Dear List:
I found an error when I called the 'gee' function. I cannot solve and explain
it. There are no errors when I used the 'geeglm' function. Both functions fit
the gee model. The project supervisor recommends me to use the 'gee' function.
But I cannot explain to him why this error happens. Would you help me solve
this problem? I appreciate your help.
In this project I will use the 'gee' or 'geeglm' and 'glmer' to fit the
simulated multivariate count responses. I generated the data like this:
Set β0 = β1 = 1, μ0 = 3, and n = 50.
For each 1 ≤ i ≤ n,
Simulate xi from N (1, 1).
Simulate zi0 and zit from
zi0 follows i.d. Poisson (μ0) ,
zit | xi follows i.d. Poisson (μit) , 1 ≤ t ≤ 3,
log (μit) = log(E (zit | xi)) = β0t + xiβ1t = 1+xi.
Let yit = zi0 + zit, 1 ≤ t ≤ 3.
So my data frame, let me call it 'simdata', the first 10 rows look like this:
id y.1 y.2 y.3 x
1 3 5 6 -0.06588626
2 6 7 6 -0.08265981
3 6 8 13 0.58307719
4 22 21 28 2.21099940
5 5 12 8 1.06299869
6 8 21 24 1.47615784
7 11 8 9 0.83748390
8 16 15 16 1.67011313
9 9 7 7 -0.14181264
10 31 37 40 2.56751453
This is the longitudinal data. I will change its shape to analyze it. The
changed 'newdata' looks like this:
id x time y
1 -0.06588626 1 3
2 -0.08265981 1 6
3 0.58307719 1 6
4 2.21099940 1 22
5 1.06299869 1 5
6 1.47615784 1 8
7 0.83748390 1 11
8 1.67011313 1 16
9 -0.14181264 1 9
10 2.56751453 1 31
...........................
1 -0.06588626 2 5
2 -0.08265981 2 7
3 0.58307719 2 8
4 2.21099940 2 21
5 1.06299869 2 12
6 1.47615784 2 21
7 0.83748390 2 8
8 1.67011313 2 15
9 -0.14181264 2 7
10 2.56751453 2 37
...........................
1 -0.06588626 3 6
2 -0.08265981 3 6
3 0.58307719 3 13
4 2.21099940 3 28
5 1.06299869 3 8
6 1.47615784 3 24
7 0.83748390 3 9
8 1.67011313 3 16
9 -0.14181264 3 7
10 2.56751453 3 40
...........................
My data 'y' comes from x. So their correlations are not independent. What
does the argument 'corstr' mean it defined in the function. I tried all
choices. But the error was still there. Here was the function I used in my
programming:
mfit1 <-
gee(y~x,data=newdata,family=poisson(link="log"),id=id,corstr="exchangeable")
GEE: GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
gee S-function, version 4.13 modified 98/01/27 (1998)
Model:
Link: Logarithm
Variance to Mean Relation: Poisson
Correlation Structure: Exchangeable
Call:
gee(formula = y ~ x, id = id, data = newdata, family = poisson(link = "log"),
corstr = "exchangeable")
Number of observations : 150
Maximum cluster size : 1
Coefficients:
(Intercept) x
1.5849653 0.7937203
Estimated Scale Parameter: 1.162505
Number of Iterations: 1
Working Correlation[1:4,1:4]
Error in print(x$working.correlation[1:4, 1:4], digits = digits) :
subscript out of bounds
Is this kind of data not fit for the function 'gee'? Because when I tested
this two functions by using the R data 'warpbreaks' they worked perfect
although some returned objects were different. I used the following to do it:
1. (summary(gee(breaks ~ tension, id=wool, data=warpbreaks,
corstr="exchangeable"))
2. summary(geeglm(breaks ~ tension, id=wool, data=warpbreaks,
corstr="exchangeable"))).
The first one is from the example of ?gee file.
I will attach the part of my programming as .R file. You can excute in R
software. Thanks a lot.
I appreciate your help.
Best regards.
Sincerely,
Cynthia Wu
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