```Date: Wed, 3 Aug 2005 17:39:07 +0200 Reply-To: Marta García-Granero Sender: "SPSSX(r) Discussion" From: Marta García-Granero Organization: Asesoría Bioestadística Subject: Re: zeros in predictors and categorical regression In-Reply-To: <2081161075.1123067625@SSW0243.ssw.buffalo.edu> Content-Type: text/plain; charset=ISO-8859-15 Hi Gene eabe> I found something unexpected and don't understand the underlying math of eabe> it. I was doing a logistic regression with a single categorical predictor eabe> (IV) with 8 values. The frequencies on the IV shows no zeros (i.e., no eabe> values with zero frequency). A crosstabs of the IV with the DV shows two eabe> cells with zeros. When i run a logisitic regression the contrasts eabe> representing those two cells have B coefficients of about -19 and standard eabe> errors of 12,000 to 13,000. In two words, extremely large. I figure that i eabe> if i combine several cells; i can get rid of the huge SEs. What i don't eabe> understand is the arithmetic that yields to the huge SEs. Is it related to eabe> the solution of the underlying equation for the logistic regression. Or, eabe> could it be due to a collinearity problem? Perhaps a hugely technical eabe> question, but i'm confident at least a few understand intimately the eabe> algebra of logistic regression. Logistic regression works with Odds Ratio. In a 2x2 contingency table: Outcome+ Outcome- RF+ a b RF- c d The OR is = (a·d)/(b·c) If any b or c cell is null, then the OR can't be computed You must collapse categories to avoid empty cells. There is no other way around that problem. Regards, Marta mailto:biostatistics@terra.es ```

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