geographical cross mapping cardinality
# S4 method for sf
gcmc(
data,
cause,
effect,
libsizes = NULL,
E = 3,
k = pmin(E^2),
tau = 1,
style = 1,
lib = NULL,
pred = NULL,
dist.metric = "L2",
threads = detectThreads(),
detrend = FALSE,
parallel.level = "low",
bidirectional = TRUE,
progressbar = TRUE,
nb = NULL
)# S4 method for SpatRaster
gcmc(
data,
cause,
effect,
libsizes = NULL,
E = 3,
k = pmin(E^2),
tau = 1,
style = 1,
lib = NULL,
pred = NULL,
dist.metric = "L2",
threads = detectThreads(),
detrend = FALSE,
parallel.level = "low",
bidirectional = TRUE,
progressbar = TRUE,
grid.coord = TRUE
)
A list
xmapcross mapping results
cscausal strength
varnamenames of causal and effect variables
bidirectionalwhether to examine bidirectional causality
observation data.
name of causal variable.
name of effect variable.
(optional) number of spatial units used (input needed: vector - spatial vector, matrix - spatial raster).
(optional) embedding dimensions.
(optional) number of nearest neighbors.
(optional) step of spatial lags.
(optional) embedding style (0 includes current state, 1 excludes it).
(optional) libraries indices (input requirement same as libsizes).
(optional) predictions indices (input requirement same as libsizes).
(optional) distance metric (L1: Manhattan, L2: Euclidean).
(optional) number of threads to use.
(optional) whether to remove the linear trend.
(optional) level of parallelism, low or high.
(optional) whether to examine bidirectional causality.
(optional) whether to show the progress bar.
(optional) neighbours list.
(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).
columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))
# \donttest{
g = gcmc(columbus,"hoval","crime",E = 7,k = 18)
g
# }
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