Re: [R] R + Hadoop on Amazon

it and will update you once it's up. > > Jamie Olson > > > On Wed, Nov 7, 2012 at 5:32 AM, Vignesh Prajapati wrote: > >> Hello All, >> >> Having some issue with local machine, I need to locate myself on Amazon >> for running R and Hadoop with Amaz

[R] R + Hadoop on Amazon

Hello All, Having some issue with local machine, I need to locate myself on Amazon for running R and Hadoop with Amazon instance. After searching a lot, I can't able to take a decision for choosing Image for Amazon instance. Can any one using R + Hadoop on Amazon. Thanks [[alternative H

[R] Problem in rhipe.lm.R

Hello All, I am new to Rhipe, my goal is to perform linear regression with Rhipe(R and Hadoop) on data stored at HDFS. But while I am adding R file which have rhlm function gives an error like *> source(file="/media/SYSTEM@/ML/R_HADOOP/**rhipe.lm.R*"*) *Error in source(file = "/media/SYSTEM@/ML/

[R] Memory issue with svm modeling in R

As I found the memory problem with local machine/micro instance(amazon) for building SVM model in R on large dataset(2,01,478 rows with 11 variables), then I have migrated our micro instance to large instance at Amazon. Still I have memory issue with large amazon instance while developing R model f

[R] Svm modeling :: Error in which.max(votematrix[, x]) : subscript out of bounds

Having a classification problem, I am using SVM for prediction in R. In dataset, there are integer as well as categorical variables. I got error while predicting with predict method. svp3c <- ksvm(input_dataset3$isCRgt3~., data=input_dataset3,type="C-svc") p3<-predict(svp3c,newdata=input_d

[R] Regression Problem

Hello all, I am new to R, I am learning regression and logistic modeling with categorical predictor variables, when there is only one predictor categorical variable i can use as.numeric() but when more than two variable then what is solution? can anyone suggest me? Thanks vignesh

[R] Inaccurate prediction in R

Hello, After development of recommendation engine with the R, before removal of outliers from data-set value of residual standard error was 1351 and after removal of outlier its 100. Still there is no accurate prediction which gives 10% correct(near) prediction. For more fitting i also have tried

[R] Improvement of Regression Model

Hello folks, I am on learning phase of R. I have developed Regression Model over six predictor variables. while development, i found my all data are not very linear. So, may because of this the prediction of my model is not exact. Here is the summary of model : Call: lm(formula = y ~ x_1 +