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robfreq |
Robust Frequency
Estimation |

**DESCRIPTION**- Estimates a sum of sinusoidal signals robustly using elemental set estimators to
initialize an MM estimation scheme. Simultaneously has high breakdown and high efficiency
under normal errors.

Note that to use this function you will also need to obtain pronyfreq. **USAGE**`robfreq(y,nfreq=1,s=NULL,breakdown=0.5,trace=F,maxregs=6000)`**REQUIRED ARGUMENTS**`y`numeric vector of observations. **OPTIONAL ARGUMENTS**`nfreq`number of frequencies to estimate. `scale`numeric constant giving scale of the residuals. By default this is estimated using mscale for the starting parameter values. `breakdown`numeric constant between 0 and 0.5 giving the desired breakdown point. `trace`logical constant. If true, the progress of the algorithm is printed out in compact format. `maxregs`The total number of evaluations of the LTS criterion allowed. **VALUE**- The output value is as for mmfreq.
**DETAILS**- This function implements a multstage estimation scheme for robust estimation of the
frequencies. The first stage uses the method of elemental sets to approximate the LTS
estimator. The last stage uses MM estimation with Hampel's redescending psi function. The
estimators simultaneously have high breakdown and 95% efficiency under normal errors.
The model is

y = a[1]*cos(x*f[1]) + ... + a[p]*cos(x*f[p]) + a[p+1]*sin(x*f[1]) + ... + a[2*p]*sin(x*f[p])

The values for

*f*define the freq vector while the values for*a*define the coef vector on output.

**REFERENCES**- Smyth, G. K., and Hawkins, D. M. (2000). Robust frequency estimation using elemental
sets.
*Journal of Computational and Graphical Statistics***9**, 196-214. (Abstract - Zipped PostScript) **BUGS**- The function uses pronyfreq as a first step, and that function uses compiled code. This means that you will need to download and compile the Fortran code associated with pronyfreq.
**SEE ALSO**- mmnl, mscale, rho.hampel, psi.hampel, mmfreq
**EXAMPLES**

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Gordon Smyth.
Copyright © 1996-2016. *Last modified:
10 February 2004*