sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se . Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways.

Impacts of \u3ci\u3ePhragmites australis\u3c/i\u3e Management on Wetland Plant Community Recovery, Seedbank Composition, and the Physical Environment in the Chesapeake Bay

Biological invasions have adverse economic, cultural, and ecological effects worldwide. Among the most impactful in North America is the invasion of Phragmites australis (Phragmites), a large-scale clonal grass that rapidly colonizes wetlands. Phragmites crowds out native plants and alters habitat for native fauna. In doing so, Phragmites also alters human access to water resources and has adverse economic effects, including decreasing property value, inhibiting recreational use, and limiting populations of game species. The efforts described in this dissertation are a component of a large, multidisciplinary effort to better understand the anthropogenic stressors to Chesapeake Bay, Maryland, at the land/water interface. Utah State University worked in collaboration with the Smithsonian Environmental Research Center and other academic and public organizations to address this problem from multiple directions. The diverse and extensive studies ranged from aquatic and avian faunal composition and submerged aquatic vegetation to our work on the invasive wetland grass, Phragmites. Having assessed the existing literature and its shortcomings, we conducted a large-scale, long-term study of the effects of Phragmites removal on the Bay. By removing Phragmites from plots in select sub-estuaries of Chesapeake Bay through herbicide spraying, leaving associated plots intact, and comparing both with native wetland conditions, we sought to better understand herbicidal management of Phragmites and the potential for wetland plant community recovery. Although sprayings decreased the relative cover, stem diameters, and stem densities of Phragmites, we found that herbicide treatment alone was not adequate to restore native plant communities or significantly affect seedbank composition. Our results demonstrate the resilience of Phragmites and call for a diverse range of control measures, including mowing, grazing, burning to expose the seedbank to germination, and—if economics allow—active revegetation to establish the desired plant community composition. This dissertation provides beneficial data for those who seek to manage Phragmites in wetland plant communities, but there is much work still to be done. The literature review, seedbank study, and community analysis included in this volume are components of a larger research program on Phragmites management. Future studies should, in particular, investigate revegetation and nutrient amelioration as means to recover pre-invasion vegetation

Boundary kernels for adaptive density estimators on regions with irregular boundaries

AbstractIn some applications of kernel density estimation the data may have a highly non-uniform distribution and be confined to a compact region. Standard fixed bandwidth density estimates can struggle to cope with the spatially variable smoothing requirements, and will be subject to excessive bias at the boundary of the region. While adaptive kernel estimators can address the first of these issues, the study of boundary kernel methods has been restricted to the fixed bandwidth context. We propose a new linear boundary kernel which reduces the asymptotic order of the bias of an adaptive density estimator at the boundary, and is simple to implement even on an irregular boundary. The properties of this adaptive boundary kernel are examined theoretically. In particular, we demonstrate that the asymptotic performance of the density estimator is maintained when the adaptive bandwidth is defined in terms of a pilot estimate rather than the true underlying density. We examine the performance for finite sample sizes numerically through analysis of simulated and real data sets

sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have been very few contributions to date that have focused on estimation of a relative risk function per se. Use of a variable or adaptive smoothing parameter for estimation of the individual densities has been shown to provide additional benefits in estimating relative risk and specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to an estimated risk function. To facilitate analyses in the field, the R package sparr is introduced, providing the ability to construct both fixed and adaptive kernel-smoothed densities and risk functions, identify statistically significant fluctuations in an estimated risk function through the use of asymptotic tolerance contours, and visualize these objects in flexible and attractive ways

The association between retinal vein ophthalmodynamometric force change and optic disc excavation

Aim: Retinal vein ophthalmodynamometric force (ODF) is predictive of future optic disc excavation in glaucoma, but it is not known if variation in ODF affects prognosis. We aimed to assess whether a change in ODF provides additional prognostic information. Methods: 135 eyes of 75 patients with glaucoma or being glaucoma suspects had intraocular pressure (IOP), visual fields, stereo optic disc photography and ODF measured on an initial visit and a subsequent visit at mean 82 (SD 7.3) months later. Corneal thickness and blood pressure were recorded on the latter visit. When venous pulsation was spontaneous, the ODF was recorded as 0 g. Change in ODF was calculated. Flicker stereochronoscopy was used to determine the occurrence of optic disc excavation, which was modelled against the measured variables using multiple mixed effects logistic regression. Results: Change in ODF (p=0.046) was associated with increased excavation. Average IOP (p=0.66) and other variables were not associated. Odds ratio for increased optic disc excavation of 1.045 per gram ODF change (95% CI 1.001 to 1.090) was calculated. Conclusion: Change in retinal vein ODF may provide additional information to assist with glaucoma prognostication and implies a significant relationship between venous change and glaucoma patho-physiology

Nowhere to Run; Nowhere to Hide: The Reality of Being a Law Library Director in Times of Great Opportunity and Significant Challenges

This is an edited version of remarks presented at \u27Nowhere to Run, Nowhere to Hide\u27: The Reality of Being a Law Library Director in Times of Great Opportunity and Significant Challenges, January 5, 2015, at the Association of American Law Schools Annual Meeting, Washington, D.C