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Articles 31 - 60 of 488
Full-Text Articles in Life Sciences
Check Based Stability Analysis Method And Its Application To Winter Wheat Variety Trials, Jixiang Wu, Karl Glover, Nathan Mueller
Check Based Stability Analysis Method And Its Application To Winter Wheat Variety Trials, Jixiang Wu, Karl Glover, Nathan Mueller
Conference on Applied Statistics in Agriculture
Finley-Wilson (FW) regression based stability analysis is highly dependent on the testing varieties and environments being used. In this study, we proposed a check based regression method to determine yield stability. One advantage of this method is its capability to determine yield stability through widely acceptable varieties and thus to provide more meaningful information to evaluate the potential use of new varieties. In addition, with integration a resampling technique, bootstrapping method, yield stability can be compared among different varieties/genotypes from either the same or different testing environments. As a demonstration, we applied this method to analyze the 2009- 2011 winter …
Use Of The Posterior Predictive Distribution As A Diagnostic Tool For Mixed Models, Matthew Kramer
Use Of The Posterior Predictive Distribution As A Diagnostic Tool For Mixed Models, Matthew Kramer
Conference on Applied Statistics in Agriculture
The posterior predictive distribution (the distribution of data simulated from a model) has been used to flag model-data discrepancies in the Bayesian literature, and several approaches have been developed. The approach taken here differs from the others both conceptually and as realized. It works by comparing the "distance" between the data and model (as represented by pseudo-data simulated from a model) with "distance" within the model. The distance within the model is calculated by generating pseudo-data from it, using each set of these pseudo-data to reestimate the model, and then generating pseudo-data from them, matching the way the original data …
Bayesian Inference For A Covariance Matrix, Ignacio Alvarez, Jarad Niemi, Matt Simpson
Bayesian Inference For A Covariance Matrix, Ignacio Alvarez, Jarad Niemi, Matt Simpson
Conference on Applied Statistics in Agriculture
Covariance matrix estimation arises in multivariate problems including multivariate normal sampling models and regression models where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these problems requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled inverse Wishart, hierarchical inverse Wishart, and a separation strategy as possible priors for the covariance matrix. We evaluate these priors through a simulation study and application to a real data set. Generally all priors work well with the exception of the inverse Wishart when the true variance is small relative to prior mean. In this …
Modeling Sleep And Wake Bouts In Drosophila Melanogaster, Gayla R. Olbricht, V. A. Samaranayake, Sahitya Injamuri, Luyang Wang, Courtney Fiebelman, Matthew S. Thimgan
Modeling Sleep And Wake Bouts In Drosophila Melanogaster, Gayla R. Olbricht, V. A. Samaranayake, Sahitya Injamuri, Luyang Wang, Courtney Fiebelman, Matthew S. Thimgan
Conference on Applied Statistics in Agriculture
Adequate sleep restores vital processes required for health and well-being; but the function and regulation of sleep is not well understood. Unfortunately, a definition of adequate sleep is unclear. On an hours-long timescale, consolidated and cycling sleep results in better health and performance outcomes. At shorter timescales, older studies report conflicting results regarding the relationship between sleep and wake bout durations. One approach to this problem has been to simply analyze the distribution of bout durations. While informative, this method eliminates the time relationship between bouts, which may be important. Here, we develop a model that describes the relationship between …
Multivariate Statistical Analysis Of Coleoptera Spectral Reflectance, Sarah E.M. Herberger, Bahaman Shafii, Stephen P. Cook, Christopher J. Williams, William J. Price
Multivariate Statistical Analysis Of Coleoptera Spectral Reflectance, Sarah E.M. Herberger, Bahaman Shafii, Stephen P. Cook, Christopher J. Williams, William J. Price
Conference on Applied Statistics in Agriculture
The insect order Coleoptera, commonly known as beetles, comprises 40% of all insects which in turn account for half of all identified animal species alive today. Coleopterans frequently have large elytra (the hardened front wings) that can have a wide range of colors. Spectral reflectance readings from these elytra may be used to uniquely identify coleopteran taxonomic groups. Multiple samples of eleven species of wood boring beetles were selected from the University of Idaho William Barr Entomology Museum. Spectrometer readings for each specimen were then fit to normal distribution mixture models to identify multiple peak reflectance wavelengths. Eighteen prominent peaks …
