Biostatistical Design and Analysis Using R: A Practical GuideWiley, 10 de mai. de 2010 - 546 páginas 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:
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. |
Conteúdo
Data sets | 48 |
Introductory statistical principles | 65 |
Sampling and experimental design with R | 76 |
Direitos autorais | |
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Termos e frases comuns
alternative Analysis of Variance appropriate argument assumptions axis B₁ BIOFILM block factor boxplot calculated codes coefficients Conclusions contrasts correlation covariate data frame data.aov dataset defined degrees of freedom density designs Deviance Df Sum Sq example F value Pr F-ratio fitted model fixed factor formula frequencies HABITAT homogeneity of variance hypothesis tests Import section 2.3 interaction Intercept library(biology linear mixed effects linear model linear regression log-linear models MACNALLY Mean Sq F model fit MSResid mtext multiple multiple comparisons nested normally distributed null hypothesis observations odds ratios p-value package parameter estimates predictor variables quadratic Quinn and Keough random factor replicates represent Residuals response variable S-PLUS scale scatterplot Signif specific Sq F value Sq Mean Sq statistical Step 3 Key subset Sum Sq Mean sums of squares t-test TEMPERATURE transformed treatment trends unbalanced value Pr F vector