At 01:45 PM 6/12/2007, 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.
1. Using if a factor has many levels, there is a natural order to the levels. If so, consider fitting the factor as an ordered factor. 2. Break the factor levels into 2 or 3 groups that have some rational connection. Then fit the factor with a smaller number of levels. E.g., "race" might have levels "white", "black", "asian", "pacific", "Spanish surname", "other". Consider a change to "white", "nonwhite". ================================================================ Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: [EMAIL PROTECTED] Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239 Fax: 757-467-2947 "Vere scire est per causas scire" ______________________________________________ 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.
