Thank you Nick for your valiant effort to characterize the treatment of missings as a feature. And thank you, Friedrich, for your work-around (& again to you Nick for your help on that too).
Let me just try to explain why this wasn't a feature for me today. Using the collapse statement, I was aggregating various amount fields by day. There could be multiple (and usually were) transactions per day. Once I had the aggregated amounts, I was interested in their correlations, especially the correlation of one amount with the lagged amount of another. When I start introducing erroneous zero amounts, my correlations will not be unbiased, & certainly not correct. In fact, the way I discovered this is that one colleague was computing the same correlations in SAS. For some reason, I had more observations than he. I now know that my "extra" observations were the result of collapse's treatment of missing values. I was able to get the same correlations as my colleague by deleting the observations with missing amounts but then I also lose the information on the number of transactions on those days (albeit with incomplete data). So yes, I emphatically agree with your d
iagnosis:
>I guess what Eric would in effect like Stata to do
>is to keep track of all the occurrences of
>missing so that -sum()- would produce say
>
>. + . + . + . + . + . + 42 = 42
>
>but
>
>. + . + . + . + . + . + . = .
>
>Thus, at the end of a set that were all missing,
>-sum()- would be morally compelled to say,
>"No, that initial guess of 0 doesn't apply here.
>These values are all missing, so the sum must
>be missing. I changed my mind!"
Failing such a radical change to collapse, perhaps there could be an "allmiss" parameter that would make the sum of totally missing values equal to missing.
Eric
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Eric G. Wruck
Econalytics
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