The R Book: Second Edition

The R Book: Second Edition

Volumn , Issue , 2012, Pages 1-1051

  Crawley, Michael J ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84888646936     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781118448908     Document Type: Book

References (143)

1. Agresti, A. (1990) Categorical Data Analysis. New York: John Wiley & Sons, Inc.
2. Aitkin, M., Francis, B., Hinde, J. and Darnell, R. (2009) Statistical Modelling in R. Oxford: Clarendon Press.
3. Atkinson, A.C. (1985) Plots, Transformations, and Regression. Oxford: Clarendon Press.
4. Baddeley, A. and Turner, R. (2012) spatstat: Spatial Point Pattern analysis, model-fitting, simulation, tests. Maintainer: Adrian Baddeley, Adrian.Baddeley@csiro.au.
5. Bates, D., Maechler, M. and Bolker, B. (2012) lme4: Linear mixed-effects models using S4 classes. Maintainer: lme4-author@R-forge.wu-wien.ac.at.
6. Bishop, Y.M.M., Fienberg, S.J. and Holland, P.W. (1980) Discrete Multivariate Analysis: Theory and Practice. New York: John Wiley & Sons, Inc.
7. Bivand, R. et al. (2012) spdep: Spatial dependence: weighting schemes, statistics and models. Maintainer: Roger Bivand, Roger.Bivand@nhh.no.
8. Borenstein, M., Hedges, L.V., Higgins, J.P.T. and Rothstein, H.R. (2009) Introduction to Meta-analysis. Chichester: John Wiley & Sons, Ltd.
9. Box, G.E.P. and Cox, D.R. (1964) An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26,211-246.
10. Box, G.E.P. and Jenkins, G.M. (1976) Time Series Analysis: Forecasting and Control. Oakland, CA: Holden-Day.
11. Box, G.E.P., Hunter,W.G. and Hunter, J.S. (1978) Statistics for Experimenters: An Introduction to Design, Data Analysis and Model Building. New York: John Wiley & Sons, Inc.
12. Breiman, L., Friedman, L.H., Olshen, R.A. and Stone, C.J. (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International Group.
13. Canty, A. and Ripley, B. (2012). boot: Bootstrap R (S-Plus) Functions. R package version 1.3-4.
14. Caroll, R.J. and Ruppert, D. (1988) Transformation and Weighting in Regression. New York: Chapman & Hall.
15. Casella, G. and Berger, R.L. (1990) Statistical Inference. Pacific Grove, CA: Wadsworth and Brooks/Cole.
16. Chambers, J.M. and Hastie, T.J. (1992) Statistical Models in S. Pacific Grove, CA: Wadsworth and Brooks Cole.
17. Chambers, J.M., Cleveland, W.S., Kleiner, B. and Tukey, P.A. (1983) Graphical Methods for Data Analysis. Belmont, CA: Wadsworth.
18. Chatfield, C. (1989) The Analysis of Time Series: An Introduction. London: Chapman & Hall.
19. Clark, P.J. and Evans, F.C. (1954) Distance to nearest neighbour as a measure of spatial relationships in populations. Ecology, 35,445-453.
20. Cleveland, W.S. (1993) Visualizing Data. Summit, NJ: Hobart Press.
21. Cochran, W.G. and Cox, G.M. (1957) Experimental Designs. New York: John Wiley & Sons, Inc.
22. Collett, D. (1991) Modelling Binary Data. London: Chapman & Hall.
23. Conover, W.J. (1980) Practical Nonparametric Statistics. New York: John Wiley & Sons, Inc.
24. Conover, W.J., Johnson, M.E. and Johnson, M.M. (1981) A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics, 23,351-361.
