# Re: [R] probit analysis

From: White, Charles E WRAIR-Wash DC <charles.edwin.white_at_us.army.mil>
Date: Sat 06 May 2006 - 23:24:29 EST

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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