Response Of Soybean Yield And Yield Components To Phosphorus Fertilization In South Dakota, Adams Kusi Appiah, Rebecca Helget, Yi Xu, Jixiang Wu
Response Of Soybean Yield And Yield Components To Phosphorus Fertilization In South Dakota, Adams Kusi Appiah, Rebecca Helget, Yi Xu, Jixiang Wu
Conference on Applied Statistics in Agriculture
Increased demand for soybean [Glycine max (L.) Merrill] production for industrial, human, and animal consumption has provided many incentives for farmers and producers to increase their production. In many soils used for soybean production, phosphorus (P) becomes a major limiting factor to soybean growth and grain production. A field experiment was conducted in five locations across Eastern South Dakota in 2013 to study the response of soybean yield and yield components to phosphorus fertilizer applications. The experiment was laid out in a randomized complete block (RCB) design with four replications. The treatments consisted of five P levels 0, 20, 40, …
Editor's Preface And Table Of Contents, Weixing Song
Editor's Preface And Table Of Contents, Weixing Song
Conference on Applied Statistics in Agriculture
These proceedings contain papers presented in the twenty-sixth annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 27 - April 29, 2014.
Using Functional Data Analysis To Evaluate Effect Of Shade On Body Temperature Of Feedlot Heifers During Environmental Heat Stress, F. Yang, A. M. Parkhurst, C. N. Lee, T. M. Brown-Brandl, P. E. Hillman
Using Functional Data Analysis To Evaluate Effect Of Shade On Body Temperature Of Feedlot Heifers During Environmental Heat Stress, F. Yang, A. M. Parkhurst, C. N. Lee, T. M. Brown-Brandl, P. E. Hillman
Conference on Applied Statistics in Agriculture
Heat stress can be a serious problem for cattle. Body temperature (Tb) is a good measure of an animal’s thermo-regulatory response to an environmental thermal challenge. Previous studies found that Tb increases in response to increasing ambient temperature in a controlled chamber. However, when animals are in an uncontrolled environment, Tb is subject to many uncontrolled environmental factors, such as sunshade, wind, and humidity, that increase variation in the data. Hence, functional data analysis (FDA) was applied to analyze the data with uncontrolled environmental factors as curves in the whole series of days in this study. Breed (Angus, MARCIII, MARC-I, …
Detecting Factors Associated With Springwheat Yield Stability In South Dakota Environments, Jixiang Wu, Karl Glover, William Berzonsky
Detecting Factors Associated With Springwheat Yield Stability In South Dakota Environments, Jixiang Wu, Karl Glover, William Berzonsky
Conference on Applied Statistics in Agriculture
Conventional yield stability analyses are focused on yield stability itself by using single linear regression method and/or additive main effect and multiplicative interaction (AMMI) analysis. It is likely that yield stability for a genotype is associated with many factors such as fertilizer level, soil types, weather conditions, and/or yield components. Detection of factors highly associated with yield stability, therefore, will help breeders develop cultivars adapted to diverse environments or to specific environments. In this study, we conducted correlation analysis based on both environments and genotypes for a data set with 22 spring wheat genotypes, which were evaluated in 18 environments …
Estimation Of Dose Requirements For Extreme Levels Of Efficacy, Mark West, Guy Hallman
Estimation Of Dose Requirements For Extreme Levels Of Efficacy, Mark West, Guy Hallman
Conference on Applied Statistics in Agriculture
The objective of this paper is to explore the extent of how dose-response models may be used to estimate extreme levels of efficacy for controlling insect pests and possibly other uses. Probit-9 mortality (99.9968% mortality) is a standard for treatment effectiveness in tephritid fruit fly research, and has been adopted by the United States Department of Agriculture for fruit flies and other pests. Data taken from the phytosanitary treatment (PT) literature are analyzed. These data are used to fit dose-response models with logit, probit and complimentary log-log links. The effectiveness of these models for predicting extreme levels of efficacy is …
A Simulation Study Of The Small Sample Properties Of Likelihood Based Inference For The Beta Distribution, Kevin Thompson, Edward Gbur
A Simulation Study Of The Small Sample Properties Of Likelihood Based Inference For The Beta Distribution, Kevin Thompson, Edward Gbur
