Each chapter has a README file on github that you can view by clicking on the chapter link and scrolling down (past the files). The README file contains information about the data contained in the chapter (particularly, what each variable represents) and sorts the models in the chapter by type.

If there is a * next to a model name, then the model DOES NOT currently work with RStanARM. Furthermore, multilevel models are not currently supported by RStanARM, but all other models below (without a *) are supported.

• Chapter 2 - Concepts and Methods from Basic Probability and Statistics
• Chapter 3 - Linear Regression: the Basics
• Chapter 4 - Linear Regression: Before and After Fitting the Model
• Chapter 5 - Logistic Regression
• Chapter 6 - Generalized Linear Models
• Chapter 7 - Simulation of Probability Models and Statistical Inference
• Chapter 8 - Simulation for Checking Statistical Procedures and Model Fits
• Chapter 9 - Causal Inference Using Regression on the Treatment Variable
• Chapter 10 - Causal Inference Using More Advanced Models
• Chapter 11 - Multilevel Structures
• Chapter 12 - Multilevel Linear Models: the Basics
• Chapter 13 - Multilevel Linear Models: Varying Slopes, Non-Nested Models, and Other Complexities
• Chapter 14 - Multilevel Logistic Regression
• Chapter 15 - Multilevel Generalized Linear Models
• Chapter 16 - Multilevel Modeling in Bugs and R: the Basics
• Chapter 17 - Fitting Multilevel Linear and Generalized Linear Models in Bugs and R
• Chapter 18 - Likelihood and Bayesian Inference and Computation
• Chapter 19 - Debugging and Speeding Convergence
• Chapter 20 - Sample Size and Power Calculations
• Chapter 21 - Understanding and Summarizing the Fitted Models
• Chapter 22 - Analysis of Variance
• Chapter 23 - Causal Inference Using Multilevel Models
• Chapter 24 - Model Checking and Comparison
• Chapter 25 - Missing-Data Imputation

• 2.3 CI

• 2.4 Hypothesis Testing - MISSING DATA

• 2.6 55,000 Residents - MISSING DATA

• 3.1 One Predictor

  • kidscore_momhs: linear model with one predictor
    lm (kid_score ~ mom_hs)

  • kidscore_momiq: linear model with one predictor
    lm (kid_score ~ mom_iq)

• 3.2 Multiple Predictors

  • kidiq_multi_preds: linear model with two predictors
    lm (kid_score ~ mom_hs mom_iq)

• 3.3 Interactions

  • kidiq_interaction: linear model with two predictors and interaction
    lm (kid_score ~ mom_hs mom_iq mom_hs:mom_iq)

• 3.4 Stat Inference

  • kidiq_multi_preds: linear model with two predictors
    lm (kid_score ~ mom_hs mom_iq)

• 3.5 Graph Displays

  • kidscore_momiq: linear model with one predictor
    lm (kid_score ~ mom_iq)

  • kidiq_multi_preds: linear model with two predictors
    lm (kid_score ~ mom_hs mom_iq)

  • kidiq_interaction: linear model with two predictors and interaction
    lm (kid_score ~ mom_hs mom_iq mom_hs:mom_iq)

• 3.6 Diagnostics

  • kidscore_momiq: linear model with one predictor
    lm (kid_score ~ mom_iq)

• 3.7 Prediction

  • kidiq_multi_preds: linear model with two predictors
    lm (kid_score ~ mom_hs mom_iq)

  • kidiq_validation: linear model with two predictors
    lm (ppvt ~ hs afqt)

• 4.1 Linear Transformations

• earn_height: linear model with one predictor
  lm (earnings ~ height)

• 4.2 Centering & Standardizing

• kidiq_interaction: linear model with two predictors and interaction
  lm (kid_score ~ mom_hs mom_iq mom_hs:mom_iq)

• kidiq_interaction_c: linear model with two predictors and interaction centered using mean
  lm (kid_score ~ c_mom_hs c_mom_iq c_mom_hs:c_mom_iq)

