| curve2time_unc_anchor {WaverideR} | R Documentation |
Anchor an age model including its uncertainty to a single radiometric data
Description
Anchor an age model including its uncertainty to a single radiometric which has a known uncertainty and a known uncertainty in bed location. the model also allows for the addition of gap(s) in the record with a known durations. if no single radiometric date is specified then the gap(s) will be added to the original age-model
Usage
curve2time_unc_anchor(
tracked_cycle_curve = NULL,
tracked_cycle_period = NULL,
tracked_cycle_period_unc = NULL,
tracked_cycle_period_unc_dist = "n",
achor_age = NULL,
achor_SD = NULL,
achor_depth = NULL,
achor_depth_unc = NULL,
achor_depth_unc_dist = "u",
gap_depth = NULL,
gap_dur = NULL,
gap_unc = NULL,
gap_unc_dist = "n",
n_simulations = NULL,
output = 1
)
Arguments
tracked_cycle_curve |
Curve of the cycle tracked using the
|
tracked_cycle_period |
Period of the tracked curve in kyr. |
tracked_cycle_period_unc |
uncertainty in the period of the tracked cycle |
tracked_cycle_period_unc_dist |
distribution of the uncertainty of the
tracked cycle value need to be either "u" for uniform distribution or
"n" for normal distribution |
achor_age |
age (in kyr) of the anchor |
achor_SD |
uncertainty given as 1 sd (in kyr) of the anchor |
achor_depth |
depth in (m) of the anchor |
achor_depth_unc |
uncertainty in (m) of the anchor |
achor_depth_unc_dist |
distribution of the uncertainty of the
anchor age, value need to be either "u" for uniform distribution or
"n" for normal distribution |
gap_depth |
depth(s) at which a gap is present |
gap_dur |
duration in (kyr) of the gap |
gap_unc |
uncertainty in the duration (kyr) of the gap |
gap_unc_dist |
distribution of the uncertainty of the
duration of the value need to be either "u" for uniform distribution or
"n" for normal distribution |
n_simulations |
number of time series to be modeled |
output |
if output = 1 a matrix with the predicted ages given the input for each run is given If output = 2 a matrix with 6 columns is generated, the first column is depth/height, the other columns are the quantile (0.025,0.373,0.5,0.6827,0.975) age values of the runs if output = 3 a matrix with 4 columns is generated with the first column being depth/height, column 2 is the mean tracked duration (in kyrs) column 3 is mean duration + 1 standard deviation and column 4 is mean duration - 1 standard deviation |
Value
If output = 1 a matrix with the predicted ages given the input for each run is given If output = 2 a matrix with 6 columns is generated, the first column is depth/height, the other columns are the quantile (0.025,0.373,0.5,0.6827,0.975) age values of the runs if output = 3 a matrix with 4 columns is generated with the first column being depth/height, column 2 is the mean tracked duration (in kyrs) column 3 is mean duration + 1 standard deviation and column 4 is mean duration - 1 standard deviation
Author(s)
Part of the code is based on the sedrate2time function of the 'astrochron' R package
References
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>
Examples
