Thank you for this helpful thought. So if I get it correctly it is hopeless to
try testing an interaction, but we neverless may assess if a covariate has an
impact, providing it is the same in all treatments.
> Message du 20/10/14 à 16h46
> De : "Elgin Perry"
> A : v_coudr...@voila.fr
> Copie à :
> Objet : Regression with few observations per factor level
>
> If it is reasonable to assume that the slope of the covariate is the same for
> all treatments and you have numerous treatments then you can do this by
> specifying one slope parameter for all treatments as you gave in your example
> (e.g. lm(var ~ trt + cov)). By combining slope information over treatments,
> you can obtain a reasonably precise estimate. With so few observations per
> treatment, you will not be able to estimate separate slopes for each
> treatment with any degree of precision (e.g. lm(var ~ trt + trt:cov))
Elgin S. Perry, Ph.D.
Statistics Consultant
377 Resolutions Rd.
Colonial Beach, Va. 22443
ph. 410.610.1473
Date: Mon, 20 Oct 2014 10:53:41 +0200 (CEST)
From: "V. Coudrain" < v_coudr...@voila.fr >
To: r-sig-ecology@r-project.org
Subject: [R-sig-eco] Regression with few observations per factor level
Message-ID: < 2127199056.738451413795221981.JavaMail.www@wwinf7128 >
Content-Type: text/plain; charset="UTF-8"
Hi, I would like to test the impact of a treatment of some variable using
regression (e.g. lm(var ~ trt + cov)).?
However I only have four observations per factor level. Is it still possible to
apply a regression with such a small
sample size. I think that i should be difficult to correctly estimate
variance.Do you think that I rather should compute
a non-parametric test such as Kruskal-Wallis? However I need to include
covariables in my models and I am not sure if
basic non-parametric tests are suitable for this. Thanks for any suggestion.
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