Conference on Applied Statistics in Agriculture
Researchers often collect proportion data that cannot be interpreted as arising from a set of Bernoulli trials. Analyses based on the beta distribution may be appropriate for such data. The SAS® GLIMMIX procedure provides a tool for these analyses using a likelihood based approach in the context of generalized linear mixed models. Since the t and F-distribution based inference employed in this approach relies on asymptotic properties, it is important to understand the sample sizes required to obtain reasonable approximate answers to inference questions. In addition, the complexity of the likelihood functions can lead to numerical issues for optimization algorithms …
Non-Normal Data In Agricultural Experiments, W. W. Stroup
Non-Normal Data In Agricultural Experiments, W. W. Stroup
Conference on Applied Statistics in Agriculture
Advances in computers and modeling over the past couple of decades have greatly expanded options for analyzing non-normal data. Prior to the 1990’s, options were largely limited to analysis of variance (ANOVA), either on untransformed data or after applying a variance stabilizing transformation. With or without transformations, this approach depends heavily on the Central Limit Theorem and ANOVA’s robustness. The availability of software such as R’s lme4 package and SAS® PROC GLIMMIX changed the conversation with regard to non-normal data. With expanded options come dilemmas. We have software choices – R and SAS among many others. Models have conditional and …
Multivariate Statistical Analysis Of Terrestrial Invertebrate Index Of Biotic Integrity, Bahman Shafii, William J. Price, Norm Merz, Timothy D. Hatten
Multivariate Statistical Analysis Of Terrestrial Invertebrate Index Of Biotic Integrity, Bahman Shafii, William J. Price, Norm Merz, Timothy D. Hatten
Conference on Applied Statistics in Agriculture
The Index of Biotic Integrity (IBI) is designed to measure the changes in ecological and environmental conditions as affected by human disturbances. In practice, the IBI is used in various ecological applications to detect divergence in biological integrity attributable to human actions. Last year during this conference, methodologies for developing an Avian Index of Biotic Integrity (A-IBI) were presented and discussed. The objective of this paper is to demonstrate the construction and statistical evaluation of a multi-metric terrestrial Invertebrate Index of Biotic Integrity (I-IBI) using the same multivariate statistical techniques. Canonical correlation analyses were utilized to select pertinent invertebrate metrics …
Characterizing Benthic Macroinvertebrate Community Responses To Nutrient Addition Using Nmds And Baci Analyses, Bahman Shafii, William J. Price, G. Wayne Minshall, Charlie Holderman, Paul J. Anders, Gary Lester, Pat Barrett
Characterizing Benthic Macroinvertebrate Community Responses To Nutrient Addition Using Nmds And Baci Analyses, Bahman Shafii, William J. Price, G. Wayne Minshall, Charlie Holderman, Paul J. Anders, Gary Lester, Pat Barrett
Conference on Applied Statistics in Agriculture
Nonmetric multidimensional scaling (NMDS) is an ordination technique which is often used for information visualization and exploring similarities or dissimilarities in ecological data. In principle, NMDS maximizes rank-order correlation between distance measures and distance in the ordination space. Ordination points are adjusted in a manner that minimizes stress, where stress is defined as a measure of the discordance between the two kinds of distances. Before and After Control Impact (BACI) is a classical analysis of variance method for measuring the potential influence of an environmental disturbance. Such effects can be assessed by comparing conditions before and after a planned activity. …
Thou Shall Not Brush Your Teeth While Eating Breakfast: A 7- Step Program For Researchers Previously Hurt In Data Analysis, Edzard Van Santen
Thou Shall Not Brush Your Teeth While Eating Breakfast: A 7- Step Program For Researchers Previously Hurt In Data Analysis, Edzard Van Santen
Conference on Applied Statistics in Agriculture
After years of providing statistical advice to fellow faculty members and graduate students, I have come to realize that it is not necessarily the big issues, but lack of knowledge of basic data analysis principles that get my clients into trouble. My claim is that if researchers and students internalized two basic definitions they would not have any problems analyzing most of their experiments. The definitions of Experimental Unit (EU) as the smallest physical unit to which a treatment may be applied and Experimental Error (Exp. Err.) as the variation among EUs treated alike are the basis for successful data …
