Advertisement

Using the VGAM Package

  • Thomas W. Yee
Part of the Springer Series in Statistics book series (SSS)

Abstract

This chapter looks at the VGAM package in R from a user’s point of view. We look at its general usage and naming conventions, some recommendations, common trouble shooting and tricks, S4 versus S3 nuances, and some details are given on some selected methods functions, e.g., fitted(), summary(). Many of the topics will be revision for the seasoned R user. This chapter assumes prior familiarity with R. Note that the software details presented here are subject to change.

Keywords

Model Matrix Data Frame Family Function Proportional Odds Model Constraint Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Aitkin, M., B. Francis, J. Hinde, and R. Darnell 2009. Statistical Modelling in R. Oxford: Oxford University Press.zbMATHGoogle Scholar
  2. Altman, M., J. Gill, and M. P. McDonald 2004. Numerical Issues in Statistical Computing for the Social Scientist. Hoboken: Wiley-Interscience.zbMATHGoogle Scholar
  3. Chambers, J. M. and T. J. Hastie (Eds.) 1991. Statistical Models in S. Pacific Grove: Wadsworth/Brooks Cole.Google Scholar
  4. Crawley, M. J. 2005. Statistics: An Introduction using R. Chichester: John Wiley & Sons.CrossRefGoogle Scholar
  5. Dalgaard, P. 2008. Introductory Statistics with R (Second ed.). New York: Springer.zbMATHCrossRefGoogle Scholar
  6. Davison, A. C. 2003. Statistical Models. Cambridge: Cambridge University Press.zbMATHCrossRefGoogle Scholar
  7. de Vries, A. and J. Meys 2012. R for Dummies. Chichester: Wiley.Google Scholar
  8. Faraway, J. J. 2006. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Boca Raton: Chapman and Hall/CRC.Google Scholar
  9. Faraway, J. J. 2015. Linear Models with R (Second ed.). Boca Raton: Chapman & Hall/CRC.zbMATHGoogle Scholar
  10. Fox, J. and S. Weisberg 2011. An R Companion to Applied Regression (Second ed.). Thousand Oaks: Sage Publications.Google Scholar
  11. Freedman, D. A. and J. S. Sekhon 2010. Endogeneity in probit response models. Political Analysis 18(2):138–150.CrossRefGoogle Scholar
  12. Freund, J. E. 1961. A bivariate extension of the exponential distribution. Journal of the American Statistical Association 56(296):971–977.zbMATHMathSciNetCrossRefGoogle Scholar
  13. Jones, O., R. Maillardet, and A. Robinson 2014. Introduction to Scientific Programming and Simulation Using R (Second ed.). Boca Raton, FL, USA: Chapman and Hall/CRC.zbMATHGoogle Scholar
  14. Maindonald, J. H. and W. J. Braun 2010. Data Analysis and Graphics Using R: An Example-Based Approach (Third ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  15. McCullagh, P. and J. A. Nelder 1989. Generalized Linear Models (Second ed.). London: Chapman & Hall.zbMATHCrossRefGoogle Scholar
  16. Spector, P. 2008. Data Manipulation with R. New York, USA: Springer Verlag.zbMATHGoogle Scholar
  17. Venables, W. N. and B. D. Ripley 2002. Modern Applied Statistics With S (4th ed.). New York, USA: Springer-Verlag.zbMATHCrossRefGoogle Scholar
  18. Zuur, A. F., E. N. Ieno, and E. H. Meesters 2009. A Beginner’s Guide to R. New York, USA: Springer.zbMATHCrossRefGoogle Scholar
  19. Zuur, A. F., A. A. Saveliev, and E. N. Ieno 2012. Zero Inflated Models and Generalized Linear Mixed Models with R. Newburgh, UK: Highland Statistics Ltd.Google Scholar

Copyright information

© Thomas Yee 2015

Authors and Affiliations

  • Thomas W. Yee
    • 1
  1. 1.Department of StatisticsUniversity of AucklandAucklandNew Zealand

Personalised recommendations