Modern Applied Statistics With S

By Christina Gloeckner. 2003

6,615 citations recorded by the source corpus.

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Citations in the corpus

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “This is the geyser data set supplied with the MASS package of the computing language R (Venables and Ripley, 2002).”
A data-driven approach to predict the success of bank telemarketing (2014, background): “, in credit scoring) that operates a smooth nonlinear logistic transformation over a multiple regression model and allows the estimation of class probabilities [33]:p cjxk ð Þ 1⁄4”
Uniform Convergence of Random Forests via Adaptive Concentration (2015, methodology): “The regression tree was ﬁt using the R -package tree [42]. deeper and so the model family becomes larger.”
Trait interactions help explain plant invasion success in the German flora (2008, methodology): “All statistical analyses were performed within R (R Development Core Team 2005, Version 2.2.1) using the packages MASS (Venables & Ripley 2002), multcomp (Bretz et al .”
Computational Statistics Handbook with MATLAB (2001, background): “On the other hand, there are several important topics not covered at all in the book, such as generalized linear models, mixed and random effects models, time series analysis, and survival analysis, that can be found in a book with similar scope (see Venables and Ripley 2002).”
Functional microRNA targets in protein coding sequences (2012, neutral): “This comparison is performed through a logistic regression (Venables and Ripley, 2002) between the binding categories and the presence or absence of the corresponding MRE in the true or false set of the PAR-CLIP data.”
Model-based clustering of high-dimensional data: A review (2014, methodology): “In other words, this method aims to ﬁnd an orthogonal projection of the data set in a low-dimensional linear subspace, such that the variance of the projected data is maximum.”
network: A Package for Managing Relational Data in R (2008, background): “Those unfamiliar with R may wish to peruse a text such as those of Venables & Ripley (2000, 2002) or Chambers (1998).”
An extensive experimental survey of regression methods (2019, methodology): “glmSAIC is the generalized linear model with stepwise feature se741 lection [19] using the Akaike information criterion and the stepAIC 742 function in the MASS package (model glmStepAIC in the caret model 743 list).”
Estimating the odds of survival and identifying mitigation opportunities for common bycatch in pelagic longline fisheries (2009, methodology): “We used the open-source statistical package R, with ‘MASS’ and ‘car’ packages for GLM confidence intervals and diagnostics, to implement and evaluate the models (Venables and Ripley, 2002; Fox, 2007; R Development Core Team, 2007).”
A Toolbox for Nonlinear Regression in R: The Package nlstools (2015, methodology): “Bootstrap estimates, standard errors together with median and percentile conﬁdence intervals (2.5% and 97.5% percentiles of bootstrapped estimates) [Venables and Ripley, 2002, pp. 225-226] are displayed by the generic function summary() .”
Height-diameter models for Scots pine and birch in Finland (2005, methodology): “The analysis was carried out with the R-implementation of the S-language (Chambers 1998, Venables and Ripley 2002, see http://www. r-project.org), where package nlme (Pinheiro and Bates 2000) was used for the random effects models.”
Nonparametric Analysis of Longitudinal Data in Factorial Experiments (2003, methodology): “…for longitudinal data analysis includes implementation of GLM/GLMM in R (R Development Core Team 2012) through the glmmPQL() function in MASS (Venables and Ripley 2002), the lme() function in nlme (Pinheiro, Bates, DebRoy, Sarkar, and R Development Core Team 2012), and the lmer() function in…”
Modelling and predicting fungal distribution patterns using herbarium data (2008, methodology): “With binary presence/absence data, generalized linear models (GLM) with binomial distribution and logistic link (logistic regression) are commonly used (McCullagh & Nelder, 1989; Meyer et al., 2002; Venables & Ripley, 2002; Crawley, 2005).”
Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests (2008, background): “L ´ OPEZ- TARAZ´ON AND B. SCHR¨ODER packages car (Fox, 2006 ), leaps (Miller and Lumley, 2006), MASS (Venables and Ripley, 2002), random- Forests (Liaw and Wiener, 2002) and quantregForest (Meinshausen, 2007).”
Managing fire mosaics for small mammal conservation: a landscape perspective (2012, methodology): “GLMMs were fitted using the package mass version 7.3-14 (Venables & Ripley 2002).”
A Guide to QTL Mapping with R/qtl (2009, background): “We provide very little discussion of R itself, and refer the reader to Dalgaard (2002), for a gentle introduction to R, and Venables and Ripley (2002), for a more comprehensive discussion of R.”