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
>
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