st: exploratory data analysis for finding substitutes and complements

["Dimitriy V. Masterov" <dvmaster@gmail.com>]

I have a panel data set with store-level sales data for 125 items at a
chain restaurant. My variables are quantity sold of that item in a
particular store and time. My data looks like this: store_id, week,
hot_dogs, burgers, fries, and drinks. For each item, I would like to
figure out which items are substitutes or complements. For example, I
would expect hamburgers and fries and hot dogs and fries to be
complements, while hot dogs and hamburgers to be substitutes. I would
like to group items into clusters to make some time-series graphs, but
plotting all 125 items on the same graph is messy.

My first attempt at this involved calculating pairwise correlations
between items, and grabbing those where the correlation is above some
threshold X in absolute value. This works reasonably well, but I don't
want to do this by hand for all the items and my loop-over-items
approach is slow and inefficient.

Is there a command that can accomplish this for me? Or is there a
better way of doing this using some sort of clustering algorithm?

DVM
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