robustRegBS: Robust Fitting of Linear Models using Bisquare Psi Function

Description

Using iteratively reweighted least squares (IRLS), the function calculates the optimal weights to perform m-estimator or bounded influence regression. Returns robust beta estimates, mean squared error (MSE) and prints robust ANOVA table.

Usage

robustRegBS(formula,data,tune=4.685,m=TRUE,max.it=1000,tol=1e-5,anova.table=FALSE)

Arguments

formula

Model

data

A data frame containing the variables in the model.

tune

Tuning Constant. Default value of 4.685 is 95% asymptotically efficient against outliers

If TRUE, calculates m estimates of beta. If FALSE, calculates bounded influence estimates of beta

max.it

Maximum number of iterations to achieve convergence in IRLS algorithm

tol

Tolerance level in determining convergence

anova.table

If TRUE, prints robust ANOVA table

Details

M-estimates of beta should be used when evaluating least squares estimates of beta and diagnostics show outliers. Least squares estimates of beta should be used as starting points to achieve convergence.

Bounded influence estimates of beta should be used when evaluating least squares estimates of beta and diagnostics show large values of the "Hat Matrix" diagonals and outliers.

References

Tukey,

Birch, Robust F-Test, 1983

Examples

Run this code

# NOT RUN {
data(stackloss)
robustRegBS(stack.loss~Air.Flow+Water.Temp,data=stackloss)

#If X matrix contained large values of H matrix (high influence points)
robustRegBS(stack.loss~Air.Flow+Water.Temp,data=stackloss,m=FALSE)
# }