• kidiq_interaction_c2: linear model with two predictors and interaction centered using conventional points
  lm (kid_score ~ c2_mom_hs c2_mom_iq c2_mom_hs:c2_mom_iq)

• kidiq_interaction_z: linear model with two predictors and interaction centered using z-score
  lm (kid_score ~ z_mom_hs z_mom_iq z_mom_hs:z_mom_iq)

• 4.4 Log Transformations

• logearn_height: linear model with one predictor and natural log transformation
  lm (log_earnings ~ height)

• log10earn_height: linear model with one predictor and log10 transformation
  lm (log10_earnings ~ height)

• logearn_height_male: linear model with two predictors and natural log transformation
  lm (log_earnings ~ height male)

• logearn_interaction: linear model with two predictors and interaction and natural log transformation
  lm (log_earnings ~ height male height:male)

• logearn_interaction_z: linear model with two predictors and interaction and natural log transformation centered using z-score
  lm (log_earnings ~ z_height male z_height:male)

• logearn_logheight: linear model with two predictors and log log transformation
  lm (log_earnings ~ log_height male)

• 4.5 Other Transformations

• kidscore_momwork: linear model with one factor
  lm (kid_score ~ as.factor(mom_work))

• 4.6 Regression Models for Prediction

• mesquite: linear model with six predictors
  lm (weight~ diam1 diam2 canopy_height total_height density group)

• mesquite_log: linear model with six predictors and log transformation
  lm (log_weight~ log_diam1 log_diam2 log_canopy_height log_total_height log_density group)

• mesquite_volume: linear model with one transformed predictor and log transformation
  lm (log_weight ~ log_canopy_volume)

• mesquite_vas: linear model with three predictors and three transformed predictors and log transformation
  lm (log_weight ~ log_canopy_volume log_canopy_area log_canopy_shape log_total_height log_density group)

• mesquite_va: linear model with one predictor and two transformed predictors and log transformation
  lm (log_weight ~ log_canopy_volume log_canopy_area group)

• mesquite_vash: linear model with two predictors and three transformed predictors and log transformation lm (log_weight ~ log_canopy_volume log_canopy_area log_canopy_shape log_total_height group)

• 4.7 Fitting a Series of Regressions - graph partially works

• nes: linear model with eight predictors
  lm (partyid7 ~ real_ideo race_adj age30_44 age45_64 age65up educ1 gender income)

• 5.1 Logistic Regression with One Predictor

• nes_logit: generalized linear model with logit link function and one predictor
  glm (vote ~ income, family=binomial(link="logit"))

• 5.2 Interpreting Logistic Regression Coeficients

• nes_logit: generalized linear model with logit link function and one predictor
  glm (vote ~ income, family=binomial(link="logit"))

• 5.4 Logistic Regression Wells in Bangladesh

• wells_dist: generalized linear model with logit link function and one predictor
  glm (switched ~ dist, family=binomial(link="logit"))

• wells_dist100: generalized linear model with logit link function and one predictor
  glm (switched ~ dist100, family=binomial(link="logit"))

• 5.5 Logistic Regression with Interactions

• wells_interaction: generalized linear model with logit link function and two predictors and interaction
  glm (switched ~ dist100 arsenic dist100:arsenic, family=binomial(link="logit"))

• wells_interaction_c: generalized linear model with logit link function with two predictors and interaction centered using mean
  glm (switched ~ c_dist100 c_arsenic c_dist100:c_arsenic, family=binomial(link="logit"))

• wells_daae_c: generalized linear model with logit link function and four predictors and interaction centered using mean
  glm (switched ~ c_dist100 c_arsenic c_dist100:c_arsenic assoc educ4, family=binomial(link="logit"))

• wells_dae_c: generalized linear model with logit link function and three predictors and interaction centered using mean
  glm (switched ~ c_dist100 c_arsenic c_dist100:c_arsenic educ4, family=binomial(link="logit"))

• wells_predicted: generalized linear model with logit link function and three predictors and interaction centered using mean
  glm (switched ~ c_dist100 c_arsenic c_educ4 c_dist100:c_arsenic c_dist100:c_educ4 c_arsenic:c_educ4, family=binomial(link="logit"))

