Title page for ETD etd-011106-155345

Document Type thesis

Author Name Huang, Fang

URN etd-011106-155345

Title Modeling Patterns of Small Scale Spatial Variation in Soil

Degree MS

Department Mathematical Sciences

Advisors
Dr. Jayson D. Wilbur, Advisor

Keywords
spatial variations
nested random effects models
semivariogram models
kriging methods
multiple logistic regression models
missing
multiple imputation

Date of Presentation/Defense 2005-12-09

Availability unrestricted

Abstract
The microbial communities found in soils are inherently heterogeneous and often exhibit spatial variations on a small scale. Becker et al. (2006) investigate this phenomenon and present statistical analyses to support their findings. In this project, alternative statistical methods and models are considered and employed in a re-analysis of the data from Becker. First, parametric nested random effects models are considered as an alternative to the nonparametric semivariogram models and kriging methods employed by Becker to analyze patterns of spatial variation. Second, multiple logistic regression models are employed to investigate factors influencing microbial community structure as an alternative to the simple logistic models used by Becker. Additionally, the microbial community profile data of Becker were unobservable at several points in the spatial grid. The Becker analysis assumes that the data are missing completely at random and as such have relatively little impact on inference. In this re-analysis, this assumption is investigated and it is shown that the pattern of missingness is correlated with both metabolic potential and spatial coordinates and thus provides useful information that was previously ignored by Becker. Multiple imputation methods are employed to incorporate the information present in the missing data pattern and results are compared with those of Becker.

Files
huang.pdf

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Document Type	thesis
Author Name	Huang, Fang
URN	etd-011106-155345
Title	Modeling Patterns of Small Scale Spatial Variation in Soil
Degree	MS
Department	Mathematical Sciences
Advisors	Dr. Jayson D. Wilbur, Advisor
Keywords	spatial variations nested random effects models semivariogram models kriging methods multiple logistic regression models missing multiple imputation
Date of Presentation/Defense	2005-12-09
Availability	unrestricted
Abstract The microbial communities found in soils are inherently heterogeneous and often exhibit spatial variations on a small scale. Becker et al. (2006) investigate this phenomenon and present statistical analyses to support their findings. In this project, alternative statistical methods and models are considered and employed in a re-analysis of the data from Becker. First, parametric nested random effects models are considered as an alternative to the nonparametric semivariogram models and kriging methods employed by Becker to analyze patterns of spatial variation. Second, multiple logistic regression models are employed to investigate factors influencing microbial community structure as an alternative to the simple logistic models used by Becker. Additionally, the microbial community profile data of Becker were unobservable at several points in the spatial grid. The Becker analysis assumes that the data are missing completely at random and as such have relatively little impact on inference. In this re-analysis, this assumption is investigated and it is shown that the pattern of missingness is correlated with both metabolic potential and spatial coordinates and thus provides useful information that was previously ignored by Becker. Multiple imputation methods are employed to incorporate the information present in the missing data pattern and results are compared with those of Becker.
Files	huang.pdf