Dear  R users,

I have trouble obtaining the same results for nested Anova with two fixed factors when using lm and aov functions. 

The formulas are:

> e1=aov(y~x/z)

> e2=lm(y~x/z)

 

summary(e1)

               Df Sum Sq Mean Sq F value    Pr(>F)

x              47  260.0     5.5 18.0088 < 2.2e-16 ***

x:z           195  169.6     0.9  2.8318 < 2.2e-16 ***

Residuals   14425 4430.3     0.3

---

Signif. codes:  0 ā***ā 0.001 ā**ā 0.01 ā*ā 0.05 ā.ā 0.1 ā ā 1

2 observations deleted due to missingness

 

For e2

Residual standard error: 0.5542 on 14425 degrees of freedom

  (2 observations deleted due to missingness)

Multiple R-squared: 0.08839,    Adjusted R-squared: 0.07309

F-statistic: 5.779 on 242 and 14425 DF,  p-value: < 2.2e-16

 

 

 

I prefer to use lm, as in my case I want to know the difference between the first control group and all the other factors though regression coefficients. The same is true for levels of the nested factor within each level of the main factor. 

 

Since I am fairly novice to running linear models in R, I am not sure what can cause this problem; it also seems that lm does not provide the decomposition of MS into MS(x) and MS(z) and corresponding F-test statistics. (Is this possible to estimate them from lm output?)

 

Finally, few words about the dataset: main factor x has 48 levels, repeated from 60 to 540 times and represents different patients. The nested factor z has 9 levels, but not all of them occur within levels of factor x. Although the nested factor levels are independent between each of the main factor (i.e. they samples taken from different tissues of each patients), considering the large size of the dataset I was advised on this forum to use the same encoding of levels of nested  factor z at each level of factor x. I am not sure if this influences QR decomposition and leads to differences that I observe.

I would most appreciate your help as after reading help pages I still can not understand the cause for lm vs aov discrepancy.

The dataset with three factors can be downloaded from

http://www.compbio.group.cam.ac.uk/Resources/Sergii_temp/example.RData 

 

Thank you,

Sergii  

 
 
  
----------------------------------------------
Sergii Ivakhno

PhD student

Computational Biology Group
Cancer Research UK Cambridge Research Institute
Li Ka Shing Centre
Robinson Way
Cambridge CB2 0RE
England

+44 (0)1223 404293 (O)
+44 (0)1223 404128 (F)

http://www.compbio.group.cam.ac.uk <http://www.compbio.group.cam.ac.uk/> /


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