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>> load arch >> x= arch (1:63,:); >> [ ax,mx,stdx ]=auto(x); >> subplot (2,2,1), plot (x)

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>> load arch >> x= arch (1:63,:); >> [ ax,mx,stdx ]=auto(x); >> subplot (2,2,1), plot (x) - PowerPoint PPT Presentation


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>> load arch >> x= arch (1:63,:); >> [ ax,mx,stdx ]=auto(x); >> subplot (2,2,1), plot (x) >> subplot (2,2,2), plot (x\') >> subplot (2,2,3), plot ( ax ) >> subplot (2,2,4), plot ( ax \'). plot (x). plot (x’). plot ( ax ’). plot ( ax ). boxplot (x). boxplot (x\').

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Presentation Transcript
slide1

>> load arch

>> x=arch(1:63,:);

>> [ax,mx,stdx]=auto(x);

>> subplot(2,2,1),plot(x)

>> subplot(2,2,2),plot(x\')

>> subplot(2,2,3),plot(ax)

>> subplot(2,2,4),plot(ax\')

slide2

plot(x)

plot(x’)

plot(ax’)

plot(ax)

slide5

PercentVarianceCapturedby PCA Model

Principal Eigenvalue % Variance % Variance

Component of CapturedCaptured

NumberCov(X) This PC Total

--------- ---------- ---------- ----------

1 5.34e+00 53.41 53.41

2 2.11e+00 21.12 74.53

3 1.10e+00 10.96 85.50

4 8.33e-01 8.33 93.83

5 2.55e-01 2.55 96.38

6 1.40e-01 1.40 97.78

7 1.00e-01 1.00 98.78

8 5.66e-02 0.57 99.35

9 3.61e-02 0.36 99.71

10 2.94e-02 0.29 100.00

slide14

>> rx=scale(arch,mx,stdx);

>> [newscores,resids,tsqs] = pcapro(rx,loads,ssq,reslm,tsqlm,1);

>>

>> pltscrs(newscores,samps,class)

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