Hello,
I want to fit a GEE with a user-defined link function.
For the user-defined link-function I still read
http://finzi.psych.upenn.edu/R/Rhelp01/archive/6555.html and
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/25727.html.
Only for testing purposes I added a new link function
(corlogit) in make.link (as well as in binomial) with
exactly the same code as logit before using my code.
I tried it with glm() and it works without any problem (I
get the same results using 'binomial(link="logit")' or
'binomial(link="corlogit")').
But with library(geepack) (Version: 0.2-10) I get different
results for logit and test.logit as link functions. First
there was an error, so that I had to modify 'geese.fit' (I
only added test.logit where I found logit).
With library(gee) (Version: 4.13-10) R exits without error
when fitting a GEE with binomial(link="corlogit"). Because
of the error 'unkown link' I modified 'gee' the same way as
for geepack().
I never changed the original application files, but I wrote
"own programmes" (using the original code and just adding
corlogit in the list of the link functions). Are there any
other functions where I had to add or modify something? What
else can I do?
Thank you for your help!
Johanna Brandt
(I'm using R 2.0.1 under Windows 2000)
## Example for geese() from the R-Help #####################
I took the example from the help:
> data(ohio)
> summary(geese(resp ~ age + smoke + age:smoke, id=id,
data=ohio,
+ family=binomial(link="logit"), corstr="exch",
scale.fix=TRUE))
Call:
geese(formula = resp ~ age + smoke + age:smoke, id = id,
data = ohio,
family = binomial(link = "logit"), scale.fix = TRUE,
corstr = "exch")
Mean Model:
Mean Link: logit
Variance to Mean Relation: binomial
Coefficients:
estimate san.se wald p
(Intercept) -1.90049529 0.11908698 254.6859841 0.00000000
age -0.14123592 0.05820089 5.8888576 0.01523698
smoke 0.31382583 0.18575838 2.8541747 0.09113700
age:smoke 0.07083184 0.08852946 0.6401495 0.42365667
Scale is fixed.
Correlation Model:
Correlation Structure: exch
Correlation Link: identity
Estimated Correlation Parameters:
estimate san.se wald p
alpha 0.354531 0.03582698 97.92378 0
Returned Error Value: 0
Number of clusters: 537 Maximum cluster size: 4
>
> ## Korrigiert
> summary(fit.korr <- geese(resp ~ age + smoke + age:smoke,
id=id, data=ohio,
+ family=binomial(link="corlogit"),
corstr="exch", scale.fix=TRUE))
Call:
geese(formula = resp ~ age + smoke + age:smoke, id = id,
data = ohio,
family = binomial(link = "corlogit"), scale.fix = TRUE,
corstr = "exch")
Mean Model:
Mean Link: corlogit
Variance to Mean Relation: binomial
Coefficients:
estimate san.se wald p
(Intercept) -1.12581067 0.06344341 314.8891093 0.00000000
age -0.07680433 0.03128947 6.0252497 0.01410264
smoke 0.17083868 0.10162807 2.8258236 0.09275930
age:smoke 0.03672858 0.04872412 0.5682249 0.45096515
Scale is fixed.
Correlation Model:
Correlation Structure: exch
Correlation Link: identity
Estimated Correlation Parameters:
estimate san.se wald p
alpha 0.3545883 0.03583136 97.9315 0
Returned Error Value: 0
Number of clusters: 537 Maximum cluster size: 4
## Example for gee() from the R-Help #######################
if(require(MASS)) {
data(OME)
## not fully appropriate link for these data.
(fm.korr <- gee(cbind(Correct, Trials-Correct) ~ Loud + Age
+ OME, id = ID,
data = OME, family = binomial(link="corlogit"),
corstr = "exchangeable"))
summary(fm.korr)
}