# Re-track the 110kyr eccentricity cycle in the wavelet scalogram
# from the XRF record of the Bisciaro data set of Arts (2014) and then
# add anchor it a U/Pb date of an ash bed and generate and anchored age model including uncertainty
Bisciaro_al <- Bisciaro_XRF[, c(1, 61)]
Bisciaro_al <- astrochron::sortNave(Bisciaro_al,verbose=FALSE,genplot=FALSE)
Bisciaro_al <- astrochron::linterp(Bisciaro_al, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_al <- Bisciaro_al[Bisciaro_al[, 1] > 2, ]
Bisciaro_al_wt <-
analyze_wavelet(
data = Bisciaro_al,
dj = 1 /200 ,
lowerPeriod = 0.01,
upperPeriod = 50,
verbose = FALSE,
omega_nr = 8
)
# Bisciaro_al_wt_track <-
# track_period_wavelet(
# astro_cycle = 110,
# wavelet = Bisciaro_al_wt,
# n.levels = 100,
# periodlab = "Period (metres)",
# x_lab = "depth (metres)"
# )
#
# Bisciaro_al_wt_track <- completed_series(
# wavelet = Bisciaro_al_wt,
# tracked_curve = Bisciaro_al_wt_track,
# period_up = 1.2,
# period_down = 0.8,
# extrapolate = TRUE,
# genplot = FALSE,
# keep_editable = FALSE
# )
#
# Bisciaro_al_wt_track <-
# loess_auto(
# time_series = Bisciaro_al_wt_track,
# genplot = FALSE,
# print_span = FALSE,
# keep_editable = FALSE
# )
Bisciaro_ca <- Bisciaro_XRF[, c(1, 55)]
Bisciaro_ca <- astrochron::sortNave(Bisciaro_ca,verbose=FALSE,genplot=FALSE)
Bisciaro_ca <- astrochron::linterp(Bisciaro_ca, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_ca <- Bisciaro_ca[Bisciaro_ca[, 1] > 2, ]
Bisciaro_ca_wt <-
analyze_wavelet(
data = Bisciaro_ca,
dj = 1 /200 ,
lowerPeriod = 0.01,
upperPeriod = 50,
verbose = FALSE,
omega_nr = 8
)
# Bisciaro_ca_wt_track <-
# track_period_wavelet(
# astro_cycle = 110,
# wavelet = Bisciaro_ca_wt,
# n.levels = 100,
# periodlab = "Period (metres)",
# x_lab = "depth (metres)"
# )
#
# Bisciaro_ca_wt_track <- completed_series(
# wavelet = Bisciaro_ca_wt,
# tracked_curve = Bisciaro_ca_wt_track,
# period_up = 1.2,
# period_down = 0.8,
# extrapolate = TRUE,
# genplot = FALSE,
# keep_editable = FALSE
# )
#
# Bisciaro_ca_wt_track <-
# loess_auto(
# time_series = Bisciaro_ca_wt_track,
# genplot = FALSE,
# print_span = FALSE,
# keep_editable = FALSE)
Bisciaro_sial <- Bisciaro_XRF[,c(1,64)]
Bisciaro_sial <- astrochron::sortNave(Bisciaro_sial,verbose=FALSE,genplot=FALSE)
Bisciaro_sial <- astrochron::linterp(Bisciaro_sial, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_sial <- Bisciaro_sial[Bisciaro_sial[, 1] > 2, ]
Bisciaro_sial_wt <-
analyze_wavelet(
data = Bisciaro_sial,
dj = 1 /200 ,
lowerPeriod = 0.01,
upperPeriod = 50,
verbose = FALSE,
omega_nr = 8
)
# Bisciaro_sial_wt_track <-
# track_period_wavelet(
# astro_cycle = 110,
# wavelet = Bisciaro_sial_wt,
# n.levels = 100,
# periodlab = "Period (metres)",
# x_lab = "depth (metres)"
# )
#
#
# Bisciaro_sial_wt_track <- completed_series(
# wavelet = Bisciaro_sial_wt,
# tracked_curve = Bisciaro_sial_wt_track,
# period_up = 1.2,
# period_down = 0.8,
# extrapolate = TRUE,
# genplot = FALSE,
# keep_editable = FALSE
# )
#
# Bisciaro_sial_wt_track <-
# loess_auto(
# time_series = Bisciaro_sial_wt_track,
# genplot = FALSE,
# print_span = FALSE,
# keep_editable = FALSE
# )
Bisciaro_Mn <- Bisciaro_XRF[,c(1,46)]
Bisciaro_Mn <- astrochron::sortNave(Bisciaro_Mn,verbose=FALSE,genplot=FALSE)
Bisciaro_Mn <- astrochron::linterp(Bisciaro_Mn, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_Mn <- Bisciaro_Mn[Bisciaro_Mn[, 1] > 2, ]
