The results of the data analysis are the basis for your conclusions. They contain estimates of the effects that have been studied including a measurement of uncertainty. Often, derived parameters are more meaningful than model parameters. In addition, it is helpful to present the information needed to judge the biological (or other) relevance of the results. Such information may include a plot or a measure of the total variance or the residual variance in the data so that the effects can be compared to natural variation. Figures can be an informative and easily understandable tool to communicate results. The description of a linear model in the methods section consists of the error distribution, the link function, the linear predictor, the random structure, the prior distributions, the fitting method, and how conclusions are drawn from the joint posterior distribution of the model parameters.