Hi,

I'm analyzing my data using GEE, which looks like below:

> interact <- geeglm(L ~ O + A + O:A,
+ data = data1, id = id,
+ family = binomial, corstr = "ar1")

> summary(interact)

Call:
geeglm(formula = lateral ~ ontask + attachment + ontask:attachment,
    family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1")

 Coefficients:
                   Estimate  Std.err  Wald Pr(>|W|)
(Intercept)        -1.89133  0.30363 38.80  4.7e-10 ***
O                    0.00348  0.00100 12.03  0.00052 ***
A1                  -0.21729  0.37350  0.34  0.56073
A2                  -0.14151  0.43483  0.11  0.74486
O:A1               -0.37540  0.16596  5.12  0.02370 *
O:A2               -0.27626  0.16651  2.75  0.09708 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Estimated Scale Parameters:
            Estimate Std.err
(Intercept)     1.27   0.369

Correlation: Structure = ar1  Link = identity

Estimated Correlation Parameters:
      Estimate Std.err
alpha    0.979 0.00586
Number of clusters:   49   Maximum cluster size: 533



I decided to use auto-regression as the correlation structure because of the
high auto-correlation of the dependent variable, "L".  However, because one
of the predictors, "O", also has high time dependency (high
autocorrelation), the estimate of "O" (0.00348) seems to be too small.  In
this case, how shall I interpret the parameter?  Should I be using another
analysis, such as loglm?

Thank you in advance for your help!

Sachi

	[[alternative HTML version deleted]]

