A brief introduction to linear models in R
This is a short version of the Monash Bioinformatics Platform’s “Introduction to linear models” workshop, for the Bioconductor Training Day following ABACBS 2018.
Data should be already loaded in the “data” folder on the virtual machines for the workshop, but you will need to download the R script we will be working from:
download.file("https://github.com/MonashBioinformaticsPlatform/r-linear-abacbs2018/raw/master/r-linear-files/linear_models_abacbs2018.R", "linear_models_abacbs2018.R")Files
- linear_models_abacbs2018.R
- teeth.csv
- teeth-read-counts.csv
- r-linear-files.zip - All files in a ZIP file.
Workshop notes
Key functions to remember
Built-in to R:
lm, anova, model.matrix, coef, sigma, df.residual, predict, confint, summary
I, polysplines – curve fitting:
ns, bsmultcomp – linear hypothesis tests (aka contrasts) and multiple comparisons:
glht, confint, summarylimma and edgeR – fitting many models to gene expression data:
DGEList, calcNormFactors, cpm
lmFit, contrasts.fit, eBayes, plotSA, topTableLinks
- Monash Data Fluency - sign up to the mailing list for workshop announcements
- More workshop materials from Monash Data Fluency
- Monash Bioinformatics Platform
- James, Witten, Hastie and Tibshirani (2013) “An Introduction to Statistical Learning”
- Dance of the CIs - play with confidence intervals
Author
This course has been developed for the Monash Bioinformatics Platform and Monash Data Fluency by Paul Harrison.
This work is licensed under a Creative Commons Attribution 4.0 International License.
