Dear R-helpers,


I'd like to use glmmPQL to predict binary responses based on a data.frame
data1
containing N entries (N<1000):


         target covariate1 covariate2  covariate3 ...    covariateM
cluster
134131        1 -0.30031885         0           0        -2.886870e-07
      1
 38370        1 -0.04883229         0           1        -1.105720e-07
    1
 19315        1 -0.11084267         0           0         6.362602e-07
    1
 33806        1 -0.14529289         0           0        -1.361914e-07
    1
154332        1 -0.07983748         0           1        -7.635439e-07
    1
...

 17228        0 -0.49668507         0           1        -2.954118e-07
      1
 41147        0 -0.32787902         0           1        -1.502238e-06
       1
104213        0  0.17164908         0           0        -2.119738e-06
    1
 28071        0 -2.08828495         0           0        -7.640990e-07
    1
    91        0  1.47042214         0           0        -5.661632e-07
       1


The responses are in column "target", and there are M covariates. All
observations
belong to cluster 1.

The data is to be modelled without fixed effects, i.e., a by a zero-mean
random effects model.
Additionally, the model is to be fed with a correlation structure
(to avoid double entries, a column x<-rnorm(nrow(data1),0,0.000001) is
appended):

correlation=corGaus(form=~covariate1 + covariate2 + ... +covariateM + x,
nugget=FALSE)

The data is visible to glmmPQL:

attach(data1)

Is
glmmPQLm<-glmmPQL(target~0,random=~1|cluster, data=data1,
correlation=corGaus(form=~covariate1 + covariate2 + ... +covariateM + x,
nugget=FALSE),
family="binomial") the right expression to fit the model ?


Thanks,
Don Muang

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