Hi
I am working on a Nested one-way ANOVA. I don't know how to implement
R code to test the significance of the random factor
My R code so far can only test the fixed factor :
anova(lme(PCB~Area,random=~1|Sites, data = PCBdata))
numDF denDF F-value p-value
(Intercept) 1 12 1841.7845 <.0001
Area 1 4 4.9846 0.0894
Here is my data and my hand calculation.
> PCBdata
Area Sites PCB
1 A 1 18
2 A 1 16
3 A 1 16
4 A 2 19
5 A 2 20
6 A 2 19
7 A 3 18
8 A 3 18
9 A 3 20
10 B 4 21
11 B 4 20
12 B 4 18
13 B 5 19
14 B 5 20
15 B 5 21
16 B 6 19
17 B 6 23
18 B 6 21
By hand calculation, the result should be:
Source SS DF MS
Areas 18.00 1 18.00
Sites 14.44 4 3.61
Error 20.67 12 1.72
Total 53.11 17 ---
MSareas/MSsites = 4.99 --- matching the R output
MSsites/MSE = 2.10
Conclusion is that Neither of Areas nor Sites make differences.
My R code so far can only test the fixed effect :
anova(lme(PCB~Area,random=~1|Sites, data = PCBdata))
numDF denDF F-value p-value
(Intercept) 1 12 1841.7845 <.0001
Area 1 4 4.9846 0.0894
--
Xiang Gao, Ph.D.
Department of Biology
University of North Texas
On Feb 14, 2012, at 5:36 PM, Xiang Gao wrote:> Hi > > I am working on a Nested one-way ANOVA. I don't know how to implement > R code to test the significance of the random factorHave you read what the unofficial Mixed Model FAQ says about testing for significance on random effects? http://glmm.wikidot.com/faq> > My R code so far can only test the fixed factor :That may be the intent of the authors. They may want to make it sufficiently difficult so that an adequate barrier prevents the unwary from taking some "easy way out". You probably need to describe your study (assuming this is not an assigned homework exercise) in sufficient scientific detail and do so on the mixed-models mailing list. -- David.> > anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) > numDF denDF F-value p-value > (Intercept) 1 12 1841.7845 <.0001 > Area 1 4 4.9846 0.0894 > > > Here is my data and my hand calculation. > >> PCBdata > Area Sites PCB > 1 A 1 18 > 2 A 1 16 > 3 A 1 16 > 4 A 2 19 > 5 A 2 20 > 6 A 2 19 > 7 A 3 18 > 8 A 3 18 > 9 A 3 20 > 10 B 4 21 > 11 B 4 20 > 12 B 4 18 > 13 B 5 19 > 14 B 5 20 > 15 B 5 21 > 16 B 6 19 > 17 B 6 23 > 18 B 6 21 > > By hand calculation, the result should be: > Source SS DF MS > Areas 18.00 1 18.00 > Sites 14.44 4 3.61 > Error 20.67 12 1.72 > Total 53.11 17 --- > > > MSareas/MSsites = 4.99 --- matching the R output > MSsites/MSE = 2.10 > Conclusion is that Neither of Areas nor Sites make differences. > > > My R code so far can only test the fixed effect : > > anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) > numDF denDF F-value p-value > (Intercept) 1 12 1841.7845 <.0001 > Area 1 4 4.9846 0.0894 > > > > -- > Xiang Gao, Ph.D. > Department of Biology > University of North Texas > > ______________________________________________ > R-help at r-project.org mailing list > 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.David Winsemius, MD West Hartford, CT
This post https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001819.html may help you understand why the standard p-values in some cases are not the right thing to do and what one alternative is. On Tue, Feb 14, 2012 at 3:36 PM, Xiang Gao <xianggao2006 at gmail.com> wrote:> Hi > > I am working on a Nested one-way ANOVA. I don't know how to implement > R code to test the significance of the random factor > > My R code so far can only test the fixed factor : > > anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) > ? ? ? ? ? ?numDF denDF ? F-value p-value > (Intercept) ? ? 1 ? ?12 1841.7845 ?<.0001 > Area ? ? ? ? ? ? ?1 ? ? 4 ? ?4.9846 ?0.0894 > > > Here is my data and my hand calculation. > >> PCBdata > ? Area Sites PCB > 1 ? ? A ? ? 1 ?18 > 2 ? ? A ? ? 1 ?16 > 3 ? ? A ? ? 1 ?16 > 4 ? ? A ? ? 2 ?19 > 5 ? ? A ? ? 2 ?20 > 6 ? ? A ? ? 2 ?19 > 7 ? ? A ? ? 3 ?18 > 8 ? ? A ? ? 3 ?18 > 9 ? ? A ? ? 3 ?20 > 10 ? ?B ? ? 4 ?21 > 11 ? ?B ? ? 4 ?20 > 12 ? ?B ? ? 4 ?18 > 13 ? ?B ? ? 5 ?19 > 14 ? ?B ? ? 5 ?20 > 15 ? ?B ? ? 5 ?21 > 16 ? ?B ? ? 6 ?19 > 17 ? ?B ? ? 6 ?23 > 18 ? ?B ? ? 6 ?21 > > By hand calculation, the result should be: > Source ?SS ? ? ?DF ? ? ?MS > Areas ? ? ?18.00 ?1 ? ?18.00 > Sites ? ? ? ?14.44 ?4 ? ?3.61 > Error ? ? ? ?20.67 ?12 ?1.72 > Total ? ? ? ? ? 53.11 ? 17 ? --- > > > MSareas/MSsites = 4.99 --- matching the R output > MSsites/MSE = 2.10 > Conclusion is that Neither of Areas nor Sites make differences. > > > My R code so far can only test the fixed effect : > > anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) > ? ? ? ? ? ?numDF denDF ? F-value p-value > (Intercept) ? ? 1 ? ?12 1841.7845 ?<.0001 > Area ? ? ? ? ? ? ?1 ? ? 4 ? ?4.9846 ?0.0894 > > > > -- > Xiang Gao, Ph.D. > Department of Biology > University of North Texas > > ______________________________________________ > R-help at r-project.org mailing list > 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.-- Gregory (Greg) L. Snow Ph.D. 538280 at gmail.com