Oliver Schabenberger

Bio: Oliver Schabenberger is an academic researcher from SAS Institute. The author has contributed to research in topics: Population & Generalized linear mixed model. The author has an hindex of 21, co-authored 37 publications receiving 7683 citations. Previous affiliations of Oliver Schabenberger include Michigan State University & Virginia Tech.

Arabidopsis CBF1 overexpression induces COR genes and enhances freezing tolerance.

Abstract: Many plants, including Arabidopsis, show increased resistance to freezing after they have been exposed to low nonfreezing temperatures. This response, termed cold acclimation, is associated with the induction of COR (cold-regulated) genes mediated by the C-repeat/drought-responsive element (CRT/DRE) DNA regulatory element. Increased expression of Arabidopsis CBF1, a transcriptional activator that binds to the CRT/DRE sequence, induced COR gene expression and increased the freezing tolerance of nonacclimated Arabidopsis plants. We conclude that CBF1 is a likely regulator of the cold acclimation response, controlling the level of COR gene expression, which in turn promotes tolerance to freezing.

Contemporary Statistical Models for the Plant and Soil Sciences

Abstract: Statistical Models Mathematical and Statistical Models Functional Aspects of Models The Inferential Steps o Estimation and Testing t-Tests in Terms of Statistical Models Embedding Hypotheses Hypothesis and Significance Testing o Interpretation of the p-Value Classes of Statistical Models Data Structures Introduction Classification by Response Type Classification by Study Type Clustered Data Autocorrelated Data From Independent to Spatial Data o A Progression of Clustering Linear Algebra Tools Introduction Matrices and Vectors Basic Matrix Operations Matrix Inversion o Regular and Generalized Inverse Mean, Variance, and Covariance of Random Vectors The Trace and Expectation of Quadratic Forms The Multivariate Gaussian Distribution Matrix and Vector Differentiation Using Matrix Algebra to Specify Models The Classical Linear Model: Least Squares and Alternatives Introduction Least Squares Estimation and Partitioning of Variation Factorial Classification Diagnosing Regression Models Diagnosing Classification Models Robust Estimation Nonparametric Regression Nonlinear Models Introduction Models as Laws or Tools Linear Polynomials Approximate Nonlinear Models Fitting a Nonlinear Model to Data Hypothesis Tests and Confidence Intervals Transformations Parameterization of Nonlinear Models Applications Generalized Linear Models Introduction Components of a Generalized Linear Model Grouped and Ungrouped Data Parameter Estimation and Inference Modeling an Ordinal Response Overdispersion Applications Linear Mixed Models for Clustered Data Introduction The Laird-Ware Model Choosing the Inference Space Estimation and Inference Correlations in Mixed Models Applications Nonlinear Models for Clustered Data Introduction Nonlinear and Generalized Linear Mixed Models Towards an Approximate Objective Function Applications Statistical Models for Spatial Data Changing the Mindset Semivariogram Analysis and Estimation The Spatial Model Spatial Prediction and the Kriging Paradigm Spatial Regression and Classification Models Autoregressive Models for Lattice Data Analyzing Mapped Spatial Point Patterns Applications Bibliography

SAS for Mixed Models, Second Edition

Abstract: Professionals and students with a background in two-way ANOVA and regression and a basic knowledge of linear models and matrix algebra will benefit from the topics covered.Includes a free CD-ROM with example SAS code!

An R2 statistic for fixed effects in the linear mixed model.

Abstract: Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R2 statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R2 statistic for the linear mixed model by using only a single model. The proposed R2 statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R2 statistic arises as a 1–1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R2 statistic leads immediately to a natural definition of a partial R2 statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R2 , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study. Copyright © 2008 John Wiley & Sons, Ltd.

A general and simple method for obtaining R2 from generalized linear mixed-effects models

Abstract: Summary The use of both linear and generalized linear mixed-effects models (LMMs and GLMMs) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (AIC), are usually presented as model comparison tools for mixed-effects models. The presentation of ‘variance explained’ (R2) as a relevant summarizing statistic of mixed-effects models, however, is rare, even though R2 is routinely reported for linear models (LMs) and also generalized linear models (GLMs). R2 has the extremely useful property of providing an absolute value for the goodness-of-fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, R2 can also be a quantity of biological interest. One reason for the under-appreciation of R2 for mixed-effects models lies in the fact that R2 can be defined in a number of ways. Furthermore, most definitions of R2 for mixed-effects have theoretical problems (e.g. decreased or negative R2 values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation). Here, we make a case for the importance of reporting R2 for mixed-effects models. We first provide the common definitions of R2 for LMs and GLMs and discuss the key problems associated with calculating R2 for mixed-effects models. We then recommend a general and simple method for calculating two types of R2 (marginal and conditional R2) for both LMMs and GLMMs, which are less susceptible to common problems. This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed-effects models. The proposed method has the potential to facilitate the presentation of R2 for a wide range of circumstances.

Generalized linear mixed models: a practical guide for ecology and evolution

Abstract: How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

A protocol for data exploration to avoid common statistical problems

Abstract: Summary 1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed. 2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake. 3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations. 4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.

Salt and drought stress signal transduction in plants

Abstract: Salt and drought stress signal transduction consists of ionic and osmotic homeostasis signaling pathways, detoxification (i.e., damage control and repair) response pathways, and pathways for growth regulation. The ionic aspect of salt stress is signaled via the SOS pathway where a calcium-responsive SOS3-SOS2 protein kinase complex controls the expression and activity of ion transporters such as SOS1. Osmotic stress activates several protein kinases including mitogen-activated kinases, which may mediate osmotic homeostasis and/or detoxification responses. A number of phospholipid systems are activated by osmotic stress, generating a diverse array of messenger molecules, some of which may function upstream of the osmotic stress-activated protein kinases. Abscisic acid biosynthesis is regulated by osmotic stress at multiple steps. Both ABA-dependent and -independent osmotic stress signaling first modify constitutively expressed transcription factors, leading to the expression of early response transcriptional activators, which then activate downstream stress tolerance effector genes.

Plant cellular and molecular responses to high salinity.

Abstract: ▪ Abstract Plant responses to salinity stress are reviewed with emphasis on molecular mechanisms of signal transduction and on the physiological consequences of altered gene expression that affect biochemical reactions downstream of stress sensing. We make extensive use of comparisons with model organisms, halophytic plants, and yeast, which provide a paradigm for many responses to salinity exhibited by stress-sensitive plants. Among biochemical responses, we emphasize osmolyte biosynthesis and function, water flux control, and membrane transport of ions for maintenance and re-establishment of homeostasis. The advances in understanding the effectiveness of stress responses, and distinctions between pathology and adaptive advantage, are increasingly based on transgenic plant and mutant analyses, in particular the analysis of Arabidopsis mutants defective in elements of stress signal transduction pathways. We summarize evidence for plant stress signaling systems, some of which have components analogous to t...