[R] Problem with anova.lmRob() "robust" package

[David Winsemius]

On Jul 28, 2011, at 9:13 AM, m.fenati at libero.it wrote:

>
> Dear R users,
> I'd like to known your opinion about a problem with anova.lmRob() of  
> "Robust" package that occurs when I run a lmRob() regression on my  
> dataset.
> I check my univariate model by single object anova as  
> anova(lmRob(y~x)).
> If I compare my model with the null model (y~1), I must obtain the  
> same results,
> but not for my data.
> Is it possible?
>
> My example:
>
> x<-c(rep(0,8),rep(1,8),rep(2,7))
> y<- 
> c 
> (1,0.6 
> ,-0.8,0.7,1.6 
> ,-0.2 
> ,-1.2 
> ,-3.8 
> ,-1.8 
> ,-2.6,-1.7,-2.1,-0.3,-1.4,1.4,-0.3,-0.3,0.5,0.4,-0.9,-1.6,0.4,0.4)
> library(robust)
> lmR<-lmRob(y~factor(x))
> anova(lmR)
> lmR0<-lmRob(y~1)
> anova(lmR,lmR0)
>
> If I run the code omitting the factor() (then treating "x" as  
> continuous), the results are the same..
>

I do not get the same results with that code. And the code does not  
appear to track your description, since the second model does not have  
an "x" term in it. Even when I create the model that it sounded as  
though you would have written, namely lmR0 <- lmRob(y ~ x),  it is  
clearly _not_ the same result.

 > coef(lmR)
(Intercept)  factor(x)1  factor(x)2
   0.2428571  -1.3432007  -0.4000000
 > coef(lmR0)
  (Intercept)            x
-0.509524217  0.005820355
>
> What is the explanation of these different results?

Since you didn't post your results and since your complaint was that  
they are "the same", it's hard to know what you are talking about.

-- 
David Winsemius, MD
West Hartford, CT