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BK

Fourth edition

Boca Raton ; London ; New York : CRC Press/Taylor & Francis Group, [2017]

xv, 253 stran : ilustrace ; 24 cm

ISBN 978-1-4987-2896-6 (brožováno)

Obsahuje bibliografie a rejstřík

001462994

Contents // Preface...xiii // Authors...XV // Chapter 1 The material of multivariate analysis...1 // 1.1 Examples of multivariate data...1 // 1.2 Preview of multivariate methods...10 // 1.3 The multivariate normal distribution...14 // 1.4 Computer programs...15 // References...15 // Appendix: An Introduction to R...16 // References...27 // Chapter 2 Matrix algebra...29 // 2.1 The need for matrix algebra...29 // 2.2 Matrices and vectors...29 // 2.3 Operations on matrices...31 // 2.4 Matrix inversion...33 // 2.5 Quadratic forms...34 // 2.6 Eigenvalues and eigenvectors...34 // 2.7 Vectors of means and covariance matrices...35 // 2.8 Further reading...37 // References... 37 // Appendix: Matrix Algebra in R...38 // Chapter 3 Displaying multivariate data...41 // 3.1 The problem of displaying many variables in two dimensions.41 // 3.2 Plotting index variables...41 // 3.3 The draftsman’s plot...43 // 3.4 The representation of individual data points...44 // 3.5 Profiles of variables...46 // 3.6 Discussion and further reading...47 // References...48 // x Contents // Appendix: Producing Plots in R...49 // References...51 // Chapter 4 Tests of significance with multivariate data...53 // 4.1 Simultaneous tests on several variables...53 // 4.2 Comparison of mean values for two samples: The singlevariable case...53 // 4.3 Comparison of mean values for two samples: The // multivariate case...55 // 4.4 Multivariate versus univariate tests...59 // 4.5 Comparison of variation for two samples: The

single-variable // case...60 // 4.6 Comparison of variation for two samples: The multivariate // case...61 // 4.7 Comparison of means for several samples...66 // 4.8 Comparison of variation for several samples...70 // 4.9 Computer programs...74 // References...78 // Appendix: Tests of Significance in R...79 // References...81 // Chapter 5 Measuring and testing multivariate distances...83 // 5.1 Multivariate distances...83 // 5.2 Distances between individual observations...83 // 5.3 Distances between populations and samples...86 // 5.4 Distances based on proportions...91 // 5.5 Presence-absence data...92 // 5.6 The Mantel randomization test...93 // 5.7 Computer programs...97 // 5.8 Discussion and further reading...97 // References...98 // Appendix: Multivariate distance measures in R...100 // References...101 // Chapter 6 Principal components analysis...103 // 6.1 Definition of principal components...103 // 6.2 Procedure for a principal components analysis...104 // 6.3 Computer programs...113 // 6.4 Further reading...114 // References...117 // Appendix: Principal Components Analysis (PCA) in R...118 // References...119 // Contents // xi // Chapter 7 Factor analysis...121 // 7.1 The factor analysis model...121 // 7.2 Procedure for a factor analysis...124 // 7.3 Principal components factor analysis...126 // 7.4 Using a factor analysis program to do principal components // analysis...128 // 7.5 Options in analyses...133 // 7.6 The value of factor analysis...134 // 7.7 Discussion and

further reading...134 // References...135 // Appendix: Factor Analysis in R...136 // References...137 // Chapter 8 Discriminant function analysis...139 // 8.1 The problem of separating groups...139 // 8.2 Discrimination using Mahalanobis distances...139 // 8.3 Canonical discriminant functions...140 // 8.4 Tests of significance...142 // 8.5 Assumptions...143 // 8.6 Allowing for prior probabilities of group membership...148 // 8.7 Stepwise discriminant function analysis...150 // 8.8 Jackknife classification of individuals...150 // 8.9 Assigning ungrouped individuals to groups...151 // 8.10 Logistic regression...151 // 8.11 Computer programs... 156 // 8.12 Discussion and further reading... 157 // References...157 // Appendix: Discriminant Function Analysis in R...159 // References...162 // Chapter 9 Cluster analysis... 163 // 9.1 Uses of cluster analysis...163 // 9.2 Types of cluster analysis...163 // 9.3 Hierarchic methods...164 // 9.4 Problems with cluster analysis...166 // 9.5 Measures of distance...167 // 9.6 Principal components analysis with cluster analysis...168 // 9.7 Computer programs...172 // 9.8 Discussion and further reading...173 // References...177 // Appendix: Cluster Analysis in R...178 // Reference...179 // Contents // xii // Chapter 10 Canonical correlation analysis...181 // 10.1 Generalizing a multiple regression analysis...181 // 10.2 Procedure for a canonical correlation analysis...183 // 10.3 Tests of significance...184 // 10.4 Interpreting canonical variates...185

// 10.5 Computer programs...197 // 10.6 Further reading...197 // References...199 // Appendix: Canonical Correlation in R...200 // References...201 // Chapter 11 Multidimensional scaling...203 // 11.1 Constructing a map from a distance matrix...203 // 11.2 Procedure for multidimensional scaling...204 // 11.3 Computer programs...214 // 11.4 Further reading...214 // References...215 // Appendix: Multidimensional scaling in R...216 // References...217 // Chapter 12 Ordination...219 // 12.1 The ordination problem...219 // 12.2 Principal components analysis...220 // 12.3 Principal coordinates analysis...225 // 12.4 Multidimensional scaling...231 // 12.5 Correspondence analysis...233 // 12.6 Comparison of ordination methods...238 // 12.7 Computer programs...239 // 12.8 Further reading...239 // References...240 // Appendix: Ordination methods in R...241 // References...243 // Chapter 13 Epilogue...245 // 13.1 The next step...245 // 13.2 Some general reminders...245 // 13.3 Missing values... 246 // References...247 // Index // 249