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 Algemene naslagwerken, atlassen, jaarboeken, almanakken en woordenboeken Naslagwerken algemeen
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Statistical Graphics and Multivariate Data: Methods and Applications of Biplots popularizes what is now seen to be useful and reliable methods for the visualization of multivariate data where the researcher wants to consider principal component analysis (PCA), canonical variates analysis (CVA), or various types of correspondence analysis.

Taal / Language : English

Inhoudsopgave:
1 Introduction

1.1Types of biplots

1.2 Overview of the book

1.3 Software

1.4 Notation

2 Biplot basics

2.1 A simple example revisited

2.2 The biplot as a multidimensional scatterplo

2.3 Calibrated biplot axes

2.4 Refining the biplot display

2.5 Scaling the data

2.6 A closer look at biplot axes

2.7 Adding new variables: the regression method

2.8 Biplots and large data sets

2.9 Enclosing a configuration of sample points

3 Principal component analysis biplots

3.1 An example: Risk management

3.2 Understanding PCA and constructing its biplot

3.3 Measures of fit for PCA biplots

3.4 Predictivities of newly interpolated samples

3.5 Adding new axes to a PCA biplot and defining their predictivities

3.6 Scaling the data in a PCA biplot

3.7 Functions for constructing a PCA biplot

4 Canonical variate analysis biplots

4.1 An example: Revisiting the Ocotea data of Table 3.9

4.2 Understanding CVA and constructing its biplot

4.3 Geometric interpretation of the transformation to the canonical space

4.4 CVA biplot axes

4.5 Adding new points and variables to a CVA biplot

4.6 Measures of fit for CVA biplots

4.7 Functions for constructing a CVA biplot

4.8 Continuing the Ocotea example

4.9 CVA biplots for two classes

4.10 A five classes CVA biplot example

4.11 Overlap in two dimensional biplots

5 Multidimensional Scaling and Nonlinear Biplots

5.1 Introduction

5.2 The regression method

5.3 Nonlinear Biplots

5.4 Providing nonlinear biplot axes for variables 5 12

5.5 A PCA biplot as a Nonlinear biplot

5.6 Constructing Nonlinear biplots 5

5.7 Examples

5.8 Analysis of Distance

5.9 Functions AODplot and PermutationAnova

6.1 Introduction

6.3 Statistical Analysis of the Biadditive Model

6.4 Biplots associated with Biadditive Models

6.5 Interpolating new rows or columns

6.6 Functions for constructing biadditive biplots

6.7 Examples of biadditive biplots: the wheat data

6.10 Diagnostic Biplots

7 Two way Tables: Biplots associated with Correspondence Analysis

7.1 Introduction

7.2 The Correspondence Analysis Biplot

7.3 Interpolation of new points (supplementary points) in CA biplots

7.4 Other CA related methods

7.5 Functions for constructing CA biplots

7.6 Examples

7.8 Conclusion

8 Multiple Correspondence analysis

8.1 Introduction

8.2 Multiple correspondence analysis of the Indicator Matrix

8.3 The Burt Matrix

8.4 Similarity Matrices, and the Extended Matching Coefficient (EMC)

8.5 Category Level Points (CLPs)

8.6 Homals

8.7 Correlational Approach

8.8 Categorical (Nonlinear) Principal Components Analysis

8.9 Functions for constructing MCA related biplots

8.10 Revisiting the Remuneration data: Examples of MCA and Categorical PCA biplots

9 Generalized Biplots

9.1 Introduction

9.2 Calculating inter sample distances

9.3 Constructing a generalized biplot

9.4 Reference system

9.5 The Basic Points

9.6 Interpolation

9.7 Prediction

9.8 An example

9.9 Constructing Generalized biplots

10Monoplots

10.1 Multidimensional Scaling

10.2 Monoplots related to the Covariance matrix

10.3 Skew symmetry

10.4 Area Biplots

10.5 Functions for constructing monoplots

Extra informatie:
Hardback
476 pagina's
Januari 2010
1166 gram
252 x 176 x 30 mm
John Wiley & Sons Inc

 Winkelwagen
 Levertijd: 5 tot 11 werkdagen

 Andere titels binnen de rubriek: Naslagwerken algemeen

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