Peter Tait
2008-Sep-18 18:22 UTC
[R] caret package: arguments passed to the classification or regression routine
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 Kuhn
2008-Sep-19 19:24 UTC
[R] caret package: arguments passed to the classification or regression routine
Forgot to cc... On Fri, Sep 19, 2008 at 3:22 PM, Max Kuhn <mxkuhn at gmail.com> wrote:> 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 >>> >>> ______________________________________________ >>> R-help at r-project.org mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-help >>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >>> and provide commented, minimal, self-contained, reproducible code. >>> >> >> >> >> -- >> >> Max >> > > > > -- > > Max >-- Max