R help - Oct 2012 - better example for multivariate data simulation question-please help if you can

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.?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. 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). 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

[Lightly edited for legibility.]

On Fri, Oct 12, 2012 at 7:39 PM, Andras Farkas <motyocska at 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