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Introduction to Statistical Modelling using R - Course Notes (Free to Course Participants)

Comprehensive notes and additional resources to accompany the Module 1 of the short course Statistical Ecotoxicology using R.

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R Code to accompany Statistical Ecotoxicology Workshop Notes (Free to Course Participants)
A zip file consisting of 7 text files (with .R extension). After unzipping, you can copy and paste into your R console to execute.

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Statistical Ecotoxicology using R - Course Notes (Free to Course Participants)
Comprehensive notes and additional resources to accompany the workshop Statistical Ecotoxicology using R.

Table of Contents

  1. Preliminaries

    • The nature of statistical science
    • Levels of measurement and implications for estimation and inference
    • Discrete and continuous probability models
    • Concept of sampling distributions of sample statistics
    • Frequentist and Bayesian statistical paradigms
  2. Review of basic concepts

    • Review of common discrete probability distributions:
    • uniform; bernoulli; binomial;hypergeometric; poisson; geometric; negative binomial
    • Using R's intrinsic functions to compute probabilities
  3. Linear models

    • the linear statistical model
    • OLS estimation for simple regression models
    • the GLM in matrix notation
    • ANOVA models and construction of design matrices
    • efficient coding techniques
    • more advanced models including interaction effects
    • testing hypotheses using orthogonal contrasts
  4. ACRs and bimodality

    • the relevance of multi-modal distributions in ecotoxicology
    • how to detect multi-modality
    • review of Australian Guidelines for testing for bi-modality
    • using R to perform the computations associated with the test of bi-modality
    • review of approaches too harmonizing acute and chronic toxicity data
    • discussion of options for computing an ACR from empirical daa
    • R software tools for ACR computation
  5. Software tools for SSDs

    • review of contemporary tools for fitting species sensitivity distributions:
    • R package ssdtools; on-line program MOSAIC; and BurrliOz software
    • comparison of performance of these tools with published data
  6. Model averaging and bias correction

    • strengths and weaknesses of the SSD approach
    •  model selection and estimation issues
    • assessing the adequacy of a fitted model
    • explanation of how model averaging works
    • using R to compute model weights and obtain HCx estimates from a model-averaged SSD fit
    • the impact of selection bias on SSD modelling and HCx estimation
    • how to compute a bias correction factor for an HCx - use of Excel spreadsheet
  7. Optimal experimental design

    • Simple formulae for determining number of experimental and control replicates in a C-R experiment
    • geometric interpretation of a matrix determinant; concept of D-optimality
    • using R to identify concentration spacing to achieve D-optimality using C-R threshold model
  8. Appendix A - R code for ACRs

  9. Appendix B - R code for D-optimal designs


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