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 at 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!