I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example:
#################
# dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance
x <- rep(1:10, length=30)
y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10, sd=0.6),rnorm(10, sd=1.1))
z <- gl(3,10)
plot(y~x, pch=unclass(z))
# model as three slopes with common intercept
options(contrasts=c("contr.treatment","contr.poly"))
model <- lm(y~x+x:z)# coefficient table in summary gives the intercept, first slope and the difference in slopes
summary(model)
# confint gives the confidence interval for the intercept and first slope,
# and the CIs for the _differences_
confint(model)
#################
What I'd like to report are the actual CI's for the slopes for the second and third treatments, in the same way that confint returns the parameter estimates for the first treatment. Can anyone point me in the right direction?
Thanks, David
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