`mclogit.fit.Rd`

These functions are exported and documented for use by other packages. They are not intended for end users.

```
mclogit.fit(y, s, w, X,
dispersion=FALSE,
start = NULL, offset = NULL,
control = mclogit.control())
mmclogit.fitPQLMQL(y, s, w, X, Z, d,
start = NULL,
start.Phi = NULL,
start.b = NULL,
offset = NULL, method=c("PQL","MQL"),
estimator = c("ML","REML"),
control = mmclogit.control())
```

- y
a response vector. Should be binary.

- s
a vector identifying individuals or covariate strata

- w
a vector with observation weights.

- X
a model matrix; required.

- dispersion
a logical value or a character string; whether and how a dispersion parameter should be estimated. For details see

`dispersion`

.- Z
the random effects design matrix.

- d
dimension of random effects. Typically $d=1$ for random intercepts only, $d>1$ for models with random intercepts.

- start
an optional numerical vector of starting values for the coefficients.

- offset
an optional model offset. Currently only supported for models without random effects.

- start.Phi
an optional matrix of strarting values for the (co-)variance parameters.

- start.b
an optional list of vectors with starting values for the random effects.

- method
a character string, either "PQL" or "MQL", specifies the type of the quasilikelihood approximation.

- estimator
a character string; either "ML" or "REML", specifies which estimator is to be used/approximated.

- control
a list of parameters for the fitting process. See

`mclogit.control`

A list with components describing the fitted model.