Tirtha wrote: >Dear users, >In my psychometric test i have applied logistic >regression on my data. >My >data consists of 50 predictors (22 continuous and 28 >categorical) plus >a >binary response. > >Using glm(), stepAIC() i didn't get satisfactory >result as >misclassification >rate is too high. I think categorical variables are >responsible for >this >debacle. Some of them have more than 6 level (one has >10 level). > >Please suggest some better regression model for this >situation. If >possible >you can suggest some article. > >thanking you. > >Tirtha
Hi Tirtha, Are your categorical variables really categorical? What I mean is if you variable is user's satisfaction level (0 for very unsatisfied, 1 for moderately unsatisfied, 2 for slightly unsatisfied, 4 for neutral, etc., finally 7 for very satisfied) then your variable is not really categorical (since 1 is closer to 3 than to 6) and then try what other people suggest. However, if your variable is, say, the 50-th amino acid in a certain gene (with values of 1 for the first amino acid, 2 for the second one,...,20 for the 20-th one) then your variable is really categorical (you generally can not say that amino acid 2 is much closer to amino acid 3 than to amino acid 17). In such a case I would have tried classification method which can treat categorical variables or, alternatively, may be regression trees (i.e. split on the values of categorical variables and at each "node" find regression coefficients of the continuous variables). Regards, Moshe Olshansky [EMAIL PROTECTED] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
