[R] GEE: estimate of predictor with high time dependency
Charles C. Berry
cberry at tajo.ucsd.edu
Tue Jun 8 22:11:18 CEST 2010
On Tue, 8 Jun 2010, Sachi Ito wrote:
> 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")
> geeglm(formula = lateral ~ ontask + attachment + ontask:attachment,
> family = binomial, data = firstgroupnowalking, id = id, corstr = "ar1")
> 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?
First off, do you know how to interpret main effects in the presence of an
interaction involving them?? I suspect not, but feel free to offer
evidence to the contrary and then tell us why discussing 'the estimate of
"O"' is sensible.
Secondly, without much more detail on the data it is hard to know what to
make of a question like this even if the business of main
effects/interactions is handled. As suggested, providing a minimal,
reproducible example of R code will go a long way.
> Should I be using another analysis, such as loglm?
> Thank you in advance for your help!
> [[alternative HTML version deleted]]
Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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