yaImpute: An R Package for kNN Imputation

This 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.

yaImpute: An R Package for kNN Imputation

This 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

The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.Keywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variablesKeywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variable

Plasma Plasmodium falciparum Histidine-Rich Protein-2 Concentrations Are Associated with Malaria Severity and Mortality in Tanzanian Children

Plasma Plasmodium falciparum histidine-rich protein-2 (PfHRP-2) concentrations, a measure of parasite biomass, have been correlated with malaria severity in adults, but not yet in children. We measured plasma PfHRP-2 in Tanzanian children with uncomplicated (n = 61) and cerebral malaria (n = 45; 7 deaths). Median plasma PfHRP-2 concentrations were higher in cerebral malaria (1008 [IQR 342–2572] ng/mL) than in uncomplicated malaria (465 [IQR 36–1426] ng/mL; p = 0.017). In cerebral malaria, natural log plasma PfHRP-2 was associated with coma depth (r = −0.42; p = 0.006) and mortality (OR: 3.0 [95% CI 1.03–8.76]; p = 0.04). In this relatively small cohort study in a mesoendemic transmission area of Africa, plasma PfHRP-2 was associated with pediatric malaria severity and mortality. Further studies among children in areas of Africa with higher malaria transmission and among children with different clinical manifestations of severe malaria will help determine the wider utility of quantitative PfHRP-2 as a measure of parasite biomass and prognosis in sub-Saharan Africa

Outbreaks of Mountain Pine Beetle in Lodgepole Pine Forests -- 1945 to 1975

The bulletin describes the methods used to map historical locations of mountain pine beetles between 1945 and 1975 and tables infestations between 1910 and 1975

User Access to the Forest Vegetation Simulator- Fire, Fuels, and Nutrient Budget Extensions

2003 Annual Meeting Presentatio

A Proposal to Improve Performance of the Forest Vegetation Simulator - Fire and Fuels Extension

The Forest Vegetation Simulator (FVS) and its associated Fire and Fuels Extension (FFE) have been used to provide information required by larger software systems like the Interagency Fuels Treatment - Decision Support System (IFT-DSS). Interacting with FVS in an automated fashion has been difficult, and simulations with very large numbers of stands, such as those necessary for landscape analyses for fire planning, could take a significant amount of time to process. This project was designed to: (A) develop a requirements document considering Service Oriented Architecture and how that may apply to FVS, and how FVS will be used interactively; (B) profile the FVS code to evaluate what takes the most processing time and identify possible areas for program optimization; (C) while optimizing and reducing the size of code, migrate FVS to a modern development framework such as Intel Fortran and the Visual Studio IDE; (D) identify platforms and systems that meet needs of the JFSP and other stakeholders, such as creating dynamic link libraries (DLL); and (E) specify and define the use of new technologies in the next phase of software development, such as OpenMP directives, thus implementing multithreading in the base FVS executables or extensions to take advantage of increased computing power of multicore processors

User\u27s guide to the Event Monitor : part of Prognosis Model, version 6 /

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