smap forecast
Usage
# S4 method for class 'sf'
smap(
data,
column,
target,
lib = NULL,
pred = NULL,
E = 3,
tau = 1,
k = E + 2,
theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
4, 6, 8),
nb = NULL,
threads = detectThreads(),
detrend = TRUE
)
# S4 method for class 'SpatRaster'
smap(
data,
column,
target,
lib = NULL,
pred = NULL,
E = 3,
tau = 1,
k = E + 2,
theta = c(0, 1e-04, 3e-04, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 3,
4, 6, 8),
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 in prediction.
- theta
(optional) weighting parameter for distances.
- nb
(optional) neighbours list.
- threads
(optional) number of threads to use.
- detrend
(optional) whether to remove the linear trend.
References
Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688):477-495.
Examples
columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
# \donttest{
smap(columbus,"inc","crime")
#> The suggested theta for variable crime is 1
# }