[R] Some questions on repeated measures (M)ANOVA & mixed modelswith lme4

[John Fox]

Dear Marco,

You might also take a look at ?Anova (or ?Manova) in the car package; the
last examples are for a repeated-measures ANOVA using both MANOVA and
univariate approaches, the latter with GG and HF corrections.

I hope this helps,
 John

--------------------------------
John Fox, Professor
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox 
-------------------------------- 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Marco B
> Sent: Sunday, May 13, 2007 10:22 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Some questions on repeated measures (M)ANOVA & 
> mixed modelswith lme4
> 
> Dear R Masters,
> 
> I'm an anesthesiology resident trying to make his way through 
> basic statistics. Recently I have been confronted with 
> longitudinal data in a treatment vs. control analysis. My 
> dataframe is in the form of:
> 
> subj | group | baseline | time | outcome (long) or subj | 
> group | baseline | time1 |...| time6 | (wide)
> 
> The measured variable is a continuous one. The null 
> hypothesis in this analysis is that the Group factor does not 
> significantly influence the outcome variable. A secondary 
> null hypothesis is that the Group x Time interaction is not 
> significant, either. Visual of the group means indicates the 
> outcome measure decreases linearly (more or less) over time 
> from baseline values. The time==1...time==6 intervals are 
> equally-spaced and we have equal sample sizes for the groups.
> 
> I've done a little reading around and found (at least) four 
> possible approaches:
> 
> A. Linear mixed model using lme4 with random intercept and slope with
> lmer() or lme()
> 
> B. Repeated measures ANOVA using aov() with Error() 
> stratification (found in Baron & Li, 2006), something along 
> the lines of:
> aov(outcome ~ group * time + baseline + Error(subj+subj:time))
> 
> (from: http://cran.r-project.org/doc/contrib/Baron-rpsych.pdf, p. 41)
> 
> C. "Repeated measures" MANOVA as follows (using data in wide format):
> response <- cbind(time1,time2,time3,time4,time5,time6)
> mlmfit <- lm(response ~ group)
> mlmfit1 <- lm(response ~ 1)
> mlmfit0 <- lm(response ~ 0)
> # Test time*group effect
> anova.mlm(mlmfit, mlmfit1, X=~1, test="Spherical") # Test 
> overall group effect anova.mlm(mlmfit, mlmfit1, M=~1) # Test 
> overall time effect anova.mlm(mlmfit1, mlmfit0, X=~1, 
> test="Spherical")
> 
> (taken from http://tolstoy.newcastle.edu.au/R/help/05/11/15744.html)
> 
> Now, on with the questions:
> 
> 1. This is really a curiosity. I find lmer() easier to use 
> than lme(), but the former does not allow the user to model 
> the correlation structure of the data. I figure lmer() is 
> presently assuming no within-group correlation for the data, 
> which I guess is unlikely in my example. Is there a way to 
> compare directly (maybe in terms of
> log-likelihood?) similar models fitted in lme() and lmer()?
> 
> 2. Baron & Li suggest a painful (at least for me) procedure 
> to obtain Greenhouse-Geisser or Huyn-Feldt correction for the 
> ANOVA analysis they propose. Is there a package or function 
> which simplifies the procedure?
> 
> 3. I must admit that I don't understand solution C. I can 
> "hack" it to fit my model, and it seems to work, but I can't 
> seem to grasp the overall concept, especially regarding the 
> outer and/or inner projection matrices (M & X). Could anyone 
> point me to a basic explanation of the procedure?
> 
> 4. Provided the assumptions for ANOVA hold, or that 
> deviations from them are not horrible, am I correct in saying 
> that this procedure would be the most powerful one? How would 
> you choose solution A over solution B (or viceversa)?
> 
> My sincerest gratitude to anyone who will take the time to 
> answer my questions!
> 
> Best Regards,
> 
> Marco
> 
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