25. Cook, R.D. and Weisberg, S. (1982) Residuals and Influence in Regression. New York: Chapman & Hall.
26. Cox, D.R. (1972) Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, Series B, 34,187-220.
27. Cox, D.R. and Hinkley, D.V. (1974) Theoretical Statistics. London: Chapman & Hall.
28. Cox, D.R. and Oakes, D. (1984) Analysis of Survival Data. London: Chapman & Hall.
29. Cox, D.R. and Snell, E.J. (1989) Analysis of Binary Data. London: Chapman & Hall.
30. Crawley, M.J. (2002) Statistical Computing: An Introduction to Data Analysis using S-PLUS. Chichester: John Wiley & Sons, Inc.
31. Crawley, M.J., Johnston, A.E., Silvertown, J., Dodd, M., de Mazancourt, C., Heard, M.S., Henman, D.F. and Edwards, G.R. (2005) Determinants of species richness in the Park Grass Experiment. American Naturalist, 165,348-362.
32. Cressie, N.A.C. (1991) Statistics for Spatial Data. New York: John Wiley & Sons, Inc.
33. Crowder, M.J. and Hand, D.J. (1990) Analysis of Repeated Measures. London: Chapman & Hall.
34. Dalgaard, P. (2002) Introductory Statistics with R. New York: Springer-Verlag.
35. Davidian, M. and Giltinan, D.M. (1995) Nonlinear Models for Repeated Measurement Data. London: Chapman & Hall.
36. Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge: Cambridge University Press.
37. Deevey, E.S. Jr (1947) Life tables for natural populations of animals. Quarterly Review of Biology, 22,283-314. Reprinted in W.E. Hazen (ed.) (1964) Readings in Population and Community Ecology. Philadelphia: Saunders.
38. DerSimonian, R. and Laird, N. (1986) Meta-analysis in clinical trials. Controlled Clinical Trials, 7,177-188.
39. Diggle, P.J. (1983) Statistical Analysis of Spatial Point Patterns. London: Academic Press.
40. Diggle, P.J., Liang, K.-Y. and Zeger, S.L. (1994) Analysis of Longitudinal Data. Oxford: Clarendon Press.
41. Dobson, A.J. (1990) An Introduction to Generalized Linear Models. London: Chapman & Hall.
42. Draper, N.R. and Smith, H. (1981) Applied Regression Analysis. New York: John Wiley & Sons, Inc.
43. Edwards, A.W.F. (1972) Likelihood. Cambridge: Cambridge University Press.
44. Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap. New York: Chapman & Hall.
45. Eisenhart, C. (1947) The assumptions underlying the analysis of variance. Biometrics, 3,1-21.
46. Everitt, B.S. (1994) Handbook of Statistical Analyses Using S-PLUS. Boca Raton, FL: Chapman & Hall/CRC.
47. Ferguson, T.S. (1996) A Course in Large Sample Theory. London: Chapman & Hall.
48. Fisher, L.D. and Van Belle, G. (1993) Biostatistics. New York, John Wiley & Sons, Inc.
49. Fisher, R.A. (1954) Design of Experiments. Edinburgh: Oliver and Boyd.
50. Fleming, T. and Harrington, D. (1991) Counting Processes and Survival Analysis. New York: John Wiley & Sons, Inc.
51. Fox, J. (2002) An R and S-Plus Companion to Applied Regression. Thousand Oaks, CA: Sage.
52. Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, 2nd edn. Thousand Oaks CA: Sage. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion.
53. Fox, J. and Weisberg, S. (2012) car: Companion to Applied Regression, with contributions by Douglas Bates, David Firth, Michael Friendly, Gregor Gorjanc, Spencer Graves, Richard Heiberger, Rafael Laboissiere, Georges Monette, Henric Nilsson, Derek Ogle, Brian Ripley and Achim Zeileis. Maintainer: John Fox, jfox@mcmaster.ca.
54. Gebhardt, A. (2012) akima: Interpolation of irregularly spaced data. Fortran code by H. Akima. R port by Albrecht Gebhardt. aspline function by Thomas Petzoldt. Eenhancements and corrections by Martin Maechler. Maintainer: Albrecht Gebhardt, albrecht.gebhardt@uni-klu.ac.at.
55. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004) Bayesian Data Analysis, 2nd edn. Boca Raton, FL: Chapman & Hall/CRC.
56. Gordon, A.E. (1981) Classification: Methods for the Exploratory Analysis of Multivariate Data. New York: Chapman & Hall.
57. Gosset, W.S. ('Student') (1908) The probable error of a mean. Biometrika, 6,1-25.
58. Grimmett, G.R. and Stirzaker, D.R. (1992) Probability and Random Processes. Oxford: Clarendon Press.
59. Hairston, N.G. (1989) Ecological Experiments: Purpose, Design and Execution. Cambridge: Cambridge University Press.
60. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J. and Stahel, W.A. (1986) Robust Statistics: The Approach Based on Influence Functions. New York: John Wiley & Sons, Inc.
61. Harman, H.H. (1976) Modern Factor Analysis. Chicago: University of Chicago Press.
62. Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman & Hall.
63. Hicks, C.R. (1973) Fundamental Concepts in the Design of Experiments. New York: Holt, Rinehart and Winston.
64. Hoaglin, D.C., Mosteller, F. and Tukey, J.W. (1983) Understanding Robust and Exploratory Data Analysis. New York: John Wiley & Sons, Inc.
65. Hochberg, Y. and Tamhane, A.C. (1987) Multiple Comparison Procedures. New York: John Wiley & Sons, Inc.
66. Hosmer, D.W. and Lemeshow, S. (2000) Applied Logistic Regression, 2nd edn. New York: John Wiley & Sons, Inc.
67. Hsu, J.C. (1996) Multiple Comparisons: Theory and Methods. London: Chapman & Hall.
68. Huber, P.J. (1981) Robust Statistics. New York: John Wiley & Sons, Inc.
69. Huitema, B.E. (1980) The Analysis of Covariance and Alternatives. New York: John Wiley & Sons, Inc.
70. Hurlbert, S.H. (1984) Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54,187-211.
71. Johnson, N.L. and Kotz, S. (1970) Continuous Univariate Distributions, Volume 2. New York: John Wiley & Sons, Inc.
72. Kalbfleisch, J. and Prentice, R.L. (1980) The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons, Inc.
73. Kaluzny, S.P., Vega, S.C., Cardoso, T.P. and Shelly, A.A. (1998) S+Spatial Stats. New York: Springer-Verlag.
74. Kauffman, J., Matsik, B. and Spencer, K. (2001) Beginning SQL Programming. Birmingham: Wrox Press.
75. Kendall, M.G. and Stewart, A. (1979) The Advanced Theory of Statistics. Oxford: Oxford University Press.
76. Keppel, G. (1991) Design and Analysis: A Researcher's Handbook. Upper Saddle River, NJ: Prentice Hall.
77. Khuri, A.I., Mathew, T. and Sinha, B.K. (1998) Statistical Tests for Mixed Linear Models. New York: JohnWiley & Sons, Inc.
78. Knuth, D.E. (1998) The TEXbook. Reading, MA: Addison Wesley.
79. Krause, A. and Olson, M. (2000) The Basics of S and S-PLUS. New York: Springer-Verlag.
80. Lee, P.M. (2012) Bayesian Statistics: An Introduction, 4th edn. Chichester: John Wiley & Sons, Ltd.
81. Lehmann, E.L. (1986) Testing Statistical Hypotheses. New York: John Wiley & Sons, Inc.
82. Levins, R. (1969) Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the Entomological Society of America, 15,237-240
83. Lunn, D., Spiegelhalter, D., Thomas, A. and Best, N. (2009) The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 28,3049-3082.
84. Mandelbrot, B.B. (1977) Fractals, Form, Chance and Dimension. San Francisco: Freeman.
85. Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979) Multivariate Statistics. London: Academic Press.
