From Bolker’s glmmFAQ


A good walkthrough



From stackexchange

LabDSV: R Labs for Vegetation Ecologists

  • Ordination
    • Lab 7 Principal Components Analysis
    • Lab 8 Principal Coordinates Analysis
    • Lab 9 Nonmetric Multi-Dimensional Scaling
    • Lab 10 Correspondence Analysis and Detrended Corresponence Analysis
    • Lab 11 Fuzzy Set Ordination
    • Lab 12 Canonical Correspondence Analysis
  • Cluster Analysis
    • Lab 13 Cluster Analysis
    • Lab 14 Discriminant Analysis with Tree Classifiers


PCa and PCoA explained

    • Lab 12 Canonical Correspondence Analysis
  • Cluster Analysis
    • Lab 13 Cluster Analysis
    • Lab 14 Discriminant Analysis with Tree Classifiers

Below are some blogs related to the question of interactions in ANOVA.  They might not be super-accessible for ‘yins – they are mostly discussions among well practiced stats users and not attempts to explain the issue in general terms.  One thing I think is apparent though is that I think the problem is not well defined – when someone says “how do you interpret interactions” different people think of it in terms of experiments analyzed with an ANOVA with only categorical variables, while others think in terms of regression with interactions between two continuous variables, or between a continuous and categorical variable.  I also think some people think in terms of how you interpret the result of an ANOVA table (which basically just tells you p-values) and interpretation of the means and other parameters you can get from your model.


Dynamic Ecology: Interpreting ANOVA interactions and model selection: a summary of current practices and some recommendations (also see the comments on their Poll: How do you interpret two-way ANOVAs?)

Andrew Gelman: Main effects and interactions

5 functions to do Multiple Correspondence Analysis in R

This mentions the ade4 package, and factominer


The factorminer package also does mca

FactoMineR: Multiple Correspondence Analysis



Multiple Correspondence Analysis Essentials: Interpretation and application to investigate the associations between categories of multiple qualitative variables – R software and data mining



I think this is the reference that might be the origin of PCA w/ categorical variables in ecology related fields


Hill & Smith 1976 Principal component analysis of taxonomic data with multistate discrete characters


The package ade4 has a function dudi.mca() for multiple correspondence analysis (PCA w/categorical variables) and dudi.hillsmith() which allows you to do a mix; its probably similar to that PCAmix package I sent.  ade4 is part of a suite of packages by crazy French ecologists that are REALLY into multivariate stuff- I find most of the documentation really hard to understand (lots of math, not much ecology, not perfect English).  They have a paper “The ade4 package: implementing the duality diagram for ecologists” In the Journal for Statistical Software which has a section “4. An example: dudi.hillsmith” where they analyze the “dune meadow” dataset that shows up often in vegan I think.  This example, whether analyzed with dudi.hillmsith or the functions in the PCAmix package (or whatever its name is) might be a good place to start.






Dutch sexism study comes under files




Simpson’s paradox


Simpson’s paradox visualization