R help - Aug 2000 - Warning - Naive Question Alert

In my research area, researchers are very stingy about sharing data.  In 
fact, they are frequently downright secretive about these 
morsels.  Typically, we get fed a diet of summary statistics and the 
assurance that the data are *normal* without any necessary documentation 
that they really are.

With that as background and assuming that the data really are normal, is 
there any way in R (or any of the S engines) to generate a data set that 
mimics exactly the summary properties reported in a published paper?  I 
know I can use rnorm() and mvrnorm() for this, but neither function will 
necessarily or very likely return a sample that has the *same* properties 
as the given population.  At best, I can sift though replicates until I 
find the one closest to the "original".   This approach doesn't
seem very
efficient or valid.  Is there another way to do this?




Dr. Marc R. Feldesman
email:  feldesmanm at pdx.edu
email:  feldesman at ibm.net
fax:    503-725-3905

"Don't know where I'm going.
Don't like where I've been.
There may be no exit.
But hell, I'm going in."  Jimmy Buffett

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"Marc R. Feldesman" <feldesmanm at pdx.edu> writes:
> With that as background and assuming that the data really are normal, is 
> there any way in R (or any of the S engines) to generate a data set that 
> mimics exactly the summary properties reported in a published paper?  I 
> know I can use rnorm() and mvrnorm() for this, but neither function will 
> necessarily or very likely return a sample that has the *same* properties 
> as the given population.  At best, I can sift though replicates until I 
> find the one closest to the "original".   This approach
doesn't seem very
> efficient or valid.  Is there another way to do this?
I've been using the following for a case where data were given in the
form of means and sd:

fake.trypsin<-local({
xrnorm<-function(n,xbar,s){
        t<-rnorm(n);t<-(t-mean(t))/sd(t);xbar+s*t}
g.1<-xrnorm(32,128,50.9)
g.2<-xrnorm(137,152,58.5)
g.3<-xrnorm(38,194,49.3)
g.4<-xrnorm(44,207,66.3)
g.5<-xrnorm(16,215,60)
g.6<-xrnorm(4,218,14)
trypsin<-c(g.1,g.2,g.3,g.4,g.5,g.6)
grp<-rep(1:6,c(32,137,38,44,16,4))
grpf<-factor(grp)
data.frame(trypsin,grp,grpf)})

The xrnorm function would seem to be something like what you're
getting at?

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907
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