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Power Calculation Practical

Power Calculation Practical. Benjamin Neale. Power Calculations Empirical. Attempt to Grasp the NCP from Null Simulate Data under theorized model Calculate Statistics and Perform Test Given α , how many tests p < α Power = (#hits)/(#tests). Practical: Empirical Power 1.

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Power Calculation Practical

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  1. Power Calculation Practical Benjamin Neale

  2. Power Calculations Empirical • Attempt to Grasp the NCP from Null • Simulate Data under theorized model • Calculate Statistics and Perform Test • Given α, how many tests p < α • Power = (#hits)/(#tests)

  3. Practical: Empirical Power 1 • We will Simulate Data under a model online • We will run an ACE model, and test for C • We will then submit our results and Jeff will collate the empirical values • While that is being calculated, we’ll talk about theoretical power calculations

  4. Practical: Empirical Power 2 • First get ace.mx and rprog.R from • /faculty/ben/2006/power/practical/. • We’ll talk about what the R program does before we run it

  5. Simulation of the MZs: model rGmz rCmz 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 A C E A C E C C 0 . 5477 0 . 5477 E E A A 0 . 4472 0 . 4472 0 . 7071 0 . 7071 MZ twin 1 MZ twin

  6. Redrawn MZ model 1 . 00 1 . 00 E 1 . 00 E A A A E E 0 . 7071 0 . 7071 0 . 4472 0 . 4472 MZ twin 1 MZ twin 2 C C 0 . 5477 0 . 5477 1 . 00 C

  7. When we simulate • From a path diagram, we can simulate trait values from simulating each latent trait • These latent traits are assumed to be normal (μ=0,σ2=1 or =0,2=1) • The latent trait is then multiplied by the path coefficient

  8. What’s a random normal 0.4 0.3 frequency) 0.2 0.1 0.0 -4 -2 0 2 4 x

  9. Redrawn MZ model Random Normal 1 1 . 00 1 . 00 E 1 . 00 E A A A 0 . 7071 E E 0 . 7071 0 . 4472 0 . 4472 MZ twin 1 MZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + MZ twin 2 trait:

  10. Redrawn MZ model Random Normal 1 1 . 00 1 . 00 E 1 . 00 E A A A E 0 . 7071 E 0 . 7071 0 . 4472 0 . 4472 MZ twin 1 MZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + MZ twin 2 trait: Norm1*A(0.7071) +

  11. Redrawn MZ model 1 . 00 1 . 00 E 1 . 00 E A A A E E 0 . 7071 0 . 7071 0 . 4472 0 . 4472 MZ twin 1 MZ twin 2 Random Normal 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + Norm2*C(0.5477) MZ twin 2 trait: Norm1*A(0.7071) +

  12. Redrawn MZ model 1 . 00 1 . 00 E 1 . 00 E A A A E E 0 . 7071 0 . 7071 0 . 4472 0 . 4472 MZ twin 1 MZ twin 2 Random Normal 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + Norm2*C(0.5477) MZ twin 2 trait: Norm1*A(0.7071) + Norm2*C(0.5477)

  13. Redrawn MZ model 1 . 00 1 . 00 E 1 . 00 E A A A E E 0 . 7071 0 . 7071 0 . 4472 Random Normal 3 0 . 4472 MZ twin 1 MZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + Norm2*C(0.5477) + Norm3*E(0.4472) MZ twin 2 trait: Norm1*A(0.7071) + Norm2*C(0.5477) +

  14. Redrawn MZ model 1 . 00 1 . 00 E 1 . 00 E A A A E E 0 . 7071 0 . 7071 0 . 4472 0 . 4472 Random Normal 4 MZ twin 1 MZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 MZ twin 1 trait: Norm1*A(0.7071) + Norm2*C(0.5477) + Norm3*E(0.4472) MZ twin 2 trait: Norm1*A(0.7071) + Norm2*C(0.5477) + Norm4*E(0.4472)

  15. Simulation of the DZs: model rGmz rCmz 0 . 50 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 1 . 00 A C E A C E C C 0 . 5477 0 . 5477 E E A A 0 . 4472 0 . 4472 0 . 7071 0 . 7071 MZ twin 1 MZ twin

  16. Redrawn DZ model 0 . 50 0 . 50 1 . 00 1 . 00 Asp Asp E E 0 . 50 Asp Asp 0 . 7071 Aco 0 . 7071 E E 0 . 4472 0 . 4472 Aco Aco 0 . 7071 0 . 7071 DZ twin 1 DZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 How many random normals will we need to supply a trait value for both DZ twins?

  17. Redrawn DZ model 0 . 50 0 . 50 1 . 00 1 . 00 Asp Asp E E 0 . 50 Asp Asp Note: σ2(K*X) = K2*σ2(x) When K is a constant hence 0.7071*norm5 0 . 7071 Aco 0 . 7071 E E 0 . 4472 0 . 4472 Aco Aco 0 . 7071 0 . 7071 DZ twin 1 DZ twin 2 C C 0 . 5477 0 . 5477 C 1 . 00 DZ twin 1 trait: 0.7071*Norm5*Aco(0.7071) + 0.7071*Norm6*Asp(0.7071) + Norm7*C(0.5477) + Norm8*E(0.4472) DZ twin 2 trait: 0.7071*Norm5*Aco(0.7071) + 0.7071*Norm9*Asp(0.7071) + Norm7*C(0.5477) + Norm10*E(0.4472)

  18. Simulation conditions • 50% additive genetic variance • 30% common environment variance • 20% specific environment variance

  19. Notes on the R program • When you run the R program it is essential that you change your working directory to where you saved the Mx script. • File menu then Change dir… • After changing directory, load the R program. • A visual guide to this follows this slide

  20. Picture of the menu CHANGE DIR… This is the menu item you must change to change where the simulated data will be placed Note you must have the R console highlighted

  21. Picture of the dialog box Either type the path name or browse to where you saved ACE.mx

  22. Running the R script SOURCE R CODE… This is where we load the R program that simulates data

  23. Screenshot of source code selection This is the file rprog.R for the source code

  24. How do I know if it has worked? • If you have run the R program correctly, then the file sim.fun ought to be in the directory where your rprog.R and ACE.mx is. • If not, try again or raise your hand.

  25. When you have finished • Note your likelihoods and your parameter estimates and complete the survey at: • https://ibgwww.colorado.edu/phpsurveyor/index.php?sid=4

  26. Theoretical power calculations • Either derive the power solutions by hand (though this requires lots of time and more IQ points than I have) • Use Mx to setup the variance covariance structure and use option power to generate power levels

  27. Quick note on the power calculations for Mx • Total sample size is reported at the end of the script • The sample size proportions for your groups are maintained. • For example if we say 50 MZ pairs and 100 DZ pairs, then Mx will assume 1/3 of your sample is MZ and 2/3 is DZ

  28. Time to look at a script • Open power.mx, and we’ll chat about it. • Quick overview of what the script does: • Generates the variance covariance structure under the full model (1st half) • Intentionally fits the wrong model (by dropping the parameter of interest for power calculations) (2nd half) • Based on the number of observations that you supply generates power estimates.

  29. Theoretical script • Following chatting, depending on time, here are some suggestions: • Change ratio of MZ and DZ keeping same total sample size • Drop A rather than C • Change effect sizes for A, C, or E

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