wle.glm.control: Auxiliary for Controlling GLM Robust Fitting
In wle: Weighted Likelihood Estimation

Description Usage Arguments Details Value Author(s) References See Also Examples

Auxiliary function as user interface for glm robust fitting. Typically only used when calling wle.glm or wle.glm.fit.

wle.glm.control(boot = 30, group = NULL, num.sol = 1, 
  raf = c("GKL", "PWD", "HD", "NED", "SCHI2"), tau = 0.1, 
  cutpoint = 0, powerdown = 1, delta = NULL, smooth = NULL,
  asy.smooth=0.031, tol = 10^(-6), equal = 10^(-3),
  max.iter = 500, window.size = NULL, use.asymptotic = NULL, 
  use.smooth=TRUE, mle.dispersion = FALSE, verbose = FALSE)

`boot`	interger. Number of starting points based on boostrap subsamples to use in the search of the roots.
`group`	integer. Dimension of the bootstap subsamples. The default value is max(round(size/2),var+1) where size is the number of observations and var is the number of predictors.
`num.sol`	interger. Maximum number of roots to be searched.
`raf`	type of Residual adjustment function to be used: `raf="GKL"`: Generalized Kullback-Leibler family RAF (see details), `raf="PWD"`: Power Divergence family RAF (see details), `raf="HD"`: Hellinger Distance RAF, `raf="NED"`: Negative Exponential Disparity RAF, `raf="SCHI2"`: Symmetric Chi-Squared Disparity RAF.
`tau`	positive real. Used in selecting the member of the RAF family in the case of `GKL` or `PWD`.
`cutpoint`	a value in the interval [0,1].
`powerdown`	a non negative number.
`delta`	between (0,1). Used in the construction of the weights for the Binomial family.
`smooth`	the value of the smoothing parameter; used in the evaluation of weights in the case of continuous models.
`asy.smooth`	the value of the smoothing parameter; used in the evaluation of asymptotic weights. or in the case of continuous models.
`tol`	the absolute accuracy to be used to achieve convergence of the algorithm.
`equal`	the absolute value for which two roots are considered the same. Two roots are compared using the corresponding final weights.
`max.iter`	maximum number of iterations.
`window.size`	positive real or `NULL`. The observations with a distance, in the predictors space, less than this threshold are used to estimate the conditional distribution for a given level of the predictor.
`use.asymptotic`	interger or `NULL`. The minimum number of observations for the level of the predictors under which asymptotic weights are used.
`use.smooth`	if `TRUE` the smoothed model is used in the computation of the Pearson Residuals. For now, the option is used only for the Gamma family.
`mle.dispersion`	if `TRUE` the weighted likelihood estimator for dispersion is used otherwise the weighted chi-sqaured statistics is used.
`verbose`	if `TRUE` warnings are printed.

The Generalized Kullback-Leibler family RAF is defined as:

\ln(tau*x+1)/tau

for tau > 0.

The Power Divergence family RAF is defined as:

tau*((x + 1)^(1/tau) - 1)

for 0 < tau < Inf while

\ln(x+1)

for tau=Inf.

A list with the arguments as components.

Claudio Agostinelli and Fatemah Alqallaf

Agostinelli, C. and Alqallaf, F. (2009) Robust inference in Generalized Linear Models. Manuscript in preparation.

wle.glm

### A variation on  example(wle.glm) :

## Annette Dobson's example ...
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
oo <- options(digits = 12) # to see more when tracing :
wle.glm.D93X <- wle.glm(counts ~ outcome + treatment, family=poisson(),
                control=list(glm=glm.control(trace = TRUE), 
                wle=wle.glm.control(raf='GKL', tau=0.15)))
options(oo)
coef(wle.glm.D93X)