Displaying 8 results from an estimated 8 matches for "gamboost".
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fanboost
2008 Oct 15
0
gamboost partial fit prediction
Dear useRs,
I am struggling to use gamboost function form the 'mboost' package. More
precisely, I am trying to extract the *partial fit* for each of the
covariates estimated in a model and I usually end up with this annoying: "Error
in newdata[[xname]] : subscript out of bounds ". I hope that the lack of
details in my query...
2008 Apr 26
2
Calling a stored model within the predict() function
Hi all,
First of all, I'm a novice R user (less that a week), so perhaps my code
isn't very efficient.
Using the MBoost package I created a model using the following command and
saved it to a file for later use:
model <- gamboost(fpfm,data=SampleClusterData,baselearner="bbs") # Creating
a model
save(model,file="model.RData") # Saving a model
After this, during a new R session, I want to deploy this model:
setwd("Q:/Program Files/R/library/mboost/data")
NewData <- read.table("NewData...
2010 Mar 19
0
mboost: Interpreting coefficients from glmboost if center=TRUE
...tering and
thus were not affected (as you already pointed out).
As you realized centering is of hight importance if you use glmboost as
it reduces the number of boosting iterations needed to estimate the
model and furthermore often improves the estimates. Centering is also
important if you use gamboost() and specify linear base-learners without
intercept (e.g. bols(x, intercept=FALSE)). However, in this case you
have to center the covariates yourself and take care of the intercept
correction afterwards.
You wrote you weren't aware of a mailing list for mboost but you could
write to the m...
2007 Nov 29
0
New versions of the caret (3.08) and caretLSF (1.12) packages
.... Wherever possible, caret tries to avoid re-fitting
models if it can get predictions from sub-models. For example, an object
for a boosted tree with 500 trees can often be used to get predictions
for any boosted tree with less than 500 trees. The affected models are:
pls, plsda, earth, rpart, gbm, gamboost, glmboost, blackboost, ctree,
pam, enet and lasso.
The caretLSF package is a parallel processing version of caret. The
other caret package, caretNWS, will be updated to work with the new
version of caret shortly.
Please email me at max dot kuhn at pfizer dot com with any questions or
comments
M...
2008 Sep 06
0
New caret packages
...project is now hosted on R-Forge. The homepage is
http://caret.r-forge.r-project.org/
The package currently includes model tuning/resampling for the following
models: lm, single trees (C4.5, rpart, ctree, logistic model trees), mars
(via earth), boosted models (ada, gbm, blackboost, glmboost, gamboost,
logitboost), bagged models (trees, earth, fda), randomforests (randomforest
and cforest), rule-based models (Ripper and M5 prime), discriminant models
(lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr),
nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest
shru...
2007 Nov 29
0
New versions of the caret (3.08) and caretLSF (1.12) packages
.... Wherever possible, caret tries to avoid re-fitting
models if it can get predictions from sub-models. For example, an object
for a boosted tree with 500 trees can often be used to get predictions
for any boosted tree with less than 500 trees. The affected models are:
pls, plsda, earth, rpart, gbm, gamboost, glmboost, blackboost, ctree,
pam, enet and lasso.
The caretLSF package is a parallel processing version of caret. The
other caret package, caretNWS, will be updated to work with the new
version of caret shortly.
Please email me at max dot kuhn at pfizer dot com with any questions or
comments
M...
2008 Sep 06
0
New caret packages
...project is now hosted on R-Forge. The homepage is
http://caret.r-forge.r-project.org/
The package currently includes model tuning/resampling for the following
models: lm, single trees (C4.5, rpart, ctree, logistic model trees), mars
(via earth), boosted models (ada, gbm, blackboost, glmboost, gamboost,
logitboost), bagged models (trees, earth, fda), randomforests (randomforest
and cforest), rule-based models (Ripper and M5 prime), discriminant models
(lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr),
nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest
shru...
2008 Aug 19
4
spatial probit/logit for prediction
Hello all,
I am wondering if there is a way to do a spatial error probit/logit model in R? I can't seem to find it in any of the packages. I can do it in MATLAB with Gibbs sampling, but would like to confirm the results. Ideally I would like to use this model to predict probability of parcel conversion in a future time period. This seems especially difficult in a binary outcome model