Functions optimal.params.gst()
, GST.k()
and I.k()
apply to count data collected over a network of community samples k
(species by sample matrix). A theoretical relationship between
GST(k)
statistics and local immigration numbers I(k)
, in
the context of a spatially-implicit neutral community model (Munoz et
al 2008), is used to provide GST(k)
and I(k)
statistics
any sample k.
If requested, optimal.params.gst()
further provides the user with
confidence bounds.
optimal.params.gst(D, exact = TRUE, ci = FALSE, cint = c(0.025, 0.975), nbres = 100)
GST.k(D, exact = TRUE)
I.k(D, exact = TRUE)
A vector of 0 to 1 GST(k)
numbers (specific output of GST.k
)
Number of individuals within samples (length = number of samples)
Species counts of the merged dataset (output of GST.k
and I.k
)
Immigration estimates (output of I.k
and optimal.params.gst
)
Corresponding immigration rates (output of I.k
and
optimal.params.gst
). Specific outputs of optimal.params.gst
when ci = T (bootstrap procedure)
Confidence interval of I(k)
Confidence interval of m(k)
Table of bootstrapped values of I(k)
Table of bootstrapped values of im(k)
A data table including species counts in a network of community samples (columns)
If TRUE
, exact similarity statistics are
calculated (sampling without replacement) while, if false, approximate
statistics (sampling with replacement) are considered (see Munoz et al
2008 for further statistical discussion)
Specifies whether bootstraps confidence intervals of immigration estimates are to be calculated
Bounds of the confidence interval, if ci = TRUE
Number of rounds of the bootstrap procedure for confidence interval calculation, if ci = T
Francois Munoz
Francois Munoz, Pierre Couteron and B.R. Ramesh (2008). “Beta-diversity in spatially implicit neutral models: a new way to assess species migration.” The American Naturalist 172(1): 116-127
optimal.params
,optimal.params.sloss
data(ghats)
optimal.params.gst(ghats)
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