search for: gamboost

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...

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...

...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...

.... 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...

...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...

.... 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...

...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...

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