simplex forecast

Usage

# S4 method for class 'sf'
simplex(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  threads = detectThreads(),
  detrend = TRUE,
  nb = NULL
)

# S4 method for class 'SpatRaster'
simplex(
  data,
  column,
  target,
  E = 2:10,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  threads = detectThreads(),
  detrend = TRUE,
  grid.coord = TRUE,
  embed.direction = 0
)

Arguments

data: observation data.
column: name of library variable.
target: name of target variable.
E: (optional) embedding dimensions.
k: (optional) number of nearest neighbors used.
tau: (optional) step of spatial lags.
style: (optional) embedding style (0 includes current state, 1 excludes it).
stack: (optional) whether to stack embeddings.
lib: (optional) libraries indices.
pred: (optional) predictions indices.
dist.metric: (optional) distance metric (L1: Manhattan, L2: Euclidean).
dist.average: (optional) whether to average distance.
threads: (optional) number of threads to use.
detrend: (optional) whether to remove the linear trend.
nb: (optional) neighbours list.
grid.coord: (optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).
embed.direction: (optional) direction selector for embeddings (0 returns all directions, 1-8 correspond to NW, N, NE, W, E, SW, S, SE).

Value

A list

xmap: forecast performance
varname: name of target variable
method: method of cross mapping
tau: step of time lag

References

Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
# \donttest{
simplex(columbus,"inc","crime")
#> The suggested E and k for variable crime is 5 and 6 
# }