kernel.arrangement {BAT} | R Documentation |
Functional arrangement of kernel density hypervolumes.
Description
Functional arrangement of a community, measuring the distribution of stochastic points within the total functional space at different distances.
Usage
kernel.arrangement(
comm,
stat = "rneig",
distance = seq(0, 1, 0.01),
pool = NULL,
type = "SES",
alpha = 0.05,
runs = 99,
plotValues = TRUE
)
Arguments
comm |
A 'Hypervolume' object, preferably built using function kernel.build. |
stat |
statistic to be calculated. One of c("rneig", "nnpair"), meaning "nearest neighbor" and "all neighbors" respectively. |
distance |
vector of distances to be considered in calculations |
pool |
Species pool coordinates to use for null model construction.
When |
type |
Envelope type for testing significance. One of c("ecdf", "norm", "SES"), meaning "empirical cumulative distribution", "normalized envelope" (between 0-1, 0.5 indicate randomness, more than 0.5 - clustered; less than 0.5 - inhibition), and "standardized effect size" respectively. |
alpha |
alpha value to consider in significance testing (p-value). |
runs |
number of simulations for significance testing. |
plotValues |
Whether to plot "rneig" or "nnpair" values for all distances. |
Details
This function measures the functional arrangement (Carvalho & Cardoso, subm.) of a n-dimensional hypervolume, namely the distribution of stochastic points within the total trait space from small to large functional distances.
Value
A list with observed rneig or nnpair values, the confidence limits and standard effect size.
References
Carvalho, J.C. & Cardoso, P. (subm.) Quantifying species distribution within the functional space.
Examples
## Not run:
comm = c(100,3,0,5,3)
names(comm) = c("SpA", "SpB", "SpC", "SpD", "SpE")
trait = data.frame(body = c(1,2,3,4,2), beak = c(1,5,4,1,2))
rownames(trait) = names(comm)
hv = kernel.build(comm, trait, method.hv = "svm", svm.nu = 0.01, svm.gamma = 0.25)
kernel.arrangement(hv)
## End(Not run)