similar to: Caret package: coeffcients for regression

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,

Dear R users, I have problem with extracting coefficients from a object. Here, X (predictor)and Y (response) are two matrix , I am regressing X ( dimensions 10 x 20) on each of columns of Y[,1] (10 x 1) and want to store the coefficient values. I have performed a Elastic Net regression and I want to store the coeffcients in each iteration. I got an error message . I do not

Dear R users, I am using "enet" package in R for applying "elastic net" method. In elastic net, two penalities are applied one is lambda1 for LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the analysis, I realised tht, I optimised only one lambda. ( even when I looked at the example in R, they used only one penality) So, I am

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 R users,   I am new user for elastic net. I am trying to use elasticnet library. I have marker data with 359 markers and 168 samples, and response is metabolites. I am trying to do regression between a metabolite and markers.  But i am getting the following error:   > en<-enet(marker,as.numeric(vio),lambda=0.5,normalize=FALSE,intercept=TRUE) Error in one %*% x : requires numeric

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, 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?

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);

Hi List I'm running a series of mixed models using lme, and I wonder if there is a way to sort them by AIC prior to testing using anova (lme1,lme2,lme3,....lme7) other than by hand. My current output looks like this. anova (lme.T97NULL.ml,lme.T97FULL.ml,lme.T97NOINT.ml,lme.T972way.ml,lme.T97fc. ml, lme.T97ns.ml, lme.T97min.ml) Model df AIC BIC logLik

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

hi everybody I have to do a lot of Anova with R and I would like to have another type of coefficients coding.. I explain. by default if I have 2 temperatures for an experience. 100°C or 130°C and I want to see the temperature effect on the presure I want to estimate the coefficient of each temperature. I will obtain ,with the anova, juste one coefficients for example +3,56 (for 100°C), and the

Dear R-Users My problem is quite simple: I need to use a fitted model to predict the next point (that is, just one single point in a curve). The data was divided in two parts: identification (x and y - class matrix) and validation (xt and yt - class matrix). I don't use all values in x and y but only the 10 nearest points (x[b,] and y[b,]) for each regression (b is a vector with the

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

I have completed a binomial GLM in R (details attached (finalModel.docx)) and I am trying to create a graph of observed and fitted values using the following commands: > MyData<-data.frame(time=seq(from=0,to=1323,by=1)) > Pred<-predict(M2,newdata=MyData,type="response") > plot(x=turtle$time,y=turtle$success) > lines(MyData$time,Pred) However, I get the following

Working on the caret package has exposed me to the wide variety of approaches that different authors have taken to creating predictive modeling functions (aka machine learning)(aka pattern recognition). I suspect that many package authors are neophyte R users and are stumbling through the process of writing their first R package (or R code). As such, they may not have been exposed to some of the

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

I just downloaded and installed R 2.3.0 on my Mac G5 running OS X 10.4.6. I also updated with R.app revision 3114 as recommended. Now, when I attemp to use package rgl I get the error > library(rgl) Error: package 'rgl' is not installed for 'arch=ppc' > I have tried reinstalling from CRAN using both binary and source. The source install fails, The binary install yields

Hi everyone, I cannot install package "svmpath" on R 2.8.1 on Redhat Linux .In fact I cannot install package externally.I am new to Linux so it would be of great help if you all can help me out. Regards, Subhajit. [[alternative HTML version deleted]]

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)