[R] lm and aov produce different results for nested fixed-factor anova

[Mark Difford]

Hi Sergii,

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

There is no difference between the two if you treat them equally, i.e. if
you summarize them in the same way.

## Try:
anova(e2)
summary(e1)

## Or:
summary.lm(e1)
summary(e2)

Your nested model is also unusual: customary/correct is: 

e2 <- lm( z/x - 1)

The nesting introduces an intercept for each level of the nesting factor.
Also, x presently is a factor (with 48 levels). Is that what you really want
?

Regards, Mark.



Sergii Ivakhno wrote:
> 
> 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 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 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
> 
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> 
> http://www.compbio.group.cam.ac.uk <http://www.compbio.group.cam.ac.uk/> /
> 
> 
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