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st: Canonical Correlation with -mim-
From
"Ploutz-Snyder, Robert (JSC-SK)[USRA]" <[email protected]>
To
"[email protected]" <[email protected]>
Subject
st: Canonical Correlation with -mim-
Date
Mon, 24 May 2010 13:13:32 -0500
I have a set of X predictors and a set of Y outcomes, and I want to perform a Canonical Correlation to better understand potential relationships between/among linear combinations of X's and linear combinations of Y's.
-> [FYI, there's a nice walk-through of Canonical Corr with Stata at UCLA's Academic
-> Technology Services website at http://www.ats.ucla.edu/stat/stata/dae/canonical.htm]
However there are a lot of X's and a lot of Y's, and missing data problems plague my listwise n. Thus, by Bayesian multiple imputation, I've compiled a "mim-compatible" dataset and wish to use the -mim- prefix to canonical correlation.
The rub:
The -mim- prefix command uses Rubin's rules for combining estimates on multiple imputed dataset and supports a number of Stata estimation commands, however the -canon- command is not currently among the list of supported routines. My read of the -mim- help files suggests that I should be able to specify the mim_options category(mim_type), which I assume would be cat(combine), but I'm stumped when trying to further define this option by the estimates(??) that is required when using the cat() option. There is a lot of estimation involved with canonical correlation, so I don't know what to use for the est() option??
Example:
mim, cat(combine) est() : canon sy1 sy2 sy3 sy4 = sx1x2 sx5 sx6 sx7x8 sx13 sx14
Question #1:
HAS ANYONE TRIED using -mim- with -canon-, and if so, can you provide guidance here?
Question #2:
IS IT POSSIBLE/LEGITIMATE to use some other -mim- supported command (ex. reg) to merely generate a var/covar matrix, and then somehow feed that matrix into the -canon- routine? Seems like that could be an option for getting the analysis I want, utilizing the multiply imputed data set that I have, but I'm not sure if it is possible or legitimate.
Any guidance is much appreciated.
Rob
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