Repeated measures with a non-linear time effect
Hi?J?rg, Unfortunately setting time as integer doesn't change anything whereas setting it as a factor means that the number of random effects will be equal to the number of samples. Perhaps this is an alternative:fit1 <- gls(measure~time*group,correlation=corSymm(form=~1|subject),weight=varIdent(form=~1|time),data=df) as it does capture the group and interaction effects but I think I need to contrast it with a null model and I'm not sure what would that null model be. Any idea?
On Thursday, July 21, 2016 3:27 PM, J?rg Albrecht <albrechj at staff.uni-marburg.de> wrote:
Hi Dan, most likely the model treats your time covariate as a continuous predictor. Try str(df), then time should appear as integer (int). If you specify time as a factor (with three levels: 1, 2, 3) the model will be able to estimate the time x treatment interaction separately for each time point. However, you still have to decide whether treating time as a factorial variable makes sense for your dataset. Best, J?rg
Am 21.07.2016 um 20:13 schrieb Daniel Rubi via R-sig-mixed-models <r-sig-mixed-models at r-project.org>:
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
<Rplot.png>_______________________________________________
R-sig-mixed-models at r-project.org mailing list