• 5.6 Evaluating, Checking, & Comparing

• wells_predicted: generalized linear model with logit link function with three predictors and interaction centered using mean
  glm (switc ~ c_dist100 c_arsenic c_educ4 c_dist100:c_arsenic c_dist100:c_educ4 c_arsenic:c_educ4, family=binomial(link="logit"))

• wells_predicted_log: generalized linear model with logit link function with three predictors and interaction with log transform and centered using mean
  glm (switched ~ c_dist100 c_log_arsenic c_educ4 c_dist100:c_log_arsenic c_dist100:c_educ4 c_log_arsenic:c_educ4, family=binomial(link="logit"))

• wells_predicted_log: generalized linear model with logit link function with three predictors and interaction with log transform and centered using mean
  glm (switched ~ c_dist100 c_log_arsenic c_educ4 c_dist100:c_log_arsenic c_dist100:c_educ4 c_log_arsenic:c_educ4, family=binomial(link="logit"))

• 5.7 Average Predictive Comparisons

• wells_dae: generalized linear model with logit link function and three predictors
  glm (switched ~ dist100 arsenic educ4, family=binomial(link="logit"))

• wells_dae_inter: generalized linear model with logit link function and three predictors with interaction
  glm (switched ~ dist100 arsenic educ4 dist100:arsenic, family=binomial(link="logit"))

• 5.8 Identifiability and Separating

• separation: generalized linear model with logit link function and one predictor
  glm (y ~ x, family=binomial(link="logit"))

• 6.2 Poisson, Exposure, & Overdispersion - MISSING DATA

• 6.4 Probit Regression

• wells_probit: generalized linear model with probit link function and one predictor
  glm (switc ~ dist100, family=binomial(link="probit"))

• 6.5 Ordered & Unordered Categorical Regression - MISSING DATA

• 6.7 More Complex GLM

• earnings1: generalized linear model with logit link function and two predictors
  glm (earn_pos ~ height male, family=binomial(link="logit"))

• earnings2: linear model with two predictors and log transformation
  lm (log_earn ~ height male, subset=earn>0)

• 6.8 Constructive Choice Models

• wells_logit: generalized linear model with logit link function and one predictor
  glm (switc ~ dist100, family=binomial(link="logit"))

• 7.1 Simulation of Probability Models

• 7.2 Summarizing Linear Regression Using Simulation

• earnings_interactions: linear model with two predictors and interaction and log transformation
  lm (log_earn ~ height male height:male)

• 7.3 Simulation for NonLinear Predictions

• congress: linear model with two predictors
  lm (vote_88 ~ vote_86 incumbency_88)

• 7.4 Predictive Simulation for GLM

• wells: generalized linear model with logit link function and one predictor
  glm (switc ~ dist, family=binomial(link="logit"))

• earnings1: generalized linear model with logit link function and two predictors
  glm (earn_pos ~ height male, family=binomial(link="logit"))

• earnings2: linear model with two predictors and log transformation
  lm (log_earn ~ height male, subset=earn>0)

• 8.1 Fake Data Simulation

• y_x: linear model with one predictor
  lm (y ~ x)

• 8.2 Fake Data Simulation to Understand Residual Plots

• grades: linear model with one predictor
  lm (final ~ midterm)

• 8.3 Simulating from the Fitted Model

• lightspeed: linear model with no predictors
  lm (y ~ 1)

• roaches: poisson regression model with exposure and three predictors
  glm (y ~ roach1 treatment senior, family=poisson, offset=log(exposure2))

• roaches_overdispersion: poisson overdispersion regression model with exposure and three predictors
  glm(y ~ roach1 treatment senior, family=quasipoisson, offset=log(exposure2))

• 8.4 Predictive Simulation to Check Fit of Time Series Model

• unemployment: linear model with one predictor
  lm (y ~ y_lag)

• 9.3 Randomized Experiments - convert regression.2tables

• electric_tr: linear model with one predictor
  lm (post_test ~ treatment)

