robustRegH {robustreg} | R Documentation |
Robust Fitting of Linear Models using Huber 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
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 |
max.it |
Maximum number of iterations to achieve convergence in IRLS algorithm |
tol |
Tolerance level in determining convergence |
anova.table |
If |
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)