Statistics 196A, Education 401D Spring 2018
Multilevel Modeling Using R
David Rogosa
Sequoia 224, rag{AT}stat{DOT}stanford{DOT}edu Office hours: Class meeting days, 5:30 - 6:30
Course web page: http://rogosateaching.com/stat196/
To see full course materials from Spring 2017 go here
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STATS 196A (EDUC 401D): Multilevel Modeling Using R
Multilevel data analysis examples using R.
Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data,
three-level analyses. For more information, see course website: http://rogosateaching.com/stat196/
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Rogosa, D. (PI)
STATS 196A | 1 units | Class # 20661 | Section 01 | Grading: Satisfactory/No Credit | WKS |
Wed 3:30 PM - 5:20 PM at Littlefield 104 with Rogosa, D. (PI)
Instructors: Rogosa, D. (PI)
Notes: Class meets on April 11, April 18, April 25, May 2, May 16.
Course Schedule
Five (2hr) mtgs W 3:30 - 5:20 on April 11, April 18, April 25, May 2, May 16. Littlefield 104
Weeks 1 - 4.
a. Introduction: Basic analyses for two-level nested data, normal models (UK Exam data)
b. Additional two-level (normal) models: experimental designs (Dyestuff), longitudinal data (growth curves, sleepstudy), observational data (High School and Beyond)
c. Generalized linear mixed models for counts and categorical outcomes
d. Three-level analyses (nested data and longitudinal data)
e. Specialized applications (as time permits): regression diagnostics, power calculations and design, ecological inference, survival analysis, nonlinear functional forms, mediation analysis, propensity scores and matching, imputation, item response theory
Week 5. Student presentations of multilevel data analyses
For the 1-unit enrollment in this short course, students are expected to engage in the four presentation class sessions, and for the fifth session each student makes a short (~10 min) presentation of a relevant data analysis they have conducted.
Note to auditors. We will have room for auditors.
The Registrar does have a form (no-fee) for faculty, staff, post-docs: Application for Auditor or Permit to Attend (PTA) Status
Core Sources
Many of the example presented in this short course are described in Examples from Multilevel Software Comparative Reviews Douglas Bates. Code version of MlmSoftRev
R-package containing mlmRev data examples. Bates talk on mlmRev U Bristol documentation Additional examples in core package lme4
Another set of examples: lmer for SAS PROC MIXED Users Douglas Bates Department of Statistics University of Wisconsin Madison Data sets from SAS System for Mixed Models
Overviews and additional examples from Doug Bates:
lme4: Mixed-effects modeling with R February 17, 2010 Springer (book chapters). An merged version of Bates book:[broken 2/18] lme4: Mixed-effects modeling with R January 11, 2010
R Journal intro Fitting linear mixed models in R Using the lme4 package Douglas Bates (pp.27-30)
Collection of all Doug Bates lme4 talks Fitting linear mixed-effects models using lme4, Journal of Statistical Software Douglas Bates Martin Machler Ben Bolker.
Technical topics: Mixed models in R using the lme4 package Part 4: Theory of linear mixed models
HSB and growth curve examples in John Fox lme tutorial
Another nice lmer exposition with life sciences examples: Mixed-effects models, Remko Duursma, Jeff Powell Hawkesbury Institute for the Environment, Western Sydney University. September 2016. HIE Datasets
Current version of R is R version 3.4.4 released on 2018-03-15. For references and software: The R Project for Statistical Computing Closest download mirror is Berkeley
A recent text (potentially) provides more infrastructure for this short course, but sadly it has many shortcomings. This text has free access at Stanford via crcnetbase.com Multilevel Modeling Using R
http://www.crcpress.com/product/isbn/9781466515857
Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences Published:June 23, 2014 by CRC Press
Journal of Statistical Software Book Review
Book website, including data
Week 1 April 11
1. Introductory Example. Nested data, two-levels. Goldstein Exam Data.
Exam {mlmRev} UK HS data. Subset: Coed schools exam data, mixed schools, cleaned
a. Introductory descriptive approaches for gender gap analysis (Smart First Year Student analyses using lmList, additional plots).
b. Various lmer analyses for gender gap.
Rogosa R-session basic plots models used Stat 209 Gender gap data analysis. scanned class handout
c. Residual plots, add-on regression diagnostics: packages HLMdiag , influence.ME
Rogosa session with Exam data (week 1) (ascii) resulting plots
d. more P-values, tests add-ons to lmer. Faraway text addendums: Inferential Methods for Linear Mixed Models
2. Growth Curve Modeling exercise, Brain Volume Data Analysis.
analyses from "Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85 years) measured with atlas-based parcellation of MRI" cartoon plot of Lateral Ventricles data; actual data plot of Lateral Ventricles data; development of lmer (mixed effect) growth models
3. Matrix Formulation for Mixed Effects Models (growth curves and nested data).
Week 2 April 18
0. Continue Introductory Example. Nested data, two-levels. Goldstein Exam Data.
See week 1 materials. Complete parts b, c, d
Plus Plots for random, fixed effects.
