| simplex | R Documentation |
optimal parameter search for simplex forecasting
## S4 method for signature '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 signature '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
)
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 ( |
stack |
(optional) whether to stack embeddings. |
lib |
(optional) libraries indices (input needed: |
pred |
(optional) predictions indices (input requirement same as |
dist.metric |
(optional) distance metric ( |
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 ( |
embed.direction |
(optional) direction selector for embeddings ( |
A list
xmapforecast performance
varnamename of target variable
methodmethod of cross mapping
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"))
simplex(columbus,"inc","crime")
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