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)
```

*asbio*version 1.9-7 Index]