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Summarizing Variation Matrix Algebra & Mx Michael C Neale PhD Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University.

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Presentation on theme: "Summarizing Variation Matrix Algebra & Mx Michael C Neale PhD Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University."— Presentation transcript:

1 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

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

3 Computing Mean Formula E (x i )/N Can compute with Pencil Calculator SAS SPSS Mx

4 One Coin toss 2 outcomes HeadsTails Outcome Probability

5 Two Coin toss 3 outcomes HHHT/THTT Outcome Probability

6 Four Coin toss 5 outcomes HHHHHHHTHHTTHTTTTTTT Outcome Probability

7 Ten Coin toss 9 outcomes Outcome Probability

8 Fort Knox Toss Heads-Tails De Moivre 1733 Gauss 1827 Infinite outcomes

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 Pascal's friend Chevalier de Mere 1654; Huygens 1657; Cardan /1 1/2 1/4 1/8 1/16 1/32 1/64 1/128 Frequency Probability

11 Variance  Measure of Spread  Easily calculated  Individual differences

12 Average squared deviation Normal distribution : xixi didi Variance = G d i 2 /N

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

14 Measuring Variation Squared differences Ways & Means 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:x :y:y                                 

17 :x:x :y:y  dx dy

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

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

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

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

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

23 Summary Formulae for sample statistics; i=1…N observations : = ( E x i )/N F x = E (x i - : x ) / (N) 2 2 r = F xy 2 2 x x F F xy = E (x i -: x )(y i -: y ) / (N)

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

28 Height of normal curve Probability density function :x:x xixi N (x i ) N (x i ) is the likelihood of data point x i for particular mean & variance estimates

29 Height of bivariate normal curve An unlikely pair of (x,y) values :x:x xixi yiyi :y:y

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


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