I posted this question way down at teh end of another thread realted to an
error in step, but that was stupid since it really is another matter
altogether. I should have posted it separately, as I have now done.
The code below creates a data.frame comprising three marginally noisy
surfaces. The code below (including a fix courtesy of David Winsemius that
avoids a step function error through use of the "by" function) returns
significant coefficients for regressions based on factor grp. In the case of
grp A the intercept is not significantly different from zero. However I
can't get step to consider dropping the intercept term. In the attached code
the returned function for grp A is y ~ x1+x2. However I think it should
return y ~ -1 + x1 +x2 (or I guess y ~ 0 + x1 + x2). If I include -1 in the
model designation then no intercept is returned for grps B and C. Am I not
using step properly? Or perhaps there is an alternative methodology I could
use. Any help appreciated.
# y = x1 + x2 for grp A
# y = 2 + 2x1 + 4x2 for grp B
# y = 3 + 2x1 + 4x2 + 6x1x2 for grp C
ind <- matrix(runif(200), ncol=2, dimnames=list(c(), c("x1","x2")))
d1 <- data.frame(ind, y=ind[,"x1"]+ind[,"x2"]+rnorm(100,0,0.05),
grp=rep("A",100))
d2 <- data.frame(ind,
y=2+2*ind[,"x1"]+4*ind[,"x2"]+rnorm(100,0,0.05),grp=rep("B", 100))
d3 <- data.frame(ind,
y=3+2*ind[,"x1"]+4*ind[,"x2"]+6*ind[,"x1"]*ind[,"x2"]+rnorm(100,0,0.05),grp=rep("C",100))
data2 <- rbind(d1,d2,d3)
# Fit each surface by grp
model <- y ~ x1*x2
by(data2, data2$grp, function(x) {step(lm(model, data=x))})
--
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