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From | Steve Samuels <sjsamuels@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Re: Programmatically determining if predictors have been dropped from a model |
Date | Thu, 16 Sep 2010 13:05:42 -0400 |
-- Barth, if your only goal is to keep the -do file- going, how about -capture- or -capture noisily-? Steve Steven J. Samuels sjsamuels@gmail.com On Thu, Sep 16, 2010 at 12:53 PM, Joseph Coveney <jcoveney@bigplanet.com> wrote: > Barth Riley wrote: > > I am running a series of Monte Carlo simulations using logistic regression. > Occasionally, the regression analysis will drop a variable (due to > collinearity, the variable having only one value, or the variable perfectly > predicting the outcome). I would like to know when a variable has been > dropped in order to prevent my do file from crashing (i.e., when I call test > <indep. Var> and the variable doesn't exit in the model). I have tried using > both logistic and logit functions and neither provide information on the > variables left in the model (i.e., via return list), nor does the confirm > function work with variables as they exist in a model. Does anyone have any > suggestions? > > -------------------------------------------------------------------------------- > > Isn't that kind of information in the coefficient vector (coefficients fixed to > zero, and column equation names containing the letter "o" for "omitted"), and > other ereturn matrixes, like e(Cns), e(rules) and so on? > > Joseph Coveney > > . sysuse auto > (1978 Automobile Data) > > . generate byte k = 1 > > . generate int weight1 = weight - 1 > > . logistic foreign i.k c.(weight weight1), nolog > note: 1.k omitted because of collinearity > note: weight1 omitted because of collinearity > > Logistic regression Number of obs = 74 > LR chi2(1) = 31.96 > Prob > chi2 = 0.0000 > Log likelihood = -29.054002 Pseudo R2 = 0.3548 > > ------------------------------------------------------------------------------ > foreign | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > 1.k | (omitted) > weight | .997416 .0006078 -4.25 0.000 .9962254 .9986079 > weight1 | (omitted) > ------------------------------------------------------------------------------ > > . foreach matrix in b Cns rules { > 2. matrix list e(`matrix') > 3. } > > e(b)[1,4] > foreign: foreign: foreign: foreign: > 1o. o. > k weight weight1 _cons > y1 0 -.00258739 0 6.2825993 > > e(Cns)[2,5] > foreign: foreign: foreign: foreign: _Cns: > 1o. o. > k weight weight1 _cons _r > r1 1 0 0 0 0 > r2 0 0 1 0 0 > > e(rules)[2,4] > c1 c2 c3 c4 > 1.k 4 0 0 0 > weight1 4 0 0 0 > > > * > * 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/