PERMANOVA+ add-on

Seamless interface with PRIMER 6

• Explorer tree navigation and workspace
• Data handling, multiple input/output formats (e.g., to or from Excel/Access, etc.)
• No limits on sizes of data matrices, numbers of factors, etc.
• 2d or 3d ordination graphics, with label/symbol control, vector overlays, reflections, rotations, spin, etc.
• Choose from more than 50 resemblance measures and a host of user-specified transformations/standardisations for individual variables or sets of variables.

PERMANOVA

• Dissimilarity/distance-based analysis of univariate or multivariate data in response to ANOVA experimental/sampling designs
• P-values by permutation or Monte Carlo asymptotic distributions
• Complex experimental designs (fixed or random factors, nested terms, hierarchies, interaction terms, mixed models)
• Correct construction of pseudo-F test statistic based on multivariate analogues to expected mean squares (EMS), including linear combinations of mean squares if required
• Choice of parameterisation of fixed effects in mixed models
• Choice of permutation method
• Estimation of sizes of components of variation
• Designs lacking replication (e.g., randomised blocks, split-plots, repeated measures, latin squares, etc.)
• Pair-wise comparisons among treatments or groups, also within levels or combinations of levels of other factors in a full model
• Specification and tests of contrasts and their interaction with other terms
• Pooling or excluding terms, and specify the order of terms in the model
• Unbalanced designs (including choice of the Type of SS: I, II or III)
• Asymmetrical designs (e.g., one impact site vs multiple control sites)
• Designs with covariates, including interactions with factors

PERMDISP

• Test of homogeneity of multivariate dispersions
• Distances to centroids or spatial medians in the space of the resemblance measure
• Test by permutation or classical tables
• Comparisons of beta-diversity using presence/absence data

PCO

• Principal coordinates analysis
• 2d and 3d ordinations with choice of vector overlays
• Correct treatment of negative eigenvalues
• Windows graphical interface and easy cutting and pasting

DistLM/dbRDA

• Distance-based linear models, distance-based redundancy analysis
• Models relating species (response) to environmental (predictor) variables
• Partitioning of variation, multivariate multiple regression
• Selection criteria: multivariate analogues to R2, adjusted R2, AIC, AICc, BIC
• Selection procedures: forward, backward, step-wise or "best"
• Fit variables individually or in sets
• Specify the order of fit
• Marginal and conditional or sequential tests by permutation
• Ordination of fitted values
• Superimpose vector overlays for base variables or some other set of variables, as either Pearson or Spearman raw correlations or as multiple partial correlations.

CAP

• Canonical analysis of principal coordinates
• Find axes through multivariate data clouds to predict a priori groups or quantitative gradients
• Explore inter-correlations between two sets of variables
• Discriminant analysis, canonical correlation analysis
• Leave-one-out misclassification errors (or residual SS) for model assessment
• Placement and allocation of new samples into existing models
• Ordination of canonical models, with choice of vector overlays
• Classification and prediction