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Plot a cvCOCO result

Source: R/plot_cvcoco.R
plot_cvcoco.Rd

Plots the diagnostics of a cvcoco result in the style of Acycle's plotcvcoco.m, using base graphics. Four pages can be drawn: (1) the two regularized folds and the held-out validation spectra with their frozen targets, (2) the stacked result panels: directional and consensus correlation, global p, local p and the number of resolvable astronomical periods, (3) the consensus pCOCO curve and (4) the Monte Carlo audit (null distributions of the consensus and directional maxima). Translated from Acycle v3.1 plotcvcoco.m (Mingsong Li).

Usage

plot_cvcoco(result = NULL, pages = c("spectra", "result", "pcoco", "audit"))

Arguments

result

cvCOCO result to plot: the list returned by cvcoco. Required (Default=NULL gives an error).

pages

Which pages to draw, in this order (character vector with any of):

  • "spectra": the two depth series (folds A and B) and the two held-out validation spectra with their frozen astronomical targets;

  • "result": the directional and consensus correlation, global p, local p and the number of contributing astronomical periods against sedimentation rate (cm/kyr);

  • "pcoco": the consensus pCOCO curve with its maximum marked;

  • "audit": histograms of the Monte Carlo null distributions of the consensus and directional maxima with the observed statistics.

Values that are not in this list are ignored. Default=c("spectra", "result", "pcoco", "audit").

Value

Invisibly returns NULL; called for its plots.

References

Li, M., Kump, L.R., Hinnov, L.A., Mann, M.E., 2018. Tracking variable sedimentation rates and astronomical forcing in Phanerozoic paleoclimate proxy series with evolutionary correlation coefficients and hypothesis testing. Earth and Planetary Science Letters 501, 165-179.

Author

Michiel Arts (R translation); original MATLAB code: Mingsong Li.

Examples

# \donttest{
set.seed(3)
t_kyr <- seq(0, 1600, by = 2)
etp <- sin(2 * pi * t_kyr / 405) + 0.6 * sin(2 * pi * t_kyr / 125) +
  0.8 * sin(2 * pi * t_kyr / 41) + 0.5 * sin(2 * pi * t_kyr / 23.7)
dat <- cbind(t_kyr * 5 / 100, etp + 0.5 * stats::rnorm(length(t_kyr)))
res <- cvcoco(data = dat, sr1 = 2, sr2 = 10, srstep = 0.5, n_sim = 20,
  seed = 1)
plot_cvcoco(res, pages = "result")

# }