Screencasts about Statistics and Models in R

These presentations introduce and discuss how to use R for basic statistical analysis and modeling of epidemiological data. The slides can be found here.

An overview of a few of the many statistical functions that come with base R, including summary(), barplot(), t.test() and wilcox.test(), and how to extract information from the results of statistical function.

A linear regression example using data from John Fox's "car" package and the lm() function including simple residual analysis and how to update a model.

A brief review of odds, log odds and the logistic model followed by a logistic regression example using the glm() function.

A review of the Poisson model, offset variables and the predictive interpretation of Poisson regression coefficients.

A Poisson regression example using traffic fatality data from Achim Zeileis's "AER" package.

Approaches to assessing and adjusting for overdispersion in Poisson models.

An overview of R packages including how to find, install find help and update packages from the command line.

An introduction to 2x2 table analysis in R using epitab() from Tomas Aragon's "epitools" package.

The Mantel-Haenszel adjusted odds ratio using the cc() and mhor() from Virasakdi Chongsuvivatwong's "epicalc" package.

An overview of the fundamental difference between disease risk and disease rates, and the implication of this difference for modeling time to event data.

A description of the assumptions underlying binomial models and exponential models of disease risk, and the role of Kaplan-Meier and the product limit estimator when those assumptions can not be reasonably met.

A description of the Kaplan-Meier method and illustration of how it can be implemented with basic R code.

An overview of how survival methods can be implemented in R using functions in the "survival" package.

These presentations introduce and discuss how to use R for basic statistical analysis and modeling of epidemiological data. The slides can be found here.

**Statistical Functions in R**An overview of a few of the many statistical functions that come with base R, including summary(), barplot(), t.test() and wilcox.test(), and how to extract information from the results of statistical function.

**Linear Regression in R**A linear regression example using data from John Fox's "car" package and the lm() function including simple residual analysis and how to update a model.

**Logistic Regression in R**A brief review of odds, log odds and the logistic model followed by a logistic regression example using the glm() function.

**Poisson Regression in R Part 1**A review of the Poisson model, offset variables and the predictive interpretation of Poisson regression coefficients.

**Poisson Regression in R Part 2**A Poisson regression example using traffic fatality data from Achim Zeileis's "AER" package.

**Overdispersion and the Negative Binomial Model in R**Approaches to assessing and adjusting for overdispersion in Poisson models.

**About R packages**An overview of R packages including how to find, install find help and update packages from the command line.

**Categorical Data Analysis in R Part 1**An introduction to 2x2 table analysis in R using epitab() from Tomas Aragon's "epitools" package.

**Categorical Data Analysis in R Part 2**The Mantel-Haenszel adjusted odds ratio using the cc() and mhor() from Virasakdi Chongsuvivatwong's "epicalc" package.

**Introduction to Survival Analysis in R: 1. Risks vs. Rates**An overview of the fundamental difference between disease risk and disease rates, and the implication of this difference for modeling time to event data.

**Introduction to Survival Analysis in R: 2. Binomal Model vs. Exponential Model of Disease**A description of the assumptions underlying binomial models and exponential models of disease risk, and the role of Kaplan-Meier and the product limit estimator when those assumptions can not be reasonably met.

**Introduction to Survival Analysis in R: 3. The Kaplan-Meier Method**A description of the Kaplan-Meier method and illustration of how it can be implemented with basic R code.

**Introduction to Survival Analysis in R: 4. The survival package**An overview of how survival methods can be implemented in R using functions in the "survival" package.