Just enough R
Introduction
I Getting started
1 Working with R
II Data
2 The dataframe
3 ‘Real’ data
4 Summaries
- A generalised approach
  - Fancy reshaping
5 Graphics
III Models
6 Commonly used statistics
7 Regression
8 Anova
9 Generalized linear models
- Logistic regression
10 Multilevel models
11 Mediation and covariance modelling
12 Baysian model fitting
13 Power analysis
- For most inferential statistics
- For multilevel or generalised linear models
IV Patterns
14 Learning key patterns
15 Unpicking interactions
16 Making predictions
17 Models are data
18 Dealing with quirks of R
19 Getting help
- Finding the backtick on your keyboard
V Explanations
20 Confidence and Intervals
- The problem with confidence intervals
- Forgetting that the CI depends on sample size.
21 Multiple comparisons
22 Non-independence
23 Fixed and random effects
24 Scaling predictor variables
- Standardising
- Dichotomising continuous predictors (or outcomes)
25 Non-scale outcomes
- 25.1 Link functions
  - Logistic regression
26 Building and choosing models
References

Just Enough R

‘Processing’ results

XXX TODO e.g.:

Calculate VPC/ICC from an lmer models using model %>% summary %>% as_data_frame()$varcor