dave (version 2.0)

aocc: Analysis of concentration (AOC)

Description

Given a two-dimensional matrix of vegetation data the function derives a contingency table of counts (scores presenc-absence transformed) based on input classification of rows (the vegetation releves) and columns (the species). The cells of the contingency table are then adjusted to equal weight, followed by correspondence analysis (cca). Concentration of counts is measured and an ordination plotted.

Usage

aocc(veg, o.rgr, o.sgr,...)
aoc(veg, o.rgr, o.sgr)
# S3 method for default
aocc(veg, o.rgr, o.sgr,...)
# S3 method for aocc
plot(x,...)

Arguments

veg

A data frame of vegetation releves (rows) by species (columns)

o.rgr

Group membership of rows given upon input

o.sgr

Group membership of columns given upon input

x

An object of class "aocc"

…

Further variables used for plotting

Value

An output list of class "aocc" with at least the following items:

rgrscores

Ordination scores of releve groups

sgrscores

Ordination scores of species groups

eigvar

Eigenvalues of correspondence analysis

grand.total

Grand total of contingency table

MSCC

Mean square contingency coefficient, a measure of concentration

new.relorder

Order of rows after ordering groups according to 1. axis

new.sporder

Order of columns after ordering groups according to 1. axis

cont.table

The contingency table

Details

These input parameters are typically generated by functions clust() and cutree() in the cluster package. See example below.

References

Feoli, E. & Orloci, L. 1979. Analysis of concentration and detection of underying factors in structured tables. Vegetatio 40: 49-54.

Orloci, L. & Kenkel, N. 1985. Introduction to Data Analysis. International Co-operative Publ. House, Burtonsville, MD.

Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston.

Examples

# NOT RUN {
# First, groups of releves are formed
require(vegan)
dr<- vegdist(nveg^0.5,method="bray")      # dr is distance matrix of rows
o.clr<- hclust(dr,method="ward.D2")          # this is clustering
o.rgr<- cutree(o.clr,k=3)                 # 3 row groups formed
# Now I group the columns of nveg (the species)
# the same way as for rows
ds<- vegdist(t(nveg^0.25),method="euclid")
o.cls<- hclust(ds,method="ward.D2")
o.sgr<- cutree(o.cls,k=4)                  # 4 column groups formed

o.aocc<- aocc(nveg,o.rgr,o.sgr)
plot(o.aocc)                               # double scatter plot
                                           # 3 row-, 4 column goups as points.

# If cluster analysis is not used but classification is input by row and 
# column to be processed by aocc():
o.rgr<- c(1,2,1,3,2,3,1,2,3,1,3)
o.sgr<- c(1,1,2,2,1,3,4,3,1,1,1,3,3,1,1,4,4,4,4,1,3)
o.aocc<- aocc(nveg,o.rgr,o.sgr)
plot(o.aocc)

# }