similar to: mboost: how to implement cost-sensitive boosting family

Full_Name: Ivan the Terrible Version: 2.9.2 OS: Windows XP SP3 Submission from: (NULL) (89.110.13.151) When using the method blackboost_fit of the package mboost appear following error : Error in party:::get_variables(obj at responses) : trying to get slot "responses" from an object (class "boost_data") that is not an S4 object Simple test case that produce bug:

I'm attempting to fit boosted regression trees to a censored response using IPCW weighting. I've implemented this through two libraries, mboost and gbm, which I believe should yield models that would perform comparably. This, however, is not the case - mboost performs much better. This seems odd. This issue is meaningful since the output of this regression needs to be implemented in a

Dear all, I am puzzled by R's memory usage when calling the blackboost function from package mboost to estimate a Gradient boosting model on a simulated dataset with 20 correlated variables and 100,000 obs. The blackboost object created by the function is only 15.3Mb, but R's memory usage increases by about 3.9Gb during the estimation of the model and the memory is not released even after

Dear useRs, we are happy to announce the release of mboost 2.0-0 on CRAN: http://cran.r-project.org/package=mboost This version contains major updates and changes to the implementation of the main algorithm. Some slight changes to the user-interface where necessary. Please consult the manual and the list of CHANGES below. The package 'mboost' (Model-based Boosting) implements

Dear useRs, we are happy to announce the release of mboost 2.0-0 on CRAN: http://cran.r-project.org/package=mboost This version contains major updates and changes to the implementation of the main algorithm. Some slight changes to the user-interface where necessary. Please consult the manual and the list of CHANGES below. The package 'mboost' (Model-based Boosting) implements

Sorry for the tardy reply but I just found your posting incidentally today. To make long things short: You are right about the centering. We forgot to correct the intercept if center = TRUE. We lately found the problem ourself and fixed it in the current version (mboost 2.0-3). However the problem only occurred if you extracted the coefficients. As the intercept is rarely interpretable we

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

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 can be straightforwardly compensated by examining the

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

Hi all R users, I'm trying to replicate the same results that are given in a published article after been granted the same data that the authors use. I'm having problems to determine the cointegration rank of my data set using the Johnasen's trace test. This trace test is already programmed in the package ur.ca and can be found in the function ca.jo(). After I run the ca.jo()

Working on the caret package has exposed me to the wide variety of approaches that different authors have taken to creating predictive modeling functions (aka machine learning)(aka pattern recognition). I suspect that many package authors are neophyte R users and are stumbling through the process of writing their first R package (or R code). As such, they may not have been exposed to some of the

Just having read the nice review article on boosting in the latest "Statistical Science", I would love to reproduce some of the plots inside that article, but it is not clear to me how to create the partial contribution plots for the Poisson regression. Does anyone have example code for this ? (The vignette does not offer it, I think) Thanks ! Markus [[alternative HTML version

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

Hi, I use a mboost model to predict my dependent variable on new data. I get the following warning message: In bs(mf[[i]], knots = args$knots[[i]]$knots, degree = args$degree, : some 'x' values beyond boundary knots may cause ill-conditioned bases The new predicted values are partly negative although the variable in the training data ranges from 3 to 8 on a numeric scale. In order to

Hi UseRs - I am new to R, and could use some help making out-of-sample predictions using a boosting model (the mboost command). The issue is complicated by the fact that I have panel data (time by country), and am estimating the model separately for each country. FYI, this is monthly data and I have 1986m1 - 2009m12 for 9 countries. To give you a flavor of what I am doing, here is a simple

Hi, Could anyone help me understand how the mincriterion threshold works in ctree and cforest of the party package? I've seen examples which state that to satisfy the p < 0.05 condition before splitting I should use mincriterion = 0.95 while the documentation suggests I should use mincriterion = qnorm(0.95) which would obviously feed the function a different value. Thanks in advance,

Fedora 9 R 2.7.1 I tried to install R on my Linux system using install.packages("Rcmdr", dependencies=TRUE). I received many, many, many error messages. I hope someone can suggest a fix. The output from warnings() is listed below. A more detailed list of errors from one of the failed installations is listed below the warnings: > warnings() Warning messages: 1: In