similar to: use "caret" to rank predictors by random forest model

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

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?

Hello, I am using the R randomForest package to classify variable stars. I have a training set of 1755 stars described by (too) many variables. Some of these variables are highly correlated. I believe that I understand how randomForest works and how the variable importance are evaluated (through variable permutations). Here are my questions. 1) variable importance error? Is there any ways

Hi everyone: I'm using "splom" to draw scatterplot matrix. I'm wondering how can I change the axis labels to c(1,10,100,1000,...) instead of c(1,2,3,...), and also how can I change the text size (for labels)? Thanks a lot! xcui

Hi, I wrote a function basically to first read an input data file, then open an pdf file and draw graph using "splom". When testing, I ran the function line by line, it can produce nice plot, but with like 50 warnings. However, whenever I ran this function as a whole, it can not produce any plot, the pdf file has nothing in it. It seems the "splom" function even hasn't

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls

I would like to build a forest of regression trees to see how well some covariates predict a response variable and to examine the importance of the covariates. I have a small number of covariates (8) and large number of records (27368). The response and all of the covariates are continuous variables. A cursory examination of the covariates does not suggest they are correlated in a simple fashion

Hello all, When building a CART model (specifically classification tree) using rpart, it is sometimes interesting to know what is the importance of the various variables introduced to the model. Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the

The documentation for caret::train shows a list of parameters that one can tune for each method classification/regression method. For example, for the method randomForest one can tune mtry in the call to train. But the function call to train random forests in the original package has many other parameters, e.g. sampsize, maxnodes, etc. Is there **any** way to access these parameters using train

Folks: I keep getting the following error message (I'm on Windows 7, R-2.15.2, and tried a reboot...). Thx! John > install.packages("caret")Installing package(s) into ‘C:/Program Files/R/R-2.15.2/library’ (as ‘lib’ is unspecified)trying URL 'http://streaming.stat.iastate.edu/CRAN/bin/windows/contrib/2.15/caret_5.15-045.zip'Content type 'application/zip' length

HI, Dear Andy, I run the RandomFOrest in R, and get the following resutls in variable importance: What is the meaning of MeanDecreaseAccuracy and MeanDecreaseGini? I found they are raw values, they are not scaled to 1, right? Which column if most similar to the variable rel.influence in Boosting? Thanks so much! > fit$importance 0 1

Hello, We have a system which creates thousands of regression/classification models and in cases where we have only one input variable NaiveBayes throws an error. Maybe I am mistaken and I shouldn't expect to have a model with only one input variable. We use R version 2.6.0 (2007-10-03). We use caret (v4.1.19), but have tested similar code with klaR (v.0.5.8), because caret relies on

This is the error then: error in prettyseq(1:ncol(out)) : could not find function "prettyseq" Eric Berger <ericjberger at gmail.com> schrieb am Fr., 14. Apr. 2023, 09:06: > What happens if you do the following? > > library(caret) > > ?caret::createDataPartition > > i.e. to confirm that caret is loaded, seek help on this function > > > On Fri, Apr

What happens if you do the following? > library(caret) > ?caret::createDataPartition i.e. to confirm that caret is loaded, seek help on this function On Fri, Apr 14, 2023 at 9:56?AM G?bor Malomsoki <gmalomsoki1980 at gmail.com> wrote: > Eric, > > Yes, i know, i am calling the package, but this is not working. > I saw a similar question in Stackoverflow, advising install

Eric, Yes, i know, i am calling the package, but this is not working. I saw a similar question in Stackoverflow, advising install package Rcpp, but this one i am unable to install. Eric Berger <ericjberger at gmail.com> schrieb am Fr., 14. Apr. 2023, 08:47: > You first have to load the package using the library command. > > library(caret) > > Then you can call

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

Or is there any similar function to split the dataframe to trainDF and testDF? G?bor Malomsoki <gmalomsoki1980 at gmail.com> schrieb am Fr., 14. Apr. 2023, 09:45: > > This is the error then: > error in prettyseq(1:ncol(out)) : could not find function "prettyseq" > > > > > Eric Berger <ericjberger at gmail.com> schrieb am Fr., 14. Apr. 2023, 09:06:

You should post the result of running sessionInfo(), and tell us how you installed R. Your installation seems very broken. Duncan Murdoch On 14/04/2023 3:45 a.m., G?bor Malomsoki wrote: > This is the error then: > error in prettyseq(1:ncol(out)) : could not find function "prettyseq" > > > > > Eric Berger <ericjberger at gmail.com> schrieb am Fr., 14.

Usually when using raw glmnet I let the implementation choose the lambdas. However when training via caret::train the lambda values are predetermined. Is there any way to have caret defer the lambda choices to caret::train and thus choose the optimal lambda dynamically? -- Yang Zhang http://yz.mit.edu/

I want to use caret to build a model with an algorithm that actually has no parameters to find. How do I stop it from repeatedly building the same model 25 times? library(caret) data(mdrr) LOGISTIC_model <- train(mdrrDescr,mdrrClass ,method='glm' ,family=binomial(link="logit") ) LOGISTIC_model 528