An introduction to applied multivariate analysis with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; Dordrecht ; Heidelberg ; London
Springer
[2011]
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xiv, 273 Seiten Illustrationen, Diagramme |
| ISBN: | 9781441996497 |
Internformat
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Datensatz im Suchindex
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Contents Preface. vü 1 2 Multivariate Data and Multivariate Analysis . 1 1.1 Introduction. 1.2 A brief history of the development of multivariate analysis . 1.3 Types of variables and the possible problem of missing values . 1.3.1 Missing values. 1.4 Some multivariate data sets. 1.5 Covariances, correlations, and distances. 1.5.1 Covariances. 1.5.2 Correlations. 1.5.3 Distances. 1.6 The multivariate normal densityfunction. 1.7 Summary. 1.8 Exercises. 1 3 4 5 7 12 12 14 14 15 23 23 Looking at Multivariate Data: Visualisation . 2.1 Introduction. 2.2 The scatterplot. 2.2.1 The bivariate
boxplot. 2.2.2 The convex hull of bivariate data. 2.2.3 The chi-plot. 2.3 The bubble and other glyph plots . 2.4 The scatterplot matrix. 2.5 Enhancing the scatterplot with estimated bivariate densities . 2.5.1 Kernel density estimators . 2.6 Three-dimensional plots. 2.7 Trellis graphics. 2.8 Stalactite plots. 2.9 Summary. 2.10 Exercises. 25 25 26 28 32 34 34 39 42 42 47 50 53 56 60
xii Contents Principal Components Analysis. 61 3 Introduction. 61 Principal components analysis (PCA). 61 Finding the sample principal components. 63 Should principal components be extracted from the covariance or the correlation matrix?. 65 3.5 Principal components of bivariate data with correlation coefficient r. 68 3.6 Rescaling the principal components. 70 3.7 How the principal components predict the observed covariance matrix . 70 3.8 Choosing the number of components. 71 3.9 Calculating principal components scores. 72 3.10 Some examples of the application of principal components analysis. 74 3.10.1 Head lengths of first and second sons . 74 3.10.2 Olympic heptathlon results. 78 3.10.3 Air pollution in US cities. 86 3.11 The biplot. 92 3.12
Sample size for principal components analysis. 93 3.13 Canonical correlation analysis. 94 3.13.1 Head measurements . 96 3.13.2 Health and personality. 99 3.14 Summary. 101 3.15 Exercises. 102 3.1 3.2 3.3 3.4 4 Multidimensional Scaling. 105 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 5 Introduction. 105 Models for proximity data. 105 Spatial models for proximities:Multidimensional scaling. 106 Classical multidimensional scaling. 106 4.4.1 Classical multidimensional scaling: Technical details . - 107 4.4.2 Examples of classical multidimensional scaling. 110 Non-metric multidimensional scaling.121 4.5.1 House of Representatives voting. 123 4.5.2 Judgements of World War II leaders. 124 Correspondence analysis. 127 4.6.1 Teenage
relationships.130 Summary. 131 Exercises. 132 Exploratory Factor Analysis. 135 5.1 5.2 5.3 Introduction. 135 A simple example of a factor analysis model. 136 The k-factor analysis model. 137
Contents 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 xiii Scale invariance of the k-factor model. 138 Estimating the parameters in the k-factor analysis model. 139 5.5.1 Principal factor analysis. 141 5.5.2 Maximum likelihood factor analysis. 142 Estimating the number of factors.142 Factor rotation. 143 Estimating factor scores. 147 Two examples of exploratory factor analysis. 148 5.9.1 Expectations of life. 148 5.9.2 Drug use by American college students. 151 Factor analysis and principal components analysis compared . 157 Summary. 159 Exercises. 159 6 Cluster Analysis. 163 6.1 Introduction. 163 6.2 Cluster analysis. 165 6.3 Agglomerative hierarchical clustering .166 6.3.1 Clustering jet
fighters. 171 6.4 K-means clustering. 175 6.4.1 Clustering the states of the USA on the basis of their crime rate profiles. 176 6.4.2 Clustering Romano-British pottery. 180 6.5 Model-based clustering.183 6.5.1 Finite mixture densities. 186 6-5.2 Maximum likelihood estimation in a finite mixture density with multivariate normal components. 187 6.6 Displaying clustering solutions graphically.191 6.7 Summary. 197 6.8 Exercises. 200 7 Confirmatory Factor Analysis and Structural Equation Models. 201 7.1 Introduction. 201 7.2 Estimation, identification, and assessing fit for confirmatory factor and structural equation models. 202 7.2.1 Estimation. 202 7.2.2 Identification . 203
7.2.3 Assessing the fit of a model. 204 7.3 Confirmatory factor analysis models. 206 7.3.1 Ability and aspiration.206 7.3.2 A confirmatory factor analysis model for drug use. 211 7.4 Structural equation models. 216 7.4.1 Stability of alienation.216 7.5 Summary. 222
xiv Contents 7.6 8 Exercises. 223 The Analysis of Repeated Measures Data. 225 8.1 Introduction. 225 8.2 Linear mixed-effects models for repeated measures data. 232 8.2.1 Random intercept and random intercept and slope models for the timber slippage data.233 8.2.2 Applying the random intercept and the random intercept and slope models to the timber slippage data . 235 8.2.3 Fitting random-effect models to the glucose challenge data. 240 8.3 Prediction of random effects. 247 8.4 Dropouts inlongitudinal data. 248 8.5 Summary. 257 8.6 Exercises. 257 References.259 Index. 271 |
| any_adam_object | 1 |
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| dewey-full | 519.5/35 |
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| dewey-tens | 510 - Mathematics |
| discipline | Informatik Soziologie Mathematik Wirtschaftswissenschaften |
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| id | DE-604.BV037332491 |
| illustrated | Illustrated |
| indexdate | 2025-03-18T04:02:20Z |
| institution | BVB |
| isbn | 9781441996497 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022486366 |
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| physical | xiv, 273 Seiten Illustrationen, Diagramme |
| publishDate | 2011 |
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| publisher | Springer |
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| series2 | Use R! |
| spellingShingle | Everitt, Brian 1944- Hothorn, Torsten 1975- An introduction to applied multivariate analysis with R R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4040708-1 |
| title | An introduction to applied multivariate analysis with R |
| title_auth | An introduction to applied multivariate analysis with R |
| title_exact_search | An introduction to applied multivariate analysis with R |
| title_full | An introduction to applied multivariate analysis with R Brian Everitt, Torsten Hothorn |
| title_fullStr | An introduction to applied multivariate analysis with R Brian Everitt, Torsten Hothorn |
| title_full_unstemmed | An introduction to applied multivariate analysis with R Brian Everitt, Torsten Hothorn |
| title_short | An introduction to applied multivariate analysis with R |
| title_sort | an introduction to applied multivariate analysis with r |
| topic | R Programm (DE-588)4705956-4 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | R Programm Multivariate Analyse |
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Inhaltsverzeichnis
THWS Schweinfurt Zentralbibliothek Lesesaal
| Signatur: |
2000 ST 250 R01 E93 |
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| Exemplar 1 | ausleihbar Verfügbar Bestellen |