Re: [R] improvement of Ancova analysis

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Sun, 04 May 2008 06:24:04 +0100 (BST)

For points 4 and 5, you could use a robust linear fit. One way to do that is to use rlm() from package MASS, which is used in several examples in the book that package MASS supports.

On Sun, 4 May 2008, Tobias Erik Reiners wrote:

> Dear Helpers,
>
> I just started working with R and I'm a bit overloaded with information.
>
> My data is from marsupials reindroduced in a area. I have weight(wt), hind
> foot
> lenghts(pes) as continues variables and origin and gender as categorial.
> condition is just the residuals i took from the model.
>
>> names(dat1)
> [1] "wt" "pes" "origin" "gender" "condition"
>
> my model after model simplification so far:
> model1<-lm(log(wt)~log(pes)+origin+gender+gender:log(pes))
> -->six intercepts and two slopes
>
> the problem is i have some things I can't include in my analysis:
> 1.Very different sample sizes for each of the treatments
>> tapply(log(wt),origin,length)
> captive site wild
> 119 149 19
> 2.Substantial differences in the range of values taken by the covariate (leg
> length) between treatments
>> tapply(pes,origin,var)
> captive site wild
> 82.43601 71.44442 60.42544
>> tapply(pes,origin,mean)
> captive site wild
> 147.3261 144.8698 148.2895
>
> 4.Outliers
> 5.Poorly behaved residuals
>
> thanks for the answer I am open minded to any different kind of analysis.
>
> Tobi
>
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-- 
Brian D. Ripley,                  ripley_at_stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Received on Sun 04 May 2008 - 05:27:03 GMT

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