2014 - 26th Annual Conference ProceedingsCopyright (c) 2018 Kansas State University Libraries All rights reserved.
http://newprairiepress.org/agstatconference/2014/proceedings
Recent Events in 2014 - 26th Annual Conference Proceedingsen-usTue, 20 Mar 2018 09:01:09 PDT3600DEVELOPING PREDICTION EQUATIONS FOR CARCASS LEAN MASS IN THE PRESCENCE OF PROPORTIONAL MEASUREMENT ERROR
http://newprairiepress.org/agstatconference/2014/proceedings/9
http://newprairiepress.org/agstatconference/2014/proceedings/9Sun, 27 Apr 2014 11:15:00 PDT
Published prediction equations for carcass lean mass are widely used by commercial pork producers for carcass valuation. These regression equations have been derived under the assumption that the predictors, such as back fat depth, are measured without error. In practice, however, it is known that these measurements are imperfect, with a variance that is proportional to the mean. In this paper, we consider both a linear and quadratic true relationship and compare regression fits among two methods that account for this error versus simply ignoring the additional error. We show that biased estimates of the relationship result if measurement error is ignored. Between our version of regression calibration and a Bayesian model approach, the Bayesian inference approach produced the least biased predictions. The benefits of our Bayesian approach also increased with an increase in the measurement error.
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Zachary J. Hass et al.BAYESIAN INFERENCE FOR A COVARIANCE MATRIX
http://newprairiepress.org/agstatconference/2014/proceedings/8
http://newprairiepress.org/agstatconference/2014/proceedings/8Sun, 27 Apr 2014 10:00:00 PDT
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 case, the posterior for the variance is biased toward larger values and the correlation is biased toward zero. This bias persists even for large sample sizes and therefore caution should be used when using the inverse Wishart prior.
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Ignacio Alvarez et al.USE OF THE POSTERIOR PREDICTIVE DISTRIBUTION AS A DIAGNOSTIC TOOL FOR MIXED MODELS
http://newprairiepress.org/agstatconference/2014/proceedings/7
http://newprairiepress.org/agstatconference/2014/proceedings/7Sun, 27 Apr 2014 10:18:00 PDT
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 are used to generate pseudo-data. "Distances" are calculated as the log of sums-of-squares, following ranking, and the test from comparing a mean distance to a distribution of mean distances. The power of this method compares favorably with those of standard methods, e.g. t-tests, but it is more general since it can be used for most models in the GLMM framework, whether estimated using traditional or Bayesian methods. A new kind of plot, where the distribution of the ranked pseudo-data is compared to the original data at each ranked datum, is useful for determining the region of the data where the model fails.
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Matthew KramerCHECK BASED STABILITY ANALYSIS METHOD AND ITS APPLICATION TO WINTER WHEAT VARIETY TRIALS
http://newprairiepress.org/agstatconference/2014/proceedings/6
http://newprairiepress.org/agstatconference/2014/proceedings/6Sun, 27 Apr 2014 11:00:00 PDT
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 wheat CPT (crop performance test) data collected by South Dakota State University.
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Jixiang Wu et al.MODELING RATIOS WITH POTENTIAL ZERO-INFLATION TO ASSESS SOIL NEMATODE COMMUNITY STRUCTURE
http://newprairiepress.org/agstatconference/2014/proceedings/5
http://newprairiepress.org/agstatconference/2014/proceedings/5Sun, 27 Apr 2014 12:00:00 PDT
The southern root-knot nematode (SRKN) and the weedy perennials, yellow nutsedge (YNS) and purple nutsedge (PNS) are simultaneously-occurring pests in the irrigated agricultural soils of southern New Mexico. Previous research has characterized SRKN, YNS and PNS as a mutually beneficial pest complex and has shown their enhanced population growth and survival when they occur together. In addition, it was shown that the density of nutsedge in a field could be used as a predictor of SRKN juveniles in the soil. In addition to SRKN, which is the most harmful of the plant parasitic nematodes, in southern New Mexico other species or categories of nematodes were identified and counted. Some of them are not as damaging to crop plants as SRKN, and some of them may be essential for soil health. The nematode species could be grouped into categories according to trophic level (what nematodes eat) and herbivore feeding behavior (how herbivore nematodes eat). Then three ratios of counts each were calculated for trophic and feeding behavior categories to investigate the soil nematode community structure. These proportions were modeled as functions of the weed hosts YNS and PNS by generalized linear regression models using the logit link function and three distributions: the Binomial, Zero-Inflated Binomial (ZIB) and Binomial Hurdle (BH). The latter two were used to account for potential high proportions of zeroes in the data. Formulas for the probability mass functions and moments were developed for the ZIB and BH. The SAS NLMIXED procedure was used to fit models for each of three sampling dates (May, July and September) in the two years of an alfalfa field study. General results showed that the Binomial generally provided the best fit, indicating lower zero-inflation than expected, but that ZIB and BH are often comparable. Importance of YNS and PNS predictors varied over sample dates and ratios. Specific results for one selected ratio illustrate the differences in estimated probabilities between Binomial, ZIB and BH distributions as YNS counts increase.
