Dear R users

Recently I received advice from this fine group on gee() and sample weights

One suggestion was to use geeglm()



I hope someone can help me to solve a problem that arises when converting a
code from gee to geeglm.


*Here is a code that I wrote with the original data, not weighted: *

> m1 <- gee( Bin ~    educ+agemean+ residencysize + yearx  ,  id = rad09 ,
data = Males, subset = marp1 == 1   ,  +  family =  binomial, corstr
="unstructured" )

  (Intercept)          educ       agemean residencysize         yearx
     -0.23875      -0.17931      -0.01470      -0.07418      -0.15200
> se <- summary(m1)$coefficients["yearx", "Robust S.E."]
> efrinedri <- coef(m1)["yearx"] + c(-1, 1) * se * qnorm(0.975)
> printco( y1 =  summary(m1)$coefficients["yearx", "Estimate"] , uppdown =
efrinedri )
[1] 0.85 (    0.59 ,    1.24 )


*
*
*Trying to convert it to geeglm() with sample weiht: *


m1 <- geeglm( Bin ~    educ+agemean+ residencysize + yearx  ,  id = rad09 ,
data = Males, subset = marp1 == 1   ,  family =  binomial,  weights =
Vigtpan  , corstr ="unstructured" )


*I get the following error message and not sure how to work on that. Any
suggestions appreciated*

Error in geese.fit(xx, yy, id, offset, soffset, w, waves = waves, zsca,  :
  nrow(zsca) and length(y) not match
In addition: Warning messages:
1: In eval(expr, envir, enclos) :
  non-integer #successes in a binomial glm!
2: glm.fit: algorithm did not converge
3: glm.fit: fitted probabilities numerically 0 or 1 occurred


*Regards*
*Stefan Jonsson*
*
*

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