wle.onestep {wle} | R Documentation |
This function evaluate the One-step weighted likelihood estimator for the regression and scale parameters.
wle.onestep(formula, data=list(), model=TRUE, x=FALSE, y=FALSE, ini.param, ini.scale, raf="HD", smooth=0.0320018, num.step=1, contrasts=NULL)
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 . |
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. |
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. |
Claudio Agostinelli
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.
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.
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