In cmjt/stelfi: Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder

Key stelfi functions

Summary of model fitting functions offered listing the key arguments for each. {-}

+-----------------------+-------------------------------------------------------+ | Function | Key arguments | +:======================+:======================================================+ | fit_hawkes() | - times - a vector of numeric occurrence times. | | | | | | - parameters - a vector of named starting values |
| | for $\mu$ (mu), $\alpha$ (alpha), and |
| | $\beta$ (beta). |
| | |
| | - marks - optional, a vector of marks ($m(t)$). |
+-----------------------+-------------------------------------------------------+ | fit_mhawkes() | - times - a vector of numeric occurrence times. | | | | | | - stream - character vector specifying the |
| | stream ID of each observation in times.| | | | | | - parameters - a vector of named starting values |
| | for $\mu$ (mu), $\alpha$ (alpha), and |
| | $\beta$ (beta). |
+-----------------------+-------------------------------------------------------+ | fit_hawkes_cbf() | As fit_hawkes() plus | | | | | | - background - some assumed time dependent | | | background function $\mu(t)$. |
| | |
| | - background_integral - the integral of |
| | background. |
| | |
| | - background_parameters - parameter |
| | starting values for $\mu(t)$. |
| | |
| | ( $^*$Note, $\texttt{mu}$ in parameters will be |
| | ignored) |
+-----------------------+-------------------------------------------------------+ | fit_lgcp() | - locs - a named data frame of event locations, | | | x, y, and t (optional). | | | | | | - sf - a polygon of the spatial domain. | | | | | | - smesh - a Delaunay triangulation of the spatial |
| | domain returned by INLA::inla.mesh.2d(). |
| | |
| | - tmesh - optional, a temporal mesh returned by |
| | INLA::inla.mesh.1d()). |
| | |
| | - parameters - a vector of named starting values |
| | for $\boldsymbol{\beta}$ (beta), |
| | $\text{log}(\tau)$ (log_tau), |
| | $\text{log}(\kappa)$ (log_kappa), and |
| | $\textrm{arctan}(\rho)$ (atanh_rho, optional). |
+-----------------------+-------------------------------------------------------+ | fit_mlgcp() | - locs, sf, and smesh - as fit_lgcp(). | | | | | | - marks - a matrix of marks for each |
| | observation of the point pattern. |
| | |
| | - parameters - a list of named parameters, as |
| | fit_lgcp() plus (betamarks), (betapp), |
| | (marks_coefs_pp ). |
| | |
| | - methods - integer(s) specifying mark |
| | distribution: 0, Gaussian; 1, Poisson; |
| | 2, binomial; 3, gamma. |
| | |
| | - strfixed - fixed structural parameters, |
| | depends on mark distribution. |
| | |
| | - fields - a binary vector indicating |
| | whether there is a new random field for each |
| | mark. |
+-----------------------+-------------------------------------------------------+ | fit_stelfi() | - times - as fit_hawkes(). | | | | | | - locs, sf, and smesh - as fit_lgcp(). |
| | |
| | - parameters - a list of named parameter |
| | starting values for $\mu$ (mu), $\alpha$ |
| | (alpha), $\beta$ (beta), $\sigma_x$ (xsigma)|
| | $\sigma_y$ (ysigma), and $\rho$ (rho). |
| | |
| | - GMRF - logical, should a GMRF be included as a |
| | latent spatial effect if so $\tau$ (tau) |
| | and $\kappa$(kappa) supplied to parameters. |
+-----------------------+-------------------------------------------------------+

Summary of utility and simulation functions listing the key arguments for each. {-}

+-----------------------+-----------------------+-----------------------+ | Function | Key arguments | Purpose | +:======================+:======================+:======================+ | get_coefs() | - obj - a fitted | Extract estimated | | | model object | parameter values from | | | returned by any | a fitted model. | | | one of the | | | | functions in the | | | | Table above | | +-----------------------+-----------------------+-----------------------+ | get_fields() | As fit_lgcp() and | Extract estimated | | | | mean, or standard | | | - sd - logical, | deviation, of | | | return standard | GMRF(s). | | | deviation. | | +-----------------------+-----------------------+-----------------------+ | get_weights() | - mesh - a | Calculate mesh | | | Delaunay | weights. | | | triangulation of | | | | the spatial | | | | domain returned | | | | by | | | | INLA::inla.mesh. | | | | 2d(). | | | | | | | | - sf - a polygon | | | | of the spatial | | | | domain. | | +-----------------------+-----------------------+-----------------------+ | mesh_2_sf() | - mesh - a | Transforms mesh | | | Delaunay | into a sf object. | | | triangulation of | | | | the spatial | | | | domain returned | | | | by | | | | INLA::inla.mesh. | | | | 2d(). | | +-----------------------+-----------------------+-----------------------+ | show_field() | - x - a vector of | Plots spatial random | | | values, one per | field values. | | | each smesh node. | | | | | | | | - smesh - as | | | | fit_lgcp() . | | | | | | | | - sf - as | | | | fit_lgcp(). | | | | | | | | - clip - logical, | | | | clip to domain | | +-----------------------+-----------------------+-----------------------+ | show_hawkes() | - obj - a fitted | Plot fitted Hawkes | | | model object | model. | | | returned by | | | | fit_hawkes() or | | | | fit_hawkes_cbf() | | | |. | | +-----------------------+-----------------------+-----------------------+ | show_hawkes_GOF() | - obj - as | Plot goodness-of-fit | | | show_hawkes(). | metrics for a Hawkes | | | | model. | | | - plot - logical | | | | | | | | - return_values - | | | | logical, return | | | | compensator | | | | values | | +-----------------------+-----------------------+-----------------------+ | show_lambda() | As fit_lgcp() and | Plot estimated | | | | spatial intensity | | | - clip - logical, | from a fitted | | | clip to domain | log-Gaussian Cox | | | | process model. | +-----------------------+-----------------------+-----------------------+ | sim_hawkes() | As fit_hawkes() | Simulate a Hawkes | | | | process. | +-----------------------+-----------------------+-----------------------+ | sim_lgcp() | As fit_lgcp() | Simulate a | | | | realisation of a | | | | log-Gaussian Cox | | | | process. | +-----------------------+-----------------------+-----------------------+

cmjt/stelfi documentation built on Oct. 25, 2023, 2:53 p.m.