Contains my implementation of the lecture programming problems (R scripts) of the wonderful course by Dr. Kevin Shoemaker. You can find the link of the course here.
The general focus is on model-based inference, including regression-based approaches, hierarchical/mixed models, and multi-model inference. Advanced analysis topics such as classification and regression trees, structural equation modeling, survival analysis, and species distributions models.
- Identify and contrast the major classes of statistical models used by ecologists (e.g., Bayesian vs frequentist, likelihood-based, machine learning) and explain how and why ecologists use these models.
- Apply analysis tools such as logistic regression, non-linear regression, hierarchical (mixed-effects) models, and machine-learning algorithms (e.g., Random Forest) on diverse data sets representative of those commonly considered in ecology.
- Learn to explore data sets quantitatively and graphically and to prepare data appropriately for analysis.
- Perform programming operations, statistical analysis, data visualization, and simulation modeling with the statistical computing language ‘R’.
- Critically evaluate the strength of inferences drawn from statistical models by testing major assumptions and assessing performance using tools such as cross-validation.