Algorithm for calculating fully iterated or one step Huber M-estimators of location.
huber.NR(x, c = 1.28, iter = 20)Returns iterative least squares iterations which converge to Huber's M-estimator. The first element in the vector is the sample median. The second element is the Huber one-step estimate.
A vector of quantitative data
Bend criterion. The value c = 1.28 gives 95% efficiency of the mean given normality.
Maximum number of iterations
Ken Aho
The Huber M-estimator is a robust high efficiency estimator of location that has probably been under-utilized by biologists. It is based on maximizing the likelihood of a weighting function. This is accomplished using an iterative least squares process. The Newton Raphson algorithm is used here. The function usually converges fairly quickly < 10 iterations. The function uses the Median Absolute Deviation function, mad. Note that if MAD = 0, then NA is returned.
Huber, P. J. (2004) Robust Statistics. Wiley.
Wilcox, R. R. (2005) Introduction to Robust Estimation and Hypothesis Testing, Second Edition. Elsevier, Burlington, MA.
huber.one.step, huber.mu, mad