simulate_step_trend: Simulate data from a linear trend model

In regime-shifts/abrupt: Methods for detecting abrupt change in temporal, spatial, and spatiotemporal data series

Simulate data from a linear trend model

Description

Simulate data from a linear trend model

Usage

simulate_step_trend(
  t,
  change_points,
  means,
  sampling_distribution = NULL,
  seed = NULL,
  ...
)

Arguments

t	numeric; vector of time points.
change_points	numeric; vector of change points, within t
means	numeric; vector of means for the regimes implied by change_points. Must be of length length(change_points) + 1.
sampling_distribution	function; a random number generating function, which takes as it's first argument the number of observations to sample. The second argument should be the expected value. The default, if nothing is supplied, is stats::rnorm().
seed	numeric; a seed for the simulation.
...	additional arguments that will be passed to sampling_distribution.

Examples

sims <- simulate_step_trend(1:100, change_points = c(25, 75),
                            means = c(2, 8, 4))
sims

library("ggplot2")
ggplot(sims, aes(x = t, y = y)) +
  geom_point() +
  geom_step(aes(y = trend))

regime-shifts/abrupt documentation built on Aug. 26, 2022, 3:15 p.m.