R help - Apr 2001 - contingency tables in R

Dear List:

Most of the analysis I do involves contingency tables.
 I am migrating to R from Stata and I have a number of
questions about using contingency tables in R.  I
suspect that most of the things I want to do are very
short R scripts that people on this list probably
have.  I wonder if you would be willing to share them.
 

First, the presentation of tables by table() is not
analysis-ready.  Is there a way to output the table
with the marginals, by cell, row or column
proportions,  with the test statistics (especially the
chi^2 and the log-likelihood chi^2), residuals, cross
product, and odds ratio?  

I also like to make tables that have summary
statistics of a given variable in the columns (mean,
s.d., etc.)  with each row being the value for a sub
group of the data.  How do you do this in R?  

The most complicated piece of this is contingency
tables done with sample data.  The sampling involves
several strata with different sampling weights. 
Calculating the cell (or row or column) probabilities
is relatively easy, but the other statistics can be
complicated (the design effect, the finite population
correction, the various chi^2s, and the standard
errors and confidence intervals). Also, I sometimes
make these tables with summary statistics in place of
counts or population proportions.  

Is there any way to do this stuff in R without hacking
it all myself?  

Thanks in advance for your suggestions. 

Best.

- PB

Patrick Ball, PhD.
Deputy Director
AAAS Science and Human Rights Program
http://shr.aaas.org






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>>>>> Patrick Ball writes:
> Dear List:
> Most of the analysis I do involves contingency tables.  I am migrating
> to R from Stata and I have a number of questions about using
> contingency tables in R.  I suspect that most of the things I want to
> do are very short R scripts that people on this list probably have.  I
> wonder if you would be willing to share them.
> First, the presentation of tables by table() is not analysis-ready.
> Is there a way to output the table with the marginals, by cell, row or
> column proportions, with the test statistics (especially the chi^2 and
> the log-likelihood chi^2), residuals, cross product, and odds ratio?
Not in one monolithic function, I think, and I am not sure I would like
to have such a thing, see below.  But the pieces are all there:

* Use margin.table() and prop.table() to obtain margins and proportions,
  respectively.

* Use chisq.test() [in package ctest] for the chisq analysis (test
  statistic, p-value, chisq residuals)

* Use loglin() for the LR chisq and residuals.

* Not sure about which odds ratios you want.  Function mantelhaen.test()
  in package ctest does exact conditional ones for 2 by 2 tables.

It really depends on how your data is set up.  If you have the raw
values in a data frame, I would actually recommend using xtabs() rather
than table().  Try e.g.

     data(esoph)
     x <- xtabs(cbind(ncases, ncontrols) ~ ., data = esoph)
     x
     summary(x)

the last one prints ``useful'' summary information.

To obtain pretty-printed output from multi-way tables, use ftable().
> I also like to make tables that have summary statistics of a given
> variable in the columns (mean, s.d., etc.)  with each row being the
> value for a sub group of the data.  How do you do this in R?
Use aggregate().
> The most complicated piece of this is contingency tables done with
> sample data.  The sampling involves several strata with different
> sampling weights.  Calculating the cell (or row or column)
> probabilities is relatively easy, but the other statistics can be
> complicated (the design effect, the finite population correction, the
> various chi^2s, and the standard errors and confidence
> intervals). Also, I sometimes make these tables with summary
> statistics in place of counts or population proportions.
> Is there any way to do this stuff in R without hacking it all myself?
The pieces are all there, I think, and it should be fairly simple to
combine them to reflect your personal preferences for displaying
categorical information etc.

-k
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