On 31/10/2007, at 9:52 AM, Achim Zeileis wrote: > On Tue, 30 Oct 2007, Zembower, Kevin wrote: > >> I'm trying to replicate some of the examples from my textbook in R >> (my >> text uses Minitab). In this problem, I'm trying to construct a 95% >> confidence interval for these distance measurements [1]: >> >>> # Case Study 7.4.1, p. 483 >>> x <- scan() >> 1: 62 52 68 23 34 45 27 42 83 56 40 >> 12: >> Read 11 items >>> alpha<-.95 >>> mean(x) + qt(c((1-alpha)/2, 1-((1-alpha)/2)), df=length(x)-1) * sd >>> (x) >> / sqrt(length(x)) >> [1] 36.21420 60.51307 >>> >> >> Are confidence intervals with the t distribution constructed using >> this >> type of equation, or am I overlooking a more concise, 'canned' >> approach >> that's already been programmed? Any suggestions on simplifying this? > > R offers a confint() generic with methods for various types of models. > If you consider estimation of the mean as a simple linear model > (with only > an intercept) you can do > fm <- lm(x ~ 1) > fm > to estimate the mean and then > confint(fm) > to get the confidence interval (by default at 0.95 level).
This is using a sledgehammer to crack a peanut. The use of t.test() as suggested by Peter Dalgaard is much more appropriate. cheers, Rolf Turner ###################################################################### Attention:\ This e-mail message is privileged and confid...{{dropped:9}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.