robustRegH: Robust Fitting of Linear Models using Huber Psi Function

In robustreg: Robust Regression Functions

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

robustRegH(formula,data,tune=1.345,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 1.345 is 95% asymptotically efficient against outliers
m	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 are 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.

Note

Original package written in 2006

Author(s)

Ian M. Johnson

References

P. J. Huber (1981) Robust Statistics. Wiley.

Birch (1983) Robust F-Test

See Also

robustRegBS()

Examples

data(stackloss)
robustRegH(stack.loss~Air.Flow+Water.Temp,data=stackloss)

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

Example output

Robust Regression with Huber Function
Convergence achieved after: 10 iterations

Robust Regression with Huber Function
Convergence achieved after: 8 iterations

robustreg documentation built on May 10, 2019, 9:04 a.m.