GetGP: Simulate time series of fluctuations based on a Gaussian...

In MJAlexander/distortr: Temporal smoothing methods for demographic time series

Description

Simulate time series of fluctuations based on a Gaussian Process, using a squared exponential covariance function.

Usage

GetGP(nyears, cov.method = "matern", range = 10, smoothness = 2,
  tau = 1, l = 1, seed = 123)

Arguments

nyears	number of years of observation period
cov.method	either sqexp (squared exponential) or matern
range	parameter for Matern function. Default is 10.
smoothness	parameter for Matern function. Default is 2.
tau	the amplitude parameter. Determines the magnitude of fluctuations over time.
l	the length scale parameter. Determines the 'wigglyness' of the fluctuations.
seed	value of random seed

Value

A data frame of observation points (x) and y-values

See Also

calcSigma

Examples

res <- GetGP(50, cov.method = "sqexp",tau = 1, l = 1)
ggplot(data = res, aes (x = x, y = y)) + geom_line() + geom_point() + theme_bw()

MJAlexander/distortr documentation built on July 17, 2020, 4:06 p.m.