spEDM (version 1.9)

smap: optimal parameter search for smap forecasting

Description

optimal parameter search for smap forecasting

Usage

# S4 method for sf
smap(
  data,
  column,
  target,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  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,
  nb = NULL
)
# S4 method for SpatRaster
smap(
  data,
  column,
  target,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  stack = FALSE,
  lib = NULL,
  pred = NULL,
  dist.metric = "L2",
  dist.average = TRUE,
  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,
  grid.coord = TRUE,
  embed.direction = 0
)

Value

A list

xmap

forecast performance

varname

name of target variable

method

method of cross mapping

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 (input needed: vector - spatial vector, matrix - spatial raster).

pred

(optional) predictions indices (input requirement same as lib).

dist.metric

(optional) distance metric (L1: Manhattan, L2: Euclidean).

dist.average

(optional) whether to average distance.

theta

(optional) weighting parameter for distances.

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).

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",E = 5,k = 6)
# }