roblm {roblm}R Documentation

MM-regression estimators

Description

MM-regression estimators

Usage

roblm(formula, data = list(), weights, na.action, 
        model = TRUE, x = FALSE, y = FALSE, singular.ok = TRUE, 
        contrasts = NULL, offset = NULL, control = roblm.control())

Arguments

formula formula
data data
weights weights
na.action na.action
model model
x x
y y
singular.ok singular.ok
contrasts contrasts
offset offset
control control

Details

This function computes and MM-regression estimator as described in Yohai (1987). It uses a bi-square re-desceding score function, and by default it returns a highly robust and highly efficient estimator (with 50% breakdown point and 95% asymptotic efficiency for normal errors). It uses an S-estimator (Rousseeuw and Yohai, 1984) for the errors scale. This estimator is also computed with a bi-square score function. This function uses the Fast-S algorithm of Salibian-Barrera and Yohai (2005) to compute the S-estimators.

Standard errors are computed using the stable asymptotic formula of Croux, Dhaene and Hoorelbeke (2004). There is also the option of using the Robust Bootstrap of Salibian-Barrera and Zamar (2002). See roblm.control.

Value

An object of class roblm. A list that includes the following components:

coef The MM-regression estimator
scale The S-scale estimator
s The auxiliary S-regression estimator
cov The estimated covariance matrix of the regression coefficients
residuals Residuals associated with the MM-estimator
fitted.values Fitted values associated with the MM-estimator
converged TRUE if the IRWLS iterations converged

Note

Author(s)

Matias Salibian-Barrera

References

Rousseeuw and Yohai (1984); Yohai (1987); Salibian-Barrera and Yohai (2005)

See Also

roblm.control

Examples

data(coleman.dat, package='roblm')
summary( roblm(y~., data=coleman.dat, control = 
        roblm.control(compute.roboot=TRUE) ) )

[Package roblm version 0.5-1 Index]