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Ecological Models and Data in R Benjamin M. Bolker
Publisher: Princeton University Press

In exchange for mathematical tools, finance provides ecology with a wealth of data. Other good resources are Logistic Regression Models (Chapman &amp; Hall/CRC Texts in Statistical Science) and Modern Regression Techniques Using R: A Practical Guide . First, he explored the logistic map x_{t + 1} = r x_t(1 - x_t) May and colleagues make these connections precise by building analytic models for toy financial systems and then using their experience and tools from theoretical ecology to solve these models. 1a, see Table 1 for model selection results for Experiment I), with highest foraging rates in the high culling treatments. Bolker BM (2008) Ecological models and data in R. Princeton, N.J.: Princeton University Press. Linear mixed effects models are a powerful technique for the analysis of ecological data, especially in the presence of nested or hierarchical variables. R Development Core Team (2010). R^2 for linear mixed effects models. I just noticed that Ben Bolker's Ecological Models and Data in R has been published! The figures I'm interested in for this post are M <- matrix( c(20/33, -2/11, 8/33, 46/33) , ncol=2 ) A <- eigen (M)$vectors D <- diag(eigen(M)$values) N <- array( dim=c(11, 2) ) n0 <- matrix( c(2,1) ) N[1,] <- n0 for(i in 2:11){ N[i,] <- A %*% D^(i-1) %*% solve(A) %*% n0 } N <- as.data.frame(N). Introductory statistics with R. Ecological models and data in R. Inspired by (but independent of) ecology. This post is actually about replicating the figures in Otto and Day: A Biologist's Guide to Mathematical Modeling in Ecology and Evolution. Further, the cross-fertilization is not one-sided. R: A language and environment for statistical computing.