Missing Data Treatment in R: A Hands-on Illustration Using {mice} скачать в хорошем качестве

Missing Data Treatment in R: A Hands-on Illustration Using {mice}

Join us for our first in-person event in over two years! https://www.meetup.com/Oslo-useR-Grou... If you want to code along to the talk, you will need a few packages. The can be installed using the following lines of code: Install necessary package install.packages( c("mice", "VIM“, “MASS”, “lattice”), dependencies = T ) Load the mice package suppressWarnings(suppressMessages( library(mice) )) [Abstract] Missing data is the norm rather than exception in most real-life data sets. It is not only wasteful to exclude observations that contain missing values but also opens parameter estimations to potential inconsistency and bias. This meetup focuses on the fully conditional specification technique using the R package {mice} (multivariate imputation by chained equations). We will be introduced to different missing data mechanisms, their impact on estimations, and the corresponding coping strategies. We will then take a look at {mice}, particularly its functionalities for imputing missing data, and eventually showcase how to analyze imputed datasets and how to interpret the results. [Bio] The speaker, Tony Tan is a PhD candidate at Centre for Educational Measurement (CEMO), University of Oslo. He holds Economics and Accounting degrees from the Australian National University and a teaching degree from the University of Melbourne. The slides can be found here: https://github.com/tonyctan/UseR.