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Joanna Zbylut et al.MODELING SLEEP AND WAKE BOUTS IN DROSOPHILA MELANOGASTER
http://newprairiepress.org/agstatconference/2014/proceedings/4
http://newprairiepress.org/agstatconference/2014/proceedings/4Sun, 27 Apr 2014 09:10:00 PDT
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 sleep and wake bout durations using the model organism, Drosophila melanogaster, which exhibits behavioral and molecular homology to human sleep. We present an exploratory analysis of the data to gain a better understanding of the sleep bout duration distribution by considering a broader range of potential distributions than considered in previous studies. We use the results of the distribution analysis to develop a model for sleep bout durations in the fly based upon their past sleep and wake history and find that this relationship should not be ignored.
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Gayla R. Olbricht et al.Editor's Preface and Table of Contents
http://newprairiepress.org/agstatconference/2014/proceedings/3
http://newprairiepress.org/agstatconference/2014/proceedings/3Sun, 27 Apr 2014 09:00:00 PDT
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.
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Weixing SongRESPONSE OF SOYBEAN YIELD AND YIELD COMPONENTS TO PHOSPHORUS FERTILIZATION IN SOUTH DAKOTA
http://newprairiepress.org/agstatconference/2014/proceedings/2
http://newprairiepress.org/agstatconference/2014/proceedings/2Sun, 27 Apr 2014 09:05:00 PDT
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, 60, and 80lb/ac of triple superphosphate. Data for yield and yield components were collect and analyzed with several statistical methods including linear mixed model approaches and Additive Model and Multiplicative Interaction effect (AMMI) methods. There was no evidence showing that P had significant impacts on grain yield and yield components. P by environment (PE) interactions were not significant for all traits except whole pod weight. Large variation in yield and yield components were attributed to environmental conditions. Plant height, 100-pod weight, and seed weight of 100- pod had positive and significant correlations with yield in three locations; Geddes, Mitchell, and Bancroft.
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Adams Kusi Appiah et al.MULTIVARIATE STATISTICAL ANALYSIS OF COLEOPTERA SPECTRAL REFLECTANCE
http://newprairiepress.org/agstatconference/2014/proceedings/1
http://newprairiepress.org/agstatconference/2014/proceedings/1Sun, 27 Apr 2014 09:07:00 PDT
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 were identified across all taxonomic groups and genders creating a multivariate response structure. Multivariate statistical procedures including principal component and discriminant analyses were employed to assess the differentiation of taxonomic groups and genders based on spectral reflectance. The first three axes of the principal component analysis accounted for 96% of the variation and provided a clear clustering of genus and gender for a subset of taxonomic groups. The linear discriminant analysis under an assumption of multivariate normality provided a distinct classification of taxonomic groups resulting in an overall 4% misclassification rate; while the nearest neighbor discriminant analysis with a proportional prior gave an overall error rate of 5.2%. Internal bootstrap validation of the latter discriminant model yielded an average error rate of 3.5%. An external cross validation of the same model, conducted on independent samples of the same species with new individuals resulted in an average misclassification error rate of only 6.5%. Given the low error rates of misclassification, such multivariate statistical approaches are recommended for analysis of spectral reflectance in Coleoptera and other similar insect groups.
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Sarah E.M. Herberger et al.