Automating the finding of coefficients for the USL

I got to playing around with R more and I've always found that to learn a language I need to solve problems with the language. I'm sure most everybody else does the same thing. My goal was to write a R function that imported a CSV with performance information to gonkulate against.

In my case, I'm using the basic performance information from "Guerrilla Capacity Planning." I've created a CSV file with the number of procs and the resultant ray trace benchmark from Table 5.1:

   1:  C:\Users\auswipe\Desktop>cat raw_throughput.csv

   2:  p,x

   3:  1,20

   4:  4,78

   5:  8,130

   6:  12,170

   7:  16,190

   8:  20,200

   9:  24,210

  10:  28,230

  11:  32,260

  12:  48,280

  13:  64,310

Then I wrote an R function to crunch the numbers:

   1:  uslCoefficients <- function(dataFile) {

   2:    uslData <- read.csv(dataFile, header=TRUE);

   3:    uslData$c <- uslData$x / uslData$x[1];

   4:    usl <- nls(c ~ p/(1+sigma*(p-1)+kappa*p*(p-1)),

   5:               uslData,

   6:               algorithm="port",

   7:               start=c(sigma=0.0, kappa=0.0),

   8:               lower=c(0,0));

   9:    sigma <- coef(usl)["sigma"];

  10:    kappa <- coef(usl)["kappa"];

  11:    return(list(sigma=sigma, kappa=kappa));

  12:  };

The function uslCoefficient returns a list where I can reference the "sigma" and "kappa" by named index:

   1:  > uslCoef <- uslCoefficients("c:\\Users\\auswipe\\Desktop\\raw_throughput.csv")

   2:  > uslCoef["sigma"]

   3:  $sigma

   4:      sigma 

   5:  0.0497973 

   6:   

   7:  > uslCoef["kappa"]

   8:  $kappa

   9:         kappa 

  10:  1.143404e-05 

  11:   

  12:  > uslCoef

  13:  $sigma

  14:      sigma 

  15:  0.0497973 

  16:   

  17:  $kappa

  18:         kappa 

  19:  1.143404e-05 

R is pretty nifty. I doubt I'll ever make use of all the power that is available but it'll be better than writing my own stat routines.