A mixed Gaussian phylogenetic model (MGPM) represents a Gaussian phylogenetic model with shifts in the underlying parameters and, optionally, type of Gaussian stochastic process (e.g. shifts from a BM to an OU model of evolution). The function PCMFitMixed implements a recursive clade partition (RCP) search for an approximate information score optimization. A deteailed description of the algorithm is provided in Appendix A in Mitov et al. 2019a. For this documentation, it is important to note that the algorithm proceeds in three steps as follows:

Model type fits to all clades in the tree bigger than a specified (see argument minCladeSize).

RCP

Round-robin

Round-robin search for an optimal model type mapping in the best partitions found during the RCP-step. This step is optional.

PCMFitMixed(X, tree, modelTypes = MGPMDefaultModelTypes(),
subModels = c(B = "A", C = "A", D = "B", E = "D", F = "E"),
argsMixedGaussian = Args_MixedGaussian_MGPMDefaultModelTypes(),
SE = matrix(0, nrow(X), PCMTreeNumTips(tree)),
generatePCMModelsFun = PCMGenerateModelTypes, metaIFun = PCMInfo,
positiveValueGuard = Inf, scoreFun = AIC, fitMappingsPrev = NULL,

## Value

an object of S3 class PCMFitModelMappings.

## References

[Mitov et al. 2019a] Mitov, V., Bartoszek, K., & Stadler, T. (2019). Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models. Proceedings of the National Academy of Sciences of the United States of America, 35, 201813823. http://doi.org/10.1073/pnas.1813823116

[Mitov et al. 2019b] Mitov, V., Bartoszek, K., Asimomitis, G., & Stadler, T. (2019). Fast likelihood calculation for multivariate Gaussian phylogenetic models with shifts. Theoretical Population Biology. http://doi.org/10.1016/j.tpb.2019.11.005

PCMFitMixed PCMOptions