86. May, R.M. (1976) Simple mathematical models with very complicated dynamics. Nature, 261,459-467.
87. McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd edn. London: Chapman & Hall.
88. McCulloch, C.E. and Searle, S.R. (2001) Generalized, Linear and Mixed Models. New York: John Wiley & Sons, Inc.
89. Michelson, A.A. (1880) Experimental determination of the velocity of light made at the U.S. Naval Academy, Annapolis. Astronomical Papers, 1,109-145
90. Millard, S.P. and Krause, A. (2001) Using S-PLUS in the Pharmaceutical Industry. New York: Springer-Verlag.
91. Miller, R.G. (1981) Survival Analysis. New York: John Wiley & Sons, Inc.
92. Miller, R.G. (1997) Beyond ANOVA: Basics of Applied Statistics. London: Chapman & Hall.
93. Mosteller, F. and Tukey, J.W. (1977) Data Analysis and Regression. Reading, MA: Addison-Wesley.
94. Murrell, P. (2006) R Graphics. Boca Raton, FL: Chapman & Hall.
95. Nelder, J.A. and Wedderburn, R.W.M. (1972) Generalized linear models. Journal of the Royal Statistical Society, Series A, 135,370-384.
96. Neter, J., Wasserman, W. and Kutner, M.H. (1985) Applied Linear Regression Models. Homewood, IL: Irwin.
97. Neter, J., Kutner, M., Nachstheim, C. andWasserman,W. (1996) Applied Linear Statistical Models. New York: McGraw-Hill.
98. Neuwirth, E. (2012) RColorBrewer: ColorBrewer palettes. Maintainer: Erich Neuwirth, erich.neuwirth@univie.ac.at.
99. OED (2004) Oxford English Dictionary. Oxford: Oxford University Press.
100. O'Hagen, A. (1988) Probability: Methods and Measurement. London: Chapman & Hall.
101. Perry, J.N., Rothery, P., Clark, S.J., Heard, M.S. and Hawes, C. (2003) Design, analysis and statistical power of the farm-scale evaluations of genetically modified herbicide-tolerant crops. Journal of Applied Ecology, 40,17-31.
102. Pinheiro, J. and Bates, D. (2012) nlme: Linear and nonlinear mixed effects models, with Saikat DebRoy, Deepayan Sarkar and the R Core team. Maintainer: R-core, R-core@R-project.org.
103. Pinheiro, J.C. and Bates, D.M. (2000) Mixed-Effects Models in S and S-PLUS. New York: Springer-Verlag.
104. Platt, J.R. (1964) Strong inference. Science, 146,347-353.
105. Plummer, M. (2012) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. http://mcmcjags. sourceforge.net/.
106. Priestley, M.B. (1981) Spectral Analysis and Time Series. London: Academic Press.
107. Rao, P.S.R.S. (1997) Variance Components Estimation: Mixed Models, Methodologies and Applications. London: Chapman & Hall.
108. Riordan, J. (1978) An Introduction to Combinatorial Analysis. Princeton, NJ: Princeton University Press.
109. Ripley, B. (2012) tree: Classification and regression trees. Maintainer: Brian Ripley, ripley@stats.ox.ac.uk.
110. Ripley, B. and Lapsley, M. (2012) RODBC: ODBC database access. Maintainer: Brian Ripley, ripley@stats.ox.ac.uk.
111. Ripley, B.D. (1996) Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
112. Robert, C.P. and Casella, G. (1999) Monte Carlo Statistical Methods. New York: Springer-Verlag.
113. Rosner, B. (1990) Fundamentals of Biostatistics. Boston: PWS-Kent.
114. Ross, G.J.S. (1990) Nonlinear Estimation. New York: Springer-Verlag.
115. Santer, T.J. and Duffy, D.E. (1990) The Statistical Analysis of Discrete Data. New York: Springer-Verlag.
116. Schwarzer, G. (2012) meta: Meta-analysis with R. Maintainer: Guido Schwarzer, sc@imbi.uni-freiburg.de.
117. Scott, D.W. (1992) Multivariate Density Estimation. Theory, Practice and Visualization. New York: John Wiley & Sons, Inc.
