Create a new parameter set within an rgeoprofile_project
. The new
parameter set becomes the active set once created.
new_set( project, spatial_prior = NULL, source_model = "uniform", name = "(no name)", sigma_model = "single", dispersal_model = "normal", sigma_prior_mean = 1, sigma_prior_sd = 1, expected_popsize_model = "single", expected_popsize_prior_mean = 1000, expected_popsize_prior_sd = 20, sentinel_radius = 0.2, n_binom = FALSE, alpha_prior_mean = 1, alpha_prior_sd = 100, weight_prior = 1 )
project | an rgeoprofile_project, as produced by the function
|
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spatial_prior | a raster file defining the spatial prior. Precision values are taken from this raster if it is defined. |
source_model | choose prior type for source locations. Pick from "uniform" (default), "normal" (bivariate normal), "kernel" (KDE based on positive data) or "manual" (the current value of the raster) |
name | an optional name for the parameter set. |
sigma_model | set as |
dispersal_model | distribute points via a "normal", "cauchy" or "laplace" model |
sigma_prior_mean | the prior mean of the parameter sigma (km). |
sigma_prior_sd | the prior standard deviation of the parameter sigma
(km). Set to 0 to use a fixed value for sigma (fixed at
|
expected_popsize_model | set as |
expected_popsize_prior_mean | the prior mean of the expected total population size. |
expected_popsize_prior_sd | the prior standard deviation of the expected
total population size. Set to 0 to use a fixed value (fixed at
|
sentinel_radius | the observation radius of sentinel sites. |
n_binom | set to true or false, decide if a negative binomial model should be run for a set of over-dispersed count data. |
alpha_prior_mean | the prior mean alpha. |
alpha_prior_sd | the prior standard deviation of alpha. |
weight_prior | control the prior on weights for a point-pattern model |