On Wed, Feb 18, 2009 at 7:27 AM, Esmail Bonakdarian <esmail...@gmail.com> wrote:
> Gabor Grothendieck wrote:
>>
>>
>> See ?Rprof for profiling your R code.
>>
>> If lm is the culprit, rewriting your lm calls using lm.fit might help.
>
> Yes, based on my informal benchmarking, lm is the main "bottleneck", the
> rest
> of the code consists mostly of vector manipulations and control structures.
>
> I am not familiar with lm.fit, I'll definitely look it up. I hope it's
> similar
> enough to make it easy to substitute one for the other.
>
> Thanks for the suggestion, much appreciated. (My runs now take sometimes
> several hours, it would be great to cut that time down by any amount :-)
>
Yes, the speedup can be significant. e.g. here we cut the time down to
40% of the lm time by using lm.fit and we can get down to nearly 10% if
we go even lower level:
> system.time(replicate(1000, lm(DAX ~.-1, EuStockMarkets)))
user system elapsed
26.85 0.07 27.35
>
> system.time(replicate(1000, lm.fit(EuStockMarkets[,-1], EuStockMarkets[,1])))
user system elapsed
10.76 0.00 10.78
>
> system.time(replicate(1000, qr.coef(qr(EuStockMarkets[,-1]),
> EuStockMarkets[,1])))
user system elapsed
3.33 0.00 3.34
>
> lm(DAX ~.-1, EuStockMarkets)
Call:
lm(formula = DAX ~ . - 1, data = EuStockMarkets)
Coefficients:
SMI CAC FTSE
0.55156 0.45062 -0.09392
> # They call give the same coefficients:
> lm.fit(EuStockMarkets[,-1], EuStockMarkets[,1])$coef
SMI CAC FTSE
0.55156141 0.45062183 -0.09391815
>
> qr.coef(qr(EuStockMarkets[,-1]), EuStockMarkets[,1])
SMI CAC FTSE
0.55156141 0.45062183 -0.09391815
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