- forestatrisk.cellneigh(raster=None, region=None, csize=10, rank=1)[source]¶
Compute number of spatial cells and neighbours.
- Parameters:
raster – Path to raster file to compute region.
region – List/tuple of region coordinates (east, west, south, north).
csize – Spatial cell size (in km).
rank – Rank of the neighborhood (1 for chess king’s move).
- Returns:
Tuple of length 2 with number of neighbours for each cell and adjacent cells.
- forestatrisk.cellneigh_ctry(raster=None, region=None, vector=None, csize=10, rank=1)[source]¶
Compute number of spatial cells and neighbours inside country’s borders.
- Parameters:
raster – Path to raster file to compute region from.
region – List/tuple of region coordinates (east, west, south, north) if raster is not provided.
vector – Path to vector file with country’s borders.
csize – Spatial cell size (in km).
rank – Rank of the neighborhood (1 for chess king’s move).
- Returns:
Tuple of length 4 with (i) number of neighbours for each cell, (ii) adjacent cells, (iii) total number of cells inside country’s border, (iv) total number of cells from region.
- class forestatrisk.model_binomial_iCAR(suitability_formula, data, n_neighbors, neighbors, NA_action='drop', data_pred=None, eval_env=0, burnin=1000, mcmc=1000, thin=1, beta_start=0, Vrho_start=1, mubeta=0, Vbeta=1000, priorVrho=-1.0, shape=0.5, rate=0.0005, Vrho_max=10, seed=1234, verbose=1, save_rho=0, save_p=0)[source]¶
model_binomial_iCAR class.
model_binomial_iCAR class to estimates the parameters of a Binomial model with iCAR process for spatial autocorrelation in a hierarchical Bayesian framework.
- Parameters:
suitability_formula – A formula-like object that can be used to construct a design matrix (see
patsy.dmatrices).data – A dict-like object that can be used to look up variables referenced in
suitability_formula.n_neighbors – A vector of integers that indicates the number of neighbors (adjacent entities) of each spatial entity. length(n.neighbors) indicates the total number of spatial entities.
neighbors – A vector of integers indicating the neighbors (adjacent entities) of each spatial entity. Must be of the form c(neighbors of entity 1, neighbors of entity 2, … , neighbors of the last entity). Length of the neighbors vector should be equal to sum(n.neighbors).
NA_action – What to do with rows that contain missing values (see
patsy.dmatrices).data_pred – Optional dataset for predictions.
eval_env – Environment used to look up any variables referenced in suitability_formula that cannot be found in data (see
patsy.dmatrices).burnin – Number of iterations for the burnin phase.
mcmc – The number of Gibbs iterations for the sampler. Total number of Gibbs iterations is equal to burnin+mcmc. burnin+mcmc must be divisible by 10 and superior or equal to 100 so that the progress bar can be displayed.
thin – The thinning interval used in the simulation. The number of mcmc iterations must be divisible by this value.
beta_start – Starting values for beta parameters. This can either be a scalar or a p-length vector. If set to -99, estimates from a simple logistic regression are used (from scikit-learn module).
Vrho_start – Positive scalar indicating the starting value for the variance of the spatial random effects.
mubeta – Means of the priors for the beta parameters of the suitability process. mubeta must be either a scalar or a p-length vector. If mubeta takes a scalar value, then that value will serve as the prior mean for all of the betas. The default value is set to 0 for an uninformative prior.
Vbeta – Variances of the Normal priors for the beta parameters of the suitability process. Vbeta must be either a scalar or a p-length vector. If Vbeta takes a scalar value, then that value will serve as the prior variance for all of the betas. The default variance is large and set to 1000 for an uninformative flat prior.
priorVrho – Type of prior for the variance of the spatial random effects. Can be set to a fixed positive scalar, or to an inverse-gamma distribution (“1/Gamma”) with parameters shape and rate, or to a uniform distribution (“Uniform”) on the interval [0,Vrho.max]. Default set to “1/Gamma”.
shape – The shape parameter for the Gamma prior on the precision of the spatial random effects. Default value is shape=0.5 for uninformative prior.
rate – The rate (1/scale) parameter for the Gamma prior on the precision of the spatial random effects. Default value is rate=0.0005 for uninformative prior.
Vrho_max – Upper bound for the uniform prior of the spatial random effect variance. Default set to 10.
seed – The seed for the random number generator. Default set to 1234.
verbose – A switch (0,1) which determines whether or not the progress of the sampler is printed to the screen. Default is 1: a progress bar is printed, indicating the step (in %) reached by the Gibbs sampler.
save_rho – A switch (0,1) which determines whether or not the sampled values for rhos are saved. Default is 0: the posterior mean is computed and returned in the rho.pred vector. Be careful, setting save.rho to 1 might require a large amount of memory.
save_p – A switch (0,1) which determines whether or not the sampled values for predictions are saved. Default is 0: the posterior mean is computed and returned in the theta.pred vector. Be careful, setting save.p to 1 might require a large amount of memory.
- Returns:
An object of class model_binomial_iCAR.