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.
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