
ordihull
, ordispider
, ordiarrows
, ordisegments
, ordigrid
, ordiellipse
, ordicluster
and lines.spantree
.
ordisymbol(ordiplot, y, factor, col = 1, rainbow = TRUE, legend = TRUE, legend.x = "topleft", legend.ncol = 1, ...)
ordibubble(ordiplot,var,...)
ordicluster2(ordiplot, cluster, mingroups = 1, maxgroups = nrow(ordiplot$sites), ...)
ordinearest(ordiplot, dist,...)
ordivector(ordiplot, spec, lty=2,...)
ordiplot
(vegan). points
). legend
. legend
par
. ordisymbol
plots different levels of the specified variable in different symbols and different colours (if rainbow
option was selected).
Function ordibubble
draws bubble diagrams indicating the value of the specified continuous variable. Circles indicate positive values, squares indicate negative values.
Function ordicluster2
provides an alternative method of overlaying information from hierarchical clustering on an ordination diagram than provided by function ordicluster
. The method draws convex hulls around sites that are grouped into the same cluster. You can select the minimum and maximum number of clusters that are plotted (i.e. the range of clustering steps to be shown).
Function ordinearest
draws a vector from each site to the site that is nearest to it as determined from a distance matrix. When you combine the method with lines.spantree
using the same distance measure, then you can evaluate in part how the minimum spanning tree was constructed.
Function ordivector
draws a vector for the specified species on the ordination diagramme and draws perpendicular lines from each site to a line that connects the origin and the head of species vector. This method helps in the biplot interpretation of a species vector as described by Jongman, ter Braak and van Tongeren (1995).
Kindt, R. & Coe, R. (2005). Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies. http://www.worldagroforestry.org/output/tree-diversity-analysis
library(vegan)
data(dune)
data(dune.env)
Ordination.model1 <- rda(dune)
plot1 <- ordiplot(Ordination.model1, choices=c(1,2), scaling=2)
ordisymbol(plot1, dune.env, "Management", legend=TRUE,
legend.x="topleft", legend.ncol=1)
plot2 <- ordiplot(Ordination.model1, choices=c(1,2), scaling=1)
distmatrix <- vegdist(dune, method='bray')
cluster <- hclust(distmatrix, method='single')
ordicluster2(plot2, cluster)
ordinearest(plot2, distmatrix, col=2)
ordivector(plot2, "Agrostol", lty=2)
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