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Message: 1 Date: Thu, 18 Dec 2008 16:52:46 +0530
Hi, If your counts are relatively high, you might start with a log-normal or gamma distribution. What you are talking about here are species abundance distributions on which there is a large body of confusing (and often wrong) literature. A good resource is Steinhard Engen (1978), Stochastic Abundance Models, CRC press. But this may be hard to find these days, and is not for the beginner. But anyway, to just do an ANOVA, comparing treatments just taking the log and assuming a log-normal distribution may be good enough. By the way what is your response? Number of species? In that case you could do fit<-lm(log(No.Species)~Habitat, mydat) anova(fit) or glm(No.Species~Habitat, mydat,family=poisson()) anova(fit) But I am not sure this answers anything. If your habitats follow a gradient, or you have covariates describing the habitats, you may want to look at ordination methods in vegan or ade4 good luck Nicholas
From: "mujeeb rahman" <mujeebrahmanp at gmail.com> Subject: [R-sig-eco] About data distribution model To: r-sig-ecology at r-project.org Message-ID: <ac3840600812180322p47f58301x7face2587107c21b at mail.gmail.com> Content-Type: text/plain Hi I have soil fauna count data from 60 plots (locations) which comprise 15 different habitats (15 x 4). From each of the 60 plots i taken 4 soil monoliths (soil core of 25 x25 cm size) and counted all the fauna. it is sure that the data does not follow a normal distribution and we could do anova. I want to find out which dsitribution it follow and to find out which model it can fit well. (Some body says that such type of data may fit with negative binomial or poisson.) Any solutions in R?. More over I am new to R programme, I expect your kind help Mujeeb Rahman P Kerala Forest Research Institute Peechi, Thrissur, Kerala, India. [[alternative HTML version deleted]]