Biometry for Forestry and Environmental Data with Examples in R focuses on statistical methods that are widely applicable in forestry and environmental sciences, but it also includes material that is of wider interest.
· Describes the theory and applications of selected statistical methods and illustrates their use and basic concepts through examples with forestry and environmental data in R.
· Rigorous but easily accessible presentation of the linear, nonlinear, generalized linear and multivariate models, and their mixed-effects counterparts. Chapters on tree size, tree taper, measurement errors, and forest experiments are also included.
· Necessary statistical theory about random variables, estimation and prediction is included. The wide applicability of the linear prediction theory is emphasized.
· The hands-on examples with implementations using R make it easier for non-statisticians to understand the concepts and apply the methods with their own data. Lot of additional material is available at www.biombook.org.
The book is aimed at students and researchers in forestry and environmental studies, but it will also be of interest to statisticians and researchers in other fields as well.
Table of Contents
1. Introduction 2. Random Variables 3. Statistical Modeling, Estimation and Prediction 4. Linear Model 5. Linear Mixed-effects Models 6. More about Linear Mixed-eff□ects Models 7. Nonlinear (Mixed-eff□ects) Models 8. Generalized Linear (Mixed-E□ffects) Models 9. Multivariate (Mixed-Eff□ects) Models 10. Additional topics on regression 11. Modeling Tree Size 12. Taper Curves 13. Measurement Errors 14. Forest and Environmental Experiments
Lauri Mehtätalo (PhD in 2004 in Forest Sciences, University of Joensuu) is professor in applied statistics at the University of Eastern Finland and adjunct professor in forest biometrics at the University of Helsinki.
Juha Lappi (PhD in 1986 in Statistics, University of Helsinki) did his research career as a senior scientist at Finnish Forest Research Institute. His thesis and other publications thereafter were one of the very first applications of mixed-effects models in forest sciences.