ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

ASCL Code Record

[ascl:1505.011] missForest: Nonparametric missing value imputation using random forest

missForest imputes missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation and can be run in parallel to save computation time. missForest has been used to, among other things, impute variable star colors in an All-Sky Automated Survey (ASAS) dataset of variable stars with no NOMAD match.

Code site:
http://cran.r-project.org/web/packages/missForest/
Used in:
http://adsabs.harvard.edu/abs/2012ApJS..203...32R
Described in:
https://doi.org/10.1093/bioinformatics/btr597
Bibcode:
2015ascl.soft05011S

Views: 1849

ascl:1505.011
Add this shield to your page
Copy the above HTML to add this shield to your code's website.