J. Fox

Publications84
h-index31
Citations18,249
Highly Influential Citations1,902
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An R Companion to Applied Regression
TLDR
This tutorial jumps right in to the power of R without dragging you through the basic concepts of the programming language.
GETTING STARTED WITH THE R COMMANDER: A BASIC-STATISTICS GRAPHICAL USER INTERFACE TO R
  • J. Fox
  • Computer Science
  • 19 August 2005
TLDR
The purpose of this paper is to introduce and describe the basic use of the R Commander GUI and the manner in which it can be extended.
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Robust Regression in R An Appendix to An R Companion to Applied Regression, Second Edition
TLDR
This appendix describes how to use several alternative robust-regression estimators, which attempt to down-weight or ignore unusual data: M -estimators; bounded-inuence estimators; MM -estIMators; and quantile- Regression estimator, including L1 regression.
OpenMx: An Open Source Extended Structural Equation Modeling Framework
TLDR
The OpenMx data structures are introduced—these novel structures define the user interface framework and provide new opportunities for model specification and a discussion of directions for future development.
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Applied Regression Analysis, Linear Models, and Related Methods
  • J. Fox
  • Mathematics
  • 5 February 1997
PART ONE: PRELIMINARIES Statistics and Social Science What Is Regression Analysis? Examining Data Transforming Data PART TWO: LINEAR MODELS AND LEAST SQUARES Linear Least-Squares Regression…
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Effect Displays in R for Generalised Linear Models
  • J. Fox
  • Computer Science
  • 22 July 2003
This paper describes the implementation in R of a method for tabular or graphical display of terms in a complex generalised linear model. By complex, I mean a model that contains terms related by…
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Applied regression analysis and generalized linear models, 2nd ed.
TEACHER'S CORNER: Structural Equation Modeling With the sem Package in R
  • J. Fox
  • Computer Science
  • 28 June 2006
TLDR
The sem package provides basic structural equation modeling facilities in R, including the ability to fit structural equations in observed variable models by two-stage least squares, and to fit latentVariable models by full information maximum likelihood assuming multinormality.
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Bootstrapping Regression Models
Bootstrapping is a general approach to statistical inference based on building a sampling distribution for a statistic by resampling from the data at hand. The term ‘bootstrapping,’ due to Efron…
Companion to Applied Regression
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