Hi Tobias,
If you want to do inferential statistics with groups differing
systematically on the covariate, you will need to be extra careful in
your interpretation. See, e.g., Miller, G. A. & Chapman, J. P.
Misunderstanding Analysis of Covariance, Journal of Abnormal Psychology,
2001, 110, 40-48, and a lot of other similar things.
That said, with your wide variation in pes you may want to consider
restricted cubic splines ("natural splines") in Frank Harrell's Hmisc
and Design packages. At least, it would be interesting to test whether
the influence of pes really is linear, which can be done easily with
splines. See also Harrell, F. E. Regression Modeling Strategies,
Springer, 2001.
Good luck with your small furry creatures!
Stephan
Tobias Erik Reiners schrieb:
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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