Correspondence analysis is the ordination for two-way count tables: it shows how the rows and the columns of a contingency or abundance table correspond. Magic Stat runs it from the spreadsheet you already have and gives you the inertia, the chi-square and the biplot.
Correspondence analysis takes a two-way table of non-negative counts — sites by species, samples by categories, any contingency table — and places both the rows and the columns in one low-dimensional space. Rows and columns that sit close together in that space correspond more than the row and column totals alone would predict.
Everything in CA is measured on a chi-square metric: the analysis decomposes the chi-square statistic of the table, and the inertia of each axis is a share of that total. That is what forces the constraint you will meet in the dialog — the data must be non-negative and complete. Negative values, or a spreadsheet full of gaps, are not a CA table.
CA is not the tool for continuous, possibly negative variables with linear structure; that is PCA. It is also not the tool when you want to explain the table by a second set of measured variables — that is RDA or CCA. And plain CA inherits the arch effect on long gradients, which is exactly what DCA was designed to remove.
Input: a non-negative, complete table. CA is chi-square based, so for a raw count table the usual transformation is None — the chi-square metric is already part of the method.
Transformations available in the dialog: None · Z-score (standardise) · ln(1+x) · ln(x) · Hellinger · Chord · Chi-square · Square root · Range 0–1. A grouping column, sample labels and point sizes are supported for the figure.
No. PCA decomposes the variance of continuous variables on a Euclidean metric. CA decomposes the chi-square of a count table on a chi-square metric. Using PCA on a contingency table is a common mistake; CA is the appropriate ordination for that kind of table.
Plain CA is unconstrained — it only describes the table you give it. CCA constrains the ordination by a second set of environmental or explanatory variables and adds a permutation test. If you have predictors you want to explain the table with, use CCA.
CA requires non-negative, complete data. If your variables are continuous and can be negative, the appropriate method is usually PCA. If you genuinely need the chi-square framework, that should be a deliberate, stated choice, not an afterthought.
Not in this dialog. CA here fits the table you give it. If you need supplementary rows or columns, FactoMineR::CA or the ca package in R is the better tool — and we will say so rather than pretend otherwise.
No. The analysis runs on your computer. The only signal the app sends automatically is an anonymous installation counter — no data, no names, no e-mail.
Detrended correspondence analysis → Canonical correspondence analysis → Redundancy analysis →