Python and R
Hi Kenn, Thanks for the suggestions, I'll have to see if I can figure out how to convert the relatively simple call to lm with an equation and the data file to the functions you mention (or if that's even feasible). Not an expert in statistics myself, I am mostly concentrating on the programming aspects of R. Problem is that I suspect my colleagues who are providing some guidance with the stats end are not quite experts themselves, and certainly new to R. Cheers, Esmail
Kenn Konstabel wrote:
lm does lots of computations, some of which you may never need. If speed really matters, you might want to compute only those things you will really use. If you only need coefficients, then using %*%, solve and crossprod will be remarkably faster than lm # repeating someone else's example # lm(DAX~., EuStockMarkets) y <- EuStockMarkets[,"DAX"] x <- EuStockMarkets x[,1]<-1 colnames(x)[1] <- "Intercept" lm(y ~ x-1) solve(crossprod(x), t(x))%*%y # probably this can be done more efficiently # and a naive timing
> system.time( for(i in 1:1000) lm(y ~ x-1))
user system elapsed 14.64 0.33 32.69
> system.time(for(i in 1:1000) solve(crossprod(x), crossprod(x,y)) )
user system elapsed 0.36 0.00 0.36 Also lsfit() is a bit quicker than lm or lm.fit. Regards, Kenn