MICE

Home of the imputation #rstats package {mice}. Amices is a place for people interested in solving missing data problems. See &

[MICE @MICEamices]

Missing data problems? Check out all of our open access resources at https://t.co/qTfD5pRywp!

[MICE @MICEamices]

Want to meet "Mr. MICE" himself? Join our summer school 'Multiple Imputation in Practice' to meet Stef van Buuren and get hands-on training with {mice} #RStats

https://t.co/XGD4ZXOTtR

[MICE @MICEamices]

How to fairly evaluate imputation methods? @OkregK and @hioberman outline potential pitfalls for simulation studies and a proposed course of action in 'Toward a standardized evaluation of imputation methodology'

https://t.co/ZlQKXPpgnt

[MICE @MICEamices]

The MIMP summer school was a great success so far! https://t.co/5WVzCaO1g7

[MICE @MICEamices]

📢Still some seats available!📢

Get together with the mice development team in a 4-day course to learn about the state of the art in incomplete data analysis and imputation with mice

https://t.co/s3FDoHRrOU

[MICE @MICEamices]

@tmorris_mrc @ellessenne @TheStatsGeek @LopezAyalaP Thanks for the mention!

[Jeroen Ooms @opencpu]

Packages and search results on https://t.co/vudEyolAj4 now show the github stars, (transitive) dependencies / dependents pkg and an overall score of the pkg (similar to pagerank)

[MICE @MICEamices]

Now accepted to CRAN: {ggmice} visualizations for {mice} with {ggplot2} https://t.co/fm5FXO52eL

[MICE @MICEamices]

@GuyProchilo We're working on {shinymice}, an R package that offers interactive tools for the evaluation and imputation of incomplete data. A demo version of the web browser UI is available here https://t.co/ARD55VJbDI

[MICE @MICEamices]

Question about your imputation workflow? Ask us here! https://t.co/UTKslOAui4

[MICE @MICEamices]

@kdpsinghlab Always here to help!

[MICE @MICEamices]

Want to learn how you *should* develop an imputation model? See our vignette on inspecting missingness https://t.co/VTAeVJX8Fz

[Maarten van Smeden @MaartenvSmeden]

The complete R-code for Mindless Imputation (MI):
MyImp <- mice(data)

[MICE @MICEamices]

Regular reminder that mean imputation may seems like a quick and easy fix for #MissingData, but it will underestimate the variance, disturb the relations between variables, and bias almost any estimate other than the mean.

https://t.co/wI0qJ6LcOD

[Sage Methods @Sage_Methods]

All missing #data has the potential to bias any future research findings.

Take a look at this #SAGEMethodspace article for advice on how to illuminate the level of bias & assess the strength of results.

Access here: https://t.co/KbXjuzDvlO

[MICE @MICEamices]

Happy holidays from the MICE amices!

[MICE @MICEamices]

Use {mice} to synthesize!

[Gerko @OkregK]

Everyone can now use @MICEamices to generate and analyse synthetic data sets! Full sim and publication archive: https://t.co/KIIostGm7r

[MICE @MICEamices]

Faced with missing data and don't know where to start? Check out the {mice} vignettes on https://t.co/qTfD5pRywp!

[MICE @MICEamices]

@tmorris_mrc You can always post an issue on GitHub for any problems with {mice} https://t.co/Lk9IBOIqlI

[MICE @MICEamices]

We're here to help!

[Mya Roberson, MSPH, PhD @MyaLRoberson]

I have 99 problems and missing data are most of them #phdlife #epitwitter

[MICE @MICEamices]

Flexible Imputation of Missing Data (van Buuren, 2018) in the Statistics top five!

Did you know that you can also read the entire book for free at https://t.co/iCOj85RyKG?

[Rob Calver @RobCalver5]

The top five new releases in #Statistics on Amazon are @CRC_MathStats books! Just sayin... https://t.co/l40kyuONMq #rstats #DataScience #Bayesian #rshiny #Causalinference @aloctavodia @nickchk @_ColinFay @crcgrubbsd @lara_crc

[MICE @MICEamices]

You could use any (simulated) data set of your choice, and have full control of the missingness if you use @missD_ta's mice::ampute() function

[Solomon Kurz @SolomonKurz]

What's your favorite data set for teaching multiple imputation with?

#RStats