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, from MASS. Note that if MAD = 0, then NA is returned.
Usage
huber.mu(x, c = 1.28, iter = 20, conv = 1e-07)
Arguments
x
A vector of quantitative data.
c
Stop criterion. The value c = 1.28 gives 95 percent efficiency of the mean given normality.
iter
Maximum number of iterations.
conv
Convergence criterion.
Value
Returns Huber's M-estimator of location.
References
Huber, P. J. (2004) Robust Statistics. Wiley.
Wilcox, R. R. (2005) Introduction to Robust Estimation and Hypothesis Testing, Second
Edition. Elsevier, Burlington, MA.