Exploratory multivariate analysis by example using R
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| Main Author | |
|---|---|
| Other Authors | , |
| Format | Electronic eBook |
| Language | English |
| Published |
Boca Raton :
CRC Press,
[2017]
|
| Edition | Second edition. |
| Series | Series in computer science and data analysis.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781315301860 1315301865 9781523113668 1523113669 9781315301877 1315301873 1315301857 9781315301853 1315301849 9781315301846 0429225431 9780429225437 9781138196346 1138196347 |
| Physical Description | 1 online resource (xiii, 248 pages). |
Cover
Table of Contents:
- Cover; Title Page; Copyright Page; Table of Contents; Preface; 1: Principal Component Analysis (PCA); 1.1 Data
- Notation
- Examples; 1.2 Objectives; 1.2.1 Studying Individuals; 1.2.2 Studying Variables; 1.2.3 Relationships between the Two Studies; 1.3 Studying Individuals; 1.3.1 The Cloud of Individuals; 1.3.2 Fitting the Cloud of Individuals; 1.3.2.1 Best Plane Representation of NI; 1.3.2.2 Sequence of Axes for Representing NI; 1.3.2.3 How Are the Components Obtained?; 1.3.2.4 Example; 1.3.3 Representation of the Variables as an Aid for Interpreting the Cloud of Individuals.
- 1.4 Studying Variables1.4.1 The Cloud of Variables; 1.4.2 Fitting the Cloud of Variables; 1.5 Relationships between the Two Representations NI and NK; 1.6 Interpreting the Data; 1.6.1 Numerical Indicators; 1.6.1.1 Percentage of Inertia Associated with a Component; 1.6.1.2 Quality of Representation of an Individual or Variable; 1.6.1.3 Detecting Outliers; 1.6.1.4 Contribution of an Individual or Variable to the Construction of a Component; 1.6.2 Supplementary Elements; 1.6.2.1 Representing Supplementary Quantitative Variables; 1.6.2.2 Representing Supplementary Categorical Variables.
- 1.6.2.3 Representing Supplementary Individuals1.6.3 Automatic Description of the Components; 1.7 Implementation with FactoMineR; 1.8 Additional Results; 1.8.1 Testing the Significance of the Components; 1.8.2 Variables: Loadings versus Correlations; 1.8.3 Simultaneous Representation: Biplots; 1.8.4 Missing Values; 1.8.5 Large Datasets; 1.8.6 Varimax Rotation; 1.9 Example: The Decathlon Dataset; 1.9.1 Data Description
- Issues; 1.9.2 Analysis Parameters; 1.9.2.1 Choice of Active Elements; 1.9.2.2 Should the Variables Be Standardised?; 1.9.3 Implementation of the Analysis.
- 1.9.3.1 Choosing the Number of Dimensions to Examine1.9.3.2 Studying the Cloud of Individuals; 1.9.3.3 Studying the Cloud of Variables; 1.9.3.4 Joint Analysis of the Cloud of Individuals and the Cloud of Variables; 1.9.3.5 Comments on the Data; 1.10 Example: The Temperature Dataset; 1.10.1 Data Description
- Issues; 1.10.2 Analysis Parameters; 1.10.2.1 Choice of Active Elements; 1.10.2.2 Should the Variables Be Standardised?; 1.10.3 Implementation of the Analysis; 1.11 Example of Genomic Data: The Chicken Dataset; 1.11.1 Data Description
- Issues; 1.11.2 Analysis Parameters.
- 1.11.3 Implementation of the Analysis2: Correspondence Analysis (CA); 2.1 Data
- Notation
- Examples; 2.2 Objectives and the Independence Model; 2.2.1 Objectives; 2.2.2 Independence Model and X2 Test; 2.2.3 The Independence Model and CA; 2.3 Fitting the Clouds; 2.3.1 Clouds of Row Profiles; 2.3.2 Clouds of Column Profiles; 2.3.3 Fitting Clouds NI and NJ; 2.3.4 Example: Women's Attitudes to Women's Work in France in 1970; 2.3.4.1 Column Representation (Mother's Activity); 2.3.4.2 Row Representation (Partner's Work); 2.3.5 Superimposed Representation of Both Rows and Columns.