An R and S–PLUS Companion to Applied Regression

By Yvonne Freeh. 2004

628 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8a69495fb8a33e500d0007239683fd48a134a4e9

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Citations in the corpus

TEACHER'S CORNER: Structural Equation Modeling With the sem Package in R (2006, methodology): “Beyond that, R comes with a complete set of manuals, including a good introductory manual; other documentation is available on the R Web site and in a number of books (e.g., Fox, 2002; Venables & Ripley, 2002).”
influence.ME: Tools for Detecting Influential Data in Mixed Effects Models (2012, background): “DFBETAS is a standardized measure that indicates the level of influence observations have on single parameter estimates (Fox, 2002).”
Dimensions and their poles: A metric and topological approach to opposites (2011, methodology): “In Figure 5 we compare two histograms (based on an example of Fox, 2002, §3.1.1) in which the boundaries between bins in the histogram on the left were chosen to be ‘‘pretty’’ numbers, and the boundaries between bins in the histogram on the right were chosen to maximise informativeness.”
PSEUDO-R 2 IN LOGISTIC REGRESSION MODEL (2006, methodology): “Example We now turn to an example of logistic regression from Fox’s (2001) text on fitting generalized linear models.”
Mixed Effects Models and Extensions in Ecology with R (2009, background): “Other useful, non-mathematical textbooks containing regression chapters include Chambers and Hastie (1992), Fox (2002), Maindonald and Braun (2003), Venables and Ripley (2002), Dalgaard (2002), Faraway (2005), Verzani (2005) and Crawley (2002, 2005).”
Beta Regression in R (2010, methodology): “No anova() method is provided, but the general coeftest() and waldtest() from lmtest (Zeileis and Hothorn 2002), and linear.hypothesis() from car ( Fox 2002 ) can be used for Wald tests while lrtest() from lmtest provides for likelihood-ratio tests of nested models.”
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning (2020, neutral): “where ψ(·) is the penalty function associated with the kernel κ(·); see [33, 17].”
Seasonal variation in diet composition and similarity of sympatric red deer Cervus elaphus and roe deer Capreolus capreolus (2008, methodology): “ANOVA computations were performed using the 'car' package for R (Fox 2002).”
Social Status and Newspaper Readership1 (2007, background): “In figures 2 and 3, where we restrict our attention to our three ordered categories of newspaper, we graph the probabilities, under model 4, of their readership by individuals’ status score within each of our six educational categories (cf. Fox 2002).”
Carnivore conservation in practice: replicated management actions on a large spatial scale (2013, methodology): “The models were fitted in R with the sem library (Fox 2002).”
Have jellyfish in the Irish Sea benefited from climate change and overfishing? (2011, methodology): “…Testing for Structural Change in Linear Regression Models (‘strucchange’; Zeileis et al., 2002), Global validation of linear models assumptions (‘gvlma’; Pena & Slate, 2006), Testing Linear Regression Models (‘lmtest’; Zeileis & Hothorn, 2002) and Companion to Applied Regression (‘car’; Fox, 2002).”
Econometric Computing with HC and HAC Covariance Matrix Estimators (2004, methodology): “R has no built-in support for HC and HAC estimation and at the time we started writing sandwich there was only one package that implements HC (but not HAC) estimators (the car package Fox 2002) but which does not allow for as much ﬂexibility as the tools presented here. sandwich provides the functio”
Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications (2010, neutral): “income We use the data from Fox (2002) to study the relationship between the prestige and the average income of Canadian occupations4.”
1. Overcoming the Reference Category Problem in the Presentation of Statistical Models (2003, background): “Example. Fox (2002, Ch. 5) describes the logistic regression analysis of a Canadian survey of 263 married women aged 21‐30.”
Discussion of "Analysis of variance--why it is more important than ever" by A. Gelman (2005, background): “[E.g., Fox (2002) writes, “It is, from one point of view, unnecessary to consider analysis of variance models separately from the general class of linear models.”]”
Effect size, confidence interval and statistical significance: a practical guide for biologists (2007, background): “Two major biases can occur for effect statistics, especially when sample size is small. One is an inherent bias for a particular statistic and the other is a bias caused by sampling errors. The former is of little concern for the correlation coefficient (Hunter & Schmidt, 2004). The d statistics sho”