ok, so 200K predictors an 10M observations would work?
On 12/12/2013 12:12 PM, Eik Vettorazzi wrote:
it is simply because you can't do a regression with more predictors than observations. Cheers. Am 12.12.2013 09:00, schrieb Romeo Kienzler:Dear List, I'm quite new to R and want to do logistic regression with a 200K feature data set (around 150 training examples). I'm aware that I should use Naive Bayes but I have a more general question about the capability of R handling very high dimensional data. Please consider the following R code where "mygenestrain.tab" is a 150 by 200000 matrix: traindata <- read.table('mygenestrain.tab'); mylogit <- glm(V1 ~ ., data = traindata, family = "binomial"); When executing this code I get the following error: Error in terms.formula(formula, data = data) : allocMatrix: too many elements specified Calls: glm ... model.frame -> model.frame.default -> terms -> terms.formula Execution halted Is this because R can't handle 200K features or am I doing something completely wrong here? Thanks a lot for your help! best Regards, Romeo ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
