R help - Jan 2009 - power analyses for mixed effects lmer models

Hi all,

I'm new (post #1!) and I hope you'll forgive me if I'm acting like
an
idiot...

I have been asked for some power analyses for some mixed-effects models I'm
running using lmer. My studies nearly always contain mixes of
repeated-measures and between-subjects predictor variables.

As an example, suppose I want to see if men or women show a stronger word
frequency effect. I have 50 words of varying frequency that I show to 30 men
and 30 women, who are supposed to decide as quickly as possible whether it's
a real word. So my data object would end up being 3000 lines long, and look
like this:

Subject  Word  Sex  Frequency  ReactionTime
s1 w1 M 23 2543
s1 w2 M 67 1438
s1 w3 M 1 8033
...
s60 w50 F 4 1099

I analyze this with 

lmer(log(ReactionTime) ~ (Sex * Frequency) + (1|Subject) + (1|Word)

Does anyone know how I might do power analyses or compute effect sizes in
this kind of situation?

Thanks.

--Lee
-- 
View this message in context:
http://www.nabble.com/power-analyses-for-mixed-effects-lmer-models-tp21457651p21457651.html
Sent from the R help mailing list archive at Nabble.com.

My preferred method for this type of thing is to use simulation.  You have
already done the hard parts in figuring out what your data is going to look like
and how you plan to analyze it.  Now just write a function that will simulate
data according to your pattern and with the difference(s) that you want to
compute the power for, then analyzes the simulated data and returns the value of
interest (usually a single p-value, but could be something else).  Now run this
function a bunch of times (I would use the replicate function to do this) and
see how often the conclusion of interest occurs (p-val < alpha, or something
else).  This is your estimate of power.

Hope this helps,

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of LeeDetroit
> Sent: Wednesday, January 14, 2009 8:12 AM
> To: r-help at r-project.org
> Subject: [R] power analyses for mixed effects lmer models
> 
> 
> Hi all,
> 
> I'm new (post #1!) and I hope you'll forgive me if I'm acting
like an
> idiot...
> 
> I have been asked for some power analyses for some mixed-effects models
> I'm
> running using lmer. My studies nearly always contain mixes of
> repeated-measures and between-subjects predictor variables.
> 
> As an example, suppose I want to see if men or women show a stronger
> word
> frequency effect. I have 50 words of varying frequency that I show to
> 30 men
> and 30 women, who are supposed to decide as quickly as possible whether
> it's
> a real word. So my data object would end up being 3000 lines long, and
> look
> like this:
> 
> Subject  Word  Sex  Frequency  ReactionTime
> s1 w1 M 23 2543
> s1 w2 M 67 1438
> s1 w3 M 1 8033
> ...
> s60 w50 F 4 1099
> 
> I analyze this with
> 
> lmer(log(ReactionTime) ~ (Sex * Frequency) + (1|Subject) + (1|Word)
> 
> Does anyone know how I might do power analyses or compute effect sizes
> in
> this kind of situation?
> 
> Thanks.
> 
> --Lee
> --
> View this message in context: http://www.nabble.com/power-analyses-for-
> mixed-effects-lmer-models-tp21457651p21457651.html
> Sent from the R help mailing list archive at Nabble.com.
> 
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