Introduction and preliminaries
Simple manipulations; numbers and vectors
Objects, their modes and attributes
Generalized transpose of an array
Grouping, loops and conditional execution
Nonlinear least squares and maximum likelihood models
An Introduction to R
References
D. M. Bates and D. G. Watts (1988), Nonlinear Regression Analysis and Its Applications. John Wiley & Sons, New York.
Richard A. Becker, John M. Chambers and Allan R. Wilks (1988), The New S Language. Chapman & Hall, New York. This book is often called the “Blue Book”.
John M. Chambers and Trevor J. Hastie eds. (1992), Statistical Models in S. Chapman & Hall, New York. This is also called the “White Book”.
John M. Chambers (1998) Programming with Data. Springer, New York. This is also called the “Green Book”.
A. C. Davison and D. V. Hinkley (1997), Bootstrap Methods and Their Applications, Cambridge University Press.
Annette J. Dobson (1990), An Introduction to Generalized Linear Models, Chapman and Hall, London.
Peter McCullagh and John A. Nelder (1989), Generalized Linear Models. Second edition, Chapman and Hall, London.
John A. Rice (1995), Mathematical Statistics and Data Analysis. Second edition. Duxbury Press, Belmont, CA.
S. D. Silvey (1970), Statistical Inference. Penguin, London.
An Introduction to R
This introduction to R is derived from an original set of notes describing the S and S-Plus environments written by Bill Venables and David M. Smith (Insightful Corporation). We have made a number of small changes to reflect differences between the R and S programs, and expanded some of the material.