• electric_trpre: linear model with two predictors
  lm (post_test ~ pre_test treatment)

• 9.4 Treatment Interactions & Post Stratification

• electric_tr: linear model with one predictor
  lm (post_test ~ treatment)

• electric_trpre: linear model with two predictors
  lm (post_test ~ treatment pre_test)

• electric_inter: linear model with two predictors and interaction
  lm (post_test ~ pre_test treatment pre_test:treatment)

• 9.5 Observational Studies - havent started

• electric_supp: linear model with two predictors and interaction
  lm (post_test ~ supp pre_test)

• 10.3 Matching - MISSING DATA

• 10.4 Lack of Overlap when Treat Assignments is Unknown

• ideo_two_pred: linear model with two predictors
  lm (score1 ~ party x, subset=overlap)

• ideo_two_pred: linear model with two predictors
  lm (score1 ~ party x, subset=incs)

• ideo_reparam: linear model with two predictors and reparamaterization
  lm (score1 ~ party I(z*(party==0)) I(z*(party==1)), subset=incs)

• ideo_interactions: linear model with two predictors and interaction
  lm (score1 ~ party x party:x, subset=incs)

• 10.5 Causal Effects Using IV

• sesame_one_pred_a: linear model with one predictor
  lm (watched ~ encouraged)

• sesame_one_pred_b: linear model with one predictor
  lm (y ~ encouraged)

• 10.6 IV in a Regression Framework - misisng stuff at bottom

• sesame_one_pred_a: linear model with one predictor
  lm (watched ~ encouraged)

• sesame_one_pred_2b: linear model with one predictor
  lm (y ~ watched_hat)

• sesame_multi_preds_3a: linear model with three predictors and one factor
  lm (watched ~ encouraged pretest as.factor(site) setting)

• sesame_multi_preds_3b: linear model with three predictors and one factor
  lm (y ~ watched_hat pretest as.factor(site) setting)

• 11.3 Repeated Measurements, Time-Series, Cross Sections & Others - missing data

• 12.2 Partial Pooling with No Predictors

  • radon_intercept: multi-level linear model with varying intercept
    lmer (y ~ 1 (1 | county))
  • radon_intercept_chr: multi-level linear model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | county))

• 12.3 Partial Pooling with Predictors

  • radon_complete_pool: multi-level linear model with complete pooling
    lm (y ~ x)

  • radon_no_pool: multi-level linear model with no pooling
    lmer (y ~ x (1 | county))

  • radon_no_pool_chr: multi-level linear model with no pooling using the Choo-Hoffman Parametrization
    lmer (y ~ x (1 | county))

• 12.4 Fitting MLM in R

  • radon_intercept: multi-level linear model with varying intercept
    lmer (y ~ 1 (1 | county))

  • radon_no_pool: multi-level linear model with no pooling
    lmer (y ~ x (1 | county))

• 12.6 Group-Level Predictors

  • radon_group: multi-level linear model with group level predictor and individual level predictors
    lmer (y ~ x u (1 | county))

  • radon_group_chr: multi-level linear model with group level predictor and individual level predictors using the Choo-Hoffman Parametrization
    lmer (y ~ x u (1 | county))

  • radon_no_pool: multi-level linear model with no pooling
    lmer (y ~ x (1 | county))

• 12.8 Prediction

  • radon_group: multi-level linear model with group level predictor and individual level predictors
    lmer (y ~ x u (1 | county))

• 13.1 Varying Intercepts & Slopes

  • radon_vary_si: multi-level linear model with group level predictors
    lmer (y ~ x (1 x | county))

  • radon_vary_si_chr: multi-level linear model with group level predictors using the Choo-Hoffman Parametrization
    lmer (y ~ x (1 x | county))

• y_x: linear model with one predictor
  lm (y ~ x)

* [radon_inter_vary](https://github.com/stan-dev/example-models/blob/master/ARM/Ch.13/radon_inter_vary.stan): multi-level linear model with group level predictors         

lmer (y ~ x u.full x:u.full (1 x | county))

