[R] weighted regression in drc package
array chip
arrayprofile at yahoo.com
Fri May 21 18:18:44 CEST 2010
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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