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E-raamat: Biometry for Forestry and Environmental Data: With Examples in R

(University of Eastern Finland, Joensuu), (The Finnish Forest Research Institute, Suonenjoki, Finland)
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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.

Features:

·         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.

Arvustused

'I loved reading this book! I have been a big fan of Lauri Mehtätalos teaching materials since I was in graduate school, when I first came across his online R code examples. Maybe I was biased when I started reading, but the book fully lived up to my expectations.[ ...] This book fills a gap in the existing literature of applied biometrics and modelling books in the field of forestry. It contains excellent R code examples and practical comments throughout the text. Yet, the book uses rigorous notation to provide solid theory knowledge to applied readers. The book appears very useful for teaching purposes and I anticipate that it will soon be considered a standard text for graduate students in forest biometrics and modelling around the world.'

- Bianca N.I. Eskelson, Biometrics, Vol 77, No.4 2021

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.
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