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The R Book

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TLDR
The R Book is the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities, and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines.
Abstract
The high-level language of R is recognized as one of the most powerful and flexible statistical software environments, and is rapidly becoming the standard setting for quantitative analysis, statistics and graphics. R provides free access to unrivalled coverage and cutting-edge applications, enabling the user to apply numerous statistical methods ranging from simple regression to time series or multivariate analysis. Building on the success of the authors bestselling Statistics: An Introduction using R, The R Book is packed with worked examples, providing an all inclusive guide to R, ideal for novice and more accomplished users alike. The book assumes no background in statistics or computing and introduces the advantages of the R environment, detailing its applications in a wide range of disciplines. Provides the first comprehensive reference manual for the R language, including practical guidance and full coverage of the graphics facilities. Introduces all the statistical models covered by R, beginning with simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression and analysis of variance, through to generalized linear models, generalized mixed models, time series, spatial statistics, multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates and professionals in science, engineering and medicine. It is also ideal for students and professionals in statistics, economics, geography and the social sciences.

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Journal ArticleDOI

Genome sequence-based species delimitation with confidence intervals and improved distance functions

TL;DR: Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty, so it is crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes.
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A brief introduction to mixed effects modelling and multi-model inference in ecology.

TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.
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Using observation-level random effects to model overdispersion in count data in ecology and evolution

TL;DR: Simulations show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdisPersion is simply ignored, and that their ability to minimise bias is not uniform across all types of over Dispersion and must be applied judiciously.
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Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse.

TL;DR: Full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone, and favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results.
Journal ArticleDOI

Social big data

TL;DR: This paper presents a revision of the new methodologies that are designed to allow for efficient data mining and information fusion from social media and of thenew applications and frameworks that are currently appearing under the “umbrella” of the social networks, socialMedia and big data paradigms.