118. Searle, S.R., Casella, G. and McCulloch, C.E. (1992) Variance Components. New York: John Wiley & Sons, Inc.
119. Shao, J. and Tu, D. (1995) The Jackknife and Bootstrap. New York: Springer-Verlag.
120. Shumway, R.H. (1988) Applied Statistical Time Series Analysis. Englewood Cliffs, NJ: Prentice Hall.
121. Silverman, B.W. (1986) Density Estimation. London: Chapman & Hall.
122. Silvey, S.D. (1970) Statistical Inference. London, Chapman & Hall.
123. Snedecor, G.W. and Cochran, W.G. (1980) Statistical Methods. Ames: Iowa State University Press.
124. Soetaert, K., Petzoldt, T. and Setzer, R.W. (20102) deSolve: General solvers for initial value problems of ordinary differential equations (ODE), partial differential equations (PDE), differential algebraic equations (DAE), and delay differential equations (DDE). Maintainer: Thomas Petzoldt, thomas.petzoldt@tu-dresden.de.
125. Sokal, R.R. and Rohlf, F.J. (1995) Biometry: The Principles and Practice of Statistics in Biological Research. San Francisco, W.H. Freeman.
126. Spiegelhalter, D. Thomas, A., Best, N. and Lunn, D. (2003) WinBUGS User Manual. Version 1.4. http://www.mrcbsu. cam.ac.uk/bugs/winbugs/manual14.pdf.
127. Sprent, P. (1989) Applied Nonparametric Statistical Methods. London: Chapman & Hall.
128. Taylor, L.R. (1961) Aggregation, variance and the mean. Nature, 189,732-735.
129. Therneau, T.M. and Atkinson, B. (2012) rpart: Recursive partitioning. Maintainer: Brian Ripley, ripley@stats.ox.ac.uk.
130. Upton, G. and Fingleton, B. (1985) Spatial Data Analysis by Example. Chichester: John Wiley & Sons, Ltd.
131. Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with S-PLUS, 4th edn. New York: Springer-Verlag.
132. Venables,W.N., Smith, D.M. and the R Development Core Team (2012) An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics. Version 2.15.0. ISBN 3-900051-12-7. http://cran.r-project.org/.
133. Wedderburn, R.W.M. (1974) Quasi-likelihood functions, generalized linear models and the Gauss-Newton method. Biometrika, 61,439-447.
134. Weisberg, S. (1985) Applied Linear Regression. New York: John Wiley & Sons, Inc.
135. Wetherill, G.B., Duncombe, P., Kenward, M., Kollerstrom, J., Paul, S.R. and Vowden, B.J. (1986) Regression Analysis with Applications. London: Chapman & Hall.
136. Winer, B.J., Brown, D.R. and Michels, K.M. (1991) Statistical Principles in Experimental Design. New York: McGraw-Hill.
137. Wood, S.N. (2000) Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistical Society, Series B, 62,413-428.
138. Wood, S.N. (2003) Thin plate regression splines. Journal of the Royal Statistical Society, Series B, 65,95-114.
139. Wood, S.N. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association, 99,673-686.
140. Wood, S.N. (2006) Generalized Additive Models: An Introduction with R. Boca Raton, FL: CRC Press.
141. Wood, S. (2012) mgcv: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation. Maintainer: Simon Wood, simon.wood@r-project.org.
142. Yu-Sung Su and Masanao Yajima (2012) R2jags: A package for running jags from R. Maintainer: Yu-Sung Su, suyusung@tsinghua.edu.cn.
143. Zar, J.H. (1999) Biostatistical Analysis. Englewood Cliffs, NJ: Prentice Hall.