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From |
Cameron McIntosh <cnm100@hotmail.com> |

To |
STATA LIST <statalist@hsphsun2.harvard.edu> |

Subject |
RE: st: Calculation of covariance matrix for unbalanced sample? |

Date |
Thu, 3 Nov 2011 09:24:16 -0400 |

And then to boot, I snag a completely different reference from my list than I intended (still early here in Canada). Please ignore S.R. Cole et al. (2010), at least in this context... what I wanted to post was: Schneider, T. (2001). Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. Journal of Climate, 14, 853-871.http://www.gps.caltech.edu/~tapio/papers/imputation.pdfhttp://www.gps.caltech.edu/~tapio/imputation/ Cam ---------------------------------------- > From: cnm100@hotmail.com > To: statalist@hsphsun2.harvard.edu > Subject: RE: st: Calculation of covariance matrix for unbalanced sample? > Date: Thu, 3 Nov 2011 09:19:20 -0400 > > I guess I would have been disappointed in Nick if my one-liner sans source material hadn't received that comment :) I don't know anything about the precise nature of the missingness in this case, but I might suggest one paper on a EM-ridge regression approach (I don't know about a Stata implementation, just Matlab), which *may* provide a more reliable end product: > Cole, S.R., Platt, R.W., Schisterman, E.F., Chu, H., Westreich, D., Richardson, D., & Poole, C. (2010). Illustrating bias due to conditioning on a collider. International Journal of Epidemiology, 39(2), 417-420. http://ije.oxfordjournals.org/content/39/2/417.full.pdf+htmlhttp://www.gps.caltech.edu/~tapio/imputation/ > I think one might also use mi: > http://www.stata.com/stata11/mi.html > or chained equations: > Royston, P. (2009). Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. The Stata Journal 9(3), 466–477.http://ideas.repec.org/c/boc/bocode/s446602.htmlhttp://www.stata-journal.com/article.html?article=st0067_4 > White, I.R., Royston, P., & Wood, A.M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399. > I would be curious to see the differences in the finished product between these and the "unbalanced" suggestion. > Cam > > ---------------------------------------- > > From: n.j.cox@durham.ac.uk > > To: statalist@hsphsun2.harvard.edu > > Date: Thu, 3 Nov 2011 12:50:48 +0000 > > Subject: RE: st: Calculation of covariance matrix for unbalanced sample? > > > > I don't think it's anything formal. I'd just say "unbalanced". > > > > I suppose that I should just add that I know Stephen Jenkins very well and that I know he won't need a miniature warning on the fact that such a covariance matrix is a dodgy beast unlikely to be fit for further analysis and with unreliable eigenproperties and that he's fully capable of explaining that to his colleague, should the colleague need such a warning. > > > > Where's the reading list? I was fully expecting a dozen references. > > > > Nick > > n.j.cox@durham.ac.uk > > > > Cameron McIntosh > > > > Nick, Stas > > Just curious. What's the estimation method being applied below: EM, FIML, MI...? > > > > From: n.j.cox@durham.ac.uk > > > > > -makematrix- (SJ) can do this. But it's better to use Stas' custom code, which is more direct. > > > > Stas Kolenikov > > > > > I don't think there's any. I vaguely remember a discussion some time > > > back on the list about this. Here's the basic outline from scratch: > > > > > > program define pwcovmat, rclasssyntax varlistunab vars : > > > `varlist'local p : word count `vars'tempname Covmatrix `Cov' = > > > J(`p',`p',.)matrix rownames `Cov' = `vars'matrix colnames `Cov' = > > > `vars'forvalues i=1/`p' { forvalues j=`i'/`p' { local x : word `i' > > > of `vars' local y : word `j' of `vars' quietly corr `x' `y', cov > > > matrix `Cov'[`i',`j'] = r(C) matrix `Cov'[`j',`i'] = r(C) > > > }}return matrix Cov = `Cov'end // of pwcovmat > > > sysuse auto > > > corr weight price mpg, cov > > > corr weight price mpg rep, cov > > > pwcovmat weight price mpg rep > > > matrix list r(Cov) > > > > On Thu, Nov 3, 2011 at 6:00 AM, <S.Jenkins@lse.ac.uk> wrote: > > > > > > A colleague has data on a relatively large number of variables. His > > > > sample is unbalanced in the sense that each variable has some missing > > > > values. He wishes to calculate the covariance matrix for his data but > > > > without the listwise deletion of cases that is imposed by -correlation, > > > > covariance- or -matrix accum-. > > > > > > > > My first thought was that he could use -pwcorr- and loop over his > > > > variables, and build up his matrix from the saved results. But I thought > > > > there must be an easier or more straightforward way -- but Googling and > > > > -findit- have not suggested any. I guess there is a relatively easy > > > > Mata solution, but I am currently unfamiliar with that route. > > > > > > > > Suggestions using Stata or Mata please > > > > * > > * For searches and help try: > > * http://www.stata.com/help.cgi?search > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Calculation of covariance matrix for unbalanced sample?***From:*<S.Jenkins@lse.ac.uk>

**Re: st: Calculation of covariance matrix for unbalanced sample?***From:*Stas Kolenikov <skolenik@gmail.com>

**RE: st: Calculation of covariance matrix for unbalanced sample?***From:*Nick Cox <n.j.cox@durham.ac.uk>

**RE: st: Calculation of covariance matrix for unbalanced sample?***From:*Cameron McIntosh <cnm100@hotmail.com>

**RE: st: Calculation of covariance matrix for unbalanced sample?***From:*Nick Cox <n.j.cox@durham.ac.uk>

**RE: st: Calculation of covariance matrix for unbalanced sample?***From:*Cameron McIntosh <cnm100@hotmail.com>

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