Description Usage Arguments Details Value References See Also Examples

View source: R/indicator_sdr.R

Compute the ratio of low frequencies over high frequencies of the r-spectrum. It also computes a null value obtained by randomizing the matrix.

1 2 | ```
indicator_sdr(input, sdr_low_range = NULL, sdr_high_range = NULL,
nreplicates = 999)
``` |

`input` |
A matrix or a logical matrix (TRUE/FALSE), or a list of these. |

`sdr_low_range` |
The range of values (in proportion) to
use for the computation of the spectral density ratio.
For example, for the lowest 20% (default value), set |

`sdr_high_range` |
The range of values (in proportion) to
use for the computation of the spectral density ratio. For example, for
the highest 20% (default value), set |

`nreplicates` |
The number of replicates to compute for the null distribution |

SDR measures the increase in long-range correlations before a critical point.
It is the ratio of the average low frequency value over high frequency
values. In this implementation, an increase in SDR implies a "reddening"
of the r-spectrum. See also `spectral_spews`

for
a more complete description.

Low and high frequencies are averaged in order to compute the SDR. The
parameters `sdr_low_range`

and `sdr_high_range`

control which
frequencies are selected for averaging. For example
`sdr_low_range = c(0, .2)`

(default) uses the lower 20
the average of low frequencies. `sdr_high_range = c(.8, 1)`

uses the
higher 20

A list (or a list of lists if input was a list of matrices) with components:

'value': SDR of the matrix

If nreplicates is above 2, then the list has the following additional components :

'null_mean': Mean SDR of the null distribution

'null_sd': SD of SDR in the null distribution

'z_score': Z-score of the observed value in the null distribution (value minus the null mean and divided by null standard deviation)

'pval': p-value based on the rank of the observed SDR in the null distribution. A low p-value means that the indicator value is significantly higher than the null values.

Carpenter, S.R. & Brock, W.A. (2010). Early warnings of regime shifts in spatial dynamics using the discrete Fourier transform. Ecosphere

spectral_spews, rspectrum

1 2 3 4 5 | ```
## Not run:
serengeti.sdr <- indicator_sdr(serengeti, nreplicates = 499)
do.call(rbind, serengeti.sdr) # convert results to data.frame
## End(Not run)
``` |

spatialwarnings documentation built on Jan. 27, 2018, 3:01 a.m.

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