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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 calculations empirical
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
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
practical empirical power 2
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
simulation of the mzs model
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

redrawn mz model
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

when we simulate
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
what s a random normal
What’s a random normal

0.4

0.3

frequency)

0.2

0.1

0.0

-4

-2

0

2

4

x

redrawn mz model1
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:

redrawn mz model2
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) +

redrawn mz model3
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) +

redrawn mz model4
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)

redrawn mz model5
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) +

redrawn mz model6
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)

simulation of the dzs model
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

redrawn dz model
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?

redrawn dz model1
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)

simulation conditions
Simulation conditions
  • 50% additive genetic variance
  • 30% common environment variance
  • 20% specific environment variance
notes on the r program
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
picture of the menu
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

picture of the dialog box
Picture of the dialog box

Either type the path name or browse to where you saved ACE.mx

running the r script
Running the R script

SOURCE R CODE…

This is where we load the R program that simulates data

screenshot of source code selection
Screenshot of source code selection

This is the file rprog.R for the source code

how do i know if it has worked
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.
when you have finished
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
theoretical power calculations
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
quick note on the power calculations for mx
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
time to look at a script
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.
theoretical script
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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