R: Representation of Models of Formal Class "glimML"

glimML-class {aod} | R Documentation

## Representation of Models of Formal Class "glimML"

### Description

Representation of models of formal class "glimML" fitted by maximum-likelihood method.

### Objects from the Class

Objects can be created by calls of the form `new("glimML", ...)` or, more commonly, via the functions `betabin` or `negbin`.

### Slots

`CALL`

  The call of the function.

`link`

  The link function used to transform the mean: “logit”, “cloglog” or “log”.

`method`

  The type of fitted model: “BB” for beta-binomial and “NB” for negative-binomial models.

`formula`

  The formula used to model the mean.

`random`

  The formula used to model the overdispersion parameter φ.

`data`

  Data set to which model was fitted. Different from the original data in case of missing value(s).

`param`

  The vector of the ML estimated parameters b and φ.

`varparam`

  The variance-covariance matrix of the ML estimated parameters b and φ.

`fixed.param`

  The vector of the ML estimated fixed-effect parameters b.

`random.param`

  The vector of the ML estimated random-effect (correlation) parameters φ.

`logL`

  The log-likelihood of the fitted model.

`logL.max`

  The log-likelihood of the maximal model (data).

`dev`

  The deviance of the model, i.e., `- 2 * (logL - logL.max)`.

`df.residual`

  The residual degrees of freedom of the fitted model.

`nbpar`

  The number of estimated parameters, i.e., nbpar = total number of parameters - number of fixed parameters. See argument `fixpar` in `betabin` or `negbin`.

`iterations`

  The number of iterations performed in `optim`.

`code`

  An integer (returned by `optim`) indicating why the optimization process terminated.

  1

    Relative gradient is close to 0, current iterate is probably solution.

  2

    Successive iterates within tolerance, current iterate is probably solution.

  3

    Last global step failed to locate a point lower than estimate. Either estimate is an approximate local minimum of the function or `steptol` is too small.

  4

    Iteration limit exceeded.

  5

    Maximum step size `stepmax` exceeded 5 consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or `stepmax` is too small.

`msg`

  Message returned by `optim`.

`singular.hessian`

  Logical: true when fitting provided a singular hessian, indicating an overparamaterized model.

`param.ini`

  The initial values provided to the ML algorithm.

`na.action`

  A function defining the action taken when missing values are encountered.

[Package aod version 1.3 Index]