* [radon_inter_vary_chr](https://github.com/stan-dev/example-models/blob/master/ARM/Ch.13/radon_inter_vary_chr.stan): multi-level linear model with group level predictors using the Choo-Hoffman Parametrization        

lmer (y ~ x u.full x:u.full (1 x | county))

• 13.4 Understanding Correlations Between Intercepts & Slopes

• earnings_vary_si: multi-level linear model with group level predictors
  lmer (y ~ x (1 x | ethn))

• earnings_vary_si_chr: multi-level linear model with group level predictors using the Choo-Hoffman Parametrization
  lmer (y ~ x (1 x | ethn))

• 13.5 Non-Nested Models

• pilots: non-nested multi-level linear model with group level predictors
  lmer (y ~ 1 (1 | group.id) (1 | scenario.id))

• pilots_chr: non-nested multi-level linear model with group level predictors using the Choo-Hoffman Parametrization
  lmer (y ~ 1 (1 | group.id) (1 | scenario.id))

• earnings_latin_square: non-nested multi-level linear model with group level predictors
  lmer (y ~ x.centered (1 x.centered | eth) (1 x.centered | age) (1 x.centered | eth:age))

• earnings_latin_square_chr: non-nested multi-level linear model with group level predictors using the Choo-Hoffman Parametrization
  lmer (y ~ x.centered (1 x.centered | eth) (1 x.centered | age) (1 x.centered | eth:age))

• 14.1 State-Level Opinions From National Polls

  • election88: multi-level logistic regression model with group level predictors
    lmer (y ~ black female (1 | state), family=binomial(link="logit"))

  • election88_full: multi-level logistic regression model with group level predictors
    lmer (y ~ black female black:female v.prev.full (1 | age) (1 | edu) (1 | age.edu) (1 | state) (1 | region.full), family=binomial(link="logit"))

• 16.3 Fitting and Understanding a Varying Intercept Multilevel Model Using RStan

  • radon.1: varying intercept model
    lmer (y ~ x (1 | county))

  • radon.2: varying intercept and slope model
    lmer (y ~ x (1 x | county))

• 16.4 Step by Step through a Stan Model

  • radon.1: varying intercept model
    lmer (y ~ x (1 | county))

• 16.5 Adding Individual and Group Level Predictors

  • radon.pooling: pooled model
    lm (y ~ x 1)

  • radon.nopooling: varying intercept model
    lmer (y ~ x (1 | county))

• 16.6 Predictions

  • radon.2a: varying intercept and slope model with generated quantities
    lmer (y ~ x (1 x | county))

  • radon.2b: varying intercept and slope model with generated quantities
    lmer (y ~ x (1 x | county))

  • radon.2: varying intercept and slope model
    lmer (y ~ x (1 x | county))

• 16.7 Fake Data Simulation

  • radon.2: varying intercept and slope model
    lmer (y ~ x (1 x | county))

• 16.8 Principles of Modeling in Stan

  • radon.2: varying intercept and slope model
    lmer (y ~ x (1 x | county))

  • radon.3: varying intercept and slope model
    lmer (y ~ x (1 u | county))

• 17.1 Varying Intercepts and Varying Slope Models

  • 17.1_radon_multi_varying_coef: multiply varying coefficients model

  • 17.1_radon_vary_inter_slope: varying intercept and slope model

  • 17.1_radon_correlation: varying intercept and slope model with correlation between slopes and intercepts

  • 17.1_radon_wishart: scaled inverse wishart model

  • 17.1_radon_wishart2: two varying coefficients model with unmodeled individual-level coefficients

• 17.2 Varying Intercept and Slope Models with Group Level Predictors

  • 17.2_radon_multi_varying_coef: multiply varying coefficients model with group level predictors

  • 17.2_radon_vary_inter_slope: varying intercept and slope model with group level predictors

  • 17.2_radon_correlation: varying intercept and slope model with correlation between slopes and intercepts and group level predictors

  • 17.2_radon_wishart: scaled inverse wishart model with group level predictors

• 17.3 Non-nested Models

  • 17.3_flight_simulator: varying intercept model
    lmer(y ~ 1 (1 | treatment) (1 | airport))

