  Extensions of linear models are very commonly used in the analysis of
biological data. Whereas goodness of fit measures such as the coefficient of
determination (R2) or the adjusted R2 are well established for linear models,
it is not obvious how such measures should be defined for generalized linear
and mixed models. There are by now several proposals but no consensus has yet
emerged as to the best unified approach in these settings. In particular, it is
an open question how to best account for heteroscedasticity and for covariance
among observations induced by random effects. This paper proposes a new
approach that addresses this issue and is universally applicable. It is
exemplified using three biological examples.
