Yes, the analysis with a small sample size would be valid (under the assumption that the model - both fixed and random effects are correctly specified) but at some point there must be a practical assessment as to the desired precision and the costs of the consequences of either inadequate estimates or wrong acceptance or rejection of hypotheses. If it were just about the numbers from a sample and resulting P-values, we would only need statisticians and no subject-matter experts (which is clearly not the case).
And while I'm soapboxing, situations with low variability require fewer samples than situations with high variability. One can't make assessments of the adequacy of an analysis solely on the sample size. Jim Jim Baldwin Station Statistician Pacific Southwest Research Station USDA Forest Service -----Original Message----- From: r-sig-ecology-boun...@r-project.org [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of V. Coudrain Sent: Monday, October 20, 2014 8:54 AM To: ElginPerry Cc: r-sig-ecology@r-project.org Subject: Re: [R-sig-eco] Regression with few observations per factor level 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. ___________________________________________________________ Mode, hifi, maison,? J'ach?te malin. Je compare les prix avec [[alternative HTML version deleted]] ___________________________________________________________ Mode, hifi, maison,… J'achète malin. Je compare les prix avec [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology This electronic message contains information generated by the USDA solely for the intended recipients. Any unauthorized interception of this message or the use or disclosure of the information it contains may violate the law and subject the violator to civil or criminal penalties. If you believe you have received this message in error, please notify the sender and delete the email immediately. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
