Hi,
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
David, try the estimable() function in the gregmisc package. Andrew On Monday 29 March 2004 06:44, David Orme wrote:> Hi, > > 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 > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html-- Andrew Robinson Ph: 208 885 7115 Department of Forest Resources Fa: 208 885 6226 University of Idaho E : andrewr at uidaho.edu PO Box 441133 W : http://www.uidaho.edu/~andrewr Moscow ID 83843 Or: http://www.biometrics.uidaho.edu No statement above necessarily represents my employer's opinion.
You may want: lm( y ~ x:z ) This is the same model you fitted, but prametrized differently. But please check that what you REALLY want is not lm( y ~ z + x:z ) This is the model with different intercepts as well. Bendix Carstensen ---------------------- Bendix Carstensen Senior Statistician Steno Diabetes Center Niels Steensens Vej 2 DK-2820 Gentofte Denmark tel: +45 44 43 87 38 mob: +45 30 75 87 38 fax: +45 44 43 07 06 bxc at steno.dk www.biostat.ku.dk/~bxc ----------------------> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of David Orme > Sent: Monday, March 29, 2004 4:44 PM > To: r-help at stat.math.ethz.ch > Subject: [R] Confidence Intervals for slopes > > > Hi, > > 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 > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo> /r-help > PLEASE > do read the posting guide! > http://www.R-project.org/posting-guide.html >