 4.1-0:

- In randomForest(), if sampsize is given, the sampling is now done without replacement, in addition to stratified by class. Therefore sampsize can not be larger than the class frequencies.
- In classification randomForest, checks are added to avoid trees with only the root node.
- Fixed a bug in the Fortran code for classification that caused segfault on some system when encountering a tree with only root node.
- The help page for predict.randomForest() now states the fact that when newdata is not specified, the OOB predictions from the randomForest object is returned.
- plot.randomForest() and print.randomForest() were not checking for
existence of performance (err.rate or mse) on test data correctly.

R-packages mailing list

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https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Fri Jul 09 00:22:50 2004

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