[R] weighted regression in drc package

[array chip]

Hi, I am currently trying to do dose-response curves
using weighted 4-parameter model (4PL). The weighting was based on
1/(expected variance) derived from historical data. I tried both drm() from drc package, and nls(), found very different
results derived from drm() vs. nls() using "weights=" argument. 

d1<-read.table("d1.txt",sep='\t',header=T,row.names=1)

drm(y~x,weights=1/exp((-1.8286)+0.05481*y-0.000405225*(y-48.34177)^2),data = d1,fct = LL.4(),control=drmc(relTol=1e-12))
b:(Intercept)  c:(Intercept)  d:(Intercept)  e:(Intercept)  
      -1.5859         0.7823        47.3591        38.7717

nls(y~c+(d-c)/(1+(x/e)^b),weights=1/exp((-1.8286)+0.05481*y-0.000405225*(y-48.34177)^2),data=d1,
start=list(b=-1, c =0, d=100, e=150))
       b        c        d        e 
-1.0076  -0.1143 104.9470 152.5558

But if I run unweighted dose-response curve, both drm() and nls() gave the same results:

drm(y~x,data = d1,fct = LL.4(),control=drmc(relTol=1e-12))
b:(Intercept)  c:(Intercept)  d:(Intercept)  e:(Intercept)  
      -1.1416         0.6986       102.8613       135.9371 

nls(y~c+(d-c)/(1+(x/e)^b),data=d1, start=list(b=-1, c =0, d=100, e=150))
Nonlinear regression model
       b        c        d        e 
-1.1416   0.6987 102.8613 135.9373

Any suggestions would be appreciated!

John


      
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