fuzzyq {FuzzyQ} | R Documentation |
Fuzzy Quantification of Common and Rare Species in Ecological Communities
Description
Perform fuzzy clustering of each species based on their abundance and occupancy.
Usage
fuzzyq(
M,
diss = "gower",
rm.absent = FALSE,
sorting = TRUE,
keep.Diss = FALSE,
std = FALSE,
wgts = c(1, 1),
...
)
Arguments
M |
A matrix or data frame of species abundances (columns). Each row represents a site. |
diss |
String. Specify the dissimilarity coefficient to be used. Default
is "gower". See |
rm.absent |
Logical. Whether or not absent species are to be removed from the calculations. |
sorting |
Logical. If |
keep.Diss |
Logical. Whether or not the species dissimilarity matrix
shoudl be returned. The default is |
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 |
wgts |
an optional numeric vector of length 2. To be used if diss = "gower", specifying weights for occupancy and abundance, respectively. Default is 1 each as in Gower's original formula. |
... |
Arguments to be passed to function |
Value
A list of class fuzzyq
containing the following:
A_O
Abundance-occupancy information for each species.
Diss
Object of class dist with pairwise dissimilarities among species based on A_O. (only if
keep.Diss = TRUE)
.spp
Clustering metrics per species: Cluster membership (where 0 and 1 denote allocation to the rare and common category, respectively), Silhouette Widths and Commonness Indices).
global
Community level clustering metrics: Average silhouette widths per cluster and globally, Mean commonness indices per cluster and Normalized Dunn's coefficient.
See Also
fanny
and daisy
in
package cluster
Examples
data(antsA)
FQAnts <- fuzzyq(antsA, sorting = TRUE)