Thank you for your reply. I think I have a poor understanding of this distribution but, if I understand your answer albeit roughly, then to get a mean of 100 I need to select a mu and derive the sd using sqrt(2*(log(100)-mu)).
That helps a lot. My application is in modeling/simulating failure/repair processes which I have read are typically log-normal. I should now be able to get the result i expect to get. Thanks again. On Sun, May 4, 2008 at 3:11 PM, Berwin A Turlach <[EMAIL PROTECTED]> wrote: > G'day Phil, > > On Sun, 4 May 2008 14:05:09 +1000 > phil colbourn <[EMAIL PROTECTED]> wrote: > > > rlnorm takes two 'shaping' parameters: meanlog and sdlog. > > > > meanlog would appear from the documentation to be the log of the > > mean. eg if the desired mean is 1 then meanlog=0. > > These to parameters are the mean and the sd on the log scale of the > variate, i.e. if you take the logarithm of the produced numbers then > those values will have the given mean and sd. > > If X has an N(mu, sd^2) distribution, then Y=exp(X) has a log-normal > distribution with parameters mu and sd. > > R> set.seed(1) > R> y <- rlnorm(10000, mean=3, sd=2) > R> summary(log(y)) > Min. 1st Qu. Median Mean 3rd Qu. Max. > -4.343 1.653 2.968 2.987 4.355 10.620 > R> mean(log(y)) > [1] 2.986926 > R> sd(log(y)) > [1] 2.024713 > > > > I noticed on wikipedia lognormal page that the median is exp(mu) and > > that the mean is exp(mu + sigma^2/2) > > > > http://en.wikipedia.org/wiki/Log-normal_distribution > > Where mu and sigma are the mean and standard deviation of a normal > variate which is exponentiated to obtain a log normal variate. And > this holds for the above example (upto sampling variation): > > R> mean(y) > [1] 143.1624 > R> exp(3+2^2/2) > [1] 148.4132 > > > So, does this mean that if i want a mean of 100 that the meanlog > > value needs to be log(100) - log(sd)^2/2? > > A mean of 100 for the log-normal variate? In this case any set of mu > and sd for which exp(mu+sd^2/2)=100 (or mu+sd^2/2=log(100)) would do > the trick: > > R> mu <- 2 > R> sd <- sqrt(2*(log(100)-mu)) > R> summary(rlnorm(10000, mean=mu, sd=sd)) > Min. 1st Qu. Median Mean 3rd Qu. Max. > 4.010e-04 1.551e+00 7.075e+00 1.006e+02 3.344e+01 3.666e+04 > R> mu <- 4 > R> sd <- sqrt(2*(log(100)-mu)) > R> summary(rlnorm(10000, mean=mu, sd=sd)) > Min. 1st Qu. Median Mean 3rd Qu. Max. > 0.9965 25.9400 56.0200 101.2000 115.5000 3030.0000 > R> mu <- 1 > R> sd <- sqrt(2*(log(100)-mu)) > R> summary(rlnorm(10000, mean=mu, sd=sd)) > Min. 1st Qu. Median Mean 3rd Qu. Max. > 9.408e-05 4.218e-01 2.797e+00 8.845e+01 1.591e+01 7.538e+04 > > Note that given the variation we would expect in the mean in the last > example, the mean is actually "close enough" to the theoretical value > of 100: > > R> sqrt((exp(sd^2)-1)*exp(2*mu + sd^2)/10000) > [1] 36.77435 > > HTH. > > Cheers, > > Berwin > > =========================== Full address ============================= > Berwin A Turlach Tel.: +65 6515 4416 (secr) > Dept of Statistics and Applied Probability +65 6515 6650 (self) > Faculty of Science FAX : +65 6872 3919 > National University of Singapore > 6 Science Drive 2, Blk S16, Level 7 e-mail: [EMAIL PROTECTED] > Singapore 117546 > http://www.stat.nus.edu.sg/~statba<http://www.stat.nus.edu.sg/%7Estatba> > -- Phil " Someone else has solved it and posted it on the internet for free " [[alternative HTML version deleted]] ______________________________________________ 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.
