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
______________________________________________
R-help@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.
--
Brian D. Ripley, [EMAIL PROTECTED]
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
______________________________________________
R-help@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.
