Computing a principal coordinates analysis of releves (rows, see pco) and subsequently the correlations with all species (columns). Two ordinations are plotted, one for releves and an arrow-plot for species. Species are restricted to the list given in sel.sp and species names are abbreviated upon request (see make.cepnames).
Given a correlation matrix of rows or columns this selects the variable sharing a maximum variance with all others and declares this rank 1. Reduces the matrix (covariances, correlations) by the contribution of the variable ranked first. Repeats the process to derive consecutive ranks until no variance is left.
Given a vegetation data frame with grouped rows (releves) indicator value analysis (funcion indval) or analysis of variance (aoc) is performed on columns (species) and these are ordered by decreasing IndVal (function indval()) or F-value (aov()) accordingly.
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.
A dynamic model of succession on alp Stabelchod in the Swiss Nationl Park using differential equations and numerial integration. 6 species guilds are considered. Space is conceived as a grid of 30 times 40 cells. Typical simulation time is around 500yr.
Flexible shortest path adjustment is a heuristic ordination method attempting to adjust pattern to ecological situations. It erases long distances in the resemblance matrix and replaces these by the sum of intermediate steps. Subsequent ordination uses function pco.
Given a data frame of a multivariate (vegetation) time series this plots a pco ordination using circles with diameters proportional to rate of change (velocity), a pco ordination pco using cirlces with diameters proportional to change in velocity (acceleration) and three velocity profiles with differently transformed species scores (from quantitative to qualitative).
Given 2 alternative classifications (g groups) of rows in a data frame of vegetation data, confusion matrix, C, is derived first. Using the first classification a matrix of row centroids is derived (using function centroid) of wich a g by g distance matrix, W, is computed (correlation transformed to distance). Cost factor, cf, is the sum of element by element multiplication of C and W respectively, cf=sum(CW).
Mimics traditional manual ordering of vegetation data table by (i) clustering rows and columns (hclust), (ii) rearranging the resulting groups according to the first AOC axis (aocc), (iii) rearranging rows and columns inside groups based on CA (cca), (iv) Putting high resolving species on top of the table (aoc). Also offers variants for ordering.
Identifies outliers based on the nearest neighbour criterion. It starts by computing a matrix of distances (correlation, r, used as distance, dr=(1-r)/2). Variables with nearest neighbour distance larger than parameter thresh are considered outliers.
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