Hi Roger,
Thank you for your reply. To my understanding, changing the regression method
only helps to speed up the computation, but not necessarily solve the problem
with 99th percentile that p-values for all the factors are 1.0. I wonder how I
should interpret the result for 99th percentile, w
You could try method = "pin".
Sent from my iPhone
> On Nov 16, 2014, at 1:40 AM, Yunqi Zhang wrote:
>
> Hi William,
>
> Thank you very much for your reply.
>
> I did a subsampling to reduce the number of samples to ~1.8 million. It
> seems to work fine except for 99th percentile (p-values f
Hi William,
Thank you very much for your reply.
I did a subsampling to reduce the number of samples to ~1.8 million. It
seems to work fine except for 99th percentile (p-values for all the
features are 1.0). Does this mean I’m subsampling too much? How should I
interpret the result?
tau: [1] 0.25
You can time it yourself on increasingly large subsets of your data. E.g.,
> dat <- data.frame(x1=rnorm(1e6), x2=rnorm(1e6),
x3=sample(c("A","B","C"),size=1e6,replace=TRUE))
> dat$y <- with(dat, x1 + 2*(x3=="B")*x2 + rnorm(1e6))
> t <- vapply(n<-4^(3:10),FUN=function(n){d<-dat[seq_len(n),];
print
Hi all,
I'm using quantreg rq() to perform quantile regression on a large data set.
Each record has 4 fields and there are about 18 million records in total. I
wonder if anyone has tried rq() on a large dataset and how long I should
expect it to finish. Or it is simply too large and I should subsa
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