# FastQn: Robust, Efficient and Fast Scale Estimate In robcor: Robust Correlations

 FastQn R Documentation

## Robust, Efficient and Fast Scale Estimate

### Description

Compute the robust scale estimator FastQn, an efficient alternative to the MAD, a fast alternative to the Qn.

### Usage

```FastQn(x, center = median(x), scale = mad(x, center))

fqn(x, center = median(x), scale = mad(x, center))

s_FastQn(x, mu.too = FALSE, center = median(x), ...)
```

### Arguments

 `x` numeric vector of observations. `center` optionally, the center: defaults to the median. `scale` optionally, the basic scale: defaults to the median absolute deviation. `mu.too` logical indicating if the `center` should also be returned for `s_FastQn()`. `...` potentially further arguments for `s_FastQn()` passed to `FastQn()`.

### Details

This function computes one-step M-estimate of scale based on provided robust estimate (defaults to the MAD). It gives 50% breakdown point and Gaussian efficiency about 80%.

The `fqn` function is a shorter alias, like `sd` and `mad`.

### Value

`FastQn()` returns a number, the FastQn robust scale estimator.

`s_FastQn(x, mu.too=TRUE)` returns a length-2 vector with location and scale; this is typically only useful for `covOGK(*, sigmamu = s_FastQn)` or `robcor(*, scaler = s_FastQn)`.

### Author(s)

Paul Smirnov <s.paul@mail.ru>

### References

Smirnov, P. O., Shevlyakov, G. L. (2010). On Approximation of the Qn-Estimate of Scale by Fast M-Estimates. In Book of Abstracts: International Conference on Robust Statistics (ICORS 2010) (pp. 94-95). Prague, Czech Republic.

`mad`, `Qn`.

### Examples

```set.seed(153)
x <- sort(c(rnorm(80), rt(20, df = 1)))
s_FastQn(x, mu.too=TRUE)
FastQn(x)
```

robcor documentation built on June 27, 2022, 9:06 a.m.