Biostatistics with R : an introductory guide for field biologists / Jan Lepš, University of South Bohemia, Czech Republic, Petr Šmilauer, University of South Bohemia, Czech Republic.

Format:

Book

Author/Creator:

Lepš, Jan, 1953- author.

Šmilauer, Petr, 1967- author.

Contributor:

Clarence J. Marshall Memorial Library Fund.

Language:

English

Subjects (All):

Biometry.

R (Computer program language).

Epidemiologic Methods.

Medical Subjects:

Biometry.

Epidemiologic Methods.

Physical Description:

xvi, 365 pages : illustrations ; 25 cm

Place of Publication:

Cambridge, UK ; New York, NY : Cambridge University Press, 2020.

Summary:

"Biostatistics with R provides a straightforward introduction on how to analyse data from the wide field of biological research, including nature protection and global change monitoring. The book is centred around traditional statistical approaches, focusing on those prevailing in research publications. The authors cover t tests, ANOVA and regression models, but also the advanced methods of generalised linear models and classification and regression trees. Chapters usually start with several useful case examples, describing the structure of typical datasets and proposing research-related questions. All chapters are supplemented by example datasets and thoroughly explained, step-by-step R code demonstrating the analytical procedures and interpretation of results. The authors also provide examples of how to appropriately describe statistical procedures and results of analyses in research papers. This accessible textbook will serve a broad audience of interested readers, from students, researchers or professionals looking to improve their everyday statistical practice, to lecturers of introductory undergraduate courses"-- Provided by publisher.

Contents:

Machine generated contents note: 1. Basic Statistical Terms, Sample Statistics

1.1. Cases, Variables and Data Types

1.2. Population and Random Sample

1.3. Sample Statistics

1.4. Precision of Mean Estimate, Standard Error of Mean

1.5. Graphical Summary of Individual Variables

1.6. Random Variables, Distribution, Distribution Function, Density Distribution

1.7. Example Data

1.8. How to Proceed in R

1.9. Reporting Analyses

1.10. Recommended Reading

2. Testing Hypotheses, Goodness-of-Fit Test

2.1. Principles of Hypothesis Testing

2.2. Possible Errors in Statistical Tests of Hypotheses

2.3. Null Models with Parameters Estimated from the Data: Testing Hardy-Weinberg Equilibrium

2.4. Sample Size

2.5. Critical Values and Significance Level

2.6. Too Good to Be True

2.7. Bayesian Statistics: What is It?

2.8. The Dark Side of Significance Testing

2.9. Example Data

2.10. How to Proceed in R

2.11. Reporting Analyses

2.12. Recommended Reading

3. Contingency Tables

3.1. Two-Way Contingency Tables

3.2. Measures of Association Strength

3.3. Multidimensional Contingency Tables

3.4. Statistical and Causal Relationship

3.5. Visualising Contingency Tables

3.6. Example Data

3.7. How to Proceed in R

3.8. Reporting Analyses

3.9. Recommended Reading

4. Normal Distribution

4.1. Main Properties of a Normal Distribution

4.2. Skewness and Kurtosis

4.3. Standardised Normal Distribution

4.4. Verifying the Normality of a Data Distribution

4.5. Example Data

4.6. How to Proceed in R

4.7. Reporting Analyses

4.8. Recommended Reading

5. Student's t Distribution

5.1. Use Case Examples

5.2. T Distribution and its Relation to the Normal Distribution

5.3. Single Sample Test and Paired t Test

5.4. One-Sided Tests

5.5. Confidence Interval of the Mean

5.6. Test Assumptions

5.7. Reporting Data Variability and Mean Estimate Precision

5.8. How Large Should a Sample Size Be?

5.9. Example Data

5.10. How to Proceed in R

5.11. Reporting Analyses

5.12. Recommended Reading

6. Comparing Two Samples

6.1. Use Case Examples

6.2. Testing for Differences in Variance

6.3. Comparing Means

6.4. Example Data

6.5. How to Proceed in R

6.6. Reporting Analyses

6.7. Recommended Reading

7. Non-parametric Methods for Two Samples

7.1. Mann

Whitney Test

7.2. Wilcoxon Test for Paired Observations

7.3. Using Rank-Based Tests

7.4. Permutation Tests

7.5. Example Data

7.6. How to Proceed in R

7.7. Reporting Analyses

7.8. Recommended Reading

8. One-Way Analysis of Variance (ANOVA) and Kruskal-Wallis Test

8.1. Use Case Examples

8.2. ANOVA: A Method for Comparing More Than Two Means

8.3. Test Assumptions

8.4. Sum of Squares Decomposition and the F Statistic

8.5. ANOVA for Two Groups and the Two-Sample t Test

8.6. Fixed and Random Effects

8.7. F Test Power

8.8. Violating ANOVA Assumptions

8.9. Multiple Comparisons

8.10. Non-parametric ANOVA: Kruskal

Wallis Test

8.11. Example Data

8.12. How to Proceed in R

8.13. Reporting Analyses

8.14. Recommended Reading

9. Two-Way Analysis of Variance

9.1. Use Case Examples

9.2. Factorial Design

9.3. Sum of Squares Decomposition and Test Statistics

9.4. Two-Way ANOVA with and without Interactions

9.5. Two-Way ANOVA with No Replicates

9.6. Experimental Design

9.7. Multiple Comparisons

9.8. Non-parametric Methods

9.9. Example Data

9.10. How to Proceed in R

9.11. Reporting Analyses

9.12. Recommended Reading

10. Data Transformations for Analysis of Variance

10.1. Assumptions of ANOVA and their Possible Violations

10.2. Log-transformation

10.3. Arcsine Transformation

10.4. Square-Root and Box

Cox Transformation

10.5. Concluding Remarks

10.6. Example Data

10.7. How to Proceed in R

10.8. Reporting Analyses

10.9. Recommended Reading

11. Hierarchical ANOVA, Split-Plot ANOVA, Repeated Measurements

11.1. Hierarchical ANOVA

11.2. Split-Plot ANOVA

11.3. ANOVA for Repeated Measurements

11.4. Example Data

11.5. How to Proceed in R

11.6. Reporting Analyses

11.7. Recommended Reading

12. Simple Linear Regression: Dependency Between Two Quantitative Variables

12.1. Use Case Examples

12.2. Regression and Correlation

12.3. Simple Linear Regression

12.4. Testing Hypotheses

12.5. Confidence and Prediction Intervals

12.6. Regression Diagnostics and Transforming Data in Regression

12.7. Regression Through the Origin

12.8. Predictor with Random Variation

12.9. Linear Calibration

12.10. Example Data

12.11. How to Proceed in R

12.12. Reporting Analyses

12.13. Recommended Reading

13. Correlation: Relationship Between Two Quantitative Variables

13.1. Use Case Examples

13.2. Correlation as a Dependency Statistic for Two Variables on an Equal Footing

13.3. Test Power

13.4. Non-parametric Methods

13.5. Interpreting Correlations

13.6. Statistical Dependency and Causality

13.7. Example Data

13.8. How to Proceed in R

13.9. Reporting Analyses

13.10. Recommended Reading

14. Multiple Regression and General Linear Models

14.1. Use Case Examples

14.2. Dependency of a Response Variable on Multiple Predictors

14.3. Partial Correlation

14.4. General Linear Models and Analysis of Covariance

14.5. Example Data

14.6. How to Proceed in R

14.7. Reporting Analyses

14.8. Recommended Reading

15. Generalised Linear Models

15.1. Use Case Examples

15.2. Properties of Generalised Linear Models

15.3. Analysis of Deviance

15.4. Overdispersion

15.5. Log-linear Models

15.6. Predictor Selection

15.7. Example Data

15.8. How to Proceed in R

15.9. Reporting Analyses

15.10. Recommended Reading

16. Regression Models for Non-linear Relationships

16.1. Use Case Examples

16.2. Introduction

16.3. Polynomial Regression

16.4. Non-linear Regression

16.5. Example Data

16.6. How to Proceed in R

16.7. Reporting Analyses

16.8. Recommended Reading

17. Structural Equation Models

17.1. Use Case Examples

17.2. SEMs and Path Analysis

17.3. Example Data

17.4. How to Proceed in R

17.5. Reporting Analyses

17.6. Recommended Reading

18. Discrete Distributions and Spatial Point Patterns

18.1. Use Case Examples

18.2. Poisson Distribution

18.3. Comparing the Variance with the Mean to Measure Spatial Distribution

18.4. Spatial Pattern Analyses Based on the K-function

18.5. Binomial Distribution

18.6. Example Data

18.7. How to Proceed in R

18.8. Reporting Analyses

18.9. Recommended Reading

19. Survival Analysis

19.1. Use Case Examples

19.2. Survival Function and Hazard Rate

19.3. Differences in Survival Among Groups

19.4. Cox Proportional Hazard Model

19.5. Example Data

19.6. How to Proceed in R

19.7. Reporting Analyses

19.8. Recommended Reading

20. Classification and Regression Trees

20.1. Use Case Examples

20.2. Introducing CART

20.3. Pruning the Tree and Crossvalidation

20.4. Competing and Surrogate Predictors

20.5. Example Data

20.6. How to Proceed in R

20.7. Reporting Analyses

20.8. Recommended Reading

21. Classification

21.1. Use Case Examples

21.2. Aims and Properties of Classification

21.3. Input Data

21.4. Similarity and Distance

21.5. Clustering Algorithms

21.6. Displaying Results

21.7. Divisive Methods

21.8. Example Data

21.9. How to Proceed in R

21.10. Other Software

21.11. Reporting Analyses

21.12. Recommended Reading

22. Ordination

22.1. Use Case Examples

22.2. Unconstrained Ordination Methods

22.3. Constrained Ordination

Methods

22.4. Discriminant Analysis

22.5. Example Data

22.6. How to Proceed in R

22.7. Alternative Software

22.8. Reporting Analyses

22.9. Recommended Reading

Appendix A First Steps with R Software

A.1. Starting and Ending R, Command Line, Organising Data

A.2. Managing Your Data

A.3. Data Types in R

A.4. Importing Data into R

A.5. Simple Graphics

A.6. Frameworks for R

A.7. Other Introductions to Work with R.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Clarence J. Marshall Memorial Library Fund.

Other Format:

Online version: Lepš, Jan, 1953- Biostatistics with r

ISBN:

9781108480383

1108480381

9781108727341

1108727344

OCLC:

1157982301

Publisher Number:

99987477166