• 17.4 Multilevel Logistic Regression

  • 17.4_multilevel_logistic: multilevel logistic regression model

• 17.5 Multilevel Poisson Regression

  • 17.5_multilevel_poisson: multilevel poisson regression model

• 17.6 Multilevel Ordered Categorical Regression

  • 17.6_multilevel_ordered_categorical: multilevel ordered categorical regression model

• 17.7 Latent-data Parameterizations of GLM

  • 17.7_latent_glm: latent-data parameterization of multilevel logistic regression model

  • 17.7_robit: robit regression model

• 18.3 Bayes for Classical and Multilevel Regression

  • radon.1: varying intercept model
    lmer (y ~ x (1 | county))

  • radon.2: varying intercept and slope model
    lmer (y ~ x (1 u | county))

  • radon.pool: pooled model
    lm (y ~ x 1)

  • radon.nopooling: varying intercept model
    lmer (y ~ x (1 | county))

• 18.5 Censored Data

  • weight: centered linear model lm (y ~ c_height 1)

  • weight_censored: censored weight model

• [19.2 General Methods for Reducing Computational Requirements] (https://github.com/stan-dev/example-models/blob/master/ARM/Ch.19/19.2_GeneralMethodsForReducingComputationalRequirements.R)

• 19.3 Simple Linear Transformations

• 19.4 Redundant Parameters & Intentionally Non-identifiable Models

  • radon: multi-level liner model with varying intercept
    lmer (y ~ 1 (1 | county))

  • radon_chr: multi-level liner model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | county))

  • radon_redundant: multi-level liner model with varying intercept and redundant parameterization
    lmer (y ~ 1 (1 | county))

  • radon_redundant_chr: multi-level liner model with varying intercept and redundant parameterization and the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | county))

  • pilots: multi-level linear model with varying intercept and redundant parameterization
    lmer (y ~ 1 (1 | treatment) (1 | airport))

  • election88: multi-level logistic regression model with redundant parameterization
    lmer (y ~ female black female:black (1 | age) (1 | edu) (1 | age_edu) (1 | state), family=binomial(link="logit"))

• 19.5 Parameter Expansion

  • pilots_expansion: multi-level linear model with varying intercept and parameter expansion
    lmer (y ~ 1 (1 | treatment) (1 | airport))

  • election88_expansion: multi-level logistic regression model with parameter expansion
    lmer (y ~ female black female:black (1 | age) (1 | edu) (1 | age_edu) (1 | state), family=binomial(link="logit"))

  • item_response: multi-level logistic regression model with parameter expansion
    lmer (y ~ a:g (a:g | k,j) (g:b | k), family=binomial(link="logit"))

• 19.6 Using Redundant Parameters for Modeling

  • 8_schools: multi-level linear model with redundant parameterization

• 20.5 Multilevel Power Calculation Using Fake-Data Simulation

  • hiv: multi-level linear model with varying slope and intercept
    lmer (y ~ time (1 time | person)

  • hiv_chr: multi-level linear model with varying slope and intercept using the Choo-Hoffman Parametrization
    lmer (y ~ time (1 time | person)

  • hiv_inter: multi-level linear model with interaction and varying slope and intercept
    lmer (y ~ time:treatment (1 time | person)

  • hiv_inter_chr: multi-level linear model with interaction and varying slope and intercept using the Choo-Hoffman Parametrization
    lmer (y ~ time:treatment (1 time | person)

• 21.2 Superpopulation & Finite-Population Variances

  • finite_populations: linear model with appropriate calculations for calculating the standard deviation of a finite population
    lm (g ~ u_1 u)

• 21.3 Contrasts & Comparisons of Multilevel Coefficients

• 21.5 R^2 & Explained Variance

  • r_sqr: multi-level linear model with appropriate calculations for R^2
    lmer (y ~ 1 (1 x | county))

• 21.6 Summarizing the Amount of Partial Pooling

  • radon_vary_intercept_a: multi-level linear model with varying intercept set up to calculate pooling factors
    lmer (y ~ x (1 | county))

