lag-1 autocorrelation coefficient

The lag_1 function calculates the lag-1 autocorrelation coefficient using a windowed analysis monte carlo analysis

Usage

lag_1(
  data = NULL,
  n_sim = 10,
  run_multicore = FALSE,
  n_cores = NULL,
  win_max = NULL,
  win_min = NULL,
  verbose = FALSE
)

Arguments

data

Two-column matrix or data frame: depth or time in column 1 and the proxy value in column 2; it is sorted and linearly interpolated to its mean sampling interval. Required (Default=NULL).

n_sim

Number of Monte Carlo simulations (integer, > 0). In every simulation the record is resampled at a random sampling interval (truncated normal around the mean interval) and a random window size between win_min and win_max is used; the output is the mean and standard deviation over the simulations. Default=10.

run_multicore

Run the simulations in parallel on several cores (logical, TRUE or FALSE). Results do not depend on it. Default=FALSE.

n_cores

Number of cores used when run_multicore = TRUE (integer or NULL). NULL gives an automatic, safe number (physical cores minus one, at most 4, limited by the free memory); change this default with options(waverider.n_cores = ...). A value > 1 also switches on parallel computing. Results do not depend on the number of cores. Default=NULL.

win_max

Largest width of the moving window in which the lag-1 autocorrelation is computed, in the unit of the first column of data (numeric, > 0). Required (Default=NULL).

win_min

Smallest width of the moving window, in the unit of the first column of data (numeric, > 0; the window width of each simulation is drawn uniformly between win_min and win_max). Required (Default=NULL).

verbose

Show a progress bar of the simulations (logical, TRUE or FALSE). Default=FALSE.

Value

Returns a matrix which contains 3 columns column 1: depth/time matrix column 2: mean autocorrelation coefficient column 3: sd autocorrelation coefficient

Author

Michiel Arts

Examples

# \donttest{
#The example uses the magnetic susceptibility data set of Pas et al., (2018).
# perform the CWT
mag_wt <- analyse_wavelet(data = mag,
dj = 1/100,
lowerPeriod = 0.1,
upperPeriod = 254,
verbose = FALSE,
omega_nr = 10)

#Track the 405 kyr eccentricity cycle in a wavelet spectra

#mag_track <- track_period_wavelet(astro_cycle = 405,
#                                   wavelet=mag_wt,
#                                   n.levels = 100,
#                                   periodlab = "Period (meters)",
#                                   x_lab = "depth (meters)")

#Instead of tracking, the tracked solution data set mag_track_solution is used
mag_track <- mag_track_solution

mag_track_complete <- completed_series(
  wavelet = mag_wt,
  tracked_curve = mag_track,
  period_up = 1.2,
  period_down = 0.8,
  extrapolate = TRUE,
  genplot = FALSE
)

# smooth the tracking of the 405 kyr eccentricity cycle
mag_track_complete <- loess_auto(time_series = mag_track_complete,
genplot = FALSE, print_span = FALSE)
#convert period in meters to sedrate depth vs time
mag_track_time<- curve2tune(data=mag,
                           tracked_cycle_curve=mag_track_complete,
                           tracked_cycle_period=405,
                           genplot = FALSE,
                           keep_editable=FALSE)

mag_lag_1 <- lag_1(data = mag_track_time,n_sim = 10,
run_multicore = FALSE,
win_max = 505,
win_min = 150,
verbose=FALSE)

# }