simplex forecast
Usage
# S4 method for class 'sf'
simplex(
data,
column,
target,
lib = NULL,
pred = NULL,
E = 1:10,
tau = 1,
k = E + 2,
nb = NULL,
threads = detectThreads(),
detrend = TRUE
)
# S4 method for class 'SpatRaster'
simplex(
data,
column,
target,
lib = NULL,
pred = NULL,
E = 1:10,
tau = 1,
k = E + 2,
threads = detectThreads(),
detrend = TRUE
)
Arguments
- data
observation data.
- column
name of library variable.
- target
name of target variable.
- lib
(optional) libraries indices.
- pred
(optional) predictions indices.
- E
(optional) embedding dimensions.
- tau
(optional) step of spatial lags.
- k
(optional) number of nearest neighbors used.
- nb
(optional) neighbours list.
- threads
(optional) number of threads to use.
- detrend
(optional) whether to remove the linear trend.
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
# }