Generalized Additive Models: An Introduction with RNow in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also e |
Contents
CHAPTER 1 Linear Models | 1 |
CHAPTER 2 Generalized Linear Models | 59 |
CHAPTER 3 Introducing GAMs | 121 |
CHAPTER 4 Some GAM Theory | 145 |
mgcv | 221 |
CHAPTER 6 Mixed Models and GAMMs | 277 |
APPENDIX A Some Matrix Algebra | 331 |
APPENDIX B Solutions to Exercises | 341 |
| 379 | |
Back cover | 385 |
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Common terms and phrases
additive model anova approach approximation basis dimension basis functions binomial coefficients columns component confidence intervals consider constraints convergence covariance matrix cross validation cubic spline curve data frame degrees of freedom derivatives deviance diagonal distribution effective degrees evaluate example exponential family factor variable Figure fitted model fitted values fixed effects GCV score given Hence Intercept knots least squares linear mixed model linear model linear predictor Link function log-likelihood mean method minimize model checking model fit model matrix normal null obtained orthogonal orthogonal matrix P-IRLS p-value parameter estimates parameterization penalized regression spline Poisson predict problem QR decomposition quasi-likelihood random effects random variables residual plots response data response variable simple simulated smooth function smooth terms smoother smoothing parameters statistic sum of squares tensor product thin plate spline tree variance vector wiggliness Xẞ zero σ²