Description Usage Arguments Value See Also Examples
Constructor for bayesPO_modelclass
objects, built to facilitate
the use of the fitting function. The output of this function has the
necessary signature for the fit_bayesPO function to start the model fit.
1 2 3 4 5 6 7 8 9 10 11 12 13  bayesPO_model(
po,
intensitySelection,
observabilitySelection,
intensityLink = "logit",
observabilityLink = "logit",
initial_values = 1,
joint_prior = prior(beta = NormalPrior(rep(0, length(intensitySelection) + 1), 10 *
diag(length(intensitySelection) + 1)), delta = NormalPrior(rep(0,
length(observabilitySelection) + 1), 10 * diag(length(observabilitySelection) + 1)),
lambdaStar = GammaPrior(1e10, 1e10)),
verbose = TRUE
)

po 
A matrix whose rows represent the presenceonly data and the columns the covariates observed at each position. 
intensitySelection 
Either a numeric or character vector and represents the selection of covariates used for the intensity set. If numeric it is the positions of the columns and if character, the names of the columns. 
observabilitySelection 
Either a numeric or character vector and represents the selection of covariates used for the observability set. If numeric it is the positions of the columns and if character, the names of the columns. 
intensityLink 
A string to inform what link function the model has with respect to the intensity covariates. Current version accepts 'logit'. 
observabilityLink 
A string to inform what link function the model has with respect to the observabilitycovariates. Current version accepts 'logit'. 
initial_values 
Either a single integer, a single

joint_prior 
A 
verbose 
Set to 
A bayesPO_model
object with the requested slots. It is ready
to be used in the fit_bayesPO
function.
initial
, prior
and
fit_bayesPO
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40  # Let us simulate some data to showcase the creation of the model.
beta < c(1, 2)
delta < c(3, 4)
lambdaStar < 1000
total_points < rpois(1, lambdaStar)
random_points < cbind(runif(total_points), runif(total_points))
# Find covariate values to explain the species occurrence.
# We give them a Gaussian spatial structure.
Z < MASS::mvrnorm(1, rep(0, total_points), 3 * exp(as.matrix(dist(random_points)) / 0.2))
# Thin the points by comparing the retaining probabilities with uniforms
# in the log scale to find the occurrences
occurrences < log(runif(total_points)) <= log1p(exp(beta[1]  beta[2] * Z))
n_occurrences < sum(occurrences)
occurrences_points < random_points[occurrences,]
occurrences_Z < Z[occurrences]
# Find covariate values to explain the observation bias.
# Additionally create a regular grid to plot the covariate later.
W < MASS::mvrnorm(1, rep(0, n_occurrences), 2 * exp(as.matrix(dist(occurrences_points)) / 0.3))
# Find the presenceonly observations.
po_sightings < log(runif(n_occurrences)) <= log1p(exp(delta[1]  delta[2] * W))
n_po < sum(po_sightings)
po_points < occurrences_points[po_sightings, ]
po_Z < occurrences_Z[po_sightings]
po_W < W[po_sightings]
# Now we create the model
model < bayesPO_model(po = cbind(po_Z, po_W),
intensitySelection = 1, observabilitySelection = 2,
intensityLink = "logit", observabilityLink = "logit",
initial_values = 2, joint_prior = prior(
NormalPrior(rep(0, 2), 10 * diag(2)),
NormalPrior(rep(0, 2), 10 * diag(2)),
GammaPrior(1e4, 1e4)))
# Check how it is.
model

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