Hi,
I have repeated measures from two groups (treatment and control), three subjects in each, over three time points.
Here's the data in an R data.frame:df <- data.frame(subject=rep(c("T1","T2","T3","C1","C2","C3"),3), group=rep(c(rep("T",3),rep("C",3)),3), time=c(rep(1,6),rep(2,6),rep(3,6)), measure=c(0,253,155,16,232,251,1035,1014,760,98,239,87,371,60,47,0,260,190), col=rep(c(rep("red",3),rep("blue",3)),3), stringsAsFactors=F)
The plot shows the time x group interaction:
R code for producing the plot:
plot(df$time,df$measure,col=df$col,xlab="time",ylab="measure")
legend("topleft",legend=c("treatment","control"),col=c("red","blue"),pch=1)
My question is what model to use to capture the?time x group interaction.
I thought:library(lmerTest)fit <- lmer(measure~time+group+time*group+(time|subject),data=df)
might do it.
But the summary of this model doesn't really capture that:> summary(fit)Linear mixed model fit by REMLt-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']Formula: measure ~ time + group + time * group + (time | subject)?Data: df
REML criterion at convergence: 210
Scaled residuals:?
? ?Min ? ? 1Q Median ? ? 3Q ? ?Max?
-1.228 -0.448 -0.163 ?0.275 ?1.923
Random effects:
?Groups ? Name ? ? ? ?Variance Std.Dev. Corr
?subject ?(Intercept) 0.00e+00 0.00e+00 ? ??
? ? ? ? ? time ? ? ? ?3.06e-16 1.75e-08 ?NaN
?Residual ? ? ? ? ? ? 1.05e+05 3.25e+02 ? ??
Number of obs: 18, groups: ?subject, 6
Fixed effects:
? ? ? ? ? ? Estimate Std. Error ? ? df t value Pr(>|t|)
(Intercept) ? 168.89 ? ? 286.35 ?13.78 ? ?0.59 ? ? 0.56
time ? ? ? ? ? -8.17 ? ? 132.55 ?13.78 ? -0.06 ? ? 0.95
groupT ? ? ? ?218.33 ? ? 404.96 ?13.78 ? ?0.54 ? ? 0.60
time:groupT ? ?19.83 ? ? 187.46 ?13.78 ? ?0.11 ? ? 0.92
Correlation of Fixed Effects:
? ? ? ? ? ? (Intr) time ? groupT
time ? ? ? ?-0.926 ? ? ? ? ? ? ?
groupT ? ? ?-0.707 ?0.655 ? ? ??
time:groupT ?0.655 -0.707 -0.926
So my question is what model to use?
Thanks a lot,Dan
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