[R] Get significant codes from a model output fit with GEE package

[Dennis Murphy]

Hi:

If you're asking about the p-values, here's a reproducible example
from the gee package:

library('gee')
m <- gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable")

# Nosing around the summary method return object:
> names(summary(m))
 [1] "call"                "version"             "nobs"
 [4] "residual.summary"    "model"               "title"
 [7] "coefficients"        "working.correlation" "scale"
[10] "error"               "iterations"
> summary(m)$coefficients    # Found it...
             Estimate Naive S.E.   Naive z Robust S.E.  Robust z
(Intercept)  36.38889   3.069434 11.855246    5.774705  6.301428
tensionM    -10.00000   3.910008 -2.557539    7.463905 -1.339781
tensionH    -14.72222   3.910008 -3.765266    3.731952 -3.944912
> class(summary(m)$coefficients)
[1] "matrix"

# Assuming your significance tests are two sided,
naivePval <- 2 * pnorm(-abs(summary(m)$coefficients[, 3]))
> naivePval
 (Intercept)     tensionM     tensionH
2.021302e-32 1.054156e-02 1.663716e-04
> robustPval <- 2 * pnorm(-abs(summary(m)$coefficients[, 5]))
> robustPval
 (Intercept)     tensionM     tensionH
2.949162e-10 1.803165e-01 7.982945e-05

HTH,
Dennis

On Mon, Aug 15, 2011 at 11:29 AM, david oseguera montiel
<davidoseguera at yahoo.com.mx> wrote:
> Does anyone know how could I get the significant codes from mixed model
> output fitted with a GEE package?
> The output I got is the following:
>
>  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:     Exchangeable
>
> Call:
> gee(formula = bru ~ +grac + locc + dekc, id = flo, data = all,
>    family = binomial(link = "logit"), corstr = "exchangeable")
>
> Summary of Residuals:
>        Min          1Q      Median          3Q         Max
> -0.41935925 -0.15355800 -0.09403907 -0.03244056  0.98117731
>
>
> Coefficients:
>                         Estimate    Naive S.E.    Naive z      Robust S.E.
>  Robust z
> (Intercept) -0.3254043  0.1640277 -1.983837   0.1375856 -2.365105
> graczero    -1.6884041  0.4978737 -3.391230   0.5222118 -3.233179
> loccM       -1.3815591  0.2202543 -6.272563   0.2256275 -6.123185
> dekclow     -0.5583225  0.2487267 -2.244723   0.2293527 -2.434341
>
> Estimated Scale Parameter:  1.025636
>
> I much appreciate any help.
> David
>
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