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Convert the re-tracked curve results to a depth time space with uncertainty

Source: R/curve2time_unc.R
curve2time_unc.Rd

Converts the re-tracked curve results from retrack_wt_MC function to a depth time space while also taking into account the uncertainty of the tracked astronomical cycle

Usage

curve2time_unc(
  tracked_cycle_curve = NULL,
  tracked_cycle_period = NULL,
  tracked_cycle_period_unc = NULL,
  tracked_cycle_period_unc_dist = "n",
  n_simulations = NULL,
  output = 1
)

Arguments

tracked_cycle_curve

Re-tracked cycle with its uncertainty (matrix or data frame, three columns): depth in metres in column 1, the mean tracked frequency in cycles per metre in column 2 and its standard deviation (1 sigma, cycles per metre) in column 3, e.g. the output of retrack_wt_MC. Rows with NA are dropped and the curve is interpolated to its median depth spacing. Required (Default=NULL gives an error).

tracked_cycle_period

Duration of the tracked cycle in kyr (single numeric value, > 0, e.g. 405 for the long eccentricity cycle). Required (Default=NULL gives an error).

tracked_cycle_period_unc

Uncertainty of tracked_cycle_period in kyr (single numeric value, >= 0): the standard deviation when tracked_cycle_period_unc_dist="n", the half-width of the range when "u". Required (Default=NULL gives an error).

tracked_cycle_period_unc_dist

Distribution from which the duration of the tracked cycle is drawn in every simulation (character): "n" normal or "u" uniform. Default="n".

n_simulations

Number of age models to simulate (integer, > 0); in every simulation one duration and one quantile of the frequency uncertainty (applied along the whole curve) are drawn. Simulations whose age model is not monotonic are discarded. Required (Default=NULL gives an error).

output

Form of the result (integer): 1 a matrix with the simulated ages (kyr) at every depth, one column per kept simulation; 2 a matrix with depth, the mean tracked frequency and the 0.02275, 0.15866, 0.5, 0.84134 and 0.97725 quantiles of the simulated ages (median, +-1 and +-2 standard deviations of a normal distribution); 3 a matrix with depth, the mean and the standard deviation of the simulated ages. Default=1.

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 (0.02275, 0.15866, 0.5, 0.84134, 0.97725) 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

References

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

Author

Based on the sedrate2time function of the 'astrochron' R package

Examples

# \donttest{
# 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 generate and age model including uncertainty

Bisciaro_al <- Bisciaro_XRF[, c(1, 61)]
Bisciaro_al <- astrochron::sortNave(Bisciaro_al,verbose=FALSE,genplot=FALSE)
#>  * Duplicates found
#>  * Duplicates values will be averaged.
#>  * Number of samples post-averaging= 287 
Bisciaro_al <- astrochron::linterp(Bisciaro_al, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_al <- Bisciaro_al[Bisciaro_al[, 1] > 2, ]

Bisciaro_al_wt <-
 analyse_wavelet(
   data = Bisciaro_al,
   dj = 1 /50 ,
   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)
#>  * Duplicates found
#>  * Duplicates values will be averaged.
#>  * Number of samples post-averaging= 287 
Bisciaro_ca <- astrochron::linterp(Bisciaro_ca, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_ca <- Bisciaro_ca[Bisciaro_ca[, 1] > 2, ]

Bisciaro_ca_wt <-
 analyse_wavelet(
   data = Bisciaro_ca,
   dj = 1 /50 ,
   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)
#>  * Duplicates found
#>  * Duplicates values will be averaged.
#>  * Number of samples post-averaging= 287 
Bisciaro_sial <- astrochron::linterp(Bisciaro_sial, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_sial <- Bisciaro_sial[Bisciaro_sial[, 1] > 2, ]

Bisciaro_sial_wt <-
 analyse_wavelet(
   data = Bisciaro_sial,
   dj = 1 /50 ,
   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)
#>  * Duplicates found
#>  * Duplicates values will be averaged.
#>  * Number of samples post-averaging= 287 
Bisciaro_Mn <- astrochron::linterp(Bisciaro_Mn, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_Mn <- Bisciaro_Mn[Bisciaro_Mn[, 1] > 2, ]

Bisciaro_Mn_wt <-
 analyse_wavelet(
   data = Bisciaro_Mn,
   dj = 1 /50 ,
   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)
#>  * Duplicates found
#>  * Duplicates values will be averaged.
#>  * Number of samples post-averaging= 287 
Bisciaro_Mg <- astrochron::linterp(Bisciaro_Mg, dt = 0.01,verbose=FALSE,genplot=FALSE)
Bisciaro_Mg <- Bisciaro_Mg[Bisciaro_Mg[, 1] > 2, ]

Bisciaro_Mg_wt <-
 analyse_wavelet(
   data = Bisciaro_Mg,
   dj = 1 /50 ,
   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(tracked_cycle_curve = bisc_retrack,
tracked_cycle_period = 110,
tracked_cycle_period_unc = ((135-110)+(110-95))/2,
tracked_cycle_period_unc_dist = "n",
n_simulations = 20,
output = 1)


# }