RoverS {fractal} | R Documentation

## Estimate the Hurst coefficient by rescaled range (R/S) method

### Description

The series is partitioned into m groups. The R/S statistic is computed as described in the references, the number of groups is increased, and the calculation is repeated. A log-log plot of R/S versus number of groups is, ideally, linear, with a slope related to H, so H can be determined by linear regression.

### Usage

    RoverS(x, n.block.min=2, scale.ratio=2, scale.min=8)

### Arguments

`x` | a vector containing a uniformly-sampled real-valued time series.
`n.block.min` | minimum number of blocks in partitioning the data. Must be at least 2. Default: `2`.
`scale.min` | minimum number of data values allowed in a block This may be restricted so the statistic evaluated within each group is from a reasonable sample. Default: `8`.
`scale.ratio` | ratio of successive scales to use in partitioning the data. For example, if `scale.min=8` and `scale.ratio=2`, the first scale will be 8, the second scale 16, the third scale 32, and so on. Default: `2`.

### Value

estimated Hurst parameter H of the time series.

### References

B.B. Mandelbrot and J.R. Wallis (1969), Water Resources Research, 5, 228–267.

See summary in M.S. Taqqu and V. Teverovsky (1998), On Estimating the Intensity of Long-Range Dependence in Finite and Infinite Variance Time Series, in A practical Guide to Heavy Tails: Statistical Techniques and Applications, 177–217, Birkhauser, Boston.

### See Also

`hurstBlock`, `hurstACVF`, `dispersion`.

### Examples

    RoverS(ocean)

[Package fractal version 1.0-1 Index]