similar to: Classification methods - which one?

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,

Hello, I have binary labels from a nearest shrunken centroids prediction (package pamr). I need to obtain a ROC curve for this test. What is the easiest way to obtain one? Paolo Sonego

Hi all, In Tibshirani's PNAS paper about nearest shrunken centroid analysis of microarrays (PNAS vol 99:6567), they used cross validation to choose the amount of shrinkage used in the model, and then test the performance of the model with the cross-validated shrinkage in separate independent testing set. If I don't have the luxury of having independent testing set, can I just use the

Dear List, I am trying to modify the xlab and ylab for a current figure that was plotted by a package, I searched through the low level plotting command and they do not seem to contain how to do this (the only way is to use xlab, ylab as arguments in "plot" command, which I can not do since the plot is plotted using some other package, not by my own script). Is there any command for

Dear all, I have a problem when using some classification functions (Kmeans, PAM, FANNY...) with a distance matrix, and i would to understand how it proceeds for the positioning of centroids after one execution step. In fact, in the classical formulation of the algorithm, after each step, to re-position the center, it calculates the distance between any elements of the old cluster and its

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

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

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

Dear all, maybe a simple problem but I found no solution for my problem. I have a matrix Y with 23 000 rows and 220 colums. The entries are "A", "B" or "C". I want to extract all rows (as a matrix ) of the matrix Y where all entries of a row are (for example) "A". Is there any solution? I tried the stringr- package but i doesn't work out. All the best

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

Hello, How can I do a cross validation in R? Thank You!

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

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 must be missing something here...Essentially, a short piece of code works if it's standalone, but doesn't work if it's divided into two functions. The code that works is: ################### WORKS ############### library(pamr) set.seed(120) x <- matrix(rnorm(1000*20),ncol=20) y <- sample(c(1:4),size=20,replace=TRUE) mydata <- list(x=x,y=y)

Hallo, I just installed all needed packages for my project on my PC. But I cannot load all at one time. I now want to load limma. How can I realize the following plan: I want to install for example limma inclusive all needed other sub packages (add-on). Can anyone tell me the corresponding command? Thanks, Corinna Here is the result of the command library(): Pakete in Library

Dear all, i have microarray data of 3 classes of patients. It's not a time course experiment only steady state. I used a rule-based method to classify the groups by the expression of the genes. This works out so far. Nevertheless I want to check my results with an other method. Therefore I look for one and want to ask you, what you suggest. I have 3 different patient groups, only the steady

Dear Stuff, I am trying to enjoy "PAM", but I could not recognize which sample is in which branches in the clustering tree because of too many samples and size limitation for my monitor. I mean, I did unsupervised clustering with "PAM" ( data2$newy<-pamr.makeclasses(data2) ) with 275 primary samples, but samples were piled up at the point of each branch, so I cannot

Dear maintainers and R-devel, Several orphaned CRAN packages are about to be archived due to outstanding QC problems, but have CRAN and BioC packages depending on them which would be broken by the archival (and hence need archiving alongside). Therefore we are looking for new maintainers taking over maintainership for one or more of the following packages: R2HTML SemiPar cghseg hexbin lgtdl

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 =

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 +