Dear R users; I'm looking for some hint about how to deal with the following situation:
Response = Y Factor A = levels: 0, 1 Factor B = levels: 0, 1 Factor C = levels: 1,2,3,4 Model: Logistic 3-parms. where th1~1+A+C, th2~1+C; th3~1 For 'simplicity' (for me) I'm using the SAS contrast parameterization. The output looks like Beta p-value th1.(Intercept) 550 <0.000 th1.A1 -15 <0.000 th1.B1 5 <0.032 th1.C1 -12 <0.001 th1.C2 -5 0.022 th1.C3 -3 0.222 th2.(Intercept) ...... ......etc if we look at the results, we may conclude that level 3 for Factor C is not statiscally significant. The question is: How can I remove this level of this factor from the analysis? Let's say that the final results looks like Model: Logistic 3-parms. where th1~1+A+C, th2~1+C; th3~1, but C with levels 1,2 and 4 only Beta p-value th1.(Intercept) 560 <0.000 th1.A1 -15 <0.000 th1.B1 5 <0.032 th1.C1 -15 <0.001 th1.C2 -8 0.031 th2.(Intercept) ...... ......etc I tried replacing Factor C by 4 different columns, say FACTORC_1, FACTOR_C2, FACTOR_C3, and FACTOR_C4 each one of them with 0 or 1, and the model I tried was f1<-nlme(Y~SSlogis(X,th1,th2,th3)|Subject,fixed=list(th1~A+B+FACTORC_1+FACTOR_C2, etc but, as I expected, the model can't be solved I will appreciate any help ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
