Compute the ratio of low frequencies over high frequencies of the r-spectrum. It also computes a null value obtained by randomizing the matrix.
indicator_sdr(input, sdr_low_range = NULL, sdr_high_range = NULL,
nreplicates = 999)A matrix or a logical matrix (TRUE/FALSE), or a list of these.
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_low_range
to c(0, .2).
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 sdr_high_range to
c(.8, 1).
The number of replicates to compute for the null distribution
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.
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_sews 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
Carpenter, S.R. & Brock, W.A. (2010). Early warnings of regime shifts in spatial dynamics using the discrete Fourier transform. Ecosphere
spectral_sews, rspectrum
# NOT RUN {
# }
# NOT RUN {
serengeti.sdr <- indicator_sdr(serengeti, nreplicates = 499)
do.call(rbind, serengeti.sdr) # convert results to data.frame
# }
# NOT RUN {
# }
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