[Lightly edited for legibility.] On Fri, Oct 12, 2012 at 7:39 PM, Andras Farkas <motyoc...@yahoo.com> wrote: > Dear All, > > [A] few weeks ago I have posted a question on the R help listserv that some > of you have responded to with a great solution, would like to thank you for > that again. I thought I would reach out to you with the issue I am trying to > solve now. I have posted the question a few days ago, but probably it was not > clear enough, so I thought i try it again. [\n\n] > > At times I have a multivariate example on my hand with known information of > means, SDs and medians for the variables, and the covariance matrix of those > variables. Occasionally, these parameters have a strong enough relationship > between them that a covariance matrix can be established. Please see attached > document as an example. [\n\n]
> Usually when I (a medicine people) simulate (and it is not to say that this > is the best approach), we use a lognormal distribution to avoid from negative > values being generated because physiologic variables almost are never > negative (we also really do not know better, unfortunatelly). For the most > part I use another software that is capable of reproducing reasonable means > and medians and SD if I enter the covariance matrix, but that is not a free > resource (so I can not share the solutions with others), nor does it have the > Sweave option for standard reports like R does that can be distributed for > free. Unfortunately in R I am having a hard time figuring the solution out. I > have tried to use the multivariate normal distribution function mvrnorm from > the MASS package, or the Mvnorm from mvtnorm package, but will get negative > values simulated, which I can not afford, also, at times the simulated means, > medians and SDs are quiet different from what I started with (which may be! due to the assumption I make with regards to the distribution of the data). [\n\n] > > I was wondering if anyone would be willing to provide some thoughts on how > you think one should try to attempt to simulate in R a multivariate > distribution with covariance matrix (using the attached data as an example) > that would result in reasonable means, medians and SD as compared to the > original values? While to have a better idea about the actual distribution of > the data would probably be invaluable to accurately reproduce the data (and > to choose a probability distribution to simulate with), often times in the > medical literature we only have information available similar to what I have > attached, (and we make the assumption of it being log normally distributed as > I have mentioned it above). I would greatly appreciate your help, > > Sincerely, > > Andras > ______________________________________________ Hi Andras, It seems that your attachment did not make it through the mail server: you probably need to include it inline as plain text if it's a reasonable size. Anyways, I believe your problem is that mvrnorm() et al generate multivariate _normals_, not multivariate lognormals. Perhaps have a look at these functions: http://rss.acs.unt.edu/Rdoc/library/compositions/html/rlnorm.html You might also think about truncated normals. Cheers, Michael ______________________________________________ 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.
