Chapter 4: Distance-based linear models (DISTLM) and distance-based redundancy analysis (dbRDA)
Key references
Method: Legendre & Anderson (1999), McArdle & Anderson (2001)
Permutation methods: Freedman & Lane (1983), Anderson & Legendre (1999), Anderson & Robinson (2001), Anderson (2001b)
4.1 General description
Key references Method:, Permutation methods: , , , DISTLM is a routine for analysing and ...
4.2 Rationale
Just as PERMANOVA does a partitioning of variation in a data cloud described by a resemblance mat...
4.3 Partitioning
Consider an (N × p) matrix of response variables Y, where N = the number of samples and p = the n...
4.4 Simple linear regression (Clyde macrofauna)
In our first example of DISTLM, we will examine the relationship between the Shannon diversity (H...
4.5 Conditional tests
More generally, when X contains more than one variable, we may also be interested in conditional ...
4.6 (Holdfast invertebrates)
To demonstrate conditional tests in DISTLM, we will consider the number of species inhabiting hol...
4.7 Assumptions & diagnostics
Thus far, we have only done examples for a univariate response variable in Euclidean space, using...
4.8 Building models
In many situations, a scientist may have measured a large number of predictor variables that coul...
4.9 Cautionary notes
Before proceeding, a few cautionary notes are appropriate with respect to building models. First,...
4.10 (Ekofisk macrofauna)
We shall now use the DISTLM tool to identify potential parsimonious models for benthic macrofauna...
4.11 Visualising models: dbRDA
We may wish to visualise a given model in the multivariate space of our chosen resemblance matrix...
4.12 Vector overlays in dbRDA
Something which certainly should come as no surprise is to see the X variables playing an importa...
4.13 dbRDA plot for Ekofisk
Let us examine the constrained dbRDA ordination for the parsimonious model obtained earlier using...
4.14 Analysing variables in sets (Thau lagoon bacteria)
In some situations, it is useful to be able to partition variability in the data cloud according ...
4.15 Categorical predictor variables (Oribatid mites)
Sometimes the predictor variables of interest are not quantitative, continuous variables, but rat...
4.16 DISTLM versus BEST/ BIOENV
On the face of it, the DISTLM routine might be thought of as playing a similar role to PRIMER’s B...