Modern analysis of biological data :: generalized linear models in R /
Book written "for people who have only a basic knowledge of statistics (for example, people who have attended only a Basic statistics/Biostatistics course) but who need to correctly analyse the data resulting from their observations or experiments"--Page IX
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Brno :
Masaryk University Press,
2016.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Book written "for people who have only a basic knowledge of statistics (for example, people who have attended only a Basic statistics/Biostatistics course) but who need to correctly analyse the data resulting from their observations or experiments"--Page IX |
| Beschreibung: | 1 online resource (245 pages) : graphs |
| Bibliographie: | Includes bibliographical references (pages 237-238) and index. |
| ISBN: | 9788021081062 8021081066 |
Internformat
MARC
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| 100 | 1 | |a Pekár, Stano, |e author. | |
| 245 | 1 | 0 | |a Modern analysis of biological data : |b generalized linear models in R / |c Stano Pekar, Marek Brabec. |
| 246 | 3 | 0 | |a Generalized linear models in R |
| 264 | 1 | |a Brno : |b Masaryk University Press, |c 2016. | |
| 300 | |a 1 online resource (245 pages) : |b graphs | ||
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| 504 | |a Includes bibliographical references (pages 237-238) and index. | ||
| 520 | |a Book written "for people who have only a basic knowledge of statistics (for example, people who have attended only a Basic statistics/Biostatistics course) but who need to correctly analyse the data resulting from their observations or experiments"--Page IX | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Intro -- CONTENTS -- FOREWORD -- 1 INTRODUCTION -- 1.1 How to read the book -- 1.2 Types of variables -- 1.3 Conventions -- 2 STATISTICAL SOFTWARE -- 2.1 The R environment -- 2.2 Installation and use of R -- 2.3 Basic operations -- 2.4 Data frames -- 3 EXPLORATORY DATA ANALYSIS (EDA) -- 3.1 Expected value -- 3.2 Variance -- 3.3 Confidence intervals -- 3.4 Summary tables -- 3.5 Plots -- 3.5.1 Distribution plots -- 3.5.2 Scatter plots -- 3.5.3 Box plots -- 3.5.4 Lattice plots -- 3.5.5 Interaction plots -- 3.5.6 Bar plots -- 3.5.7 Paired plots -- 3.5.8 3D plots -- 3.5.9 Plots with whiskers -- 3.5.10 Curves -- 4 STATISTICAL MODELLING -- 4.1 Regression model -- 4.2 General linear model -- 4.3 Generalized linear model -- 4.4 Searching for the "correct" model -- 4.5 Model selection -- 4.6 Model diagnosis -- 5 THE FIRST TRIAL -- 5.1 An Example -- 5.2 EDA -- 5.3 Presumed model -- 5.4 Statistical analysis -- 5.4.1 ANOVA table of Type I -- 5.4.2 Nonlinear trends -- 5.4.3 Removal of model terms -- 5.4.4 Comparison of levels using contrasts -- 5.4.5 Contrasts and the model parameterization -- 5.4.6 Posterior simplification -- 5.4.7 Diagnosis of the final model -- 5.5 Conclusion -- 6 SYSTEMATIC PART -- 6.1 Regression -- 6.2 ANOVA and ANODEV -- 6.3 ANCOVA and ANCODEV -- 6.4 Syntax of the systematic part -- 7 RANDOM PART -- 7.1 Continuous measurements -- 7.2 Counts and frequencies -- 7.3 Relative frequencies -- 8 GAUSSIAN DISTRIBUTION -- 8.1 Description of LM and GLM -- 8.2 Regression -- 8.3 Weighted regression -- 8.4 Multiple regression -- 8.5 Two-way ANOVA -- 8.6 One-way ANCOVA -- 9 GAMMA AND LOGNORMAL DISTRIBUTIONS -- 9.1 Description of the gamma model -- 9.2 Description of the lognormal model -- 9.3 Regression -- 9.4 Two-way ANODEV -- 9.5 Two-way ANCOVA -- 10 POISSON DISTRIBUTION -- 10.1 Description of the Poisson model -- 10.2 One-way ANODEV. | |
