[R] "Re-creating" distributions

[R. Michael Weylandt]

Short answer: no, those are (in general) insufficient parameters to
characterize a distribution.

Long answer: unfortunately, it's not uncommon that those "summary
statistics" are the only ones reported based on someone or other's
limited experience with the Gaussian. There are a few things you could
try, but each of them has problems:

i) Pretend like your data is in fact normal and use those parameters
because they do uniquely characterize a normal distribution. MASS
(among others) provides a multivariate normal distribution [mvrnorm]
if you have a covariance matrix available.

ii) If you have reason to imagine another distribution [guided by
domain knowledge], try to get its parameters in so far as possible by
moment matching. Covariance structures are much harder for the general
case though.

iii) If you can get something that resembles original data, simply
work by bootstrapping / imputation.

Hope this helps,
Michael

On Thu, Jun 7, 2012 at 3:34 PM, Andras Farkas <motyocska at yahoo.com> wrote:
> Dear All,
>
> I often have to work with certain models in which I try to "reproduce" a distribution the best I can with very little known information avaible. Is there a package or function in R that could best reproduce a probability distribution using only the mean, median and SD values availble without knowing the actual distribution type to begin with and/or the covariance matrix (for more then 1 data set)? All I usually have reported availble is mean, median and SD. I hope I made my question clear enough...
>
> thanks,
>
> Andras
>
>
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