I''m running brglm with binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1''s; (3) k IVs, there are n * k unique rows; (4) when brglm is run, at least 1 IV is reported as involving a singularity. I''ve tried recoding the n IV''s using (n-1) indicator variables: brglm produces a result without reporting singularities. How should I go about computing estimates for the offending IVs? Is there a better way? I''m interested primarily in the reliability of the parameter estimates. -- View this message in context: http://www.nabble.com/Multicollinearity-with-brglm--tp22814696p22814696.html Sent from the R help mailing list archive at Nabble.com.
I'm running brglm to do binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1's; (I think this is the source of the problem) (3) there are n * k unique rows, where n is as much as 10; (4) when brglm is run, at least 1 IV is reported as involving a singularity and this occurs for nearly every choice of k, n. How should I go about computing estimates for the offending IVs? I'm interested primarily in the reliability of the parameter estimates. -- View this message in context: http://www.nabble.com/Multicollinearity-with-brglm--tp22814696p22827727.html Sent from the R help mailing list archive at Nabble.com.
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