kenkellner/R-tutorials

Short R and BUGS tutorials for beginners.
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data
models
.gitignore
01_basic_operations.R
02_importing_data.R
03_basic_statistics.R
04_figures.R
05_conditionals_loops_functions.R
06_glms_glmms.R
07_maximum_likelihood.R
08_JAGS_intro.R
09_glms_JAGS.R
10_randomeffects_JAGS.R
11_debugging_JAGS.R
13_resampling.R

tutorials

Short R and JAGS tutorials for beginners, mainly for use in various classes I have taught.

The tutorials are presented in a suggested order. The data directory contains data files (CSV) used in the examples. The model directory contains JAGS models used in example Bayesian analyses. The R files include step-by-step comments and observations.

Summary of tutorial contents:

1. Basic operations in R including math, simple functions, data formats and containers

2. Importing data into R from spreadsheets; manipulating data in R data frames; saving R objects and workspaces

3. Basic statistical tests in R, e.g. t-tests, ANOVA, simple linear regression

4. Creating and customizing plots and figures in R

5. Conditionals and true/false statements; looping structures in R (for, while loops); writing custom functions

6. Fitting generalized linear models and generalized linear mixed models in R using glm(), lme(), and glmmPQL()

7. A brief introduction to maximum likelihood estimation in R using optim()

8. Introduction to JAGS (Just Another Gibbs Sampler) and the R interface package R2jags for doing Bayesian analysis in R

9. Generalized linear models (logistic and Poisson) fit in JAGS

10. Mixed models in JAGS (i.e., including both fixed and random effects)

11. A demonstration of common errors when running analyses using JAGS

12. More advanced models fit in a Bayesian framework including occupancy and N-mixture models

13. Introduction to resampling tests in R (randomization/permutation tests)

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