gsba604

Links to R labs

Session	Link
Tutorial on R basics	Click here @1
Tutorial on R Markdown	Click here
R-Lab 1	Click here
R-Lab 2	Click here
R-Lab 3	Click here
R-Lab 4	Click here
R-Lab 5	Click here
Tutorial on making an R Package	Click here @2
R-Lab 6	Click here
R-Lab 7	Click here@ 3
R-Lab 8	Click here@ 3
R-Lab 9	Click here
R-Lab 10	Part A and Part B @2

@1^[prepared by Rudy Angeles for Stats 305 @ Stanford]

@2^[prepared by Trambak Banerjee]

@3^[directly from Jared Knowles notes]

The R labs are from the following resources:

J.J. Faraway. Practical Regression and Anova using R
G. James, D. Witten, T. Hastie, and R. Tibshirani. An introduction to statistical learning. New York: springer. 2013.
A. Agresti. Foundations of Linear and Generalized Linear Models, Wiley 2015.
A. Gelman and Hill. Data Analysis Using Regression and Multilevel Hierarchical Models, 2007.

Referencing of R-labs with Class Notes

Module 1. Linear Regression Methods in Fixed, Moderate and High dimensions
- Ordinary Least Squares (OLS)
  - Estimation [R-lab 1]
    - BLUE, Gauss-Markov Theorem
  - Confidence Sets [R-lab 2]
    - Confidence Interval for a single Linear Parametric Function (LPF)
    - Confidence Regions for multiple LPFs
    - Simultaneous Confidence Intervals for multiple LPFs
    - Prediction Interval
  - Hypothesis Testing [R-lab 2]
    - Testing for the signifance of a single LPF
      - Testing for the signifance of individual predictors: t-test
    - ANOVA Table and testing hypothesis involving several LPFs
      - Testing for the signifance of the entire model: F-test
- Variable/Model Selection [R-Lab 3]
  - Forward Selection
  - Backward Selection
  - AIC, BIC, Cp
  - Best Subset
  - Cross Validation (CV)
- Penalized/Shrinkage Methods For Moderate and High dimensional problems [R-lab 3]
  - Multicollinearity and VIF
  - Curse of Dimensionality
  - Ridge
  - Lasso
  - Computational complexity, path algorithms, LARS and glmnet
  - Selecting the penalty parameter by CV
  - Elastic Net
  - Group Lasso
  - Fused Lasso
- Categorical Predictors [R-lab 4]
- Least Squares in Heteroskedastic Models [R-lab 4]
  - Generalized Least Squares
  - Weighted Least Squares
- Quantile Regression [R-lab 4]
- M-Estimation [R-lab 4]
  - Huber loss and Robustness
Module 2. Non-Linear Regression
- Transforming the Response: Box-Cox method [R-lab 5]
- Transforming the Predictors [R-lab 5]
  - Polynomial Regression
  - Regression Splines
  - Local Regression
  - Generalized Additive Models (GAM)
  - Dimension Reduction Methods
    - Principal Component Regression
    - Partial Least Squares
Module 3. Generalized Linear Models (GLM)
- Popular Models [R-lab 6]
  - Binary Data (Logit and Probit)
  - Multinomial Response Models
  - Count Data (Poisson GLM)
  - Models for Contingency tables
  - Connections between logistic and log-linear models
  - Quasi-likelihood Models
- General framework for studying theory and methods for GLMs
  - Moving Beyond Gaussianity
  - Exponential Dispersion Family
  - Divergence and general inference principles
- Random Effects, Correlated Responses and GLMM [R-lab 9]
  - Normal Linear Mixed Model [R-lab 7]
  - Generalized Linear Mixed Model (GLMM) [R-lab 8]
- High-dimensional (HD) Versions
Module 4. Causal Inference
- Causal Inference using regression on the treatment variables [R-lab 10 A]
- Causal inference using more advanced models [R-lab 10 B]

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