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Multivariate Data AnalysisPowerPoint Presentation

Multivariate Data Analysis

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Multivariate Data Analysis. Principal Component Analysis. Principal Component Analysis (PCA). Singular Value Decomposition Eigenvector / eigenvalue calculation. Data Matrix (IxK). Reduce variables Improve projections Remove noise Find outliers Find classes. K. X. I. PCA.

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### Multivariate Data Analysis

### Conclusions

### What is rank?

### ANOVA

### ANOVA

### More nomenclature

Principal Component Analysis

Principal Component Analysis (PCA)

- Singular Value Decomposition
- Eigenvector / eigenvalue calculation

Data Matrix (IxK)

- Reduce variables
- Improve projections
- Remove noise
- Find outliers
- Find classes

K

X

I

PCA

- Example with 2 variables, 6 objects
- Find best (most informative) direction in space
- Describe direction
- Make projection

x2

x1

x2

x1

X = t1p1’ + t2p2’ + ... + tApA’ + E

X=TP’+E

X : properly preprocessed (IxK)

T: Score matrix (IxA)

P: loading matrix (KxA)

E: residual matrix (IxK)

ta: score vector

pa: loading vector

The Wine ExamplePeople magazineWise & Gallagher

Wine Beer Spirit LifeEx HeartD

France

Italy

Switz

Austra

Brit

U.S.A.

Russia

Czech

Japan

Mexico

63.5000 40.1000 2.5000 78.0000 61.1000

58.0000 25.1000 0.9000 78.0000 94.1000

46.0000 65.0000 1.7000 78.0000 106.4000

15.7000 102.1000 1.2000 78.0000 173.0000

12.2000 100.0000 1.5000 77.0000 199.7000

8.9000 87.8000 2.0000 76.0000 176.0000

2.7000 17.1000 3.8000 69.0000 373.6000

1.7000 140.0000 1.0000 73.0000 283.7000

1.0000 55.0000 2.1000 79.0000 34.7000

0.2000 50.4000 0.8000 73.0000 36.4000

Beer Wine Spirit LifeEx HeartD

Mean

20.9900 68.2600 1.7500 75.9000 153.8700

24.9270 38.6718 0.9132 3.2128 110.8182

Standard

Deviation

Scores = positions of objects in multivariate space

Loadings = importance of original variables for new directions

Try to explain a large enough portion of X (46+32 = 78%)

The Apricot Example

Manley & Geladi

Mathematical rank = max(min(I,K))

Gives zero residual

Effective rank = A

Separates model from noise

SS

SS%

SS%cum

Comp#

1

2

3

4

5

6

7

8

9

10

68.8269

1.2843

0.0463

0.0045

0.0007

0.0003

0.0002

0.0001

0.0000

0.0000

98.10

1.83

0.07

0.01

0.00

0.00

0.00

0.00

0.00

0.00

98.10

99.93

100

Total

70.1634

100

Score 1 (98%)

SStot = l1 + l2 + l3 +...+ l(I or K)

SStot = SS1 + SS2 + SS3 +...+ SS(I or K)

From largest to smallest!

ANOVA

X = TP’ + E

data = model + residual

SStot = SSmod + SSres

R2 = SSmod / SStot = 1 - SSres / SStot

Coefficient of determination (often in %)

Examples

Wines R2 = SSmod = 78% SSres = 22%2 Comp.

Apricots 1 R2 = SSmod = 99.93% SSres = 0.07%

2 Comp.

Apricots 2 R2 = SSmod = 100% SSres = ±0.0%

3 Comp.

Loading 23

Wavelength, nm

Loading 2

Score = Latent Variable

Loading vector = Eigenvector

Effective rank = Pseudorank = Model dimensionality = Number of components

SSa = Eigenvalue

Singular value = SSa1/2

An analysis sequence

- 1. Scale, mean-center data
- 2. Calculate a few components
- 3. Check scores, loadings
- 4. Find outliers, groupings, explain
- 5. Remove outliers

An analysis sequence

- 6. Scale, mean-center data
- 7. Calculate enough components
- 8. Try to detemine pseudorank
- 9. Check score plots
- 10. Check loading plots
- 11. Check residuals

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