Skip to Content
Data Analysis with R, Second Edition - Second Edition
book

Data Analysis with R, Second Edition - Second Edition

by Tony Fischetti
March 2018
Beginner to intermediate content levelBeginner to intermediate
570 pages
13h 42m
English
Packt Publishing
Content preview from Data Analysis with R, Second Edition - Second Edition

So how does mice come up with the imputed values?

Let's focus on the univariate case, where only one column contains missing data and we use all the other (completed) columns to impute the missing values before generalizing to a multivariate case.

mice actually has a few different imputation methods up its sleeve, each best suited for a particular use case. mice will often choose sensible defaults based on the data type (continuous, binary, non-binary categorical, and so on).

The most important method is what the package calls the norm method. This method is very much like stochastic regression. Each of the m imputations is created by adding a normal noise term to the output of a linear regression predicting the missing variable. What makes ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Hands-On Exploratory Data Analysis with R

Hands-On Exploratory Data Analysis with R

Radhika Datar, Harish Garg
Bayesian Data Analysis, Third Edition, 3rd Edition

Bayesian Data Analysis, Third Edition, 3rd Edition

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
R: Data Analysis and Visualization

R: Data Analysis and Visualization

Tony Fischetti, Brett Lantz, Jaynal Abedin, Hrishi V. Mittal, Bater Makhabel, Edina Berlinger, Ferenc Illés, Milán Badics, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, István Margitai, Balázs Márkus, Péter Medvegyev, Julia Molnár, Balázs Árpád Szucs, Ágnes Tuza, Tamás Vadász, Kata Váradi, Ágnes Vidovics-Dancs

Publisher Resources

ISBN: 9781788393720Supplemental Content