socalrug / advanced_regression_2019-10-05

Advanced Regression Models with R Applications

OCRUG - Advanced Regression Models with R Applications

Sharpen your Data Science skills with this is a hands-on workshop on advanced regression techniques in R.

About this Event

In this workshop we will talk about variety of regression models, give their definitions, discussing goodness-of-fit criteria, presenting fitted models, interpreting estimated regression coefficients, and using the fitted models for prediction. The models will be limited to: linear regression, Box-Cox transformation, gamma regression, ordinary logistic regression, Poisson regression, beta regression, longitudinal (repeated measures) regression, and hierarchical model.

The workshop is designed to be hands-on. Participants are required to bring laptops and be ready to write R, analyzing data and interpreting results. For each model, we present an example with a complete R code, and then will an exercise to work on. Workshop participants should be familiar with algebraic expressions of different probability distributions, and have a fundamental knowledge of simple linear regression: normally distributed random error, continuous and categorical independent variables (requiring creating dummy variables).

The material covered by the workshop will be taken from my recently published book “Advanced Regression Models with SAS and R Applications”, CRC Press, 2018.

We will have a limited number of books for sale. You can purchase the book and get it signed by Dr. Olga.

Biography of Dr. Olga Korosteleva

Dr. Olga Korosteleva, is a professor of Statistics at the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received her Bachelor’s degree in Mathematics in 1996 from Wayne State University in Detroit, and a Ph.D. in Statistics from Purdue University in West Lafayette, Indiana, in 2002. Since then she has been teaching mostly Statistics courses in the Master’s program in Applied Statistics at CSULB, and loving it!

Dr. Olga is an undergraduate advisor for students majoring in Mathematics with an option in Statistics. She is also the faculty supervisor for the Statistics Student Association. She is also the immediate past-president of the Southern California Chapter of the American Statistical Association (SCASA). Dr. Olga is the editor-in-chief of SCASA’s monthly eNewsletter and the author (co-author) of four statistical books.

Event Details

When: October 5, 2019

• Saturday: 8:30 AM - 04:30 PM

Where:

University of California, Irvine -- Paul Merage School of Business

4293 Pereira Drive

Irvine, CA 92617

• Google Maps
• Directions & Parking Information
• Rooms
  • SB1 2100 - Main event room
  • SB1 3rd floor patio - meals

Registration

• Cost: $25
• Register through EventBright

Rules

• All participants must register for the event and have a valid ticket to attend. If there is space you can register at the door.
• All participants must abide by the OCRUG Code of Conduct, including the R Consortium and the R Community Code of Conduct.

WiFi Access

• Connect to SSID: UCInet Mobile
• Go to https://oit.uci.edu/reg
• register your device as a guest

If you have problems, please call OIT support line at [phone] option 3

GitHub Repo

OCRUG GitHub Repo: https://github.com/ocrug/

Please install git and clone the following repo before the event and pull before the start of the event

command:

git clone https://github.com/ocrug/advanced_regression.git

Event Repo: https://github.com/ocrug/advanced_regression

If you would like to make thing easier during the course you can install a package that has all the code and data already loaded. It also has all the data used in the textbook, both examples and exercises.

Repo: https://github.com/ocrug/AdvancedRegression

You can install the package with:

install.packages('devtools', dependencies = TRUE)
devtools::install_github("https://github.com/ocrug/AdvancedRegression")
library(AdvancedRegression)

There is some documentation. Check it out by looking at the docs for a function such as:

? AICC

Slack Channel

A slack channel has been set up for the hackathon. This will be used for general announcements but it is also a great source for you to ask questions to other participants.

If you have not created an account on our slack group, create one using the following link:

Slack Group Sign-up: https://ocrug-slack.herokuapp.com

Once you have an account, sign in (you can do it on a web browser or download an app on your phone or desktop).

Slack channel: https://ocrug.slack.com

The channel for the course is regression-2019

Check your setup

Since this event depends on you have an R setup that is functional with the correct packages and version of R, we highly recommend that you run the check_setup.r before the event. If you have issues, please reach out to use in the slack channel (see above) to get help.

Twitter

Please follow us on twitter, oc_rug, and also tweet about the event with the hash tag #OCRUG

Resources

• RStudio Cheat Sheets

  • 1-page note sheets covering data science fundamentals and useful R packages.

• R for Data Science

  • Comprehensive book on the complete data science workflow, including data importing/cleaning, visualization, and data analysis
  • Focus on tidyverse packages
  • Accessible for beginners who have a basic grasp of R

• Tidyverse, Main Site

  • This is the hub website for the core tidyverse packages
  • Check out the Packages section and associated links for helpful information on using the packages.

• Advanced R, 2nd Edition

  • This book digs into the details of R.
  • A great resource for more advanced users wanting to learning more about R under the hood.
  • There is also a 1st Edition of the book.

Food

Food, drinks and snacks will be provided throughout the event. We will have vegetarian options available. Please feel free to bring any additional food for yourself if you would like to supplement the meals or if you have other specific dietary constraints.

• Saturday

  • Lunch: mexican (tacos, rice & beans, chips & salsa)

• Snacks and Drinks

  • Coffee
  • Soft drinks
  • Water
  • Various snacks, TBD (e.g. fruit, chips, nuts, granola bars)

Schedule

Start	End	Activity	Slides	Location
08:30	09:00	Sign-in		SB1 Lobby
09:00	09:30	Introduction and computer setup		SB1 2100
09:30	10:30	Linear Regression - definition, fitted model, interpretation of estimated regression coefficients, prediction, R application	2-15	SB1 2100
10:30	11:00	Gamma regression	16-29	SB1 2100
11:00	11:15	Break
11:15	11:40	Logistic regression	30-40	SB1 2100
11:40	12:10	Poisson regression	41-49	SB1 2100
12:10	12:30	Zero-inflated Poisson regression	50-63	SB1 2100
12:30	01:30	Lunch		Patio
01:30	02:00	Beta regressions	64-74	SB1 2100
02:00	02:30	Longitudinal normal regression	75-83	SB1 2100
02:30	02:45	Break
02:45	03:15	Longitudinal normal regression: Exercise	84-89	SB1 2100
03:15	03:45	Longitudinal logistic regression	90-100	SB1 2100
03:45	04:15	Hierarchical normal models	101-107	SB1 2100
04:15	04:30	Wrap-up		SB1 2100