Skip to Main Content
Users guide to the Most Similar Neighbor Imputation Program Version 2Author(s): Nicholas L. Crookston; Melinda Moeur; David Renner
Source: Gen. Tech. Rep. RMRS-GTR-96. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 35 p.
Publication Series: General Technical Report (GTR)
Station: Rocky Mountain Research Station
View PDF (176.26 KB)
DescriptionThe Most Similar Neighbor (MSN, Moeur and Stage 1995) program is used to impute attributes measured on some sample units to sample units where they are not measured. In forestry applications, forest stands or vegetation polygons are examples of sample units. Attributes from detailed vegetation inventories are imputed to sample units where that information is not measured. MSN performs a canonical correlation analysis between information measured on all units and the detailed inventory data to guide the selection of measurements to impute. This report presents an introductory discussion of Most Similar Neighbor imputation and shows how to run the program. An example taken from a forest inventory application is presented with notes on other applications and experiences using MSN. Technical details of the way MSN works are included. Information on how to get and install the program and on computer system requirements is appended.
- 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.; Moeur, Melinda; Renner, David. 2002. Users guide to the Most Similar Neighbor Imputation Program Version 2. Gen. Tech. Rep. RMRS-GTR-96. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 35 p.
Keywordscanonical correlation, imputation, forest inventory, forest planning, landscape analysis
- Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data
- A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables
- Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A.
XML: View XML