[R] mixed models lmer function help!!

Bert Gunter bgunter.4567 at gmail.com
Mon Jun 19 22:08:40 CEST 2017

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.


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 at 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
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