| 505 | 8 | |a 10.3 Overdispersion and underdispersion -- 10.4 Multiple regression -- 10.5 One-way ANCODEV -- 10.6 Three-way ANODEV -- 11 NEGATIVE-BINOMIAL DISTRIBUTION -- 11.1 Description of the negative-binomial model -- 11.2 One-way ANODEV -- 12 BINOMIAL DISTRIBUTION -- 12.1 Description of the binomial model -- 12.2 Two-way ANODEV -- 12.3 Overdispersion and underdispersion -- 12.4 Regression -- 12.5 One-way ANCODEV -- 12.6 Binary one-way ANCODEV -- REFERENCES -- INDEX -- Subject index -- R functions and their arguments. | |
| 650 | 0 | |a Quantitative research. |0 http://id.loc.gov/authorities/subjects/sh2007000909 | |
| 650 | 0 | |a Analysis of variance. |0 http://id.loc.gov/authorities/subjects/sh85004782 | |
| 650 | 0 | |a Gaussian distribution. |0 http://id.loc.gov/authorities/subjects/sh85053556 | |
| 650 | 0 | |a Lognormal distribution. |0 http://id.loc.gov/authorities/subjects/sh85078134 | |
| 650 | 0 | |a Poisson distribution. |0 http://id.loc.gov/authorities/subjects/sh85103956 | |
| 650 | 0 | |a Distribution (Probability theory) |0 http://id.loc.gov/authorities/subjects/sh85038545 | |
| 650 | 6 | |a Recherche quantitative. | |
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| 700 | 1 | |a Brabec, Marek, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Pekar, Stano. |t Modern analysis of biological data. |d Brno : Masaryk University Press, 2016 |z 9788021080195 |w (OCoLC)991599485 |
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| contents | Intro -- CONTENTS -- FOREWORD -- 1 INTRODUCTION -- 1.1 How to read the book -- 1.2 Types of variables -- 1.3 Conventions -- 2 STATISTICAL SOFTWARE -- 2.1 The R environment -- 2.2 Installation and use of R -- 2.3 Basic operations -- 2.4 Data frames -- 3 EXPLORATORY DATA ANALYSIS (EDA) -- 3.1 Expected value -- 3.2 Variance -- 3.3 Confidence intervals -- 3.4 Summary tables -- 3.5 Plots -- 3.5.1 Distribution plots -- 3.5.2 Scatter plots -- 3.5.3 Box plots -- 3.5.4 Lattice plots -- 3.5.5 Interaction plots -- 3.5.6 Bar plots -- 3.5.7 Paired plots -- 3.5.8 3D plots -- 3.5.9 Plots with whiskers -- 3.5.10 Curves -- 4 STATISTICAL MODELLING -- 4.1 Regression model -- 4.2 General linear model -- 4.3 Generalized linear model -- 4.4 Searching for the "correct" model -- 4.5 Model selection -- 4.6 Model diagnosis -- 5 THE FIRST TRIAL -- 5.1 An Example -- 5.2 EDA -- 5.3 Presumed model -- 5.4 Statistical analysis -- 5.4.1 ANOVA table of Type I -- 5.4.2 Nonlinear trends -- 5.4.3 Removal of model terms -- 5.4.4 Comparison of levels using contrasts -- 5.4.5 Contrasts and the model parameterization -- 5.4.6 Posterior simplification -- 5.4.7 Diagnosis of the final model -- 5.5 Conclusion -- 6 SYSTEMATIC PART -- 6.1 Regression -- 6.2 ANOVA and ANODEV -- 6.3 ANCOVA and ANCODEV -- 6.4 Syntax of the systematic part -- 7 RANDOM PART -- 7.1 Continuous measurements -- 7.2 Counts and frequencies -- 7.3 Relative frequencies -- 8 GAUSSIAN DISTRIBUTION -- 8.1 Description of LM and GLM -- 8.2 Regression -- 8.3 Weighted regression -- 8.4 Multiple regression -- 8.5 Two-way ANOVA -- 8.6 One-way ANCOVA -- 9 GAMMA AND LOGNORMAL DISTRIBUTIONS -- 9.1 Description of the gamma model -- 9.2 Description of the lognormal model -- 9.3 Regression -- 9.4 Two-way ANODEV -- 9.5 Two-way ANCOVA -- 10 POISSON DISTRIBUTION -- 10.1 Description of the Poisson model -- 10.2 One-way ANODEV. 10.3 Overdispersion and underdispersion -- 10.4 Multiple regression -- 10.5 One-way ANCODEV -- 10.6 Three-way ANODEV -- 11 NEGATIVE-BINOMIAL DISTRIBUTION -- 11.1 Description of the negative-binomial model -- 11.2 One-way ANODEV -- 12 BINOMIAL DISTRIBUTION -- 12.1 Description of the binomial model -- 12.2 Two-way ANODEV -- 12.3 Overdispersion and underdispersion -- 12.4 Regression -- 12.5 One-way ANCODEV -- 12.6 Binary one-way ANCODEV -- REFERENCES -- INDEX -- Subject index -- R functions and their arguments. |
