ctmc2glm {ctmcmove} | R Documentation |

Transforms a "ctmc" object and covariate rasters into data suitable for analysis using Poisson GLM software (like glm in R).

ctmc2glm(ctmc, stack.static, stack.grad, crw = TRUE, normalize.gradients = FALSE, grad.point.decreasing = TRUE, include.cell.locations=TRUE,directions=4,zero.idx=integer())

`ctmc` |
A "ctmc" object (output from "path2ctmc"). |

`stack.static` |
A rasterStack object, where each layer in the stack is a location-based covariate. |

`stack.grad` |
A rasterStack object, where each layer in the stack is a directional gradient-based covariate |

`crw` |
Logical. If TRUE (default), an autocovariate is created for each cell visited in the CTMC movement path. The autocovariate is a unit-length directional vector pointing from the center of the most recent grid cell to the center of the current grid cell. |

`normalize.gradients` |
Logical. Default is FALSE. If TRUE, then all gradient covariates are normalized by dividing by the length of the gradient vector at each point. |

`grad.point.decreasing` |
Logical. If TRUE, then the gradient covariates are positive in the direction of decreasing values of the covariate. If FALSE, then the gradient covariates are positive in the direction of increasing values of the covariate (like a true gradient). |

`include.cell.locations` |
Logical. If TRUE, then the x and y locations of the centers of the (1) current and (2) neighboring raster cells will be returned for each row in the created data matrix. |

`directions` |
Integer. Either 4 (indicating a "Rook's neighborhood" of 4 neighboring grid cells) or 8 (indicating a "King's neighborhood" of 8 neighboring grid cells). |

`zero.idx` |
Integer vector of the indices of raster cells that are not passable and should be excluded. These are cells where movement should be impossible. Default is zero.idx=integer(). |

This code creates one data row for each possible transition from each grid cell visited by the CTMC path.

`z` |
Response variable (either zero or 1) for analysis using GLM software. |

`X` |
Matrix of predictor variables for analysis using GLM software. Created from the location-based and gradient-based covariates. |

`tau` |
Offset for each row in a Poisson GLM with log link. |

`t` |
Vector of the time each raster grid cell was entered |

Ephraim M. Hanks

Hanks, E. M.; Hooten, M. B. & Alldredge, M. W. Continuous-time Discrete-space Models for Animal Movement The Annals of Applied Statistics, 2015, 9, 145-165

## For example code, do ## ## > help(ctmcMove)

[Package *ctmcmove* version 1.2.9 Index]