Ecological Models and Data in R (eBook)

Benjamin M. Bolker (Autor)

eBook Download: EPUB

2008
408 Seiten
Princeton University Press (Verlag)
978-1-4008-4090-8 (ISBN)

Infos

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling) Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples

Autor

Benjamin M. Bolker is a theoretical ecologist in the departments of Mathematics & Statistics and Biology at McMaster University.

Produkt-Details

• Verlagsort: Princeton
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber ► Freizeit / Hobby ► Sammeln / Sammlerkataloge
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
• Themenwelt: Naturwissenschaften ► Geowissenschaften
• Themenwelt: Technik
• Schlagworte: Accuracy and precision • algorithm • Analysis of covariance • Analysis of Variance • Approximation • Bayesian • Bayesian inference • Bayesian Statistics • Beta-binomial distribution • Big O notation • Binomial Distribution • Calculation • Cartesian coordinate system • chain rule • combination • Confidence interval • Correlation and dependence • Covariance matrix • Covariate • Credible interval • curve fitting • data set • Degrees of freedom (statistics) • Delta method • Derivative • Ecology • Empirical distribution function • Equation • estimation • Estimator • expected value • exponential distribution • exponential function • frequentist inference • functional response • Function (mathematics) • Gamma distribution • histogram • Hyperbolic function • inference • Likelihood Function • Likelihood-ratio test • Linear Function • linear regression • logarithm • Logarithmic scale • logistic function • Logit • Markov Chain Monte Carlo • Maxima and minima • maximum likelihood estimation • Michaelis–Menten kinetics • Model Selection • Multilevel Model • Multivariate normal distribution • negative binomial distribution • Nelder–Mead method • Normal distribution • null hypothesis • Observational error • Overdispersion • Parameter • Parameter (computer programming) • parametrization • Point Estimation • Poisson distribution • posterior probability • power law • predation • Prediction • Prior probability • Probability • Probability Distribution • Proportionality (mathematics) • p-value • Quantile • Quantity • Random Variable • Realization (probability) • Scale Parameter • shape parameter • Special case • square root • standard deviation • standard error • Statistic • Statistical hypothesis testing • statistical power • statistical significance • statistician • Statistics • stochastic • Subset • summary statistics • Summation • Taylor series • Trade-off • Variable (mathematics) • Variance • WinBUGS
• ISBN-10: 1-4008-4090-2 / 1400840902
• ISBN-13: 978-1-4008-4090-8 / 9781400840908