glimML-class: Representation of Models of Formal Class "glimML"
In aod: Analysis of Overdispersed Data

glimML-class

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 \phi.

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 \phi.

varparam

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

fixed.param

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

random.param

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

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.

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