  • radon_vary_intercept_b: multi-level linear model with varying intercept set up to calculate pooling factors
    lmer (y ~ x (1 | county))

• 21.7 Adding a Predictor can Increase Residual Variance

  • radon_vary_intercept_floor: multi-level linear model with varying intercept
    lmer (y ~ u x (1 | county))

  • radon_vary_intercept_floor_chr: multi-level linear model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ u x (1 | county))

  • radon_vary_intercept_floor2: multi-level linear model with varying intercept
    lmer (y ~ u x x_mean (1 | county))

  • radon_vary_intercept_floor2_chr: multi-level linear model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ u x x_mean (1 | county))

  • radon_vary_intercept_nofloor: multi-level linear model with varying intercept
    lmer (y ~ u (1 | county))

  • radon_vary_intercept_nofloor_chr: multi-level linear model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ u (1 | county))

• 21.8 Multiple Comparisons and Statistical Significance

  • multiple_comparisons: multi-level linear model that serves as a multiple comparisons example
    lmer (y ~ theta (theta | j))

• 22.1 Classical ANOVA

• 22.4 Doing ANOVA Using MLM

  • anova_radon_nopred: multi-level linear model with varying intercept and set up for ANOVA
    lmer (y ~ 1 (1 | county))

  • anova_radon_nopred_chr: multi-level linear model with varying intercept and set up for ANOVA using the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | county))

• 23.1 Multilevel Aspects of Data Collection

  • electric_1a: multi-level linear model with varying intercept and slope
    lmer (y ~ 1 (1 | pair) (treatment | grade))

  • electric_1a_chr: multi-level linear model with varying intercept and slope using the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | pair) (treatment | grade))

  • electric_1b: multi-level linear model with varying intercept and slope
    lmer (y ~ treatment pre_test (1 | pair))

  • electric_1b_chr: multi-level linear model with varying intercept and slope using the Choo-Hoffman Parametrization
    lmer (y ~ treatment pre_test (1 | pair))

  • electric_1c: multi-level linear model with group level factors
    lmer (y ~ 1 (1 | pair) (treatment | grade) (pre_test | grade))

  • electric_1c_chr: multi-level linear model with group level factors using the Choo-Hoffman Parametrization
    lmer (y ~ 1 (1 | pair) (treatment | grade) (pre_test | grade))

  • electric_one_pred: linear model with one predictor
    lm (post_test ~ treatment)

  • electric_multi_preds: linear model with two predictors
    lm (post_test ~ treatment pre_test)

• 23.3 Treatments Applied at Different Levels

  • electric: multi-level linear model with varying intercept
    lmer (y ~ treatment (1 | pair))

  • electric_chr: multi-level linear model with varying intercept using the Choo-Hoffman Parametrization
    lmer (y ~ treatment (1 | pair))

• 23.4 Instrumental Variables & Multilevel Modeling

  • sesame_street1: multi-level linear model using multivariate normal

  • sesame_street2: multi-level linear model using multivariate normal

• 24.2 Behavioral Learning Experiments

  • dogs: multi-level logit regression model

  • dogs_log: multi-level model using binomial distribution

  • dogs_check: multi-level model using binomial distribution

• 25.0 Missing Data in R and Bugs

• 25.4 Random Imputation of a Single Variable

  • earnings: linear model with ten predictors
    lm (earnings ~ male over65 white immig educ_r workmos workhrs_top any_ssi any_welfare any_charity)

  • earnings_pt1: logistic regression model with eight predictors
    glm (earnings ~ male over65 white immig educ_r any_ssi any_welfare any_charity, family=binomial(link="logit"))

  • earnings_pt2: linear model with eight predictors
    lm (earnings ~ male over65 white immig educ_r any_ssi any_welfare any_charity)

• 25.5 Imputation of Several Missing variables

  • earnings2: mlinear model with eleven predictors
    lm (earnings ~ interest male over65 white immig educ_r workmos workhrs_top any_ssi any_welfare any_charity)