On Wed, 17 Dec 2003, Brandon Vaughn wrote: > Thanks to everyone who wrote in with suggestions. I will check out the > books mentioned. > > The book I mentioned "Resampling: The New Statistics" is actually available > free online at: > > http://www.resample.com/content/text/index.shtml > > It seems pretty good as an introduction. But then again, I am new at this > concept.
An introduction to what? (It seems to confuse resampling and simulation-based inference.) > Does anyone know right off hand how to do simple simulation with R? Like > for instance, in the book mentioned above, there is an example of figuring > out the probability that a company with 20 trucks with have 4 or more fail > on a given day (the probability that any given truck fails is .10). So the > way they do it is to simulate uniform numbers from 1 to 10, and let the > number 1 represent a defective truck. So here is the setup in the program > Resampling Stat: > > REPEAT 400 [repeat simulation 400 times] > GENERATE 20 1,10 a [generate 20 numbers between 1 and 10; store > in vector a] > COUNT a = 1 b [count the number of 1's and store in vector b] > SCORE b z [keep track of each trial in vector z] > END [repeat process] > COUNT z > 3 k [count the number of times trials more than 3 and > store] > DIVIDE k 400 kk [convert to probability and store] > PRINT kk [print result] > > This seems like a simple problem, and seemingly simple process in Resampling > Stats. Any idea on how to get started doing this in R? However, the number of failures is a binomial variate, so it is much simpler in R, for example cnts <- rbinom(400, 20, 0.1) mean(cnts >= 4) However, doing 1 million runs was almost instantaneous on my machine. And the expected answer is pbinom(3, 20, 0.1, lower=FALSE) As a matter of terminology, this is not resampling as usually defined, so I do wonder exactly what it is you are after. For resampling in the usual sense, I would echo Jason's recommendation of Davison and Hinkley's CUP book. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
