[R] understanding the 4 parameter logisitc regression

[1Rnwb]

I have questions regarding  
test=data.frame(cbind(conc=c(25000, 12500, 6250, 3125, 1513, 781, 391,
195, 97.7, 48.4, 24, 12, 6, 3, 1.5, 0.001),
 il10=c(330269, 216875, 104613, 51372, 26842, 13256, 7255, 3049, 1849, 743,
480, 255, 241, 128, 103, 50)))

nls(log(il10)~A+(B-A)/(1+(conc/xmid )^scal),data=test,
+             start = list(A=3.5, B=15,
+               xmid=600,scal=1/2.5))
Nonlinear regression model
  model:  log(il10) ~ A + (B - A)/(1 + (conc/xmid)^scal) 
   data:  test 
          A           B        xmid        scal 
 14.7051665   3.7964534 607.9822962   0.3987786 
 residual sum-of-squares:  0.1667462 

I did not understand how these values  A=3.5, B=15,xmid=600,scal=1/2.5  were 
obtained by Jim in the posting here
http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg25500.html.

I would appreciate a little help here to understand the 4-parameter
logisitic regression for processing of standard curve for ELISA/MUltiplex
Immunoassays.

Thanks and happy holidays
sharad
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