Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Mosi Ifatunji <ifatunji@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Correlation matrices using multiply imputed datasets |

Date |
Mon, 21 Oct 2013 17:29:14 -0400 |

Colleagues, I have been asked by reviewers to include a correlation matrix of my key independent variables. My analysis employed multiple imputation so I will need to generate correlation matrices using multiply imputed data. Some time ago I wrote the list asking about this and was generously provided with the following two fixes: First, someone offered that I could obtain the estimates using the the -mi- command set, using: mi convert wide, clear mi register imputed employ lperinc educ1 educ2 educ3 educ4 workexp motivation efficacy ysm mi impute mvn employ lperinc educ2 educ3 educ4 workexp motivation efficacy ysm, emonly mat Sigma = r(Sigma_em) /* save EM estimate of the variance-covariance (VC) matrix */ _getcovcorr Sigma, corr shape(full) /* convert VC to a correlation matrix */ mat C = r(C) matlist C This works fine but ends up excluding the first education category from the correlation matrix and does not include standard errors or tests of statistical significance. The second strategy was to write a short program, creating a new command -micorr- : cap program drop ecorr program ecorr, eclass version 12 syntax [varlist] [if] [in] [aw fw] [, * ] if (`"`weight'"'!="") { local wgt `weight'`exp' } marksample touse correlate `varlist' `if' `in' `wgt', `options' tempname b V mata: st_matrix("`b'", vech(st_matrix("r(C)"))') local p = colsof(`b') mat `V' = J(`p',`p',0) local cols: colnames `b' mat rownames `V' = `cols' eret post `b' `V' [`wgt'] , obs(`=r(N)') esample(`touse') eret local cmd ecorr eret local title "Lower-diagonal correlation matrix" eret local vars "`varlist'" end cap program drop micorr program micorr, rclass tempname esthold _estimates hold `esthold', nullok restore qui mi estimate, cmdok: ecorr `0' tempname C_mi mata: st_matrix("`C_mi'", invvech(st_matrix("e(b_mi)")')) mat colnames `C_mi' = `e(vars)' mat rownames `C_mi' = `e(vars)' di di as txt "Multiple-imputation estimate of the correlation matrix" di as txt "(obs=" string(e(N_mi),"%9.0g") ")" matlist `C_mi' return clear ret matrix C_mi = `C_mi' end Then use the new command to generate estimates: micorr employ lperinc educ1 educ2 educ3 educ4 workexp motivation efficacy ysm This also works fine. The estimates between the two strategies are the same; very confirming :-) This second way has the advantage of providing me with the estimate for the first education category. However, it also does not provide me with estimates of the standard errors or tests of significance. Although I am fairly proficient at using Stata I have not progressed to the level of programming my own commands. I am hoping that someone might be able to edit either of the two above strategies (preferably the second) in a way that provides the user with estimates of the standard error and/or tests of statistical significance? Any help would be greatly appreciated. Best, -- Mosi * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

- Prev by Date:
**Re: st: ROC-curves** - Next by Date:
**RE: st: How to test the equality of marginal effects after running probit, oprobit or mprobit models** - Previous by thread:
**st: Panel VAR: Unit root tests/Cointegration** - Next by thread:
**st: DEA Inefficiencies** - Index(es):