From: Rolf Turner <r.turner_at_auckland.ac.nz>

Date: Tue, 03 Jun 2008 09:10:07 +1200

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 02 Jun 2008 - 22:06:42 GMT

Date: Tue, 03 Jun 2008 09:10:07 +1200

On 3/06/2008, at 2:56 AM, Daniel Brewer wrote:

> I have some data with two categorises plus/minus (p53) and a

*> particular
**> time (Time) and the outcome is a continuous vairable (Result). I
**> set up
**> a maximum model.
**> ancova <- lm(Result~Time*p53)
**>> summary(ancova)
**> ..
**> Coefficients:
**> Estimate Std. Error t value Pr(>|t|)
**> (Intercept) 0.05919 0.55646 0.106 0.916
**> Time -0.02134 0.01785 -1.195 0.241
**> p53plus 0.17059 0.78696 0.217 0.830
**> Time:p53plus 0.11887 0.02524 4.709 4.62e-05 ***
**> ..
**>
**>> From a plot of the data and the result of the linear model it
**>> looks like
**> the two categories share the same intercept. How do I define this? I
**> tried this:
**>> ancova2<-update(ancova,~.-1)
**>> summary(ancova2)
**> Call:
**> lm(formula = Result ~ Time + p53 + Time:p53 - 1)
**> ..
**> Coefficients:
**> Estimate Std. Error t value Pr(>|t|)
**> Time -0.02134 0.01785 -1.195 0.241
**> p53minus 0.05919 0.55646 0.106 0.916
**> p53plus 0.22977 0.55646 0.413 0.682
**> Time:p53plus 0.11887 0.02524 4.709 4.62e-05 ***
**>
**> But I do not think that is doing what I want.
*

You're right --- that's not what you want. That simply gives you a
different

parameterization of the original model. Use either

fit1 <- lm(Result ~ Time + Time:p53)

or

fit2 <- lm(Result ~ Time:p53)

which give 2 different parameterizations of the model you want.

cheers,

Rolf Turner

P. S. I believe that it's strictly speaking incorrect to refer to
such model as an

``ancova'' model. My recollection is that the ``classical'' use of
the term ancova

refers to models in which the slopes are identical but the intercepts
(possibly)

different. So a model in which both slopes and intercepts are
allowed to differ

from level to level of a factor is not an ancova model. But it's all
just jargon

anyway.

R. T.

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