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| discipline | Mathematik |
| format | Electronic eBook |
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| indexdate | 2024-11-27T13:27:54Z |
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| publisher | Masaryk University Press, |
| record_format | marc |
| spelling | Pekár, Stano, author. Modern analysis of biological data : generalized linear models in R / Stano Pekar, Marek Brabec. Generalized linear models in R Brno : Masaryk University Press, 2016. 1 online resource (245 pages) : graphs text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 237-238) and index. Book written "for people who have only a basic knowledge of statistics (for example, people who have attended only a Basic statistics/Biostatistics course) but who need to correctly analyse the data resulting from their observations or experiments"--Page IX Print version record. Intro -- CONTENTS -- FOREWORD -- 1 INTRODUCTION -- 1.1 How to read the book -- 1.2 Types of variables -- 1.3 Conventions -- 2 STATISTICAL SOFTWARE -- 2.1 The R environment -- 2.2 Installation and use of R -- 2.3 Basic operations -- 2.4 Data frames -- 3 EXPLORATORY DATA ANALYSIS (EDA) -- 3.1 Expected value -- 3.2 Variance -- 3.3 Confidence intervals -- 3.4 Summary tables -- 3.5 Plots -- 3.5.1 Distribution plots -- 3.5.2 Scatter plots -- 3.5.3 Box plots -- 3.5.4 Lattice plots -- 3.5.5 Interaction plots -- 3.5.6 Bar plots -- 3.5.7 Paired plots -- 3.5.8 3D plots -- 3.5.9 Plots with whiskers -- 3.5.10 Curves -- 4 STATISTICAL MODELLING -- 4.1 Regression model -- 4.2 General linear model -- 4.3 Generalized linear model -- 4.4 Searching for the "correct" model -- 4.5 Model selection -- 4.6 Model diagnosis -- 5 THE FIRST TRIAL -- 5.1 An Example -- 5.2 EDA -- 5.3 Presumed model -- 5.4 Statistical analysis -- 5.4.1 ANOVA table of Type I -- 5.4.2 Nonlinear trends -- 5.4.3 Removal of model terms -- 5.4.4 Comparison of levels using contrasts -- 5.4.5 Contrasts and the model parameterization -- 5.4.6 Posterior simplification -- 5.4.7 Diagnosis of the final model -- 5.5 Conclusion -- 6 SYSTEMATIC PART -- 6.1 Regression -- 6.2 ANOVA and ANODEV -- 6.3 ANCOVA and ANCODEV -- 6.4 Syntax of the systematic part -- 7 RANDOM PART -- 7.1 Continuous measurements -- 7.2 Counts and frequencies -- 7.3 Relative frequencies -- 8 GAUSSIAN DISTRIBUTION -- 8.1 Description of LM and GLM -- 8.2 Regression -- 8.3 Weighted regression -- 8.4 Multiple regression -- 8.5 Two-way ANOVA -- 8.6 One-way ANCOVA -- 9 GAMMA AND LOGNORMAL DISTRIBUTIONS -- 9.1 Description of the gamma model -- 9.2 Description of the lognormal model -- 9.3 Regression -- 9.4 Two-way ANODEV -- 9.5 Two-way ANCOVA -- 10 POISSON DISTRIBUTION -- 10.1 Description of the Poisson model -- 10.2 One-way ANODEV. 10.3 Overdispersion and underdispersion -- 10.4 Multiple regression -- 10.5 One-way ANCODEV -- 10.6 Three-way ANODEV -- 11 NEGATIVE-BINOMIAL DISTRIBUTION -- 11.1 Description of the negative-binomial model -- 11.2 One-way ANODEV -- 12 BINOMIAL DISTRIBUTION -- 12.1 Description of the binomial model -- 12.2 Two-way ANODEV -- 12.3 Overdispersion and underdispersion -- 12.4 Regression -- 12.5 One-way ANCODEV -- 12.6 Binary one-way ANCODEV -- REFERENCES -- INDEX -- Subject index -- R functions and their arguments. Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Analysis of variance. http://id.loc.gov/authorities/subjects/sh85004782 Gaussian distribution. http://id.loc.gov/authorities/subjects/sh85053556 Lognormal distribution. http://id.loc.gov/authorities/subjects/sh85078134 Poisson distribution. http://id.loc.gov/authorities/subjects/sh85103956 Distribution (Probability theory) http://id.loc.gov/authorities/subjects/sh85038545 Recherche quantitative. Analyse de variance. Loi de Gauss (Statistique) Distribution lognormale. Distribution de Poisson. Distribution (Théorie des probabilités) distribution (statistics-related concept) aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Analysis of variance fast Distribution (Probability theory) fast Gaussian distribution fast Lognormal distribution fast Poisson distribution fast Quantitative research fast Brabec, Marek, author. Print version: Pekar, Stano. Modern analysis of biological data. Brno : Masaryk University Press, 2016 9788021080195 (OCoLC)991599485 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1543330 Volltext |
| spellingShingle | Pekár, Stano Brabec, Marek Modern analysis of biological data : generalized linear models in R / Intro -- CONTENTS -- FOREWORD -- 1 INTRODUCTION -- 1.1 How to read the book -- 1.2 Types of variables -- 1.3 Conventions -- 2 STATISTICAL SOFTWARE -- 2.1 The R environment -- 2.2 Installation and use of R -- 2.3 Basic operations -- 2.4 Data frames -- 3 EXPLORATORY DATA ANALYSIS (EDA) -- 3.1 Expected value -- 3.2 Variance -- 3.3 Confidence intervals -- 3.4 Summary tables -- 3.5 Plots -- 3.5.1 Distribution plots -- 3.5.2 Scatter plots -- 3.5.3 Box plots -- 3.5.4 Lattice plots -- 3.5.5 Interaction plots -- 3.5.6 Bar plots -- 3.5.7 Paired plots -- 3.5.8 3D plots -- 3.5.9 Plots with whiskers -- 3.5.10 Curves -- 4 STATISTICAL MODELLING -- 4.1 Regression model -- 4.2 General linear model -- 4.3 Generalized linear model -- 4.4 Searching for the "correct" model -- 4.5 Model selection -- 4.6 Model diagnosis -- 5 THE FIRST TRIAL -- 5.1 An Example -- 5.2 EDA -- 5.3 Presumed model -- 5.4 Statistical analysis -- 5.4.1 ANOVA table of Type I -- 5.4.2 Nonlinear trends -- 5.4.3 Removal of model terms -- 5.4.4 Comparison of levels using contrasts -- 5.4.5 Contrasts and the model parameterization -- 5.4.6 Posterior simplification -- 5.4.7 Diagnosis of the final model -- 5.5 Conclusion -- 6 SYSTEMATIC PART -- 6.1 Regression -- 6.2 ANOVA and ANODEV -- 6.3 ANCOVA and ANCODEV -- 6.4 Syntax of the systematic part -- 7 RANDOM PART -- 7.1 Continuous measurements -- 7.2 Counts and frequencies -- 7.3 Relative frequencies -- 8 GAUSSIAN DISTRIBUTION -- 8.1 Description of LM and GLM -- 8.2 Regression -- 8.3 Weighted regression -- 8.4 Multiple regression -- 8.5 Two-way ANOVA -- 8.6 One-way ANCOVA -- 9 GAMMA AND LOGNORMAL DISTRIBUTIONS -- 9.1 Description of the gamma model -- 9.2 Description of the lognormal model -- 9.3 Regression -- 9.4 Two-way ANODEV -- 9.5 Two-way ANCOVA -- 10 POISSON DISTRIBUTION -- 10.1 Description of the Poisson model -- 10.2 One-way ANODEV. 10.3 Overdispersion and underdispersion -- 10.4 Multiple regression -- 10.5 One-way ANCODEV -- 10.6 Three-way ANODEV -- 11 NEGATIVE-BINOMIAL DISTRIBUTION -- 11.1 Description of the negative-binomial model -- 11.2 One-way ANODEV -- 12 BINOMIAL DISTRIBUTION -- 12.1 Description of the binomial model -- 12.2 Two-way ANODEV -- 12.3 Overdispersion and underdispersion -- 12.4 Regression -- 12.5 One-way ANCODEV -- 12.6 Binary one-way ANCODEV -- REFERENCES -- INDEX -- Subject index -- R functions and their arguments. Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Analysis of variance. http://id.loc.gov/authorities/subjects/sh85004782 Gaussian distribution. http://id.loc.gov/authorities/subjects/sh85053556 Lognormal distribution. http://id.loc.gov/authorities/subjects/sh85078134 Poisson distribution. http://id.loc.gov/authorities/subjects/sh85103956 Distribution (Probability theory) http://id.loc.gov/authorities/subjects/sh85038545 Recherche quantitative. Analyse de variance. Loi de Gauss (Statistique) Distribution lognormale. Distribution de Poisson. Distribution (Théorie des probabilités) distribution (statistics-related concept) aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Analysis of variance fast Distribution (Probability theory) fast Gaussian distribution fast Lognormal distribution fast Poisson distribution fast Quantitative research fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2007000909 http://id.loc.gov/authorities/subjects/sh85004782 http://id.loc.gov/authorities/subjects/sh85053556 http://id.loc.gov/authorities/subjects/sh85078134 http://id.loc.gov/authorities/subjects/sh85103956 http://id.loc.gov/authorities/subjects/sh85038545 |
| title | Modern analysis of biological data : generalized linear models in R / |
| title_alt | Generalized linear models in R |
| title_auth | Modern analysis of biological data : generalized linear models in R / |
| title_exact_search | Modern analysis of biological data : generalized linear models in R / |
| title_full | Modern analysis of biological data : generalized linear models in R / Stano Pekar, Marek Brabec. |
| title_fullStr | Modern analysis of biological data : generalized linear models in R / Stano Pekar, Marek Brabec. |
| title_full_unstemmed | Modern analysis of biological data : generalized linear models in R / Stano Pekar, Marek Brabec. |
| title_short | Modern analysis of biological data : |
| title_sort | modern analysis of biological data generalized linear models in r |
| title_sub | generalized linear models in R / |
| topic | Quantitative research. http://id.loc.gov/authorities/subjects/sh2007000909 Analysis of variance. http://id.loc.gov/authorities/subjects/sh85004782 Gaussian distribution. http://id.loc.gov/authorities/subjects/sh85053556 Lognormal distribution. http://id.loc.gov/authorities/subjects/sh85078134 Poisson distribution. http://id.loc.gov/authorities/subjects/sh85103956 Distribution (Probability theory) http://id.loc.gov/authorities/subjects/sh85038545 Recherche quantitative. Analyse de variance. Loi de Gauss (Statistique) Distribution lognormale. Distribution de Poisson. Distribution (Théorie des probabilités) distribution (statistics-related concept) aat MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Analysis of variance fast Distribution (Probability theory) fast Gaussian distribution fast Lognormal distribution fast Poisson distribution fast Quantitative research fast |
| topic_facet | Quantitative research. Analysis of variance. Gaussian distribution. Lognormal distribution. Poisson distribution. Distribution (Probability theory) Recherche quantitative. Analyse de variance. Loi de Gauss (Statistique) Distribution lognormale. Distribution de Poisson. Distribution (Théorie des probabilités) distribution (statistics-related concept) MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Analysis of variance Gaussian distribution Lognormal distribution Poisson distribution Quantitative research |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1543330 |
| work_keys_str_mv | AT pekarstano modernanalysisofbiologicaldatageneralizedlinearmodelsinr AT brabecmarek modernanalysisofbiologicaldatageneralizedlinearmodelsinr AT pekarstano generalizedlinearmodelsinr AT brabecmarek generalizedlinearmodelsinr |