RDA-CCA analysis is a method combining Redundancy Analysis (RDA) and Canonical Correlation Analysis (CCA) to study the relationship between environmental factors and community structure. It reveals patterns in data through dimensionality reduction and assesses correlations between variables.

Input

Environmental Factors Table A tab-separated text file containing row and column headers, where each row represents a sample, The first column header must be 'SampleID', and the second column must be named 'Category' (for group classification), and from the third column onwards, each column represents an environmental factor.

A tab-separated text file containing row and column headers, where each row represents a species (unique and non-repeating), and each column represents a sample.

Output

Environmental factors are represented by arrows. The quadrant in which the arrow is located indicates the positive or negative correlation between the environmental factor and the ordination axis. The length of the line connecting the arrow to the origin represents the degree of correlation between a certain environmental factor and the community and species distribution. The longer the line, the greater the correlation; conversely, the shorter the line, the smaller the correlation. The angle between the arrow's line and the ordination axis (RDA1 and RDA2, the first and second principal components) represents the degree of correlation between the environmental factor and the ordination axis. The smaller the angle, the higher the correlation; conversely, the larger the angle, the lower the correlation.