Yes, thanks that's very useful
Apart from checking the fit with nls() as you suggested, I've also used Prism,
which gave the following results
Equation 1
Best-fit values
BOTTOM 10.96
TOP 106.4
LOGEC50 -5.897
HILLSLOPE 0.9501
EC50 1.2670e-006
Std. Error
BOTTOM 2.196
TOP 9.337
LOGEC50 0.1439
HILLSLOPE 0.2270
95% Confidence Intervals
BOTTOM 6.301 to 15.61
TOP 86.62 to 126.2
LOGEC50 -6.202 to -5.592
HILLSLOPE 0.4689 to 1.431
EC50 6.2750e-007 to 2.5560e-006
Goodness of Fit
Degrees of Freedom 16
R² 0.9622
Absolute Sum of Squares 787.5
Sy.x 7.015
Data
Number of X values 20
Number of Y replicates 1
Total number of values 20
Number of missing values 0
In other words: also in line with the drc 1.6-3 and nls() results
As for the scaling: yes this is useful because I can't predict whether
concentrations are in molar, micromolar,..., right now I indeed scaled
dose-values "manually", it's better/ more robust when the drm-function takes
care of that
I suppose this also means I don't have to do the log transformation anymore?
Thanks (both of you) for your swift feedback
Hans
-----Original Message-----
From: Christian Ritz [mailto:r...@life.ku.dk]
Sent: vrijdag 22 mei 2009 11:30
To: Hans Vermeiren
Cc: r-help@r-project.org; marc_schwa...@me.com
Subject: Re: [R] drc results differ for different versions
Hi Hans,
I hope I can resolve your problems below (Marc, thank you very much for cc'ing
me on your
initial response!).
Have a look at the following R lines:
## Fitting the model using drm() (from the latest version)
m1<- drm(response ~ dose, data = d, fct = LL.4())
summary(m1)
plot(m1)
## Checking the fit by using nls()
## (we have very good guesses for the parameter estimates)
m2 <- nls(response ~ c + (d - c)/(1 + (dose/e)^b), data=d, start=list(b=-0.95,
c=10,
d=106, e=1.2745e-06))
summary(m2)
The standard errors agree quite well. The minor discrepancies between to two
fits are
attributable to different numerical approximations of the variance-covariance
matrix being
used in drm() and nls().
So I would use the latest version of 'drc', especially for datasets with really
small
doses. One recent change to drm() was to incorporate several layers of scaling
prior to
estimation (as well as subsequent back scaling after estimation):
1) scaling of parameters with the same scale as the x axis
2) scaling of parameters with the same scale as the y axis
3) scaling of parameters in optim()
The effect of scaling is to temporarily "convert" the dataset (and the model)
to scales
that are more convenient for the estimation procedure. Any feedback on this
would be much
appreciated.
Therefore it should also not be necessary to manually do any scaling prior to
using drm()
(like what you did). Compare, for instance, your specification of drm() to mine
above.
Is this explanation useful?!
Christian
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