R help - Feb 2012 - how to test the random factor effect in lme

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 factor
Have 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