mixed models lmer function help!!
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 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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