Extending the Linear Model with R Faraway J.
Publisher: Chapman & Hall/CRC

Boca Raton, FL: Chapman & Hall/CRC. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. Here we extend that study to compare carbohydrate restriction to fat restriction, using the change in relative fat measured by dual energy X ray absorptiometry (DXA), an accurate method to assess percent fat [20,21], as a phenotype. Boca Raton, FL, Chapman & Hall/CRC; 2004. C# code fits the four-parameter logistic to the given data. Dunn, Analytical Biochemistry 343 (2005) pp 54-65. By extending the initial conditions vector by one element, and changing the model function delegate we could use the same code to fit the FiveParameterLogistic function. The fifth is on Dynamic Linear Models mainly using Kalman filter and a Bayesian approach, which would extend the depth of statsmodels in time series analysis. We hypothesized that genes representative of food intake, energy homeostasis, and Faraway JJ: Linear Models with R. The five-parameter logistic: A characterization and comparison with the four-parameter logistic, Paul G. Many GSoC slots we will get, but we have the chance this year to get a large increase in the speed of development of statsmodels, and we can reduce the number of cases where someone needs to run to R, or Stata, or Matlab because there is no implementation for a statistical analysis available in Python. Data and R code from all chapters are available from www.highstat.com. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. [CRC Texts in Statistical Science Series] OpenURL.