huber.NR {asbio} | R Documentation |
Huber M-estimator iterative least squares algorithm
Description
Algorithm for calculating fully iterated or one step Huber M-estimators of location.
Usage
huber.NR(x, c = 1.28, iter = 20)
Arguments
x |
A vector of quantitative data |
c |
Bend criterion. The value |
iter |
Maximum number of iterations |
Details
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.
Value
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.
Author(s)
Ken Aho
References
Huber, P. J. (2004) Robust Statistics. Wiley.
Wilcox, R. R. (2005) Introduction to Robust Estimation and Hypothesis Testing, Second Edition. Elsevier, Burlington, MA.
See Also
Examples
x<-rnorm(100)
huber.NR(x)