Robin Hankin <[EMAIL PROTECTED]> writes: > Hi > > I have a vector x of length n. I am interested in x[1] > being different from the other observations (ie x[-1]). > > My null hypothesis is that x[1] > is drawn from a Gaussian distribution of the same > mean as observations x[-1], which are assumed > to be iid Gaussian. The (unknown) variance > of x[1] is assumed to be the same as the > variance of x[-1]. > > > This should be an unpaired t-test. > > But > > > > x <- c(23,25,29,27,30,30) > > t.test(x=x[1] , y=x[-1]) > Error in t.test.default(x = x[1], y = x[-1]) : > not enough 'x' observations > > > > > > What arguments do I need to send to t.test() to test my null?
You can't. Shouldn't be too much of a problem to modify t.test.default to stop it complaining. (It's not quite enough to remove the check for nx < 2, though. You also need to deal with var(x) being NA if x has length one.) Alternatively, just write up the formula for the t statistic: > x <- c(23,25,29,27,30,30) > (x[1]-mean(x[-1]))/sqrt(var(x[-1])*(1+1/(length(x)-1))) [1] -2.189595 > 2*pt(-2.1896,4) [1] 0.09373392 -- O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [email protected] 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.
