WaverideR (version 0.4.1)

flmw: Fit linear models to spectral peaks extracted from the wavelet spectra to astronomical cycles multiplied by sedimentation rate x

Description

The flmw function is used calculate the linear correlation for a list of astronomical cycles transformed using a range of sedimentation rates and then compared to spectral peaks of a wavelet spectra

Usage

flmw(
  wavelet = NULL,
  sedrate_low = NULL,
  sedrate_high = NULL,
  spacing = NULL,
  cycles = c(NULL),
  x_lab = "depth",
  y_lab = "sedrate",
  run_random = FALSE,
  rand_simulations = 1000,
  run_multicore = FALSE,
  genplot = FALSE,
  palette_name = "rainbow",
  color_brewer = "grDevices",
  plot_res = 2,
  keep_editable = FALSE,
  verbose = FALSE
)

Value

Returns a list which contains 10 elements element 1: slope coefficient element 2: r squared element 3: nr of components element 4: difference to the origin element 5: slope coefficient percentile element 6: r squared percentile element 7: nr of components percentile, element 8: difference to the origin percentile element 9: y-axis values of the matrices which is sedimentation rate element 10: x-axis values of the matrices which is depth

Arguments

wavelet

Wavelet object created using the analyze_wavelet function

sedrate_low

Minimum sedimentation rate (cm/kyr)for which the sum of maximum spectral power is calculated for.

sedrate_high

Maximum sedimentation rate (cm/kyr) for which the sum of maximum spectral power is calculated for.

spacing

Spacing (cm/kyr) between sedimentation rates

cycles

Astronomical cycles (in kyr) for which the combined sum of maximum spectral power is calculated for

x_lab

label for the y-axis Default="depth"

y_lab

label for the y-axis Default="sedrate"

run_random

run multiple simulation to calculate percentile against the 0 hypothesis

rand_simulations

nr of simulations to calculate percentile against the 0 hypothesis

run_multicore

run simulation using multiple cores Default=FALSE the simulation is run at x-2 cores to allow the 2 remaining processes to run background processes

genplot

Generate plot Default="FALSE"

palette_name

Name of the color palette which is used for plotting. The color palettes than can be chosen depends on which the R package is specified in the color_brewer parameter. The included R packages from which palettes can be chosen from are; the 'RColorBrewer', 'grDevices', 'ColorRamps' and 'Viridis' R packages. There are many options to choose from so please read the documentation of these packages Default=rainbow. The R package 'viridis' has the color palette options: “magma”, “plasma”, “inferno”, “viridis”, “mako”, and “rocket” and “turbo” To see the color palette options of the The R pacakge 'RColorBrewer' run the RColorBrewer::brewer.pal.info() function The R package 'colorRamps' has the color palette options:"blue2green", "blue2green2red", "blue2red", "blue2yellow", "colorRamps", "cyan2yellow", "green2red", "magenta2green", "matlab.like", "matlab.like2" and "ygobb" The R package 'grDevices' has the built in palette options:"rainbow", "heat.colors", "terrain.colors","topo.colors" and "cm.colors" To see even more color palette options of the The R pacakge 'grDevices' run the grDevices::hcl.pals() function

color_brewer

Name of the R package from which the color palette is chosen from. The included R packages from which palettes can be chosen are; the RColorBrewer, grDevices, ColorRamps and Viridis R packages. There are many options to choose from so please read the documentation of these packages. "Default=grDevices

plot_res

options 1-8 option 1: slope coefficient, option 2: r squared, option 3: nr of components, option 4: difference to the origin , option 5: slope coefficient percentile option 6: r squared percentile, option 7: nr of components percentile, option 8: difference to the origin percentile Default=2

keep_editable

Keep option to add extra features after plotting Default=FALSE

verbose

Print text Default=FALSE.

Author

Based on the eAsm function of the 'astrochron' R package and the 'eCOCO' and 'COCO' function of the 'Acycle' software

References

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>

Acycle: Time-series analysis software for paleoclimate research and education, Mingsong Li, Linda Hinnov, Lee Kump, Computers & Geosciences,Volume 127,2019,Pages 12-22,ISSN 0098-3004, <doi:10.1016/j.cageo.2019.02.011>

Tracking variable sedimentation rates and astronomical forcing in Phanerozoic paleoclimate proxy series with evolutionary correlation coefficients and hypothesis testing, Mingsong Li, Lee R. Kump, Linda A. Hinnov, Michael E. Mann, Earth and Planetary Science Letters,Volume 501, T2018,Pages 165-179,ISSN 0012-821X,<doi:10.1016/j.epsl.2018.08.041>

Examples

# \donttest{
#estimate sedimentation rate for the magnetic susceptibility record
# of the Sullivan core of Pas et al., (2018).

mag_wt <- analyze_wavelet(data = mag,
dj = 1/100,
lowerPeriod = 0.1,
upperPeriod = 254,
verbose = FALSE,
omega_nr = 10)

sedrates <- flmw(wavelet = mag_wt,
    sedrate_low = 0.5,
    sedrate_high = 4,
    spacing = 0.05,
    cycles = c(2376,1600,1180,696,406,110),
    x_lab = "depth",
    y_lab = "sedrate",
    run_random = FALSE,
    rand_simulations = 50, # increase to get better constrainted resutls
    run_multicore = FALSE,
    genplot = FALSE,
    palette_name = "rainbow",
    color_brewer = "grDevices",
    plot_res = 2,
    keep_editable=FALSE,
    verbose=FALSE)
# }