13. New standardisation options

13.1 Overview

PRIMER 8 offers a host of new options for standardising data (either samples or variables), via t...

13.2 Analysing cumulative standardised data

Rationale Suppose we have data where the variables consist of different size classes of mussels (...

13.3 Example: Mussel sizes in the Gulf of Alaska

To implement the new standardisation routine in PRIMER 8 and (simultaneously) demonstrate the uti...

13.4 Example: Gulf of Maine invertebrates - functional resemblance

There are many situations where the standardisation of samples is required as a pre-treatment pri...

Introduction

New Statistical Methods in P8

New Tools & Utilities in P8

1.1 Expansion from P7 to P8

1.2 Definitions of statistics

1.3 Biotic data: summary stats

1.4 Split summary stats results by groups

1.5 Environmental data: summary stats

2.1 What is an empirical distribution?

2.2 Example: Empirical distributions of oyster sizes

3.1 Plots of empirical densities

3.2 Example: Dotplot of oyster sizes

3.3 Example: Violin plot of kelp holdfast volumes

4.1 Wilcoxon signed-rank test

4.2 Example: Plankton hauls

4.3 Mann-Whitney U test

4.4 Example: Snapper in marine reserves

4.5 Kruskal-Wallis test

4.6 Example: A bivalve species from Ekofisk

4.7 Kolmogorov-Smirnov test

4.8 Example: Sizes of oysters

4.9 Test of Association

4.10 Example: Ekofisk diversity

4.11 Example: Associations between species

Overview of new 'Design' options and tools

6.1 Overview - Allow heterogeneity

6.2 ANOVA in a nutshell

6.3 The Behrens-Fisher problem (BFP)

6.4 Multivariate Behrens-Fisher problem

6.5 Solution to the multivariate BFP

6.6 Example: one-way PERMANOVA allowing heterogeneity

6.7 Heterogeneity in more complex designs

6.8 Example: two-way crossed PERMANOVA allowing heterogeneity

7.1 Overview - Finite factors

7.2 Dichotomy: fixed vs random factors

7.3 Not a dichotomy: a progression from fixed to random

7.4 Example: environmental impact on molluscs

7.5 Broader implications for detecting impact

8.1 Designs lacking replication

8.2 Example: Split-plot - Woodstock vegetation

8.3 Example: Repeated measures - Victorian avifauna

9.1 Why group covariables together?

9.2 Periodic and cyclical models

9.3 Example: Annual monthly cycles - B.C. macroalgae

10.1 Ordinations for multi-factor designs

10.2 Main effects plot

10.3 Interaction plot

10.4 Example: NZ fish assemblages

11.1 What are 'residual' distances?

11.2 Example: Plankton (revisited)

12.1 Overview - Control charts

12.2 Classical univariate control chart

12.3 Classical multivariate control chart

12.4 Bivariate normal example: NZ fish

12.5 Dissimilarity-based multivariate control chart

12.6 Additional notes on implementing control charts

12.7 Example: Birds from Grand Forks

13.1 Overview

13.2 Analysing cumulative standardised data

13.3 Example: Mussel sizes in the Gulf of Alaska

13.4 Example: Gulf of Maine invertebrates - functional resemblance

14.1 Overview

14.2 Example: NE Pacific groundfish vs depth

15.1 New default colour palette

15.2 New selection options

15.3 Re-name levels of a factor (or indicator)

15.4 Add customised values/labels to graphical axes

15.5 Split data sheet by factor/indicator

15.6 Line plots for samples

15.7 Output group-level stats from dispersion (or variability) weighting

15.8 Output diagnostic plots from CAP

15.9 New diagnostics for PCA/PCO plots

13. New standardisation options

13.1 Overview

13.2 Analysing cumulative standardised data

13.3 Example: Mussel sizes in the Gulf of Alaska

13.4 Example: Gulf of Maine invertebrates - functional resemblance

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