Overview

These labs are meant for incoming graduate students. No prior experience with R is assumed, but students should have taken a basic undergraduate-level statistics course.

Schedule

Week 1 - Introduction to R

Overview of R basics. Topics include:

  • Installing R
  • Creating and indexing vectors and data.frames
  • Computing summary statistics
  • Importing and exporting data
  • Help pages

Week 2 - t-tests

t-tests with and without the t.test function. Topics include:

  • Two-sample t-test
  • Paired t-test
  • Graphics
    • Histograms
    • Boxplots

Week 3 - One-way ANOVA

Analysis of data from completely randomized designs. Topics include:

  • aov vs lm
  • Means, effects, and SEs with model.tables
  • Computing the ANOVA table by hand
  • Multiple comparisons with TukeyHSD

Week 4 - Contrasts, estimation, and power

Three somewhat unrelated topics in this lab.

  1. Creating orthogonal contrasts to test specific hypotheses
  2. Moving beyond null hypothesis tests to focus on effect sizes and confidence intervals
  3. Power analysis for t-tests and one-way ANOVA
    • power.t.test and power.anova.test

Week 5 - Assumptions of ANOVA, transformations, and nonparametrics

Testing assumptions and dealing with violations using transformations or nonparametrics.

  • Extracting residuals with resid
  • Shapiro-Wilk test with shapiro.test
  • Graphical assessments
  • Log, sqrt, asin, transformations
  • Kruskal-Wallis test with kruskal.test
  • Wilcoxon rank sum test, aka Mann-Whitney test, with wilcox.test

Week 6 - Randomized complete block design

Accounting for extraneous sources of variation using blocked designs

  • Blocked ANOVA by hand and with aov
  • Random block effects using Error in aov

Week 7 - AxB Factorial

Factorial designs with just two factors. Analysis and options for presenting the results.

Week 8 - Nested Designs

Nested designs in which experimental units are subsampled.

  • aov with multiple Error strata
  • More flexible model fitting with the lme function in the nlme package
  • The multcomp package for multiple comparisons

Week 9 - Split-plot Designs

Split-plot designs in which treatments are applied to both whole-units and sub-units in a blocked design.

  • aov with multiple Error strata
  • More flexible model fitting with the lme function in the nlme package
  • The multcomp package for multiple comparisons

Week 10 - Repeated Measures Designs

Repeated measures designs in which observations are recorded on each “subject” on multiple time periods. The interaction of the treatment variable and time is of interest.

  • Univariate approach using aov and adjusted p-values
  • Multivariate approach using manova with Wilks’ lambda or Pillai’s trace

Week 11 - ANCOVA

Analysis of covariance in which one explantory variable is a factor, and the other is a continuous variable.

  • Model fitting with lm
  • Predictions with predict

Week 12 - Linear models

Stepping back to look at linear models

Week 13 - Generalized linear models

Logistic regression and Poisson regression with glm

Week 14 - Model selection

Model selection and multi-model inference