Arguments
param
The vector of regression and not regression parameters.
xdat
$(\mathbf{x}_1 , \mathbf{x}_2 , \ldots ,
\mathbf{x}_n )^\top$, where the matrix $\mathbf{x}_i,\,i=1,\ldots,n$ for a given unit will
depend on the times of observation for that unit ($j_i$) and will have
number of rows $j_i$, each row corresponding to one of the $j_i$ elements
of $y_i$ and $p$ columns where $p$ is the number of covariates including
the unit first column to account for the intercept. This xdat matrix is
of dimension $(N\times p),$ where $N =\sum_{i=1}^n j_i$ is the total
number of observations from all units.
ydat
$(y_1 , y_2 , \ldots , y_n )^\top$,
where the response data vectors $y_i,\,i=1,\ldots,n$
are of possibly different lengths for different units. In particular,
we now have that $y_i$ is ($j_i \times 1$), where $j_i$ is the number of
observations on unit $i$. The total number of observations from all units
is $N =\sum_{i=1}^n j_i$. The ydat are the collection of data vectors
$y_i, i = 1,\ldots,n$ one from each unit which summarize all the data
together in a single, long vector of length $N$.
margmodel
Indicates the marginal model.
Choices are “poisson” for Poisson, “bernoulli” for Bernoulli,
and “nb1” , “nb2” for the NB1 and NB2 parametrization
of negative binomial in Cameron and Trivedi (1998).
link
The link function.
Choices are “log” for the log link function, “logit” for
the logit link function, and “probit” for
the probit link function.