similar to: New versions of the caret (3.08) and caretLSF (1.12) packages

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

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

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

Like earth can be trained simultaneously for degree and nprune, is there a way to train ctree simultaneously for mincriterion and maxdepth? Also, I notice there are separate methods ctree and ctree2, and if both options are attempted to tune with one method, the summary averages the option it doesn't support. The full log is attached, and notice these lines below for

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,

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

Hello, I would like to perform a Partial least square discriminate analysis (PLSDA) in R. To do this I use the package mixOmics. I could perform the PLSDA in R. however I would also like to perform a leave-one-out cross validation in order to assess the performance of my model. My supervisor told me that I should focus on the R2/Q2 ratios. However when I read the instruction for running the

Greetings, I have been exploring the use of the caret package to conduct some plsda modeling. Previously, I have come across methods that result in a R2 and Q2 for the model. Using the 'iris' data set, I wanted to see if I could accomplish this with the caret package. I use the following code: library(caret) data(iris) #needed to convert to numeric in order to do regression #I

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 <-

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'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

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

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") When I try to do a PLSDA using my own data. The impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get

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

Hi, I am using caretNWS on a RHEL x86_64 system and I am getting an error message that is nearly identical to the one occuring in http://www.r-project.org/nosvn/R.check/r-release-macosx-ix86/caretNWS-00check.txt Error in socketConnection(serverHost, port = port, open = "a+b", blocking = TRUE) : unable to open connection Calls: system.time ... .local -> tryCatch -> tryCatchList

Hello, I would like to do a partial least square discriminant analysis (PLSDA) in R using the package "ropls" Which is in R available via the R command : source("https://bioconductor.org/biocLite.R") I try to do a PLSDA to illustrate the impact of two genders (AP,C) on 5 compounds measured in persons (samples) should be illustrated. When I try to do a PLSDA I get the warning