Comparing Functional Data Analysis And Hysteresis Loops When Testing Treatments For Reducing Heat Stress In Dairy Cows, S. Maynes, A. M. Parkhurst, J. B. Gaughan, T. L. Mader
Comparing Functional Data Analysis And Hysteresis Loops When Testing Treatments For Reducing Heat Stress In Dairy Cows, S. Maynes, A. M. Parkhurst, J. B. Gaughan, T. L. Mader
Conference on Applied Statistics in Agriculture
Various techniques are commonly used to reduce heat stress, including sprayers and misters, shading, and changes in feed. Oftentimes studies are performed where researchers do not control the times when animals use shading or other means available to reduce heat stress, making it hard to test differences between treatments. Two methods are used on data from a study where Holstein cows were given free access to weight activated “cow showers.” Functional data analysis can be used to model body temperature as a function of time and environmental variables such as the Heat Load Index. Differences between treatment groups can be …
Five Things I Wish My Mother Had Told Me, About Statistics That Is, Philip M. Dixon
Five Things I Wish My Mother Had Told Me, About Statistics That Is, Philip M. Dixon
Conference on Applied Statistics in Agriculture
I present five short stories, each describing something I wish I had known and appreciated earlier in my statistical life. The five are Simpson's paradox is everywhere, numerical optimization algorithms can be deceived, you can't always trust the Satterthwaite approximation, BLUP's are wonderful things, and It's good to know Reverend Bayes.
On The Small Sample Behavior Of Generalized Linear Mixed Models With Complex Experiments, Julie Couton, Walt Stroup
On The Small Sample Behavior Of Generalized Linear Mixed Models With Complex Experiments, Julie Couton, Walt Stroup
Conference on Applied Statistics in Agriculture
Generalized linear mixed models (GLMMs), regardless of the software used to implement them (R, SAS, etc.), can be formulated as conditional or marginal models and can be computed using pseudo-likelihood, penalized quasi-likelihood, or integral approximation methods. While information exists about the small sample behavior of GLMMs for some cases- notably RCBDs with Binomial or count data- little is known about GLMMs for continuous proportions (e.g. Beta) or time-to-event (e.g. Gamma) data or for more complex designs such as the split-plot. In this presentation we review the major model formulation and estimation options and compare their small sample performance for cases …
Editor's Preface And Table Of Contents, Weixing Song
Editor's Preface And Table Of Contents, Weixing Song
Conference on Applied Statistics in Agriculture
These proceedings contain papers presented in the twenty-fifth annual Kansas State University Conference on Applied Statistics in Agriculture, held in Manhattan, Kansas, April 28 - April 30, 2013.
Variation Analysis For Fiber Quality Traits Among Different Positionsin Eight Upland Cotton Cultivars, Yi Xu, Johnie N. Jenkins, Jack C. Mccarty, Jixiang Wu
Variation Analysis For Fiber Quality Traits Among Different Positionsin Eight Upland Cotton Cultivars, Yi Xu, Johnie N. Jenkins, Jack C. Mccarty, Jixiang Wu
Conference on Applied Statistics in Agriculture
Equivalencyof fiber quality within a plant of upland cotton, Gossypium hirsutum L., is very important. There are several traits within a plant that can be used to measure fiber quality and five of those traits will be investigated. Eight representative upland cultivars were grown at the Plant Science Research Farm at Mississippi State University in 1986 and five fiber traits: micronaire, fiber elongation, 2.5% and 50% span length, and fiber strength, were measured at different plant locations. The analysis of the study was modeled after a crop stability analysis with plant locations being treated as environments in the analysis. Three …
Towards Better Fdr Procedures For Discrete Test Statistics, Xiongzhi Chen, R. W. Doerge
Towards Better Fdr Procedures For Discrete Test Statistics, Xiongzhi Chen, R. W. Doerge
Conference on Applied Statistics in Agriculture
The false discovery rate (FDR) has been a widely used error measure in situations where a large number of tests are conducted simultaneously. Most methods that control the FDR at a prespeci ed level, or estimate the FDR of a multiple testing procedure (FDR procedures), were essentially developed for continuous test statistics. As such, their performances need to be carefully assessed when applied to discrete test statistics. We review some popular FDR procedures, point out a key reason for their excessive conservativeness when applied to discrete p-values, and suggest an improvement for these methods for such p-values.
