The model becomes nonlinear when you add the natural response rate. In
R, that means that you switch from using the glm function to using the
nls function. As long as you're willing to use logistic regression
instead of Probit analysis, nls has a 'self starting' option (SSLogis)
for a three parameter logistic model. The third parameter will be your
natural response rate. Unless you are looking at the tails of the
distribution, the Probit and logistic models will agree closely. If you
are highly motivated to use Probit analysis, you can use SSLogis to
figure out how to do that.

 

With regard to the SAS Probit procedure, it's been a few years since I
last used it but I wasn't happy when I did. The natural response rates I
got from that procedure were often unrealistic. As always, it's a good
idea to plot your data. 

 

Chuck

------------------------------

 

Message: 55

Date: Sat, 6 May 2006 15:08:33 +0800

From: "Jinsong Zhao" <jszhao@mail.hzau.edu.cn>

Subject: 

To: "r-help" <r-help@stat.math.ethz.ch>

Message-ID: <346899462.32251@eyou.net>

Content-Type: text/plain;     charset="gb2312"

 

Dear all,

 

I have a very simple set of data and I would like to analyze them

with probit analysis. 

...

 

I use glm(y ~ log10(dose), family=binomial(link=probit)) to

do probit analysis, however, I have to exclude the first 

observation. In an experimental design, the first observation 

may be as a control group. I think it should not be simple 

excluded from the datasets when constructing a model.

 

I refered to SAS online doc for probit procedure, it use the first 

observation to estimate the natural (threshold) response rate (C). 

And the final model will be:

 

p = Pr(Y = 0) = C + (1 - C)F(x'\beta)  

 

However, I don't know how to considered the control effect 

when using glm().

 

Any suggestions will be really appreciated.

 

Best Regards,

 

Jinsong Zhao

 

 


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