- Lila Leatherman made the recommendation to retanin the OOB object from a randomForest model. This has been added to the
yaifunction as oob = c(FALSE, TRUE) and also results the randomForest argument predict.all = TRUE
yaImpute (Crookston & Finley 2007) Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.
Crookston NL, Finley AO (2007). "yaImpute: An R Package for kNN Imputation." Journal of Statistical Software, 23(10). ISSN 1548-7660, http://www.jstatsoft.org/v23/i10.
Bugs: Users are encouraged to report bugs here. Go to issues in the menu above, and press new issue to start a new bug report, documentation correction or feature request. You can direct questions to jeffrey_evans@tnc.org.
To install yaImpute in R use install.packages() to download current stable release from CRAN
or, for the development version, run the following (requires the remotes package):
remotes::install_github("jeffreyevans/yaImpute")