search for: glmboost

I'm getting started in R, and I'm trying to use one of the gradient boosting packages, mboost. I'm already installed the package with install.packages("mboost") and loaded it with library(mboost). My problem is that when I attempt to call glmboost, I get a message that " Error in glmboost() : no applicable method for "glmboost" ". Does anybody have an idea of what kind of problem this is indicative of? Kyle Ellrott

...rcept is rarely interpretable we didn't have a closer look and thus it took some time until we found this bug. The model predictions however always took care of the centering 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 cen...

I'm running R 2.10.1 with mboost 2.0 in order to build predictive models . I am performing prediction on a binomial outcome, using a linear function (glmboost). However, I am running into some confusion regarding centering. (I am not aware of an mboost-specific mailing list, so if the main R list is not the right place for this topic, please let me know.) The boost_control() function allows for the choice between center=TRUE and center=FALSE. If I selec...

I am having trouble predicting new data with a model created from package mboost: > mb1<-glmboost(as.formula(formula1),data=data_train,control=boost_control(mstop=400,nu=.1)) > f.predict<-predict(mb1,newdata=data_train) Error in scale.default(X, center = cm, scale = FALSE) : length of 'center' must equal the number of columns of 'x' Ultimately I want to predict &quot...

...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 Max ______...

...able. The 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, ne...

...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 Max ______...

...able. The 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, ne...