Advancing deterministic algorithms for mixed-effects modelling in R (13frg180)


(McMaster University)


The Banff International Research Station will host the "Advancing deterministic algorithms for mixed-effects modelling in R" workshop from to .

Statistical analysis is simpler when data have been sampled independently. But independent sampling is often impractical or impossible in real-world studies. Consider, for example, a study on the health of urban Canadians. Two individuals sampled from the same city would be more likely to share symptoms than individuals sampled from different cities. Such a study would lead to statistical dependence among individuals from the same city, which may systematically bias conclusions. However, it is possible to conduct statistical analyses with the potential to correct for such biases -- a technique called mixed effects modelling. This technique posses many technical and conceptual challenges that do not arise in the analysis of independent samples. For example, mixed effects modelling requires relatively more computer time than techniques designed for independent samples, potentially inhibiting the use of these techniques on large data sets. Further, for certain statistical problems, software for mixed effects modelling may not even exist or may be prohibitively expensive. Our central goal with this focused research group is to expand the range and quality of free and open-source computational tools available for analyzing data with mixed models.
