Description Usage Arguments Details Value References See Also Examples

A function of class `measurement_model`

that calculates likelihood,
gradient, hessian, and partial derivatives of nuisance parameters and the
Laplacian generalized inverse, using the "maximum likelihood population
effects" model of Clarke et al (2002) with a non-negative slope
between genetic and resistance distance.

1 2 3 4 5 6 7 8 9 10 11 |

`E` |
A submatrix of the generalized inverse of the graph Laplacian (e.g. a covariance matrix) |

`S` |
A matrix of observed genetic distances |

`phi` |
Nuisance parameters (see details) |

`nu` |
Unused |

`gradient` |
Compute gradient of negative loglikelihood with regard to |

`hessian` |
Compute Hessian matrix of negative loglikelihood with regard to |

`partial` |
Compute second partial derivatives of negative loglikelihood with regard to |

`nonnegative` |
Force slope to be nonnegative? |

`validate` |
Numerical validation via package |

The nuisance parameters `phi`

are the intercept ("alpha"), slope ("beta"), negative log residual
deviation ("tau"), and logit-transformed correlation parameter ("rho") of the MLPE regression. If not supplied, `phi`

is
is estimated via maximum likelihood using package `corMLPE`

(github.com/nspope/corMLPE) and `nlme::gls`

.

TODO: formula

A list containing:

`covariance` |
rows/columns of the generalized inverse of the graph Laplacian for a subset of target vertices |

`objective` |
(if |

`fitted` |
((if |

`boundary` |
(if |

`gradient` |
(if |

`hessian` |
(if |

`gradient_E` |
(if |

`partial_E` |
(if |

`partial_S` |
(if |

`jacobian_E` |
(if |

`jacobian_S` |
(if |

Clarke et al. TODO

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
library(raster)
data(melip)
covariates <- raster::stack(list(altitude=melip.altitude, forestcover=melip.forestcover))
surface <- conductance_surface(covariates, melip.coords, directions = 8)
# inverse of graph Laplacian at null model (IBD)
laplacian_inv <- radish_distance(theta = matrix(0, 1, 2),
formula = ~forestcover + altitude,
data = surface,
radish::loglinear_conductance,
covariance = TRUE)$covariance[,,1]
mlpe(laplacian_inv, melip.Fst) #without 'phi': return MLE of phi
mlpe(laplacian_inv, melip.Fst, phi = c(0., 0.5, -0.1))
``` |

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