event.nab.2 is 0/1 and I dichotomized va to get va.2 to see if I could
get geeglm to work. glm has no problem with the data but geeglm chokes.
Each subject (patient.id) has at most 2 observations and more than 3/4
of the subjects have 2 observations. I have even worse problems trying
to use glmmPQL from MASS and worse still trying to use lmer from lme4.
But I figured a marginal model would work. (geeglm seems to work OK with
most of the explanatory factors in the data set I have but a couple of
them show similar problems.)
>
summary(glm(event.nab.2~va.2,family=binomial(link="logit"),data=test))
Call:
glm(formula = event.nab.2 ~ va.2, family = binomial(link = "logit"),
data = test)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3787 -0.3787 -0.2246 -0.2246 2.7177
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.5993 0.1804 -14.41 < 2e-16 ***
va.2(84, Inf] -1.0685 0.3435 -3.11 0.00187 **
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 363.19 on 958 degrees of freedom
Residual deviance: 352.28 on 957 degrees of freedom
AIC: 356.28
Number of Fisher Scoring iterations: 6
summary(geeglm(event.nab.2~va.2,family=binomial(link="logit"),id=patient.id,cor="exch",data=test))
Error in geese.fit(xx, yy, id, offset, soffset, w, waves = waves,
zsca, :
nrow(zsca) and length(y) not match
> head(test)
patient.id event.nab.2 va.2
1 1 0 (-Inf,84]
2 1 0 (-Inf,84]
3 2 0 (84, Inf]
4 2 0 (84, Inf]
5 3 0 (84, Inf]
6 3 0 (84, Inf]
I'm using R 2.3.1 and the latest version of geepack. I get a similar
error message if I use va which is continuous.
I don't know what the error message from geeglm/geese means.
Rick B.
