Skip to Main Content
yaImpute: An R package for kNN imputationAuthor(s): Nicholas L. Crookston; Andrew O. Finley
Source: Journal of Statistical Software. 23(10). 16 p.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
PDF: View PDF (483.8 KB)
DescriptionThis article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest attribute estimation and mapping. The impetus to writing the yaImpute is a growing interest in nearest neighbor imputation methods for spatially explicit forest inventory, and a need within this research community for software that facilitates comparison among different nearest neighbor search algorithms and subsequent imputation techniques. yaImpute provides directives for defining the search space, subsequent distance calculation, and imputation rules for a given number of nearest neighbors. Further, the package offers a suite of diagnostics for comparison among results generated from different imputation analyses and a set of functions for mapping imputation results.
R example code from the paper
Data sets for examples
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationCrookston, Nicholas L.; Finley, Andrew O. 2008. yaImpute: An R package for kNN imputation. Journal of Statistical Software. 23(10). 16 p.
Keywordsmultivariate, imputation, Mahalanobis, random forests, correspondence analysis, canonical correlation, independent component analysis, most similar neighbor, gradient nearest neighbor, mapping predictions
- Imputed forest structure uncertainty varies across elevational and longitudinal gradients in the western Cascade mountains, Oregon, USA
- The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases
- Partitioning error components for accuracy-assessment of near-neighbor methods of imputation
XML: View XML