[R] comparing 3 datasets

[pauljohn@ukans.edu]

I have 3 datasets with the same variables.  I want to find out what
differences there are between the three, to know if an experimental
condition has an effect.  So I decided first to make histograms.  So I
created this handy "histomatic" function that creates a picture with the
3 histograms on a single image:

I thought I was being clever, but in the end, no!

#read in 3 tables worth
NoFlagMod0<-read.table("NoFlagMod0.txt",header=TRUE);
RandMastMod0<-read.table("RandMastMod0.txt",header=TRUE);
NoMastMod0<-read.table("NoMastMod0.txt",header=TRUE);

#here's my magical function
histomatic <- function (s1,s2,s3,var){
  if (is.numeric (s2[[var]])) {     
  par(mfrow=c(3,1));
  hist(s1[[var]],breaks=40,xlab=var);
  
  hist(s2[[var]], breaks=40,xlab=var);
  hist(s3[[var]], breaks=40,xlab=var);
  }
}
#cycle through all the variables, just grab names from first set.
nameList<-names(RandMastMod0);
par(ask=Yes)
for (var in nameList) histomatic(NoFlagMod0,RandMastMod0,NoMastMod0,var)

I knew I wanted a pretty fine grained display, so I set breaks at 40.
Other than that, I don't know for sure what else I want.

Here's the problem:
The histograms shown do not have the same ranges.  SInce the datasets
are slightly different, the ranges displayed are different, so they are
difficult to compare visually.  Is there a solution?

Other than that, if you have other ideas about comparing 3 datasets, i'm
glad to hear.  I'm especially curious to know if there is a significance
test of the hypothesis that 3 samples are drawn from a common
distribution. (apart from testing the means with an F test, that is).  

-- 
Paul E. Johnson                       email: pauljohn at ukans.edu
Dept. of Political Science            http://lark.cc.ukans.edu/~pauljohn
University of Kansas                  Office: (785) 864-9086
Lawrence, Kansas 66045                FAX: (785) 864-5700
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