poissonL2T: Fitting of Poisson Generalized Linear Models using MT method...

In poissonMT: Robust M-Estimators Based on Transformations for Poisson Model

Description

poissonL2T is used to fit generalized linear models by MT method with L2 rho function. The model is specified by the x and y components. Since the L2 rho function is used the method is not robust.

Usage

  poissonL2T(x, y, start = NULL, tol = 1e-08, maxit = 100,
  m.approx = NULL, mprime.approx = NULL, na.to.zero = TRUE)

Arguments

x	design matrix of dimension n * p.
y	vector of observations of length n.
start	starting values for the parameters in the linear predictor.
tol	convergence tolerance for the parameter vector.
maxit	integer specifying the maximum number of IRWLS iterations.
m.approx	a function that return the value, for each linear predictor, that makes the estimating equation Fisher consistent. If NULL the default internal function is used.
mprime.approx	a function that return the value, for each linear predictor, corresponding to the first derivative of m.approx. If NULL the default internal function is used.
na.to.zero	logical, should the eventual NA in the coefficients be replaced by 0?

Value

A vector with the estimated coefficients.

Author(s)

Claudio Agostinelli, Marina Valdora and Victor J. Yohai

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.

See Also

poissonMT

Examples

  data(epilepsy)
  x <- model.matrix( ~ Age10 + Base4*Trt, data=epilepsy)
  poissonMTsetwd(tempdir())
  Efit4 <- poissonL2T(x=x, y=epilepsy$Ysum)

poissonMT documentation built on May 2, 2019, 11:01 a.m.