search for: mxkuhn

I was trying to produced bagged importance estimates of attributes in earth using the caret package with the following commands:   fit2 <- bagEarth(loyalty ~ ., data=model1, B = 10)   bagImpGCV <- varImp(fit2,value="gcv") My bootstrap estimates are produced however the second command "varImp" produces the following error:    Error in UseMethod("varImp") : no

Everyone, I've got code in my package that uses LogitBoost from the caTools package. caTools does not have a namespace. My package also uses loads RWeka, which has a namespace, and also has a function called LogitBoost. After loading both packages, how can I be specific about running the version from caTools (since caTools:::LogitBoost won't work)? Thanks, Max

The CRAN policy page (https://cran.r-project.org/web/packages/policies.html) implies that there is a formal procedure for orphaning a package but none is mentioned in the Extensions manual (https://cran.r-project.org/doc/manuals/r-devel/R-exts.html). This page (https://cran.r-project.org/src/contrib/Orphaned/README) implies that one would simply resubmit the package to CRAN with the text

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

Everyone, I'm working with scatter plots with different colored symbols (via lattice). I'm currently using these colors for points and lines: col1 <- c(rgb(1, 0, 0), rgb(0, 0, 1), rgb(0, 1, 0), rgb(0.55482458, 0.40350876, 0.04166666), rgb(0, 0, 0)) plot(seq(along = col1), pch = 16, col = col1, cex = 1.5) I'm also using these with transparency (alpha

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've had a lot of requests for additions to the reproducible research task view that fall into a grey area (to me at least). For example, roxygen2 is a tool that broadly enable reproducibility but I see it more as a tool for better programming. I'm about to check in a new version of the task view that includes packrat and checkpoint, as they seem closer to reproducible research, but also

Hello, I'm training a set of data with Caret package using an elastic net (glmnet). Most of the time train works ok, but when the data set grows in size I get the following error: Error en { : task 1 failed - "arguments imply differing number of rows: 9, 10" and several warnings like this one: 1: In eval(expr, envir, enclos) : model fit failed for Resample01 My call to train

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

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

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +

I second the motion for a Programming Tools CRAN Task View. I would also think it could contain things like Rcpp, R6, etc. -Greg > On Jan 22, 2015, at 10:20 AM, Max Kuhn <mxkuhn at gmail.com> wrote: > > I've had a lot of requests for additions to the reproducible research > task view that fall into a grey area (to me at least). > > For example, roxygen2 is a tool that broadly enable reproducibility > but I see it more as a tool for better program...

Somewhere I've seen an example of an xyplot() where the key was placed in a location of a missing panel. For example, if there were 3 conditioning levels, the panel grid would look like: 34 12 In this (possibly imaginary) example, there were scatter plots in locations 1:3 and location 4 had no conditioning bar at the top, only the key. I can find examples of putting the legend outside of

I would like to suggest a Reproducible Research CRAN task view. This was discussed briefly in one of the useR! sessions this year. >From quick browse through CRAN, I counted 19 packages that were associated with Sweave or other methods for reproducible research. I think that we've gotten to a point where some additional documentation that enumerates/compares/contrasts the various packages

For a package, I need to write a csv version of a data set to an R object. Right now, I use: out <- capture.output( write.table(x, sep = ",", na = "?", file = "", quote = FALSE,

Hello All, Have recently started learning Sweave and Knitr. Am now trying to learn odfWeave as well. Things went pretty smoothly with Sweave and Knitr but I'm having some trouble with odfWeave. My understanding was that odfWeave should work in pretty much the same way as Sweave. With odfWeave, you set up an input .odt file in a folder, run that file through the odfWeave function, and then

Hy, I got the following problem when trying to build a rpart model and using everything but LOOCV. Originally, I wanted to used k-fold partitioning, but every partitioning except LOOCV throws the following warning: ---- Warning message: In nominalTrainWorkflow(dat = trainData, info = trainInfo, method = method, : There were missing values in resampled performance measures. ----- Below are some

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

I've been updating a package and, when installing a local devel version, I get an error "object 'confusionMatrix' not found whilst loading namespace". Looking around online, it appears that this might be related to loading a specific RData file, but it doesn't seem to be the case AFAICT. I've installed the devel version in the last week without issues and the

Hello everyone, I'm stumped. I'd like to create a scatterplot matrix with circular reference lines. Here is an example in 2d: library(ellipse) set.seed(1) dat <- matrix(rnorm(300), ncol = 3) colnames(dat) <- c("X1", "X2", "X3") dat <- as.data.frame(dat) grps <- factor(rep(letters[1:4], 25)) panel.circ <- function(x, y, ...) { circ1