geographical convergent cross mapping

Usage

# S4 method for class 'sf'
gccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 1,
  k = E + 1,
  nb = NULL,
  trendRM = TRUE,
  progressbar = TRUE
)

# S4 method for class 'SpatRaster'
gccm(
  data,
  cause,
  effect,
  libsizes,
  E = 3,
  tau = 1,
  k = E + 3,
  RowCol = NULL,
  trendRM = TRUE,
  progressbar = TRUE
)

Arguments

data: The observation data.
cause: Name of causal variable.
effect: Name of effect variable.
libsizes: A vector of library sizes to use.
E: (optional) The dimensions of the embedding.
tau: (optional) The step of spatial lags.
k: (optional) Number of nearest neighbors to use for prediction.
nb: (optional) The neighbours list.
trendRM: (optional) Whether to remove the linear trend.
progressbar: (optional) whether to print the progress bar.
RowCol: (optional) Matrix of selected row and cols numbers.

Value

A list.

xmap: cross-mapping prediction outputs
varname: names of causal and effect variable

Examples

columbus = sf::read_sf(system.file("shapes/columbus.gpkg", package="spData")[1],
                       quiet=TRUE)
# \donttest{
g = gccm(columbus, "HOVAL", "CRIME", libsizes = seq(5,45,5))
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
g
#>   libsizes CRIME->HOVAL HOVAL->CRIME
#> 1        5   0.08036917    0.2360232
#> 2       10   0.11368175    0.3207803
#> 3       15   0.08758561    0.3509119
#> 4       20   0.10310575    0.3638976
#> 5       25   0.11585893    0.3814201
#> 6       30   0.12976658    0.4063601
#> 7       35   0.14387488    0.4355102
#> 8       40   0.15749070    0.4612822
#> 9       45   0.19913895    0.4838568
plot(g, ylimits = c(0,0.65))

# }