Thomas Schu <th.schumann <at> gmx.de> writes: > I´m faced with following problem: > Given is a sample where the sample size is 12, the sample mean is 30, and > standard deviation is 4.1. > Based on a Student-t distribution i´d like to simulate randomly 500 possible > mean values within a two-tailed 95% confidence interval. > Calculation of the lower and upper limit of the two-tailed confidence > interval is the easy part. > > m <- 30 #sample mean > s <- 4.1 #standard deviation > n <- 12 #sample size > quant <- qt(0.975,df=11)*s/sqrt(n)#student-t with two tailed )95% confidence > interval > l <- m-quant# lower limit > h <- m+quant# upper limit > > 500 randomly simulated values are computable with the rt() command but this > command does not consider the 95% confidence interval.
Perhaps: simulate more values than you need and take a subset: allvals <- rt(1000,df=11) ## 1000 samples is overkill: slightly more than ## 500*(1.05) should be large enough subvals <- (allvals[abs(allvals)<qt(0.975,df=11)]) vals <- m+subvals[1:500]*s/sqrt(n) I'm subsetting before transforming, it seems slightly easier. ______________________________________________ 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.
