similar to: [caret package] [trainControl] supplying predefined partitions to train with cross validation

Hi, I run R 2.10.1 under ubuntu 10.04 LTS (Lucid Lynx) and klaR version 0.6-4. I compute a model over a 2 classes dataset (composed of 700 examples). To that aim, I use the function NaiveBayes provided in the package klaR. When I then use the prediction function : predict(my_model, new_data). I get the following warning : "In FUN(1:747[[747L]], ...) : Numerical 0 probability with

I have carefully read the CARET documentation at: http://caret.r-forge.r-project.org/training.html, the vignettes, and everything is quite clear (the examples on the website help a lot!), but I am still a confused about the relationship between two arguments to trainControl: "method" "index" and the interplay between trainControl and the data splitting functions in caret

I want to use the caret package and found out about the timingSamps obtion to obtain the time which is needed to predict results. But, as soon as I set a value for this option, the whole model generation fails. Check this example: ------------------------- library(caret) tc=trainControl(method='LGOCV', timingSamps=10) tcWithout=trainControl(method='LGOCV')

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data =

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-bag (oob) error estimate from the model instead of resampling, which (in theory) is largely

Hello! I have a question. I am working with createFolds: folds<- trainControl(method='cv', index=createFolds(data$Score,list = TRUE)) I need to iterate over folds to extract the indexes from each fold. For example, if I do folds$index$Fold01, it contains: 5 11 17 29 44 50 52 64 65 I need to iterate over each $Fold_i to extract the indexes, but I can't do it because I

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

Hello all, I have searched and have not yet identified a solution so now I am sending this message. In short, I need to split my data into training, validation, and testing subsets that keep all observations from the same sites together ? preferably as part of a cross validation procedure. Now for the longer version. And I must confess that although my R skills are improving, they are not so

The following code fails to train a nnet model in a random dataset using caret: nR <- 700 nCol <- 2000 myCtrl <- trainControl(method="cv", number=3, preProcOptions=NULL, classProbs = TRUE, summaryFunction = twoClassSummary) trX <- data.frame(replicate(nR, rnorm(nCol))) trY <- runif(1)*trX[,1]*trX[,2]^2+runif(1)*trX[,3]/trX[,4] trY <-

Hey all, Does anyone know how we can get train set and test set for each fold of 5 fold cross validation in Caret package? Imagine if I want to do cross validation by random forest method, I do the following in Caret: set.seed(12) train_control <- trainControl(method="cv", number=5,savePredictions = TRUE) rfmodel <- train(Species~., data=iris, trControl=train_control,

Hello, I have set up a Samba-Server (samba-3.0.23a-0.1.34.x86_64.rpm and after that samba-3.0.23c-0.1.36.x86_64.rpm) as PDC and tdbsam-passdb backend. I can add XP-Computers and Users to the Domain, but the Domain-Users has problems with Access-Rights on the Win-XP-Systems. The Reason is (i think so) that samba has not generated the typical Groups: Result from 'net groupmap list' =>

Hi, I've searched R-help and haven't found an answer. I have a set of data from which I can create a classification tree using rpart. However, what I'd like to do is predefine the blank structure of the binary tree (i.e., which nodes to include) and then use a package like rpart to fit for the optimal splitting criteria at each of the predefined nodes. Does such a package exist?

Hi all: I'm writing a research prototype on LLVM 1.9. Given a module ,what is the right way to get the SSA-based llvm-IR? As I know , llvmgcc generates SSA-based bytecode. But if a module is constructed by hand, how can I transform it into a SSA-based llvm? Thanks. Ying -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hello, Ying. > But if a module is constructed by hand, how can I transform it into a > SSA-based llvm? LLVM IR is *always* in SSA form, even if you're constructing module by hands (Verifier pass actually does the check and reject invalid code). If you want to eliminate memory accesses and transform them to registers & phi's you might want to run mem2reg pass also. -- With best

Hello everyone, Clang seems to only define __PIE__ when both –fPIC and –fPIE is used in the command line whereas gcc defines both __PIC__ and __PIE__. Is this intended or a bug in clang? Thanks. Command line: clang -fPIC -fPIE -dM -E - < /dev/null | grep __PI -- Tareq

Hi all, Does anyone knows how to add a new variable to the predefined variables sent by asterisk to AGI script? regards -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20090304/80ba7bd7/attachment.htm

Hi, I have a linear model ("mod1 <- lm(V3~V1+V2) and I would like to get the model's prediction at values of V1 and V2 not included in the original sample. samp <- read.table("data.dat",nrows=100) attach(samp) out.poly <- lm(V3 ~ V1 + V2) If I try to use out.poly to predict values for the function I run into problems. It seems that it isn't possible to use a new

Dear list,   running a hierachical cluster analysis I want to define a number of objects that build a cluster already. In other words: I want to force some of the cases to be in the same cluster from the start of the algorithm.   Any hints? Thanks in advance!   Derik [[alternative HTML version deleted]]

Hello, I am trying to include legend text with math symbols from a predefined character variable that is read in from a file. ? If there is only one line of text in the legend, the following, although cumbersome, works for me: ? > LegendText = " 'U' [infinity], '=10 m/s' "?? # (read in from a file) ??> LegendName = paste("bquote(paste(",LegendText,

Dear R helpers, Suppose I have following data.frame. df <- data.frame(category = c("treat_A", "treat_A", "treat_A", "treat_A", "treat_A", "treat_A", "treat_A", "treat_A", "treat_B", "treat_B", "treat_B", "treat_B", "treat_B", "treat_B",