ent_sp_simpson {divent} | R Documentation |
Spatially Explicit Simpson's Entropy
Description
Simpson's entropy of the neighborhood of individuals, up to a distance (Shimatani 2001).
Usage
ent_sp_simpson(
X,
r = NULL,
correction = c("isotropic", "translate", "none"),
check_arguments = TRUE
)
ent_sp_simpsonEnvelope(
X,
r = NULL,
n_simulations = 100,
alpha = 0.05,
correction = c("isotropic", "translate", "none"),
h0 = c("RandomPosition", "RandomLabeling"),
global = FALSE,
check_arguments = TRUE
)
Arguments
X |
a spatialized community
(A dbmss::wmppp object with |
r |
a vector of distances. |
correction |
the edge-effect correction to apply when estimating the number of neighbors or the K function with spatstat.explore::Kest. Default is "isotropic". |
check_arguments |
if |
n_simulations |
the number of simulations used to estimate the confidence envelope. |
alpha |
the risk level, 5% by default. |
h0 |
A string describing the null hypothesis to simulate. The null hypothesis may be "RandomPosition": points are drawn in a Poisson process (default) or "RandomLabeling": randomizes point types, keeping locations unchanged. |
global |
if |
Value
ent_sp_simpson
returns an object of class fv
,
see spatstat.explore::fv.object.
There are methods to print and plot this class.
It contains the value of the spatially explicit Simpson's entropy
for each distance in r
.
ent_sp_simpsonEnvelope
returns an envelope object spatstat.explore::envelope.
There are methods to print and plot this class.
It contains the observed value of the function,
its average simulated value and the confidence envelope.
References
Duranton G, Overman HG (2005).
“Testing for Localisation Using Micro-Geographic Data.”
Review of Economic Studies, 72(4), 1077–1106.
doi:10.1111/0034-6527.00362.
Shimatani K (2001).
“Multivariate Point Processes and Spatial Variation of Species Diversity.”
Forest Ecology and Management, 142(1-3), 215–229.
doi:10.1016/s0378-1127(00)00352-2.
Examples
# Generate a random community
X <- rspcommunity(1, size = 1000, species_number = 3)
# Calculate the entropy and plot it
autoplot(ent_sp_simpson(X))
# Generate a random community
X <- rspcommunity(1, size = 100, species_number = 3)
# Calculate the entropy and plot it
autoplot(ent_sp_simpsonEnvelope(X, n_simulations = 10))