lag_1 | R Documentation |
The lag_1
function calculates the lag-1 autocorrelation coefficient using a windowed analysis
monte carlo analysis
lag_1(
data = NULL,
n_sim = 10,
run_multicore = FALSE,
win_max = NULL,
win_min = NULL,
verbose = FALSE
)
data |
Input data set should consist of a matrix with 2 columns with first column being depth and the second column being a proxy |
n_sim |
number of simulations to be ran |
run_multicore |
Run function using multiple cores |
win_max |
maximum window size |
win_min |
minimum window size |
verbose |
print text |
Returns a matrix which contains 3 columns column 1: depth/time matrix column 2: mean autocorrelation coefficient column 3: sd autocorrelation coefficient
Michiel Arts
#The example uses the magnetic susceptibility data set of Pas et al., (2018).
# perform the CWT
mag_wt <- analyze_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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.