setting parameters equal in lm

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

ir. Thierry Onkelinx
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-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at 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.

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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