1. A mess, because you failed to read and follow the posting guide: This is a **plain text** mailing list, which means that html can get mangled, as you have demonstrated.
2. And wrong list: the r-sig-mixed-models list is where this would be more suitable. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Jun 19, 2017 at 9:29 AM, Shelby Leonard via R-help <r-help@r-project.org> wrote: > Hi,I have tumor growth curve data for a bunch of different mice in various > groups. I want to compare the growth curves of the different groups to see if > timing of drug delivery changed tumor growth.I am trying to run a mixed > models with repeated measures over time with each mouse as a random effect > with linear and quadratic terms for time.This took me a long time to figure > out and I just wanted to make sure I did it correctly and I am interpreting > it correctly. This is the code I ran > Rtumor<-lmer(volume~Group+Time+(1|Subject), > data=Rtumor)summary(Rtumor)Rtumor.null=lmer(volume~Time+(1|Subject), > data=Rtumor, REML=FALSE)Rtumor.full=lmer(volume~Group+Time+(1|Subject), > data=Rtumor, REML=FALSE)anova(Rtumor.null,Rtumor.full) > Here is my output Rtumor<-lmer(volume~Group+Time+(1|Subject), data=Rtumor)> > summary(Rtumor)Linear mixed model fit by REML ['lmerMod']Formula: volume ~ > Group + Time + (1 | Subject) Data: Rtumor > REML criterion at convergence: 1541.2 > Scaled residuals: Min 1Q Median 3Q Max -1.8006 -0.6348 > -0.0658 0.3903 4.7551 > Random effects: Groups Name Variance Std.Dev. Subject (Intercept) > 3.197e-09 5.654e-05 Residual 3.348e+05 5.786e+02Number of obs: > 101, groups: Subject, 11 > Fixed effects: Estimate Std. Error t value(Intercept) -495.520 > 303.619 -1.632Group 24.350 115.615 0.211Time 79.653 > 7.886 10.101 > Correlation of Fixed Effects: (Intr) Group Group -0.933 Time > -0.300 -0.007 >> Rtumor.null=lmer(volume~Time+(1|Subject), data=Rtumor, REML=FALSE)> >> Rtumor.full=lmer(volume~Group+Time+(1|Subject), data=Rtumor, REML=FALSE)> >> anova(Rtumor.null,Rtumor.full)Data: RtumorModels:Rtumor.null: volume ~ Time >> + (1 | Subject)Rtumor.full: volume ~ Group + Time + (1 | Subject) >> Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)Rtumor.null 4 >> 1576.5 1586.9 -784.24 1568.5 Rtumor.full 5 1578.4 >> 1591.5 -784.22 1568.4 0.0457 1 0.8307There were 50 or more >> warnings (use warnings() to see the first 50) > > > My questions are1) Did I do this correctly?2) Do I still need to run it again > with quadratic terms for time?, If so, how do I do this?3) If I am > understanding these results correctly, they say There is no difference > between these groups on volume growth curves > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
