Description Usage Arguments Details Value Note Author(s) References Examples
This calclulates and plots average similarities of rows (releves) in a square, classified matrix. Correlation coefficient is used as similarity, upon plotting proportionas to the surface of circles. Hence, diagonal elements exhibit similarity of groups, off-diagonals all similarities to the remaining groups.
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veg |
This is a vegetation data frame, releves are rows, species columns |
rmember |
Group membership of the rows or columns, typically taken from cluster analysis |
use |
Either "rows" or "columns" |
y |
Transformation of species scores: x'= x exp(y) |
... |
Capacity. Adjusts plot size to the number of groups. |
x |
An object of class "mxplot" |
capacity |
The number of group symbols that fit on one page |
The distance measure used is "correlation used as distance". See reference.
An object of class "mxplot" with at least the following items:
order |
Dimension of the similarity matrix (equal to the number of groups ng) |
mmatrix |
The ng x ng matrix of average group similarity |
levels |
The ng group names (a vector of character variables) |
Plot parameter capacity only affects the format of plot. Just try.
Otto Wildi
Wildi, O. 2017. Data Analysis in Vegetation Ecology. 3rd ed. CABI, Oxfordshire, Boston.
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Loading required package: cluster
Loading required package: labdsv
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
This is labdsv 2.0-1
convert existing ordinations with as.dsvord()
Attaching package: ‘labdsv’
The following object is masked from ‘package:stats’:
density
Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-7
Loading required package: nnet
Attaching package: ‘nnet’
The following object is masked from ‘package:mgcv’:
multinom
Loading required package: tree
The "ward" method has been renamed to "ward.D"; note new "ward.D2"
Call:
mxplot.default(veg = sveg, rmember = groups, use = "rows", y = 0.5)
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