# [R-sig-ME] Repeated measures with a non-linear time effect

Jörg Albrecht albrechj at staff.uni-marburg.de
Thu Jul 21 21:27:25 CEST 2016

```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,

Jörg

> 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
>
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