Statistical Considerations When Using Hysteresis To Estimate Internal Heat Load In Dairy Cows, S. Maynes, A. M. Parkhurst
Statistical Considerations When Using Hysteresis To Estimate Internal Heat Load In Dairy Cows, S. Maynes, A. M. Parkhurst
Conference on Applied Statistics in Agriculture
Water is often used to manage heat stress in dairy cattle. Sprinklers are often placed over the feed bunk or used while cattle are waiting to be milked, however in this experiment cattle were given control over water with a cow-activated shower. Previous studies have focused on how wetting can lower body emperature or reduce respiration rates. An alternative way to investigate this management practice is to examine internal heat loads. Internal heat load can be quantified by fitting a hysteresis loop to daily field data. The hysteresis loop is formed by a phase diagram of body temperature versus an …
The Nuances Of Statistically Analyzing Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge
The Nuances Of Statistically Analyzing Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge
Conference on Applied Statistics in Agriculture
High-throughput sequencing technologies, in particular next-generation sequencing (NGS) technologies, have emerged as the preferred approach for exploring both gene function and pathway organization. Data from NGS technologies pose new computational and statistical challenges because of their massive size, limited replicate information, large number of genes (high-dimensionality), and discrete form. They are more complex than data from previous high-throughput technologies such as microarrays. In this work we focus on the statistical issues in analyzing and modeling NGS data for selecting genes suitable for further exploration and present a brief review of the relevant statistical methods. We discuss visualization methods to assess …
Treatment Heterogeneity And Potential Outcomes In Linear Mixed Effects Models, Troy E. Richardson, Gary L. Gadbury
Treatment Heterogeneity And Potential Outcomes In Linear Mixed Effects Models, Troy E. Richardson, Gary L. Gadbury
Conference on Applied Statistics in Agriculture
Studies commonly focus on estimating a mean treatment effect in a population. However, in some applications the variability of treatment effects across individual units may help to characterize the overall effect of a treatment across the population. Consider a set of treatments, {T,C}, where T denotes some treatment that might be applied to an experimental unit and C denotes a control. For each of N experimental units, the duplet {rTᵢ,rCᵢ}, i = 1,2, … , N, represents the potential response of the i th experimental unit if treatment were applied and the response …
Identifying Spectra Important For Prediction Of Senescent Grassland Canopy Structure, Rebecca Phillips, Nicanor Saliendra, Mark West
Identifying Spectra Important For Prediction Of Senescent Grassland Canopy Structure, Rebecca Phillips, Nicanor Saliendra, Mark West
Conference on Applied Statistics in Agriculture
Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of vegetation properties to ensure conservation of grassland structure for wildlife and forage for livestock. Spectral imaging data may be useful in assessment of large (>100,000 ha) landscapes, as in the Grand River National Grassland (GRNG), South Dakota. Here, we examined the predictive potential for the Advanced High Resolution Spectrometer (AVIRIS) to estimate mixed-grass prairie canopy structural attributes (photosynthetically active vegetation (kg PV ha-1), non-photosynthetically active vegetation (kg NPV ha-1), total standing crop (kg …
Exploration Of Reactant-Product Lipid Pairs In Mutant-Wild Type Lipidomics Experiments, Lianqing Zheng, Gary L. Gadbury, Jyoti Shah, Ruth Welti
Exploration Of Reactant-Product Lipid Pairs In Mutant-Wild Type Lipidomics Experiments, Lianqing Zheng, Gary L. Gadbury, Jyoti Shah, Ruth Welti
Conference on Applied Statistics in Agriculture
High-throughput metabolite analysis is very important for biologists to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the …
