similar to: Major update: mboost 2.0-0 released

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

Some time ago I came across the function object.size() to estimate the size of an R object. I don't know if the behavior of the function is intended to be quite "user unfriendly" as it is right now or if just nobody was thinking/caring about it. I have two suggestions to improve it: - Why is it named object.size() and not just size()? The latter would be far more intuitive and

mboost contains a blackboost method to build tree-based boosting models. I tried to write my own "cost-sensitive" ada family. But obviously my understanding to implement ngradient, loss, and offset functions is not right. I would greatly appreciate if anyone can help me out, or show me how to write a cost-sensitive family, thanks! Follows are some families I wrote ngradient <-

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

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

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:

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

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

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

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

I just upgraded to Ubuntu 8.10 (i386) from 8.04. After the upgrade, I ran update.packages(.libPaths()[1]) in R to get the packages installed from source up to date too. Unfortunately, two packages could not be updated: mclust and mboost. In both cases, the error I got mentioned lf77blas. Here's the output for mboost: * Installing *source* package 'mboost' ... ** libs gcc -std=gnu99

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

Hi all, I am currently updating our package gamboostLSS which depends on package mboost *and* on package gamlss.dist. From mboost we use a lot of the fitting infrastructure and from gamlss.dist we obtain the relevant loss functions (aka families) used for fitting and corresponding quantile functions. Furthermore, we use the Family() function from package mboost. However, if I depend on both

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

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

Hi all, anyone has an idea how I could fix this? \donttest{ ## Set colors from colorspace package with a fallback col <- try(colorspace::rainbow_hcl(5), silent = TRUE) %||% rainbow(5) } The problem is that this makes R CMD check freak out (http://www.r-project.org/nosvn/R.check/r-devel-linux-x86_64-debian-clang/falsy-00check.html) if the colorspace package is not declared as a dependency.

On 08/12/2014 9:40 AM, G?bor Cs?rdi wrote: > Hi all, > > anyone has an idea how I could fix this? > > \donttest{ > ## Set colors from colorspace package with a fallback > col <- try(colorspace::rainbow_hcl(5), silent = TRUE) %||% rainbow(5) > } > > The problem is that this makes R CMD check freak out >

I'm unable to open an SPSS file over my network. If I copy it to my local C:/ drive I can read it. I saved the command (in a "crib sheet" text file) in order to avoid all the typing, so I'm pretty sure I've done it before. I verified that the file I'm trying to read is OK. This is what happens: > SurveyData <-

Dear List I just downloaded and installed R 2.12.0 and then installed R Commander . First it got RCmdr and Car, and then suggested for other packages for utilizing the full functionality- I clicked yes! I got 140 packages installed!!! Cran Mirror was UCLA... Here is the list. Is this intentional- I can see some packages like snow and multicore which are desirable but quite optional.(see list