dglm.control {dglm}

Auxiliary for controlling double glm fitting

Description

Auxiliary function as user interface for fitting double generalized linear models. Typically only used when calling dglm.

Usage

dglm.control(epsilon = 1e-007, maxit = 50, trace = FALSE, ...)

Arguments

epsilon

positive convergence tolerance epsilon; the iterations converge when (|L_o - L|)/(|L_o| + 1) > \epsilon, where L_o is minus twice the values of log-likelihood on the previous iteration, and L is minus twice the values of log-likelihood on the current.

maxit

integer giving the maximal number of outer iterations of the alternating iterations.

trace

logical indicating if (a small amount of) output should be produced for each iteration.

...

not currently implemented

Details

When 'trace' is true, calls to 'cat' produce the output for each outer iteration. Hence, 'options(digits = *)' can be used to increase the precision; see the example for glm.control.

Author(s)

Gordon Smyth, ported to R by Peter Dunn ([e-mail])

References

Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47–60.

Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics, 10, 696-709.

Verbyla, A. P., and Smyth, G. K. (1998). Double generalized linear models: approximate residual maximum likelihood and diagnostics. Research Report, Department of Statistics, University of Adelaide.

See Also

dglm-class, dglm

Examples

### A variation on  example(dglm) :
# Continuing the example from  glm, but this time try
# fitting a Gamma double generalized linear model also.
clotting <- data.frame(
      u = c(5,10,15,20,30,40,60,80,100),
      lot1 = c(118,58,42,35,27,25,21,19,18),
      lot2 = c(69,35,26,21,18,16,13,12,12))
         
# The same example as in  glm: the dispersion is modelled as constant
out <- dglm(lot1 ~ log(u), ~1, data=clotting, family=Gamma)
summary(out)

# Try a double glm 
oo <- options()
options(digits=12) # See more details in tracing
out2 <- dglm(lot1 ~ log(u), ~u, data=clotting, family=Gamma,
   control=dglm.control(epsilon=0.01, trace=TRUE))
   # With this value of epsilon, convergence should be quicker
   # and the results less reliable (compare to example(dglm) )

summary(out2)
options(oo)