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}}


