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From |
"Joao Pedro W. de Azevedo" <jazevedo@provide.com.br> |

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

Subject |
st: RE: RE: multicollinearity test for probit? |

Date |
Wed, 10 Dec 2003 23:30:01 -0200 |

Dear Matt, Another suggestion might be to apply the following tests: Bartlett's test for sphericity; and, Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Both tests are often used prior to PCA or Factor Analysis and test precisely to which extent the variables used in the model are correlated. These tests can be implemented in STATA through the ado file FACTORTEST . ssc install factortest Cheers, JP -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox Sent: Wednesday, December 10, 2003 8:12 PM To: statalist@hsphsun2.harvard.edu Subject: st: RE: multicollinearity test for probit? As I understand it, multicollinearity on the right-hand side is much the same irrespective of what is on the left-hand side or what link function is contemplated. So I don't see that you are obliged to do it retrospectively. The world divides here into those who think in terms of some test (e.g. through an omnibus or portmanteau test statistic) and those who want to examine structure or look for potential problems in an exploratory way. As someone firmly in the latter camp, I've no idea if there's some overall test which supposedly maps all pertinent information into a single statistic. If they tell you there is one, its merits are probably exaggerated. Three things spring to mind. There are probably thirty others, and my three may not even be among the most important. 1. -_rmcoll- ============ Check it out. 2. -pca- ======== One of the best general ways of looking for structure is to look at the principal components of the predictors. The eigenvalues give a quick guide to whether you have clusters of variables. Perhaps the best single diagnostic is not among the standard outputs: it is the correlations between the original variables and the components. -makematrix- on SSC gives a relatively painless way of getting those concisely and directly. Sometimes you look at the principal components and see some structure that you then realise could be presented and explained in some other way, say directly in terms of the correlation matrix. It's like climbing up the North Face with pitons and ropes and goodness knows what, but when you get to the top you see that there was an easier way to climb up there all the time. (I've never done this, but I saw Clint Eastwood do it once. Principal components, that is.) 3. scatterplot matrix ===================== This can be a good way of scanning a few dozen predictors. You are just on the look out for scatter plots with strongly diagonal patterns. (But watch out for outliers too.) Nick n.j.cox@durham.ac.uk Matt A. Barreto > Is there a similar command to vif following regress when > using probit or > oprobit (or logit/ologit) to test for multicollinearity > among independent > variables in a probit equation? * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: RE: multicollinearity test for probit?***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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