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Summarizing Variation Matrix Algebra & Mx

Summarizing Variation Matrix Algebra & Mx. Michael C Neale PhD Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University 19 th International workshop on Methodology Twin and Family Studies. Overview. Mean/Variance/Covariance Calculating Estimating by ML

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Summarizing Variation Matrix Algebra & Mx

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  1. Summarizing VariationMatrix Algebra & Mx Michael C Neale PhDVirginia Institute for Psychiatric and Behavioral GeneticsVirginia Commonwealth University19th International workshop on Methodology Twin and Family Studies

  2. Overview • Mean/Variance/Covariance • Calculating • Estimating by ML • Matrix Algebra • Normal Likelihood Theory • Mx script language

  3. Computing Mean • Formula E(xi)/N • Can compute with • Pencil • Calculator • SAS • SPSS • Mx

  4. One Coin toss 2 outcomes Probability 0.6 0.5 0.4 0.3 0.2 0.1 0 Heads Tails Outcome

  5. Two Coin toss 3 outcomes Probability 0.6 0.5 0.4 0.3 0.2 0.1 0 HH HT/TH TT Outcome

  6. Four Coin toss 5 outcomes Probability 0.4 0.3 0.2 0.1 0 HHHH HHHT HHTT HTTT TTTT Outcome

  7. Ten Coin toss 9 outcomes Probability 0.3 0.25 0.2 0.15 0.1 0.05 0 Outcome

  8. Fort Knox Toss Infinite outcomes 0.5 0.4 0.3 0.2 0.1 0 -4 -3 -2 -1 0 1 2 3 4 Heads-Tails De Moivre 1733 Gauss 1827

  9. Dinosaur (of a) Joke • Elk: The Theory by A. Elk brackets Miss brackets. My theory is along the following lines. • Host: Oh God. • Elk: All brontosauruses are thin at one end, much MUCH thicker in the middle, and then thin again at the far end.

  10. Pascal's Triangle Probability Frequency 1 1 1 1 2 1 1 3 3 1 1 4 6 4 1 1 5 10 10 5 1 1 6 15 20 15 6 1 1 7 21 35 35 21 7 1 1/1 1/2 1/4 1/8 1/16 1/32 1/64 1/128 Pascal's friend Chevalier de Mere 1654; Huygens 1657; Cardan 1501-1576

  11. Variance • Measure of Spread • Easily calculated • Individual differences

  12. Average squared deviation Normal distribution : xi di -3 -2 -1 0 1 2 3 Variance =Gdi2/N

  13. Measuring Variation Weighs & Means • Absolute differences? • Squared differences? • Absolute cubed? • Squared squared?

  14. Measuring Variation Ways & Means • Squared differences Fisher (1922) Squared has minimum variance under normal distribution

  15. Covariance • Measure of association between two variables • Closely related to variance • Useful to partition variance

  16. Deviations in two dimensions :x + + + + + + + + + + + + + + :y + + + + + + + + + + + + + + + + + + +

  17. Deviations in two dimensions :x dx + dy :y

  18. Measuring Covariation Concept: Area of a rectangle • A square, perimeter 4 • Area 1 1 1

  19. Measuring Covariation Concept: Area of a rectangle • A skinny rectangle, perimeter 4 • Area .25*1.75 = .4385 .25 1.75

  20. Measuring Covariation Concept: Area of a rectangle • Points can contribute negatively • Area -.25*1.75 = -.4385 1.75 -.25

  21. Measuring Covariation Covariance Formula: Average cross-product of deviations from mean F = E(xi- :x)(yi - :y) xy N

  22. Correlation • Standardized covariance • Lies between -1 and 1 r = F xy xy 2 2 F* F y x

  23. Summary Formulae for sample statistics; i=1…N observations : = (Exi)/N Fx = E (xi - :x) / (N) 2 2 Fxy= E(xi-:x)(yi-:y) / (N) r= F xy xy 2 2 FF x x

  24. Variance covariance matrix Several variables Var(X) Cov(X,Y) Cov(X,Z) Cov(X,Y) Var(Y) Cov(Y,Z) Cov(X,Z) Cov(Y,Z) Var(Z)

  25. Variance covariance matrix Univariate Twin Data Var(Twin1) Cov(Twin1,Twin2) Cov(Twin2,Twin1) Var(Twin2) Only suitable for complete data Good conceptual perspective

  26. Conclusion • Means and covariances • Basic input statistics for “Traditional SEM” • Easy to compute • Can use raw data instead

  27. Likelihood computation Calculate height of curve -1

  28. Height of normal curve Probability density function :x N(xi) -3 -2 -1 0 1 2 3 xi N(xi) is the likelihood of data point xi for particular mean & variance estimates

  29. 0.5 0.4 0.3 3 2 1 0 -3 -2 -1 -1 0 -2 1 -3 2 3 Height of bivariate normal curve An unlikely pair of (x,y) values :y yi :x xi

  30. Exercises: Compute Normal PDF • Get used to Mx script language • Use matrix algebra • Taste of likelihood theory

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