BookDOI
Modern Applied Statistics with S
W. N. Venables,Brian D. Ripley +1 more
- Iss: 1
TLDR
A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.Abstract:
A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods The emphasis is on presenting practical problems and full analyses of real data setsread more
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Journal ArticleDOI
Effect size, confidence interval and statistical significance: a practical guide for biologists.
TL;DR: This article extensively discusses two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta‐analysis.
Journal ArticleDOI
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TL;DR: MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.
Book
Negative Binomial Regression
TL;DR: In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.
Journal ArticleDOI
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
TL;DR: It is found that the Illumina sequencing data are highly replicable, with relatively little technical variation, and thus, for many purposes, it may suffice to sequence each mRNA sample only once (i.e., using one lane).
Journal ArticleDOI
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review
Carsten F. Dormann,Jana M. McPherson,Miguel B. Araújo,Roger Bivand,Janine Bolliger,Gudrun Carl,Richard G. Davies,Alexandre H. Hirzel,Walter Jetz,W. Daniel Kissling,Ingolf Kühn,Ralf Ohlemüller,Pedro R. Peres-Neto,Björn Reineking,Boris Schröder,Frank M. Schurr,Robert J. Wilson +16 more
TL;DR: In this paper, the authors describe six different statistical approaches to infer correlates of species distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations.