So far, we have explored models where we have different fixed levels for each effect. This makes a lot of sense when we have a set of possible levels for an effect that we control and are interested in measuring. It also makes sense when we have a blocking effect that has a finite (and small) set values (for example, the sex or occupation of a person). In some cases, we will have a huge amount of levels that will be generally unimportant, for example, if we want to measure whether a drug is effective, and we are dealing with multiple observations per person, we want to add a blocking effect for a person. In these cases, we are not interested in the effect per se, but we certainly want to use it as a control variable for our model. A model that uses proper blocks, will be more efficient: think of ANOVA as a method of attributing variability to factors. If we have...
- Tech Categories
- Best Sellers
- New Releases
- Books
- Videos
- Audiobooks
Tech Categories Popular Audiobooks
- Articles
- Newsletters
- Free Learning
You're reading from R Statistics Cookbook
Product typeBook
Published inMar 2019
Reading LevelExpert
PublisherPackt
ISBN-139781789802566
Edition1st Edition
Languages
Tools
Concepts
Author (1)
Francisco Juretig
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig
The rest of the page is locked
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime
You have been reading a chapter from
R Statistics CookbookPublished in: Mar 2019Publisher: PacktISBN-13: 9781789802566
© 2019 Packt Publishing Limited All Rights Reserved
Author (1)
Francisco Juretig
Francisco Juretig has worked for over a decade in a variety of industries such as retail, gambling and finance deploying data-science solutions. He has written several R packages, and is a frequent contributor to the open source community.
Read more about Francisco Juretig