I don't know how it ties into the tools car gives you, but one (quick
and dirty) way to do this is to simply regress on
Y ~ aX2 + b(X1+X3)
or in R code something like:
lm(Y ~ X2 + I(X1+X3), data = data.set)
which gives a linear model you can play around with. Note the I()
function [that's the capital letter immediately preceding J] which
tells R to interpret that term "AsIs"
Hope this helps,
Michael
On Mon, May 28, 2012 at 11:14 PM, Dustin Fife <fife.dus...@gmail.com> wrote:
> 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
>
> [[alternative HTML version deleted]]
>
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