optimal parameter search for simplex forecasting
# S4 method for 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 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
)
A list
xmapforecast performance
varnamename of target variable
methodmethod of cross mapping
observation data.
name of library variable.
name of target variable.
(optional) embedding dimensions.
(optional) number of nearest neighbors used.
(optional) step of spatial lags.
(optional) embedding style (0 includes current state, 1 excludes it).
(optional) whether to stack embeddings.
(optional) libraries indices (input needed: vector - spatial vector, matrix - spatial raster).
(optional) predictions indices (input requirement same as lib).
(optional) distance metric (L1: Manhattan, L2: Euclidean).
(optional) whether to average distance.
(optional) number of threads to use.
(optional) whether to remove the linear trend.
(optional) neighbours list.
(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).
(optional) direction selector for embeddings (0 returns all directions, 1-8 correspond to NW, N, NE, W, E, SW, S, SE).
Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734-741.
columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))
# \donttest{
simplex(columbus,"inc","crime")
# }
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