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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...
4.15 Categorical predictor variables (Oribatid mites)
Sometimes the predictor variables of interest are not quantitative, continuous variables, but rat...
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 ...
4.13 dbRDA plot for Ekofisk
Let us examine the constrained dbRDA ordination for the parsimonious model obtained earlier using...
4.12 Vector overlays in dbRDA
Something which certainly should come as no surprise is to see the X variables playing an importa...
4.11 Visualising models: dbRDA
We may wish to visualise a given model in the multivariate space of our chosen resemblance matrix...
4.10 (Ekofisk macrofauna)
We shall now use the DISTLM tool to identify potential parsimonious models for benthic macrofauna...
4.8 Building models
In many situations, a scientist may have measured a large number of predictor variables that coul...
4.7 Assumptions & diagnostics
Thus far, we have only done examples for a univariate response variable in Euclidean space, using...
4.6 (Holdfast invertebrates)
To demonstrate conditional tests in DISTLM, we will consider the number of species inhabiting hol...
1.13 PERMANOVA versus ANOSIM
The analysis of similarities (ANOSIM), described by is also available within PRIMER and can be u...
1.10 Running PERMANOVA
To run PERMANOVA on the Ekofisk data, click on the resemblance matrix and select PERMANOVA+ > PER...
1.6 Test by permutation
An appropriate distribution for the pseudo-F statistic under a true null hypothesis is obtained b...
4.5 Conditional tests
More generally, when X contains more than one variable, we may also be interested in conditional ...
4.4 Simple linear regression (Clyde macrofauna)
In our first example of DISTLM, we will examine the relationship between the Shannon diversity (H...
4.3 Partitioning
Consider an (N × p) matrix of response variables Y, where N = the number of samples and p = the n...
4.2 Rationale
Just as PERMANOVA does a partitioning of variation in a data cloud described by a resemblance mat...
3.9 PCO versus MDS
We recommend that, for routine ordination to visualise multivariate data on the basis of a chosen...
3.8 Distances among centroids (Okura macrofauna)
In chapter 1, the difficulty in calculating centroids for non-Euclidean resemblance measures was ...
3.7 PCO versus PCA (Clyde environmental data)
Principal components analysis (PCA) is described in detail in chapter 4 of . As stated earlier, P...