repeated measures: lme(r) vs manova
Hi Nathan, Here is a nice simulation study comparing LME to MANOVA and several other traditional methods (I hope you don't mind the interdisciplinary transfer): Gueorguieva, R., & Krystal, J. H. (2004). Move Over ANOVA. Archives of General Psychiatry, 61, 310-317. The bottom line: In longitudinal designs, LME has better power in presence of missing values. CU, Martin
-----Original Message----- From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed- models-bounces at r-project.org] On Behalf Of Nathan Lemoine Sent: Monday, March 19, 2012 10:32 PM To: r-sig-mixed-models at r-project.org Subject: [R-sig-ME] repeated measures: lme(r) vs manova Hi all, Sorry in advance for the length of this post, but I've searched around and couldn't find anything that addressed this issue: I recently ran into the issue of deciding on the appropriate way to
analyze a
repeated measures design. We enriched quadrats to measure productivity and we monitored them for three years. Four quadrats were nested within plots. Here are the data: "plot" "quad" "nut" "t1" "t3" "t4" "1" 1 "A" "nut" 17.69130435 70.4 57.8 "2" 1 "A" "no nut" 65.4173913 125.8 109.9 "3" 1 "B" "nut" 19.56521739
103.2
100.8 "4" 1 "B" "no nut" 89.03636364 131.3 99.1 "5" 1 "C" "nut"
29.88723404
25.7 29.9 "6" 1 "C" "no nut" 45.45454545 113.1 110.6 "7" 1 "D" "nut" 18.28181818 60.9 67.7 "8" 1 "D" "no nut" 68.88888889 136 95 "9" 2 "A"
"nut"
35.41666667 61.6 16 "10" 2 "A" "no nut" 40.90909091 59.4 64.7 "11" 2 "B"
"nut"
34.14255319 26.7 23.1 "12" 2 "B" "no nut" 36.27021277 71.6 47.2 "13" 2 "C" "nut" 13.33333333 20.9 26.4 "14" 2 "C" "no nut" 7.118181818 31.2 19.1 "15"
2
"D" "nut" 20 30.9 27.8 "16" 2 "D" "no nut" 19.34893617 31.3 16.7 "17" 3
"A"
"nut" 22.22222222 130.7 163.6 "18" 3 "A" "no nut" 32.90869565 83.8 86.2
"19" 3
"B" "nut" 38.29787234 99 110.1 "20" 3 "B" "no nut" 38.83636364 127.1 115.2 "21" 3 "C" "nut" 38.88888889 81.7 193.7 "22" 3 "C" "no nut" 28.98888889
72.1
103.8 "23" 3 "D" "nut" 50 111.3 117.7 "24" 3 "D" "no nut" 26.86666667 94.2
113
"25" 4 "A" "nut" 63.63636364 128.4 114.8 "26" 4 "A" "no nut" 108.8956522
121
80.7 "27" 4 "B" "nut" 104.4444444 146.5 102.2 "28" 4 "B" "no nut"
84.74444444
111.5 109.9 "29" 4 "C" "nut" 71.31111111 86.2 118.4 "30" 4 "C" "no nut" 115.9555556 131.4 141.9 "31" 4 "D" "nut" 75.65555556 141.5 92.5 "32" 4 "D" "no nut" 108.9888889 146.6 122.2 "33" 5 "A" "nut" 20.2 57.4 14.6 "34" 5
"A" "no
nut" 12.34489796 55.4 13.4 "35" 5 "B" "nut" 48.98888889 56.3 28.7 "36" 5
"B"
"no nut" 35.65555556 55.8 17.6 "37" 5 "C" "nut" 22.22222222 45.9 7.3 "38"
5
"C" "no nut" 9.088888889 55.6 20.5 "39" 5 "D" "nut" 64.44444444 86.1 61.7
"40"
5 "D" "no nut" 15.65555556 75.7 41.8 "41" 6 "A" "nut" 22.22222222 101.1
69.8
"42" 6 "A" "no nut" 53.33333333 171.2 113.5 "43" 6 "B" "nut" 37.87777778
111.1 66.8 "44" 6 "B" "no nut" 46.96666667 120.8 83.8 "45" 6 "C" "nut"
17.87777778 120.7 84 "46" 6 "C" "no nut" 21.21212121 116.3 76.8 "47" 6 "D"
"nut" 24.01304348 86.1 64.6 "48" 6 "D" "no nut" 29.51034483 112.5 51.9
The basic question is: When is it appropriate to use a MANOVA-based
repeated measures design over a mixed effects model?
For example, the MANOVA approach:
library(car)
repeated.manova <- lm(cbind(t1,t3,t4)~nut+plot+quad, data=manova.data)
Manova(repeated.manova)
nut is not significant and there are 40 denominator df.
If I set up the data and run lme:
mixed.dat <- melt(manova.data, id=c("plot","quad","nut"))
colnames(mixed.dat)[4:5] <- c("time","prod") mixed.dat$time <-
as.numeric(mixed.dat$time))
library(nlme)
lme.repeated <- lme(prod~nut, random=~nut|time, data=mixed.dat)
anova(lme.repeated)
Gives 140 denominator df. I'm also not sure this is the appropriate set up
for
a repeated measures design. Running the following code seems more in line with what I've read to take into account the correlation in observations within the same plot: lme.repeated2 <- lme(prod~nut*time, random=~time|plot, data=mixed.dat) anova(lme.repeated2) This model seems much more appropriate, as observations within plots are now allowed to be correlated, but there is still a huge difference between the MANOVA-based approach and the mixed-effects-based approach, as the mixed-effects model gives me a significant result. The MANOVA assumes that I have three (correlated) observations on 48 independent units, whereas the lme approach assumes that I have 144 observations on correlated units. Also not sure if that interpretation is correct. Alternatively, I used lmer() for non-nested, multilevel models allowing observations to be correlated in space and time: repeated.mixed3 <- lmer(prod~nut + (1|plot) + (1|time), data=mixed.dat) repeated.mixed4 <- lmer(prod~ (1|plot) + (1|time), data=mixed.dat) anova(repeated.mixed3, repeated.mixed4) This approach also gives me a significant result. Which of these is the
most
appropriate? The differences between lme and lmer are trivial (in this
case),
but the difference between the MANOVA approach and mixed-effects is substantial. I figure the MANOVA approach is probably in correct on
account
of the nested design, but my question extends to situations when the
design
is not nested. Thanks in advance for your help, Nathan
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