Student-t distributed random value generation within a confidence interval?

Ben Bolker · 2012-11-23T19:41:03Z

Thomas Schu 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 s n <- 12 #sampl

Ben Bolker

Fri, Nov 23, 2012 11:41 AM

Thomas Schu <th.schumann <at> gmx.de> writes:

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.

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Thomas Schu Student-t distributed random value generation within a confidence interval? Nov 23 Ben Bolker Student-t distributed random value generation within a confidence interval? Nov 23 Thomas Schu Student-t distributed random value generation within a confidence interval? Nov 24