Bisciaro_Mn_wt <-
analyze_wavelet(
data = Bisciaro_Mn,
dj = 1 /200 ,
lowerPeriod = 0.01,
upperPeriod = 50,
verbose = FALSE,
omega_nr = 8
)
# Bisciaro_Mn_wt_track <-
# track_period_wavelet(
# astro_cycle = 110,
# wavelet = Bisciaro_Mn_wt,
# n.levels = 100,
# periodlab = "Period (metres)",
# x_lab = "depth (metres)"
# )
#
#
# Bisciaro_Mn_wt_track <- completed_series(
# wavelet = Bisciaro_Mn_wt,
# tracked_curve = Bisciaro_Mn_wt_track,
# period_up = 1.2,
# period_down = 0.8,
# extrapolate = TRUE,
# genplot = FALSE,
# keep_editable = FALSE
# )
# Bisciaro_Mn_wt_track <-
# loess_auto(
# time_series = Bisciaro_Mn_wt_track,
# genplot = FALSE,
# print_span = FALSE,
# keep_editable = FALSE
# )
Bisciaro_Mg <- Bisciaro_XRF[,c(1,71)]
Bisciaro_Mg <- astrochron::sortNave(Bisciaro_Mg,verbose=FALSE,genplot=FALSE)
Bisciaro_Mg <- astrochron::linterp(Bisciaro_Mg, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_Mg <- Bisciaro_Mg[Bisciaro_Mg[, 1] > 2, ]
Bisciaro_Mg_wt <-
analyze_wavelet(
data = Bisciaro_Mg,
dj = 1 /200 ,
lowerPeriod = 0.01,
upperPeriod = 50,
verbose = FALSE,
omega_nr = 8
)
# Bisciaro_Mg_wt_track <-
# track_period_wavelet(
# astro_cycle = 110,
# wavelet = Bisciaro_Mg_wt,
# n.levels = 100,
# periodlab = "Period (metres)",
# x_lab = "depth (metres)"
# )
#
#
# Bisciaro_Mg_wt_track <- completed_series(
# wavelet = Bisciaro_Mg_wt,
# tracked_curve = Bisciaro_Mg_wt_track,
# period_up = 1.2,
# period_down = 0.8,
# extrapolate = TRUE,
# genplot = FALSE,
# keep_editable = FALSE
# )
#
# Bisciaro_Mg_wt_track <-
# loess_auto(
# time_series = Bisciaro_Mg_wt_track,
# genplot = FALSE,
# print_span = FALSE,
# keep_editable = FALSE)
wt_list_bisc <- list(Bisciaro_al_wt,
Bisciaro_ca_wt,
Bisciaro_sial_wt,
Bisciaro_Mn_wt,
Bisciaro_Mg_wt)
#Instead of tracking, the tracked solution data sets Bisciaro_al_wt_track,
#Bisciaro_ca_wt_track, Bisciaro_sial_wt_track, Bisciaro_Mn_wt_track,
# Bisciaro_Mn_wt_track and Bisciaro_Mg_wt_track are used
data_track_bisc <- cbind(Bisciaro_al_wt_track[,2],
Bisciaro_ca_wt_track[,2],
Bisciaro_sial_wt_track[,2],
Bisciaro_Mn_wt_track[,2],
Bisciaro_Mg_wt_track[,2])
x_axis_bisc <- Bisciaro_al_wt_track[,1]
bisc_retrack <- retrack_wt_MC(wt_list = wt_list_bisc,
data_track = data_track_bisc,
x_axis = x_axis_bisc,
nr_simulations = 20,
seed_nr = 1337,
verbose = FALSE,
genplot = FALSE,
keep_editable = FALSE,
create_GIF = FALSE,
plot_GIF = FALSE,
width_plt = 600,
height_plt = 450,
period_up = 1.5,
period_down = 0.5,
plot.COI = TRUE,
n.levels = 100,
palette_name = "rainbow",
color_brewer = "grDevices",
periodlab = "Period (metres)",
x_lab = "depth (metres)",
add_avg = FALSE,
time_dir = TRUE,
file_name = NULL,
run_multicore = FALSE,
output = 5,
n_imgs = 50,
plot_horizontal = TRUE,
empty_folder = FALSE)
bisc_retrack_age_incl_unc <- curve2time_unc_anchor(tracked_cycle_curve = bisc_retrack,
tracked_cycle_period = 110,
tracked_cycle_period_unc = ((135-110)+(110-95))/2,
tracked_cycle_period_unc_dist = "n",
achor_age = 20609,
achor_SD = 40,
achor_depth = 7.25,
achor_depth_unc = 0.25,
achor_depth_unc_dist = "n",
gap_depth = NULL,
gap_dur = NULL,
gap_unc = NULL,
gap_unc_dist = "n",
n_simulations = 20,
output = 1)