Hello all, I am unsure of how to interpret the output from a Generalized Estimating Equation analysis of an ordinal response. I hope someone can enlighten me. The analysis was done using package 'repolr'. The data consists of a Score on a 3-point scale from 56 Subjects after repeatedly washing their hands with soap. Two soap Products were tested, each panelist washed 10 times = 10 Applications.
I'm puzzled by some aspects of the model output below from repolr--- * correlation structure changed from AR1 to Fixed * additional factors = "cuts" 1 and 2 added to model ( is this testing for the ability of GLM to detect cutpoints for the assumed latent effect?) * assume that "factor(Product)[T.2]" refers to the 2nd level of the Product factor (coded "869"), for comparison to baseline of 1st level (coded "143") Any insight is much appreciated. Thanks, Paul ================ Model ========================= This is the model that was fit, using the "repolr" package: soapfeel.mod <- repolr(formula = Score ~ factor(Product) * Application , subjects = "Subject" , data = soap.data , categories = 3 , times = c(1,2,3,4,5,6,7,8,9,10) , corstr = "ar1", tol = 0.001, scalevalue = 1, alpha = 0.5,po.test=TRUE, fixed=FALSE) ================ Output========================= > summary(soapfeel.mod[["gee"]]) GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA gee S-function, version 4.13 modified 98/01/27 (1998) Model: Link: Logit Variance to Mean Relation: Binomial Correlation Structure: Fixed Call: ogee(formula = formula, id = exdata$exdata$subjects, data = exdata$exdata, R = R_mat, b = as.numeric(coeffs), maxiter = 10, family = "binomial", corstr = "fixed", silent = TRUE, scale.fix = TRUE, scale.value = scalevalue) Summary of Residuals: Min 1Q Median 3Q Max -0.32791175 -0.13163165 -0.05841431 -0.02337869 0.97076089 Coefficients: Estimate Naive S.E. Naive z Robust S.E. factor(cuts)1 -3.0093220 0.47829379 -6.291786 0.51860442 factor(cuts)2 -1.3082469 0.41257539 -3.170928 0.40572065 factor(Product)[T.2] -0.6136790 0.58810180 -1.043491 0.69995927 Application -0.1445904 0.07672638 -1.884494 0.09077013 factor(Product)[T.2]:Application 0.2650185 0.09867353 2.685811 0.10336124 Robust z factor(cuts)1 -5.8027310 factor(cuts)2 -3.2245017 factor(Product)[T.2] -0.8767352 Application -1.5929293 factor(Product)[T.2]:Application 2.5640026 Estimated Scale Parameter: 1 Number of Iterations: 1 Working Correlation [,1] [,2] [,3] [,4] [,5] [1,] 1.000000e+00 4.271812e-01 2.375569e-01 1.014798e-01 5.643328e-02 ... ... ====================Data frame==================== > str(soap.data) 'data.frame': 560 obs. of 6 variables: $ Subject : int 1 1 1 1 1 1 1 1 1 1 ... $ Product : int 869 869 869 869 869 869 869 869 869 869 ... $ Question : int 2 2 2 2 2 2 2 2 2 2 ... $ Application: int 1 2 3 4 5 6 7 8 9 10 ... $ Score : int 3 3 3 3 3 3 3 3 3 3 ... =====================sessionInfo()================== R version 2.8.1 (2008-12-22) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] tcltk grid stats graphics grDevices datasets utils [8] methods base other attached packages: [1] Rcmdr_1.4-7 car_1.2-12 repolr_1.0 hints_1.0.1-1 [5] gee_4.13-13 effects_2.0-3 nnet_7.2-45 MASS_7.2-45 [9] lattice_0.17-20 geepack_1.0-16 loaded via a namespace (and not attached): [1] boot_1.2-35 lme4_0.999375-28 Matrix_0.999375-20 tools_2.8.0 [5] Zelig_3.4-1 Paul Prew Statistician Ecolab ESC F44, 655 Lone Oak Drive Eagan, MN 55123 CONFIDENTIALITY NOTICE: \ This e-mail communication an...{{dropped:11}} ______________________________________________ R-help@r-project.org 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.