Module 5: Model Selection

Topic: ANOVA-based forward/backward selection, AIC/BIC, mixed-effects models.

Learning Objectives

Compare models using AIC and BIC
Perform automated model selection with MuMIn
Fit mixed-effects models with lme4/MASS
Understand when to use random effects

Slides

R Code

Data

Key Packages

library(MuMIn)
library(MASS)

Assignment

HW5: Model Selection — due Module 6 Wednesday. Submit on Canvas.

---
title: "Module 5: Model Selection"
---

::: {.week-header}
**Topic:** ANOVA-based forward/backward selection, AIC/BIC, mixed-effects models.
:::

## Learning Objectives

- Compare models using AIC and BIC
- Perform automated model selection with `MuMIn`
- Fit mixed-effects models with `lme4`/`MASS`
- Understand when to use random effects

## Slides

::: {.file-list}
- [Model Selection 1 (PDF)](../Week%205/Model_selection%201.pdf)
- [Regularization and Cross-validation Slides (PDF)](../Week%205/Regularization%20and%20Cross-validation%20slides.pdf)
:::

## R Code

::: {.file-list}
- [Model Selection 1.R](../Week%205/Model%20Selection%201.R)
- [Model selection 1.2.R](../Week%205/Model%20selection%201.2.R)
:::

## Data

::: {.file-list}
- [corals.csv](../Week%205/corals.csv)
- [shootbiomass.csv](../Week%205/shootbiomass.csv)
- [genotype_yields.csv](../Week%205/genotype_yields.csv)
:::

## Key Packages

```r
library(MuMIn)
library(MASS)
```

## Assignment

**HW5: Model Selection** — due Module 6 Wednesday. Submit on Canvas.