Dear Tom,
I think you failed to generate simulated outcome from the correct model. Hence
the zero variance of your random effects. Here is a better working example.
library(lme4)
fake2 <- expand.grid(Bleach = c("Control","Med","High"), Temp =
c("Cold","Hot"), Rep = factor(seq_len(3)), ID = seq_len(8))
fake2$rep <- fake2$Bleach:fake2$Temp:fake2$Rep
SDnoise <- 0.77
SDrep <- 1
FFBleach <- c(3.27,3.21, 3.64)
RFrep <- rnorm(length(levels(fake2$rep)), sd = SDrep)
fake2$Growth <- with(fake2, FFBleach[Bleach] + RFrep[rep] + rnorm(nrow(fake2),
sd = SDnoise))
model2 <- lmer(Growth~Bleach*Temp+(1|rep),data=fake2)
str(summary(model2))
summary(model2)@coefs #to extract the t-values
Best regards,
Thierry
PS R-sig-mixed models is a better mailing list for this kind of questions.
> -----Oorspronkelijk bericht-----
> Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
> Namens Tom Wilding
> Verzonden: maandag 5 september 2011 16:17
> Aan: r-help@r-project.org
> Onderwerp: [R] Power analysis in hierarchical models
>
> Dear All
> I am attempting some power analyses, based on simulated data.
> My experimental set up is thus:
> Bleach: main effect, three levels (control, med, high), Fixed.
> Temp: main effect, two levels (cold, hot), Fixed.
> Main effect interactions, six levels (fixed)
> For each main-effect combination I have three replicates.
> Within each replicate I can take varying numbers of measurements
> (response variable = Growth (of marine worms)) but, for this example,
> assume eight). (I’m interested in changing this to see if the
> experimental power changes much).
> Total size = 3 x 2 x 3 x 8 = 144
> The script thus far goes:
> =========== start of script =================
> library(lme4)
> #Data frame structure
> Bleach=rep(c("Control","Med","High"),each=48)
> Temp= rep(rep(c("Cold","Hot"),each=24),3)
> Rep= (rep(rep(rep(c("1","2","3"),each=8),2),3))
> Ind= (rep(rep(rep(c(1:8),3),2),3))#not required for stats
>
> #Fake data (based on pilot studies), only showing a single main effect
> (bleach)
> Growth=c( rnorm(48,3.27,0.77),rnorm(48,3.21,0.77),rnorm(48,3.64,1.17))
> fake2=data.frame(Bleach,Temp,Rep,Ind,Growth);head(fake2)
> #generate factor level for lmer as per Crawley, page 649
> fake2$rep=fake2$Bleach:fake2$Temp:fake2$Rep#rep is used in the lmer
> model
> with(fake2,table(rep))#check that each rep contains 8 measurements
>
> # run alternative (?equivalent) models
> model1=aov(Growth~Bleach*Temp+Error(Bleach*Temp/Rep),data=fake2);sum
> mary(model1)
> model2=lmer(Growth~Bleach*Temp+(1|rep),data=fake2);summary(model2)#no
> te:
> see above, rep<>Rep!
> ============ end of script ==========
> I'd like to get familiar with using lme4 because it is likely that the
> final results of the experiment will be unbalanced (which precludes the
> use of aov I think). The df given by model1 seem to make sense. Any
> guidance on any of the following would be much appreciated:
> 1. Are model1 and model2 equivalent?
> 2. For model1 - is the random component correctly specified and is
> there a (simple) mechanism to get the appropriate F ratios and P
> values?
> 3. For model2 - again, is the random component correct (probably not)
> and why is the random effect (rep) variance and standard deviations so
> low (zero in most iterations)?
> 4. For both models - how do I isolate (so I can tabulate and create
> histograms) the appropriate P and/or t values? (for model2 - the
> ‘mer’ object doesn’t seem to contain the t values but maybe
> I’m missing something).
> Direction to any more generic sources of information regarding power
> analysis in hierarchical models would be gladly received.
> Thank you
> Tom.
>
>
> -------------------------------------------------------------------------
> Tom Wilding, MSc, PhD, Dip. Stat.
> Scottish Association for Marine Science,
> Scottish Marine Institute,
> OBAN
> Argyll. PA37 1QA
> United Kingdom.
> Phone (+44) (0) 1631 559214
> Fax (+44) (0) 1631 559001
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