Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

The Grammar of Graphics

Yale’s Environmental Performance Index (EPI) has emerged as the premier framework of national-scale metrics for global environmental policy analysis. Data and indicators from 180 countries gauge progress on 20+ environmental public health and ecosystem vitality goals. The framework offers a policy-relevant scorecard that highlights leaders and laggards in environmental performance, gives insight on best practices, and provides guidance for countries that aspire to be leaders in sustainability. The Yale Center for Environmental Law & Policy (YCELP) is looking for research assistants to contribute to the 2018 release of the next EPI report. For more information, visit epi.yale.edu.

Environmental Performance Index

Mycotoxins contaminating animal feed can exert toxic effects in animals and be transferred into animal products. Therefore, mycotoxin occurrence in feed should be monitored. To this end, we performed a large-scale global survey of mycotoxin contamination in feed and assessed regional differences and year-to-year variation of mycotoxin occurrence. Concentrations of aflatoxin B1, zearalenone, fumonisins, ochratoxin A, deoxynivalenol, and T-2 toxin were analyzed in 74,821 samples of feed and feed raw materials (e.g., maize, wheat, soybean) collected from 100 countries from 2008 to 2017. In total, 88% of the samples were contaminated with at least one mycotoxin. Mycotoxin occurrence showed distinct regional trends and climate was a key determinant governing these trends. In most regions, the majority of samples complied with maximum levels and guidance values for mycotoxins in animal feed that are in effect in the European Union. However, 41.1%, 38.5%, and 20.9% of samples from South Asia, Sub-Saharan Africa, and Southeast Asia, respectively, exceeded the maximum level for aflatoxin B1 (20 µg/kg). In several regions, mycotoxin concentrations in maize showed a pronounced year-to-year variation that could be explained by rainfall or temperature during sensitive periods of grain development. A large fraction of samples (64%) was co-contaminated with ≥ 2 mycotoxins. Most frequently observed mycotoxin mixtures were combinations of deoxynivalenol, zearalenone, and fumonisins, as well as fumonisins and aflatoxin B1. Deoxynivalenol and zearalenone concentrations were correlated in maize and wheat. In conclusion, according to an extensive global survey, mycotoxin (co-)contamination of animal feed is common, shows regional trends, and is governed in part by climate and weather.

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Global Mycotoxin Occurrence in Feed: A Ten-Year Survey

The Elements of Graphing Data

Bayesian inference using Markov chain Monte Carlo (MCMC) has become one of the primary methods used to infer phylogenies from sequence data. Assessing convergence is a crucial component of these analyses, as it establishes the reliability of the posterior distribution estimates of the tree topology and model parameters sampled from the MCMC. Numerous tests and visualizations have been developed for this purpose, but many of the most popular methods are implemented in ways that make them inconvenient to use for large data sets. RWTY is an R package that implements established and new methods for diagnosing phylogenetic MCMC convergence in a single convenient interface.

/pdf/rwty-r-we-there-yet-an-r-package-for-examining-convergence-zhamaj6j62.pdf

RWTY (R We There Yet): An R Package for Examining Convergence of Bayesian Phylogenetic Analyses.

This article develops a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots...

/pdf/the-generalized-pairs-plot-37ornc0sza.pdf

The Generalized Pairs Plot

Extension to ggplot2

Extension to 'ggplot2' [R package GGally version 2.0.0]

Ecological fingerprints of climate change are becoming increasingly evident at broad geographical scales as measured by species range shifts and changes in phenology. However, finer-scale species-level responses to environmental fluctuations may also provide an important bellwether of impending future community responses. Here we examined changes in abundance of butterfly species along a hydrological gradient of six montane meadow habitat types in response to drought. Our data collection began prior to the drought, and we were able to track changes for 11 years, of which eight were considered mild to extreme drought conditions. We separated the species into those that had an affinity for hydric vs. xeric habitats. We suspected that drought would favor species with xeric habitat affinities, but that there could be variations in species-level responses along the hydrological gradient. We also suspected that mesic meadows would be most sensitive to drought conditions. Temporal trajectories were modeled for both species groups (hydric vs. xeric affinity) and individual species. Abundances of species with affinity for xeric habitats increased in virtually all meadow types. Conversely, abundances of species with affinity for hydric habitats decreased, particularly in mesic and xeric meadows. Mesic meadows showed the most striking temporal abundance trajectory: Increasing abundances of species with xeric habitat affinity were offset by decreasing or stable abundances of species with hydric habitat affinity. The one counterintuitive finding was that, in some hydric meadows, species with affinity for hydric habitats increased. In these cases, we suspect that decreasing moisture conditions in hydric meadows actually increased habitat suitability because sites near the limit of moisture extremes for some species became more acceptable. Thus, species responses were relatively predictable based upon habitat affinity and habitat location along the hydrological gradient, and mesic meadows showed the highest potential for changes in community composition. The implications of these results are that longer-term changes due to drought could simplify community composition, resulting in prevalence of species tolerant to drying conditions and a loss of species associated with wetter conditions. We contend that this application of gradient analysis could be valuable in assessing species vulnerability of other taxa and ecosystems.

/pdf/gradient-based-habitat-affinities-predict-species-1czxp80o3i.pdf

Jason Crowley

Papers

The Generalized Pairs Plot

Extension to ggplot2

Extension to 'ggplot2' [R package GGally version 2.0.0]

Gradient-based habitat affinities predict species vulnerability to drought

ggobi/ggally: v2.1.1