What are some good exploratory analysis and diagnostic plots for count data?

Does anyone know of good reference material on exploratory analysis and diagnostic plots for count data?

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Yes, of course, assume for example I have count data with two crossed factors (say A and B) where each factor has 20 levels. So, each count is associated with one level of factor A and one level of factor B. And I am interested in plot that help visualize the data and also plots to use as diagnosis to help criticize models once they are tested. – user12397 Aug 17 '12 at 9:46
This blog has a pretty nice example. You can also augment two barplots to show the summed counts of Factors A and B as a multi-panel graph. For diagnostics, perhaps you can change the raw count of the mosaic plot to be residual. – Penguin_Knight Aug 17 '12 at 11:33
Very nice example. Thank you. – user12397 Aug 17 '12 at 12:40
That's pretty cool, @Penguin_Knight. With 20 levels of each factor, though, how readable would a mosaic plot be? (I'm generally a fan of mosaic plots but haven't seen one with 400 tiles in it). The first plot at that site has 45 tiles, so this one would have tiles about 1/9 the size of those. But I don't have a better solution off hand. – Peter Flom Aug 17 '12 at 15:47

Even given your extended description of your project in the comments it is, IMO, still too broad a question to give much useful advice. Specifically for analysis of categorical data (of which your count data would seem to fall within given the description), I would perhaps suggest the work of Michael Friendly. He published the books Visualizing Categorical Data and Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data. It is specific to analysis in SAS respectively R. Potentially 20 values though will be a stretch for some (many?) of the suggested categorical displays.

For diagnostic plots, I would suggest John Fox's Regression Diagnostics green book. It is in the context of linear regression models, but the majority of same diagnostics (or very similar ones) can be utilized for generalized linear models as well.

I suspect most advice given in general for EDA for continous data types could be fairly easily extended to low count data. So I wouldn't be fixated on looking for resources specific to count data. It matters more for models, but the way to graphically explore/present/diagnose those models should be fairly similar.

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Have a look at this book (and associated R package) M Friendly: Visualizing Categorical data http://www.amazon.com/Visualizing-Categorical-Data-Michael-Friendly/dp/1580256600

(Given that the author has been working for SAS, there must be some code for that system as well ...)

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