fuzzyqBoot {FuzzyQ} | R Documentation |
Apply Fuzzy Quantification of Common and Rare Species to Bootstrap Replicates
Description
Produce N replicates of the original site by species matrix or dataframe by
taking bootstrap samples of sites (rows) and apply fuzzyq
to each
replicate.
Usage
fuzzyqBoot(
M,
N = 1000,
level = "spp",
std = FALSE,
rm.absent = FALSE,
wgts = c(1, 1),
...
)
Arguments
M |
A matrix or dataframe of species abundaces (columns). Each row represents a site. |
N |
Integer. Number of bootstrap replicates desired. Default is 1,000. |
level |
String. Specifiy the type of metrics to be computed for each
bootstrap replicate. Either |
std |
Logical. Whether or not the measurements of occupancy and
abundance are to be standardized before calculating the dissimilarities.
Measurements are standardized for each variable (column), by subtracting
the variable's mean value and dividing by the variable's mean absolute
deviation. It only takes effect if |
rm.absent |
Logical. Whether or not absent species are to be removed from the calculations. |
wgts |
an optional numeric vector of length 2. To be used if |
... |
Arguments to be passed to function |
Value
A list consisting of the following:
bs.rep
Matrix of estimated metrics. Replicates are arranged in rows. If
level = "spp"
, columns represent estimates of Commonness Indices per species. Iflevel = "global"
, columns represent estimates of community-level clustering metrics: Average silhouette widths per cluster and globally, Mean commonness indices per cluster and Normalized Dunn's coefficient.level
Flag indicating whether the estimates are taken at species (
"spp"
) or community level ("global"
).
Examples
data(antsA)
FQAnts <- fuzzyq(antsA, sorting = TRUE)
# Compute species Commonness Indices of species of 1,000 bootstrap
# replicates:
BS.FQAnts <- fuzzyqBoot (antsA, N = 1e3, level='spp')
# Compute global metrics of 1,000 boostrap replicates:
BS.global <- fuzzyqBoot (antsA, N = 1e3, level='global')