wle.onestep: A One-Step Weighted Likelihood Estimator for Linear model

In wle: Weighted Likelihood Estimation

Description

This function evaluate the One-step weighted likelihood estimator for the regression and scale parameters.

Usage

wle.onestep(formula, data=list(), model=TRUE, x=FALSE, 
            y=FALSE, ini.param, ini.scale, raf="HD", 
            smooth=0.031, num.step=1, 
            contrasts=NULL, verbose=FALSE)

Arguments

formula	a symbolic description of the model to be fit. The details of model specification are given below.
data	an optional data frame containing the variables in the model. By default the variables are taken from the environment which wle.stepwise is called from.
model, x, y	logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response.)
ini.param	starting values for the coefficients.
ini.scale	starting values for the scale parameters.
raf	type of Residual adjustment function to be use: raf="HD": Hellinger Distance RAF, raf="NED": Negative Exponential Disparity RAF, raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
smooth	the value of the smoothing parameter.
num.step	number of the steps.
contrasts	an optional list. See the contrasts.arg of model.matrix.default.
verbose	if TRUE warnings are printed.

Value

wle.onestep returns an object of class "wle.onestep".

Only print method is implemented for this class.

The object returned by wle.onestep are:

coefficients	the parameters estimator.
standard.error	an estimation of the standard error of the parameters estimator.
scale	an estimation of the error scale.
residuals	the unweighted residuals from the estimated model.
fitted.values	the fitted values from the estimated model.
tot.weights	the sum of the weights divide by the number of observations.
weights	the weights associated to each observation.
f.density	the non-parametric density estimation.
m.density	the smoothed model.
delta	the Pearson residuals.
call	the match.call().
contrasts
xlevels
terms	the model frame.
model	if model=TRUE a matrix with first column the dependent variable and the remain column the explanatory variables for the full model.
x	if x=TRUE a matrix with the explanatory variables for the full model.
y	if y=TRUE a vector with the dependent variable.

Author(s)

Claudio Agostinelli

References

Agostinelli, C., (1997) A one-step robust estimator based on the weighted likelihood methodology, Working Paper n. 1997.16, Department of Statistics, University of Padova.

Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.

Agostinelli, C., Markatou, M., (1998) A one-step robust estimator for regression based on the weighted likelihood reweighting scheme, Statistics \& Probability Letters, Vol. 37, n. 4, 341-350.

Agostinelli, C., (1998) Verosimiglianza pesata nel modello di regressione lineare, XXXIX Riunione scientifica della Societ\'a Italiana di Statistica, Sorrento 1998.

See Also

wle.smooth an algorithm to choose the smoothing parameter for normal distribution and normal kernel, wle.lm a function for estimating linear models with normal distribution error and normal kernel.

Examples

#library(wle)
#library(lqs)

#data(artificial)

#result.lts <- lqs(y.artificial~x.artificial, 
#                 method = "lts")

#result.wle <- wle.onestep(y.artificial~x.artificial,
#               ini.param=result.lts$coefficients,
#               ini.scale=result.lts$scale[1])

#result.wle

Example output

Loading required package: circular

Attaching package: 'circular'

The following objects are masked from 'package:stats':

    sd, var

wle documentation built on May 29, 2017, 11:48 a.m.