simulate_sbm {castor} | R Documentation |

Given a rooted phylogenetic tree and a Spherical Brownian Motion (SBM) model for the evolution of the geographical location of a lineage on a sphere, simulate random outcomes of the model on all nodes and/or tips of the tree. The function traverses nodes from root to tips and randomly assigns a geographical location to each node or tip based on its parent's previously assigned location and the specified model parameters. The generated states have joint distributions consistent with the SBM model (Perrin 1928; Brillinger 2012). This function generalizes the simple SBM model to support time-dependent diffusivities.

simulate_sbm(tree, radius, diffusivity, time_grid = NULL, splines_degree = 1, root_latitude = NULL, root_longitude = NULL)

`tree` |
A rooted tree of class "phylo". The root is assumed to be the unique node with no incoming edge. Edge lengths are assumed to represent time intervals or a similarly interpretable phylogenetic distance. |

`radius` |
Strictly positive numeric, specifying the radius of the sphere. For Earth, the mean radius is 6371 km. |

`diffusivity` |
Either a single numeric, or a numeric vector of length equal to that of Under a planar approximation the squared geographical distance of a node from the root will have expectation |

`time_grid` |
Numeric vector of the same length as |

`splines_degree` |
Integer, either 0,1,2 or 3, specifying the polynomial degree of the provided |

`root_latitude` |
The latitude of the tree's root, in decimal degrees, between -90 and 90. If NULL, the root latitude is chosen randomly according to the stationary probability distribution of the SBM. |

`root_longitude` |
The longitude of the tree's root, in decimal degrees, between -180 and 180. If NULL, the root longitude is chosen randomly according to the stationary probability distribution of the SBM. |

For short expected transition distances this function uses the approximation formula by Ghosh et al. (2012). For longer expected transition distances the function uses a truncated approximation of the series representation of SBM transition densities (Perrin 1928).

The pair `time_grid`

and `diffusivity`

can be used to define a time-dependent diffusivity, with time counted from the root to the tips (i.e. root has time 0) in the same units as edge lengths. For example, to define a diffusivity that varies linearly with time, you only need to specify the diffusivity at two time points (one at 0, and one at the time of the youngest tip), i.e. `time_grid`

and `diffusivity`

would each have length 2. Note that `time_grid`

should cover the full time range of the tree; otherwise, `diffusivity`

will be extrapolated as a constant when needed.

If `tree$edge.length`

is missing, each edge in the tree is assumed to have length 1. The tree may include multifurcations as well as monofurcations.

A list with the following elements:

`success` |
Logical, specifying whether the simulation was successful. If |

`tip_latitudes` |
Numeric vector of length Ntips, listing simulated decimal latitudes for each tip in the tree. |

`tip_longitudes` |
Numeric vector of length Ntips, listing simulated decimal longitudes for each tip in the tree. |

`node_latitudes` |
Numeric vector of length Nnodes, listing simulated decimal latitudes for each internal node in the tree. |

`node_longitudes` |
Numeric vector of length Nnodes, listing simulated decimal longitudes for each internal node in the tree. |

Stilianos Louca

F. Perrin (1928). Etude mathematique du mouvement Brownien de rotation. 45:1-51.

D. R. Brillinger (2012). A particle migrating randomly on a sphere. in Selected Works of David Brillinger. Springer.

A. Ghosh, J. Samuel, S. Sinha (2012). A Gaussian for diffusion on the sphere. Europhysics Letters. 98:30003.

S. Louca (2021). Phylogeographic estimation and simulation of global diffusive dispersal. Systematic Biology. 70:340-359.

`simulate_ou_model`

,
`simulate_rou_model`

,
`simulate_bm_model`

,
`fit_sbm_const`

## Not run: # generate a random tree tree = generate_random_tree(list(birth_rate_intercept=1),max_tips=100)$tree # simulate SBM on the tree simulation = simulate_sbm(tree, radius=6371, diffusivity=1e4, root_latitude=0, root_longitude=0) # plot latitudes and longitudes of the tips plot(simulation$tip_latitudes,simulation$tip_longitudes) ## End(Not run)

[Package *castor* version 1.7.0 Index]