poissonMTinitialParallel: Initial Robust Estimates based on MT robust method for...
In poissonMT: Robust M-Estimators Based on Transformations for Poisson Model

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

View source: R/poissonMTinitialParallel.R

poissonMTinitialParallel is used to provides a robust initial estimate for fit generalized linear models. This is the parallel computing version. The model is specified by the x and y components.

poissonMTinitialParallel(x, y, stage2 = TRUE, alpha = c(0.025, 0.025),
  tol = 1e-04, cc = 2.3, psi = "bisquare", maxit = 20,
  zero = sqrt(.Machine$double.eps), replace.small = 0.5, start = NULL,
  na.to.zero = TRUE, parallel = c("no", "multicore", "snow"),
  ncpus = 1, cl = NULL)

`x`	design matrix of dimension n p*.
`y`	vector of observations of length `n`.
`stage2`	logical, the second stage should be performed?
`alpha`	quantile orders used in the second stage.
`tol`	convergence tolerance for the parameter vector.
`cc`	tuning constant c for Tukey's bisquare psi-function.
`psi`	the name of the `psi` function. At the moment only the `bisquare` is available.
`maxit`	integer specifying the maximum number of IRWLS iterations.
`zero`	eigenvalues smaller than `zero` will be considered exactly equal to 0.
`replace.small`	all the observations `y` smaller than `replace.small` are replaced by `replace.small` value.
`start`	eventual starting values, as a reference, for the parameters in the linear predictor.
`na.to.zero`	logical, should the eventual `NA` in the coefficients be replaced by `0`?
`parallel`	The type of parallel operation to be used. By default (`none`) no parallel is used.
`ncpus`	integer: number of processes to be used in parallel operation. Typically one would chose this to the number of available CPUs.
`cl`	An optional `parallel` or `snow` cluster for use if `parallel = "snow"`. If not supplied, a cluster on the local machine is created for the duration of the `poissonMTinitialParallel` call.

This function is the same as function poissonMTinitial, however it can takes advantage of parallel computing.

A list with the following components

`coefficients1`	initial value proposed at the end of the first stage.
`obj1`	value of the MT objective function at `coefficients1`.
`coefficients2a`	initial value proposed at the end of the first part od the second stage.
`obj2a`	value of the MT objective function at `coefficients2a`.
`coefficients2b`	initial value proposed at the end of the second part od the second stage.
`obj2b`	value of the MT objective function at `coefficients2b`.
`coefficients`	initial value proposed.
`obj`	value of the MT objective function at `coefficients`.

Claudio Agostinelli, Marina Valdora and Victor J. Yohai

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.

poissonMTinitial, poissonMT and poissonL2T

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