[R] A performance anomaly

[Peter Dalgaard]

Ajay Narottam Shah <ajayshah at mayin.org> writes:

> I wrote a simple log likelihood (for the ordinary least squares (OLS)
> model), in two ways. The first works out the likelihood. The second
> merely calls the first, but after transforming the variance parameter,
> so as to allow an unconstrained maximisation. So the second suffers a
> slight cost for one exp() and then it pays the cost of calling the first.
> 
> I did performance measurement. One would expect the second version to
> be slightly (very slightly) slower than the first. But I am finding
> this is not the case! The second version is slightly faster. How can
> this be?
> 
> On my machine (ibook @ 1.2 GHz, OS X "panther", R 2.1), I get:
> 
> > measurement(ols.lf1)
> [1] 7.45
> > measurement(ols.lf1.inlogs)
> [1] 6.9
> 
> Here is the self-contained bug-demonstration code:

What "bug"?

You're just not testing carefully enough. Timings are not perfectly
reproducible; you'll need multiple replications. I get

> replicate(50,c(measurement(ols.lf1.inlogs),measurement(ols.lf1)))
     [,1] [,2] [,3] [,4] [,5]  [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 9.10 9.60  9.5 9.70 9.95 10.00  9.3 9.85 9.55  9.95 10.05 10.20  9.65
[2,] 9.75 9.65  9.2 9.15 9.60  9.55  9.8 9.85 9.75  9.70  9.35  9.65  9.60
     [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
[1,]  9.65  9.55   9.1  9.90 10.00  9.45  9.30   9.4   9.2 10.25  9.80 10.10
[2,]  9.95  9.90   9.7  9.55  9.55  9.80  9.95  10.6   9.3 10.30  9.55  9.65
     [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
[1,]   9.6  9.50  9.55  9.65  9.85  9.70  9.75   9.7  9.25  9.85   9.8  9.75
[2,]   9.7  9.75  9.10  9.75  9.40  9.55  9.95   9.9  9.55  9.55   9.3  9.60
     [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49]
[1,]   9.7  9.25  9.65  9.35  9.95  9.95  9.75  9.20  9.55  9.65  9.80  9.75
[2,]   9.9  9.65 10.20  9.20  9.65  9.50 10.25  9.75  9.90  9.10  9.25  9.05
     [,50]
[1,]  9.55
[2,]  9.40
> x <- .Last.value
> t.test(x[1,],x[2,])

        Welch Two Sample t-test

data:  x[1, ] and x[2, ]
t = 0.284, df = 96.202, p-value = 0.777
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.1018137  0.1358137
sample estimates:
mean of x mean of y
    9.663     9.646


-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907