caret package: arguments passed to the classification or regression routine

A new version 3.41 is on

  https://r-forge.r-project.org/projects/caret/

Until later tonight, you will have to get it via

  svn checkout svn://svn.r-forge.r-project.org/svnroot/caret

and build it yourself.

Usage examples:

 library(caret)
 library(mlbench)
 data(BostonHousing)

 gbm1 <- train(medv ~ .,
               data = BostonHousing,
               "gbm",
               distribution = "laplace",
               verbose = FALSE)

 gbm2 <- train(medv ~ .,
               data = BostonHousing,
               "gbm",
               verbose = FALSE)

 gbm3 <- train(medv ~ .,
               data = BostonHousing,
               "gbm",
               verbose = FALSE,
               distribution = list(name="quantile",alpha=0.5))

Max


On Fri, Sep 19, 2008 at 2:49 PM, Max Kuhn <mxkuhn at gmail.com> wrote:

Peter,

train looks at the class of the outcome variable to determine the type
of model (regression or classification). Rather than making everyone
specify the distribution in every case, it switches between
"bernoulli" and "gaussian".

For other models, train looks at the parameters passed via ... and
will let those over-ride the automatically generated values. I can do
the same for gbm in this context (besides the tuning parameters, this
is the only argument that is automatically set for gbm).

I'll make the changes and upload a new version to

  https://r-forge.r-project.org/projects/caret/

It will probably be version 3.41.

One other thing - it is usually better to email the package
maintainers off-list for questions like this before emailing the list.

Max


On Thu, Sep 18, 2008 at 2:22 PM, Peter Tait <petertait at sympatico.ca> wrote:

Hi,
I am having problems passing arguments to method="gbm" using the train()
function.

I would like to train gbm using the laplace distribution or the quantile
distribution.

here is the code I used and the error:

gbm.test <- train(x.enet, y.matrix[,7],
 method="gbm",
 distribution=list(name="quantile",alpha=0.5), verbose=FALSE,
 trControl=trainControl(method="cv",number=5),
 tuneGrid=gbmGrid
)
Model 1: interaction.depth=1, shrinkage=0.1, n.trees=300
collapsing over other values of n.trees
Error in gbm.fit(trainX, modY, interaction.depth =
tuneValue$.interaction.depth,  :
 formal argument "distribution" matched by multiple actual arguments

The same error occured with distribution="laplace".

I also tried the following without and success :

gbm.test <- train(x.enet, y.matrix[,7],
 method="gbm",
 list(distribution="laplace", verbose=FALSE),
 trControl=trainControl(method="cv",number=2),
 tuneGrid=gbmGrid
)
Model 1: interaction.depth=1, shrinkage=0.1, n.trees=300
collapsing over other values of n.trees
Error in if (is.null(offset) || (offset == 0)) { :
 missing value where TRUE/FALSE needed
In addition: Warning message:
In gbm.fit(trainX, modY, interaction.depth = tuneValue$.interaction.depth,
 :
 NAs introduced by coercion

Any help would be appreciated.
Cheers
Peter

--

Max

--

Max