Statistics 196A, Education 401D  Spring 2019
    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:

                To see full course materials from Spring 2018 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, 
nonlinear models, three-level analyses. For more information, see course website:
Terms: Spr | Units: 1 | Grading: Satisfactory/No Credit
Instructors: Rogosa, D. (PI) 
STATS 196A | 1 units | Class # 29266 | Section 01 | Grading: Satisfactory/No Credit | WKS |  
Wed 3:30 PM - 5:20 PM at Sequoia Hall 200 with Rogosa, D. (PI) 
Instructors: Rogosa, D. (PI) 
Notes: Class meets on  April 10, April 17, April 24, May 1, May 15.
 Course Schedule
Five (2hr) mtgs W 3:30 - 5:20 on April 10, April 17, April 24, May 1, May 15.  Sequoia 200
 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 3.5.3 released on (2019-03-11) -- "Great Truth". 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
     Multilevel Modeling Using R        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 10
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   HLMdiaginfluence.ME
           Rogosa session with Exam data (week 1) (ascii)         resulting plots
     d. more P-values, tests add-ons to lmer.
       afex package with Exam data ggaplmer2
       Faraway text addendums: Inferential Methods for Linear Mixed Models
     e. Plus     Plots for random, fixed effects.

2. Matrix Formulation for Mixed Effects Models (growth curves and nested data).

Week 2 April 17
1. Recap Introductory Example. Nested data, two-levels. Goldstein Exam Data.
     merMod objects from lmer
    Add-on package merTools     merTools vignette
prediction with lmer : predict with lme4   predictInterval from merTools  prediction vignette
Rogosa session.   plots

2. Common/canonical two-level examples (measured outcome)
A. Growth Curve models and analysis. Bates Sleepstudy example (week2 Stat222).
Chap. 4 Bates book          [more Doug Bates Slides (pdf pages 8-28) ]
     Sleepstudy class handout, pdf scan     Sleepstudy, 2018 clean ascii         Individual plots (frame-by-frame)       Plot of straight-line fits         
Reduced/constrained models: growth curve example
B. 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)

3. 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

4. Data from designed experiments.(basics).
     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       Rogosa R-session
     Random effects anova recap (see Bates book Chap1,    Chap2).

Week 3 April 24
      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
glmer.nb  Fits a generalized linear mixed-effects model (GLMM) for the negative binomial family, building on glmer, and initializing via from MASS.
3.    hglm -- a different package for fitting hierarchical generalized linear models.
R Journal December 2010.    manual    vignette