similar to: Saving model and other objects from caret

I looked at the documentation of source() and summary(), and I could not find the reason why calling something like: > summary(resamps) from the command line, works (it prints the summary) whereas calling summary(resampls) from a file that I source with source("my_file.r") does not print anything. How can I get summary(resamps) to print when I source a file with this command?

I have a binary classification problem where the fraction of positives is very low, e.g. 20 positives in 10,000 examples (0.2%) What is an appropriate cross validation scheme for training a classifier with very few positives? I currently have the following setup: ======================================== library(caret) tmp <- createDataPartition(Y, p = 9/10, times = 3, list = TRUE)

Forgot to send to R-help From: Nordlund, Dan (DSHS/RDA) Sent: Thursday, February 07, 2013 2:09 PM To: 'James Jong' Subject: RE: [R] Sourcing my file does not print command outputs James, Your code seems to have ‘…’ sitting on a line all by itself (maybe should be at the end of the preceding comment? Anyway, when I eliminated that problem and sourced the script using the following call

dear all, I am a student in cs college. I would like to know how to plot infinte number of genes after using the svm. the data set i have consists of x which is a matrix of 39 cancer patients [rows] and 2000 gene names [colmns]. each cell is the value of the gene for a particular patient. there are two types of cancer people representedas factor y. here is the code: library(e1071) #load

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,

> > So i guess from your prev. email the svmModel$coefs correspond to the > "Alpha" . yes (times the sign of y!). > > Why do I see three columns in the coefs?( Is this the number of classes -1 > = Numbe of hyperplanes) yes, but in a packed format which is not trivial. I attach some explanation I sent to R-help some time ago (the guy wanted to write his own

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,

Dear all, I have used following code but everytime I encounter a problem of not having coefficients for all the variables in the predictor set. # code rm(list=ls()) library(caret) # generating response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y,

Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

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

When I try to crate a grid of parameters for training with caret I get various errors: ------------------------------------------------------------ > my_grid <- createGrid("rf") Error in if (p <= len) { : argument is of length zero > my_grid <- createGrid("rf", 4) Error in if (p <= len) { : argument is of length zero > my_grid <-

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

Hello, When evaluating different learning methods for a categorization problem with the (really useful!) caret package, I'm getting confusing results from the Kappa computation. The data is about 20,000 rows and a few dozen columns, and the categories are quite asymmetrical, 4.1% in one category and 95.9% in the other. When I train a ctree model as: model <- train(dat.dts,

Hello, I am learning caret package, and I want to use the RFE to reduce the feature. I want to use RFE coupled Random Forest (RFE+FR) to complete this task. As we know, there are a number of pre-defined sets of functions, like random Forest(rfFuncs), however,I want to tune the parameters (mtr) when RFE, and then I write code below, but there is something wrong message, How can I deal with it?

Dear all, I want to fit a random effect model with only one random factor. I do not want to include the intercept term either. The model I using now is lmer(values ~ (1|lot), data=tmp) The results are as below. How do I take out the intercept term? Or if this is not possible for the lmer function, is it possible using lme function in the "nlme" package? Thank you very much in

I have never been able to get class probabilities to work and I am relatively new to using these tools, and I am looking for some insight as to what may be wrong. I am using caret with kernlab/ksvm. I will simplify my problem to a basic data set which produces the same problem. I have read the caret vignettes as well as documentation for ?train. I appreciate any direction you can give. 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 +

Hello all! I would like to delete multiple records on a single call. Something similar to the following line. new Ajax.Request(''/myctrl/delete/1,2,3,4,5'', {asynchronous:true, evalScripts:true}); However, I don''t know how to pass multiple parameters to one action (in a AJAX call). Is it possible? How could I do that? In other words, I want the Delete action to take

Hello all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2)