poissonSSinitial: Initial Robust Estimates based on SubSampling method for fitting of Poisson Generalized Linear Models

Description

poissonSSinitial is used to provides a robust initial estimate for fit generalized linear models. The model is specified by the x and y components.

Usage

poissonSSinitial(x, y, nsubm, size = ncol(x), cc = 2.3,
  psi = "bisquare", na.to.zero = TRUE, trace.lev = 0)

Arguments

design matrix of dimension \(n \times p\).

vector of observations of length n.

nsubm

the number of subsamples to take for finding an initial estimate.

size

size of the subsamples.

tuning constant c for Tukey's bisquare psi-function.

psi

the name of the psi function. At the moment only the bisquare is available.

na.to.zero

logical, should the eventual NA in the coefficients be replaced by 0?

trace.lev

logical (or integer) indicating if intermediate results should be printed; defaults to 0 (the same as FALSE).

Value

A list with the following components

coefficients

initial value proposed.

obj

value of the MT objective function at coefficients.

nOksamples

number of subsamples without errors.

References

C. Agostinelli, M. Valdora and V.J Yohai (2018) Initial Robust Estimation in Generalized Linear Models with a Large Number of Covariates. Submitted.

M. Valdora and V.J. Yohai (2014) Robust estimators for generalized linear models. Journal of Statistical Planning and Inference, 146, 31-48.

Examples

Run this code

# NOT RUN {
  data(epilepsy)
  x <- model.matrix( ~ Age10 + Base4*Trt, data=epilepsy)
  start <- poissonSSinitial(x=x, y=epilepsy$Ysum, nsubm=100)$coefficients
  start
# }