bayesPO_model {bayesPO}

Build a model to be used in the bayesPO fitting function

Description

Constructor for bayesPO_model-class 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.

Usage

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(1e-10, 1e-10)),
  verbose = TRUE
)

Arguments

po

A matrix whose rows represent the presence-only 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 bayesPO_initial-class or a list containing bayesPO_initial-class objects. The length of the list will inform the model how many independent chains will be run. If an integer, that many initial values will be randomly generated.

joint_prior

A bayesPO_prior object.

verbose

Set to FALSE to suppress all messages to console.

Value

A bayesPO_model object with the requested slots. It is ready to be used in the fit_bayesPO function.

See Also

initial, prior and fit_bayesPO.

Examples

# 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 presence-only 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(1e-4, 1e-4)))
# Check how it is.
model