Variance Inflation Factors In Regression Models With Dummy Variables, Leigh Murray, Hien Nguyen, Yu-Feng Lee, Marta D. Remmenga, David W. Smith
Variance Inflation Factors In Regression Models With Dummy Variables, Leigh Murray, Hien Nguyen, Yu-Feng Lee, Marta D. Remmenga, David W. Smith
Conference on Applied Statistics in Agriculture
Variance Inflation Factors (VIFs) are used to detect collinearity among predictors in regression models. Textbook explanation of collinearity and diagnostics such as VIFs have focused on numeric predictors as being "co-linear" or "co-planar", with little attention paid to VIFs when a dummy variable is included in the model. This work was motivated by two regression models with high VIFs, where "standard' interpretations of causes of collinearity made no sense. The first was an alfalfa-breeding model with two numeric predictors and two dummy variables. The second was an economic model with one numeric predictor, one dummy and the numeric x dummy …
Stability Analysis For Yield And Seed Quality Of Soybean [Glycine Max (L.) Merril] Across Different Environments In Eastern South Dakota, Kaushal Raj Chaudhary, Jixiang Wu
Stability Analysis For Yield And Seed Quality Of Soybean [Glycine Max (L.) Merril] Across Different Environments In Eastern South Dakota, Kaushal Raj Chaudhary, Jixiang Wu
Conference on Applied Statistics in Agriculture
Genotype-environment interaction has always been an important and challenging issue for plant breeders in developing desirable varieties. Determination of genotype and environment is common in breeding program as it helps to find out the genotypes that have wide or specific adaptability across various environmental conditions. In this study, fifteen varieties of soybean were evaluated for stability of grain yield (ton/ha), protein content (%), and oil content (%) at six different locations of Eastern South Dakota in 2011. Mixed linear model and Additive main effects and multiplicative interactions (AMMI) were applied to detect genotype-by-environment (G*E) interactions and stability of each variety …
Gene Set Testing To Characterize Multivariately Differentially Expressed Genes, John R. Stevens, S. Clay Isom
Gene Set Testing To Characterize Multivariately Differentially Expressed Genes, John R. Stevens, S. Clay Isom
Conference on Applied Statistics in Agriculture
In a gene expression experiment (using oligo array, RNA-Seq, or other platform), researchers typically seek to characterize di erentially expressed genes based on common gene function or pathway involve-ment. The eld of gene set testing provides numerous characterization methods, some of which have proven to be more valid and powerful than others. These existing gene set testing methods focus on experimental designs where there is a single null hypothesis (usually involving association with a continuous or categorical phenotype) for each gene. Increasingly common experimental designs lead to multiple null hypotheses for each gene, and the characterization of these multivariately di …
Determining The Effectivesness Of Including Spatial Information Into A Nematode/Nutsedge Pest Complex Model, Joel Vetter, Zhining Ou, Leigh Murray, Stephen H. Thomas, Jill Schroeder
Determining The Effectivesness Of Including Spatial Information Into A Nematode/Nutsedge Pest Complex Model, Joel Vetter, Zhining Ou, Leigh Murray, Stephen H. Thomas, Jill Schroeder
Conference on Applied Statistics in Agriculture
An experiment was performed in 2005-2006 to determine if a nematode-resistant variety of alfalfa (Medicago sativa L.) can effectively reduce the pest complex consisting of yellow and purple nutsedge (YNS, Cyperus esculentus L. and PNS, C. rotundus L.) and the southern rootknot nematode (SRKN, Meloidogyne incognita (Kofoid & White) Chitwood). The alfalfa field, which had a history of severe infestation from both species of nutsedge and SRKN, was divided into 1m x 2m quadrats. In May, July and September of each year, eighty quadrats were randomly selected and counts of PNS, YNS and a soil sample (analyzed for the count …