Changing the Support of a Spatial Covariate: A Simulation Study

Researchers are increasingly able to capture spatially referenced data on both a response and a covariate more frequently and in more detail. A combination of geostatisical models and analysis of covariance methods may be used to analyze such data. However, very basic questions regarding the effects of using a covariate whose support differs from that of the response variable must be addressed to utilize these methods most efficiently. In this experiment, a simulation study was conducted to assess the following: (i) the gain in efficiency when geostatistical models are used, (ii) the gain in efficiency when analysis of covariance methods are used, and (iii) the effects of including a covariate whose support differs from that of the response variable in the analysis. This study suggests that analyses which both account for spatial structure and exploit information from a covariate are most powerful. Also, the results indicate that the support of the covariate should be as close as possible to the support of the response variable to obtain the most accurate experimental results

Effect of a Pre-Dinner Walnut Snack on Nutrient Intake Among University Students

https://openriver.winona.edu/urc2018/1021/thumbnail.jp

Design and analysis of experiments in the presence of spatial correlation

This dissertation consists of three papers written on the design and analysis of experiments in the presence of spatial correlation. The first paper discusses the use of optimality criteria in the design of experiments. In the context of linear models, an optimality criterion is developed for models that include random effects. This criterion also allows for the inclusion of fixed and/or random nuisance parameters in the model and for the presence of a general covariance structure. Also, a general formula is presented for which all previously published optimality criteria are special cases. The second paper presents a simulation study on changing the support of a spatial covariate. Researchers are increasingly able to capture spatially referenced data on both a response and a covariate more frequently and in more detail. A combination of geostatisical models and analysis of covariance methods is used to analyze such data. However, basic questions regarding the effects of using a covariate whose support differs from that of the response variable must be addressed to utilize these methods more efficiently. A simulation study was conducted to assess the effects of including a covariate whose support differs from that of the response variable in the analysis. This study suggests that the support of the covariate should be as close as possible to the support of the response variable. The third paper presents a new method for analysis of covariance with a spatial covariate. Data sets which contain measurements on a spatially referenced response and covariate are analyzed using either cokriging or spatial analysis of covariance. While cokriging accounts for the correlation structure of the covariate, it is purely a predictive tool. Alternatively, spatial analysis of covariance allows for parameter estimation yet disregards the correlation structure of the covariate. A method is proposed which both accounts for the correlation in and between the response and covariate and allows for the estimation of model parameters; also, this method allows for analysis of covariance when the response and covariate are not colocated

Variation in the U.S. Photoperiod Insensitive Sorghum Collection for Chemical and Nutritional Traits

Screening germplasm for chemical and nutritional content can be expensive and time consuming. Near infrared spectroscopy (NIRS) and application of geostatistical models can make screening more efficient. The objectives of this study were to utilize these technologies to: (i) generate chemical and nutritional values for the U.S. photoperiod insensitive sorghum collection, (ii) describe variability for those traits, (iii) identify accessions in the highest and lowest 1% for each trait, and (iv) describe relationships among the accessions. Accessions were grown at Ithaca, NE, during 2001 and 2002. Samples of grain were scanned and NIRS equations developed for starch, fat, crude protein, acid detergent fiber, and phosphorus. The NIRS generated values for each accession can be accessed on GRIN at http://www.ars-grin.gov/cgi-bin/npgs/html/desclist.pl?69; verified 22 November 2005. The highest and lowest 1% of accessions was identified for each trait by best linear unbiased predictors (BLUPs). Means and standard deviations for observed values and variances due to accessions were calculated. Rank correlations between BLUPs and observed values ranged from r = 0.82 to r = 0.92. Principal component analysis showed that much of the variation is attributable to a contrast of starch with a weighted average of fat, crude protein, acid detergent fiber, and phosphorus. Cluster analyses showed clusters on the basis of canonical values, but no geographical, taxonomical, or morphological interpretation of the clusters was apparent

Changing the Support of a Spatial Covariate: A Simulation Study

Researchers are increasingly able to capture spatially referenced data on both a response and a covariate more frequently and in more detail. A combination of geostatisical models and analysis of covariance methods may be used to analyze such data. However, very basic questions regarding the effects of using a covariate whose support differs from that of the response variable must be addressed to utilize these methods most efficiently. In this experiment, a simulation study was conducted to assess the following: (i) the gain in efficiency when geostatistical models are used, (ii) the gain in efficiency when analysis of covariance methods are used, and (iii) the effects of including a covariate whose support differs from that of the response variable in the analysis. This study suggests that analyses which both account for spatial structure and exploit information from a covariate are most powerful. Also, the results indicate that the support of the covariate should be as close as possible to the support of the response variable to obtain the most accurate experimental results