Admission requirements What you need to read before coming to class on Friday

Chapter: 1 - Introduction to the Analysis of Longitudinal Data

Lecture: Introduction to Multilevel Models for Longitudinal and Repeated Measures Data slides | watch | download


This week: Jan 5 - Jan 9

December/January

. Mon Tue Wed Thu Fri .
28 29 30 31 1 2 3
4 5 6 7 8 9 10
11 12 13 14 15 16 17

Tuesday

Introductions. What is statistical modeling? Syllabus and course description.

Friday

Introduction to iClickers, RStudio, and MPlus.

Homework

No homework is due.


Tuesday Session

What is statistical modeling?

Syllabus description

Getting ready for Friday

  • download and install R and RStudio on your computer
  • log on to MPlus server (mplus.uvic.ca) using remote desktop (we didn’t get to cover this in class, so we’ll cover this in class on Friday)

Friday Session

Mplus on UVic server

  • MPlus intro and interface
  • how to get access
  • how to connect from you computer to remote MPlus Server

Dynamic Documents with RStudio

  • big picture of scientific communication
  • markdown
  • Resources for learning R

Reports and Activities

Earning points from REPORTS

Earning points from class ACTIVITIES

  • total of 8 points each week
  • 4 points earned prior to class
  • 4 points earned during class

iClicker orientation.

  • Reviewing content for Chapter 1.
    Introducing recurring segments:
  • Guess Page
  • Guess Slide
  • Quiz question

Friday Session: Jan 9, 2015

Mplus on UVic server

Establish VPN connection to vpn1.uvic.ca using your netlink id and password. Use Remote Desktop Connection to get access to the mplus.uvic.ca server. ( Start -> All Programs -> Accessories -> Remote Desktop Connection )

Please note: When you open Remote Desktop Connection, click on the Local Resources tab and under Local devices and resources, click More and be sure that drives is checked. This will allow you to analyze your local computer data on the Mplus server and will save all of your scripts locally.

(re)view slides on Introduction to Mplus statistical software and command language.

Dynamic Documents with RStudio

The working metaphor

Scientists are data journalists who tell stories of their data.
They type text to instruct a human reader how to recreate the story in his/her mind.
They type text to instruct a computer how to manipulate data, evaluate models, print graphs, and assemble output.

Documents that contain typed instructions for both human and computer consumption that recreate a story about the data are called DYNAMIC.

See an example of a dynamic document in this markdown simulator, created in javascript by Jeroen Ooms.

markdown language uses special combinations of characters to make text strings appear differently: in bold, italics, as a heading, or as a name of the column in the table. This language is very simple and straightforward, but may take a little bit to get used to. To help you get proficient with markdown consider some of the following resources.

  • markdown guide : a well-written “breaking into” guide. Gives ample verbal description. Recommended for newbies. By John Gruber.
  • markdown cheat-sheet : brief, simple, parallel view of what the code is doing.By Mashery group.
  • markdown in R : a blog entry on getting started with rmarkdown - a version of markdown enhanced for the use with RStudio. By Jeromy Anglim.

You will need markdown to complete homework assignments and we won’t spend any dedicated lab time on it, so please, pick it up on your own.

Goals in Data Science projects

Adapting reproducible research standards, each project in data science could be conceptualized as having the following objectives:

  • Strategic Goal
    Tell a story about your data.

  • Tactical Goal
    Develop, evaluate, and interpret statistical models with which you tell a story about your data.

  • Technical Assignment
    Write a computer script that generates an electronic document reporting the statistical models with which you tell a story about your data.

Meeting Week Topics / Report due (23:59)
09 Jan 1
16 Jan 2 Markdown chapters review
23 Jan 3 R-Cheatsheet
30 Jan 4 Reporting models
06 Feb 5 Random Coefficients Models
09-14 Feb 6 Reading Break
20 Jan 7 Describing shape of WP change
27 Feb 8 Time invariant predictors
06 Mar 9 Daily diary studies
13 Mar 10 Time variant predictors
20 Mar 11 Clustered structures
27 Mar 12 Alternative metrics of time

Learning R

A GOOD PLACE TO START LEARNING R - The RStudio team collects the best online resources. Check out every link they mention, it’s worth it. In fact, do it right now.

Now we’ll use one of the resources mentioned in the link above, swirl.
Open your RStudio and execute the following code:

install.packages("swirl")  
install.packages("Rtools")  
install.packages("devtools")  
devtools::install_github(c("swirldev/swirl", "swirldev/swirlify"))  
library(swirlify)  

Follow the prompt and complete the first lesson.

I also recommend completing two free interactive courses at DataCamp: Introduction to R and Data Analysis and Statistical Inference. Their content partially overlaps with the training by two available courses by swirl package, but gives a different take and examples.

More Resources

Reports and Activities

REPORTS

NOTE: if you haven’t do so already, please register an account with disqus.

ACTIVITIES

Total of 8 points each week

4 points earned prior to class
- Quiz Question ( 2 pts)
- Page/Idea (1 pts) - Slide/Idea (1 pts)

4 points earned during class - various activities
- various point weights

Guess Page. In the current chapter, what page contains one of the most important ideas or concepts? Provide the page number and the answer why in less than 140 characters, No abbr plz! Post anonymously into the corresponding comment thread on the page for the current week of the course.

Guess Slide. In the current lecture, what slide contains one of the most important ideas or concepts? Provide the slide number and answer why in less than 140 characters, No abbr plz! Post anonymously into the corresponding comment thread on the page for the current week of the course

Quiz Question. Please write the question about the content of the current chapter/lecture that you think should appear on the final exam? Post anonymously into the corresponding comment thread on the page for the current week of the course.

NOTE Quiz Question, Guess Page, and Guess Slide must be submitted into the respective comment threads no later than 10:30 am of the Tuesday of the current week. The entries must be UNIQUE: if you post a response similar to an existing one, your response will no be accepted as valid.

Rosetta Stone. A question, a series of questions, or other activity that involves learning some programming language or comparing programming languages.

The activities for the other 4 class participation points will be wildcards - you will not know what they will be ahead of time to keep things interesting. Examples: output interpretation.

iClicker training

We will cover the basic functionality of iClickers for polling during class. Please read iClicker training guide for further details.

What to prepare for next time

  • read Chapter 2
  • do as many R tutorial with DataCamp and swirl as possible
  • complete pre-class activities (before 10:30 am Tuesday)
  • review MPlus introduction slides