1. Data from designed experiments.(abbreviated).
a. Dyestuff data, Bates book, Chapter 1 (sec 1.2, 1.3) Rogosa Dyestuff session
b. Penicillin data (also Pastes, ratbrain), Bates book, Chapter 2. From Doug Bates presentation
More factorial designs week 3 (hope).
2. Growth Curve models and analysis. Sleepstudy example (from Stat222).
Sleepstudy class handout, pdf scan Sleepstudy, clean ascii [more Doug Bates Slides (pdf pages 8-28) ] Individual plots (frame-by-frame) Plot of straight-line fits
3. Two-level nested (normal) data recap. Brief overview of HSB (High School and Beyond) analysis (from Stat209): plots and model.
a full single Bryk dataset (longform) (abbreviated) Rogosa R-session Bryk data plots, Rogosa R-session
Caution from last year: side-by-side boxplot creation and lmList subset issue
A nice teaching document from Indiana that does HSB data with every known statistical package (including lmer)
Week 3 April 25
0a. Random effects anova recap (see Bates book Chap1, Chap2).
0b. Two-level normal model wrap-up. HSB example (week2 above). Matrix Formulation for Mixed Effects Models (growth curves and nested data).
Main topic: Generalized Linear Mixed Models: counts and proportions.
nice overview: Generalized Linear Mixed Models from Encyclopedia of Statistics in Behavioral Science.
1. Dichotomous outcomes, glmer analysis examples.
a. Respiratory clinical trial from HSAUR. lmList does logistic, introducing glmer lmList, glmer for respiration data (placebo group)
b. Contraception (Bangladesh) use from Bates review Rogosa R-session glmer model slide
c. Test scores (pass/fail outcome) from Ch 8, Multilevel Modeling Using R. Rogosa R-session
2. Count outcome, GLMM poisson models.
a. Count data: Contagious bovine pleuropneumonia, data(cbpp) in lme4.
Rogosa R-session herd plots
b. Another count data example from mlmRev package, data(Mmmec): Malignant Melanoma Mortality in the European Community associated with the impact of UV radiation exposure.
Rogosa glmer session more Rogosa Mmmec session
hglm -- a different package for fitting hierarchical generalized linear models. R Journal December 2010.
Week 4 May 2
0. Revisit Count data. Poisson regression, Aids in Belgium (see Week1 Stat222); Week3 item 2b Mmmec example analysis
1. Three-level (and above) lmer examples.
Measured outcomes
a. Achieve data from Multilevel Modeling Using R book. Rogosa R-session
b. example from mlmRev package, data(Chem97): Scores on A-level Chemistry in 1997. Rogosa R-session
Count outcome.
c. data(Mmmec): Malignant Melanoma Mortality in the European Community associated with the impact of UV radiation exposure. Rogosa 3-level session
We won't get to these..........
Special and Specialized Multi-level Topics:
From week3 hglm -- a different package for fitting hierarchical generalized linear models. R Journal December 2010.
A. Nonlinear Models. nlmer in lme4 Bates presentation Orange trees, logistic, in Stat222 (week3)
B. Extensions of lme4 modeling: npmlreg Nonparametric Maximum Likelihood (NPML) estimation; hglm Hierarchical Generalized Linear Models,
Package RLRsim Title Exact (Restricted) Likelihood Ratio Tests for Mixed and Additive Models An Introduction to merTools
C. Multilevel Survival Analysis.
Mixed effects (Frailty) survival models. Package coxme Terry Therneau July 2014: Cox proportional hazards models containing Gaussian random
effects, also known as frailty models coxme manual. Maintainer Terry Therneau. Mixed Effects Cox Models Terry Therneau Mayo Clinic June 15, 2015. Additional materials. frailtyHL: A Package for Fitting Frailty Models with H-likelihood
Stat222 coxme handout plain text version
D. Mediation Analysis Package multilevel Package mediation package vignette
E. Propensity Score and Matching Methods. Package matchMulti Title Optimal Multilevel Matching using a Network Algorithm Package multilevelPSA Title Multilevel Propensity Score Analysis
F. Ecological Inference R-package eiPack: R x C Ecological Inference and Higher-Dimension Data Management. R News Oct 2007
G. Multiple Imputation. Package mitml Title Tools for Multiple Imputation in Multilevel Modeling
H. Power calculations and Design;
Package pamm Title Power Analysis for Random Effects in Mixed Models
Package simr Title Power Analysis for Generalised Linear Mixed Models by Simulation.
longpower power and sample size for linear models of longitudinal data
Package MultiRR Title Bias, Precision, and Power for Multi-Level Random Regression
Week 5 May 16
Student presentations, multilevel analysis examples.