[R] badly written code may be slow (was hard to believe speed difference)

[ripley@stats.ox.ac.uk]

If you write R like C or Java, it will often be slow.  R is a vector
language, so learn to work with it rather than against it.

For a truncated (standard) normal, try qnorm(runif(500000, a, b)) for
suitable a and b (pnorm(lower) and pnorm(upper)?)

And what are you going to do with 0.5m truncated normals that makes 70s
significant?

BTW, R has system.time() to time things.

On Tue, 6 Aug 2002, Jason Liao wrote:

> First, I love R and am grateful to be using this free and extremely
> high quality software.
>
> Recently I have been comparing two algorithms and naturally I
> programmed in R first. It is so slow that I can almost feel its pain.
> So I decided to do a comparison with Java. To draw 500,0000 truncated
> normal, Java program takes 2 second and R takes 72 seconds. Not a
> computer science major, I find it hard to understand how R can be so
> slow.

It's not the language ....

> R code:
>
> normal.truncated <- function(mean, sd, lower, upper)
> 	 {
> 	    repeat
>             {
> 		deviate = round( rnorm(1, mean, sd) );
>                 if(deviate<=upper && deviate>=lower) break;
>             }
> 	    temp <- c(deviate+.5, deviate-.5, upper+.5, lower-.5);
> 	    result <- pnorm(temp, mean, sd);
> 	    prob <- (result[1] - result[2])/(result[3] - result[4]);
>
>             return(c(deviate, prob));
>      }
>
>      print(date());
>
>      n = 500000;
>      result = numeric(n);
>
>      for(i in 1:n) result[i] = normal.truncated(0,1, -10, 10)[1]
>      print(date());
>
> Java Code:
>
>     package Mcdonald;
>     import VisualNumerics.math.*;
>     import java.util.*;
>
>     public final class normal_univariate_truncated
>     {
>        public double harvest, prob;
>        static Random ran_obj = new Random();
>
>        public normal_univariate_truncated(double mean, double var,
> double lower, double upper)
>        {
>           double sd = Math.sqrt(var);
>
>           lower -= .5;
>           upper += .5;
>           double left = (lower - mean)/sd;
>           double right = (upper - mean)/sd;
>           double prob2 = Statistics.normalCdf(right) -
> Statistics.normalCdf(left);
>
>           while(true)
>           {
>               harvest = mean + ran_obj.nextGaussian()*sd;
>               if(harvest>lower && harvest<upper) break;
>           }
>
>           harvest = Sfun.nearestInteger(harvest);
>           left = (harvest - .5 - mean)/sd;
>           right = (harvest + .5 - mean)/sd;
>           double prob1 = Statistics.normalCdf(right) -
> Statistics.normalCdf(left);
>
>           prob = prob1/prob2;
>        }
>
>
>        public static void main(String[] useless)
>        {
>            System.out.println(new Date());
>            int n = 500000;
>            double[] result = new double[n];
>            for(int i=0; i<n; i++)
>            {
>                normal_univariate_truncated obj = new
> normal_univariate_truncated(0, 1, -10, 10);
>                result[i] = obj.harvest;
>            }
>            System.out.println(new Date());
>        }
>
>     }
>
>
> =====
> Jason G. Liao, Ph.D.
> Division of Biometrics
> University of Medicine and Dentistry of New Jersey
> 335 George Street, Suite 2200
> New Brunswick, NJ 08903-2688
> phone (732) 235-8611, fax (732) 235-9777
> http://www.geocities.com/jg_liao
>
> __________________________________________________
>
>
>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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