[R] setting parameters equal in lm

[ONKELINX, Thierry]

offset() fixes the parameter to 1. So offset(I(.5*X2)) should do the trick.

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
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-----Oorspronkelijk bericht-----
Van: r-help-bounces op r-project.org [mailto:r-help-bounces op r-project.org] Namens Dustin Fife
Verzonden: dinsdag 29 mei 2012 14:56
Aan: Rui Barradas
CC: r-help
Onderwerp: Re: [R] setting parameters equal in lm

That did it. Thanks! One more follow-up questions. How do I set a parameter to a particular value? I tried I(.5*X2), but that didn't do what I expected.

On Tue, May 29, 2012 at 6:39 AM, Rui Barradas <ruipbarradas op sapo.pt> wrote:

> Hello,
>
> Your model is equivalent of
>
> y = b1(X1 + X3) + b2X2
>
> (plus error)
>
> So, use I() to add X1 and X3. You don't need to create an extra
> variable
> X13 <- X1 + X3. See the help page for it. The point on function formula.
>
> ?I
>
> linMod2 = lm(Y ~ -1 + I(X1  + X3) + X2, data=data.set)
> summary(linMod2)
> # The same.
> linMod3 = lm(Y ~ 0 + I(X1  + X3) + X2, data=data.set)
> summary(linMod3)
>
> With set.seed(1) your common slope is
>
> coef(linMod2)
> I(X1 + X3)         X2
>  0.4237869  3.3626984
>
> Also, I find it better to put 'library', 'require', etc as the first
> lines of code.
>
> Hope this helps,
>
> Rui Barradas
>
> Em 29-05-2012 04:14, Dustin Fife escreveu:
>
>  Forgive me if this is a trivial question, but I couldn't find it an
> answer
>> in former forums. I'm trying to reproduce some SAS results where they
>> set two parameters equal. For example:
>>
>> y = b1X1 + b2X2 + b1X3
>>
>> Notice that the variables X1 and X3 both have the same slope and the
>> intercept has been removed. How do I get an estimate of this
>> regression model? I know how to remove the intercept ("-1" somewhere
>> after the tilde).
>> But how about setting parameters equal? I have used the car package
>> to set up linear hypotheses:
>>
>>
>> X1 = rnorm(20, 10, 5); X2 = rnorm(20, 10, 5); X3 = rnorm(20, 10, 5) Y
>> = .5*X1 + 3*X2 + .5*X3 + rnorm(20, 0, 15) data.set =
>> data.frame(cbind(X1, X2, X3, Y)) linMod = lm(Y~X1 + X2 + X3,
>> data=data.set)
>> require(car)
>> linearHypothesis(linMod, c("(Intercept)=0", "X1-X3=0"))
>>
>> (forgive the unconventional use of the equal sign....old habit).
>> Unfortunately, the linearHypothesis is always compared to a full
>> model (where the parameters are freely estimated). I want to have an
>> ANOVA summary table for the reduced model. Any ideas? Thanks in
>> advance for the help!
>>
>>


--
Dustin Fife
PhD Student
Quantitative Psychology
University of Oklahoma

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