Biostatistical Design and Analysis Using R

First book specifically aimed at biologists/ecologists thatexplains how the new freeware statistical package "R"can be applied to their problems.
This software package is becoming increasingly popular as it ispowerful and free.

First book specifically aimed at biologists/ecologists that explains how the new freeware statistical package "R" can be applied to their problems. This software package is becoming increasingly popular as it is powerful and free.

Auteur

Murray Logan is a lecturer and researcher in the School of Biological Sciences, Monash University, Melbourne, Australia. He teaches a range of zoological and ecological courses in addition to biostatistical and R courses to undergraduate and graduate students. He also provides research design and analysis advice to a range of university, government and private organizations.

Texte du rabat

R -- the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphing exploratory data analysis and graphical summaries regression (linear, multi and non-linear) simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures) * frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.

Contenu

1 Introduction to R 1 2 Datasets 3 Introductory statistical principles 4 Sampling and experimental design with R 5 Graphical data presentation 6 Simple hypothesis testing - one and two population tests 7 Introduction to Linear models 8 Correlation and simple linear regression 9 Multiple and curvilinear regression 10 Single factor classification (ANOVA) 11 Nested ANOVA 12 Factorial ANOVA 13 Unreplicated factorial designs - randomized block and simple repeated measures 14 Partly nested designs: split plot and complex repeated measures 15 Analysis of covariance (ANCOVA) 16 Simple Frequency Analysis 17 Generalized linear models (GLM)