The R book

By Michael J. Crawley. 2022

7,477 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/93df01be5d1e665aadd3c9a51985f17f63de3e5b

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In Artificial Intelligence

Citations in the corpus

Camouflage through behavior in moths: the role of background matching and disruptive coloration (2015, background): “London (UK): Methuen & Co Ltd. Crawley MJ. 2012.”
Influence of experimental warming and shading on host–parasitoid synchrony (2010, methodology): “These analyses were carried out in the statistical computing environment R (R-Development-Core-Team, 2006), using generalized linear models with quasibinomial error structure (Crawley, 2007).”
Characterizing properties and drivers of long distance movements by elephants (Loxodonta africana) in the Gourma, Mali (2013, methodology): “Wilcoxon Rank Sum tests were used when assumptions of normality were violated as dictated by Shapiro–Wilk normality tests (Crawley, 2007).”
Changes in the Functional Composition of a Central European Urban Flora over Three Centuries (2010, methodology): “Fisher’s exact test had to be used if the number of at least one of the expected elements per time period was o5 (Crawley, 2007).”
Causality and cross-modal integration. (2009, methodology): “The minimal adequate model is obtained from the maximal model by a process of term deletion (also known as backward selection; for an introduction, see Crawley, 2007, pp.”
Using observation-level random effects to model overdispersion in count data in ecology and evolution (2014, background): “Unfortunately, few studies reporting significant predictors assess the degree of overdispersion in their models (Richards, 2008), despite explicit guidance on how to calculate dispersion parameters for GLMs (see Crawley, 2007; Zuur et al., 2009) and GLMMs (see Bolker et al., 2009).”
The tree mortality regime in temperate old-growth coniferous forests: the role of physical damage (2010, methodology): “Incidence (presence or absence) of preexisting decay fungi in trees killed by mechanical causes was modeled using generalized linear mixed models with a binomial error distribution and a logit link (Crawley 2007).”
Standard methods for maintaining adult Apis mellifera in cages under in vitro laboratory conditions (2013, methodology): “…may change with treatment) is quantitative or qualitative (i.e. diseased versus not diseased), then a generalised linear mixed model (GLMM) can be used to analyse data in which ‘cage’ is a ‘random effect’ parameter and treatment is a ‘fixed effect’ parameter (Crawley, 2005; Bolker et al., 2009).”
A hyper‐arid environment shapes an inverse pattern of the fast–slow plant economics spectrum for above‐, but not below‐ground resource acquisition strategies (2018, methodology): “Finally, to examine the effect of aridity on trait variation (CWV), we performed a series (one per trait) of general linear models (GLMs; Crawley, 2007), using a Gaussian error distribution (link function “identity”).”
Generalist foraging of pollinators: diet expansion at high density (2008, methodology): “The full models were then reduced using backward simplifications (p. 327; Crawley 2007).”
Estimating patterns of reptile biodiversity in remote regions (2013, methodology): “To take overdispersion into account, we used a quasi-Poisson family and tested significance using an F test (Crawley, 2007).”
An evaluation of the robustness of global amphibian range maps (2014, methodology): “we assumed a quasi-binomial error distribution and used an F-test to assess significance (Crawley, 2007).”