get.TMB.data.input: Internal scampr function that creates a list of data and...

In ElliotDovers/scampr: Spatially Correlated, Approximate Modelling of Point patterns in R

Internal scampr function that creates a list of data and starting parameters for scampr models

Description

Internal scampr function that creates a list of data and starting parameters for scampr models

Usage

get.TMB.data.input(
  formula,
  data,
  bias.formula,
  pa.data,
  coord.names = c("x", "y"),
  quad.weights.name = "quad.size",
  approx.type = c("variational", "laplace", "not_sre"),
  model.type = c("PO", "PA", "IDM"),
  basis.functions,
  bf.matrix.type = c("sparse", "dense"),
  starting.pars,
  latent.po.biasing = TRUE,
  po.biasing.basis.functions,
  prune.bfs = 4
)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the fixed effects of the model to be fitted. The 'response' must be a binary that indicates whether a datum is a presence or: quadrature point (for point process models)/ absence (for binary models). See GLM function for further formula details.
data	a data frame containing response and predictors within formula.
bias.formula	an object of class "formula" (or one that can be coerced to that class) OR the character string "latent". In the formula case, this is a symbolic description of the predictors included to account for bias in the presence-only data (no response term is needed). In the case of fitting an integrated data model, bias.formula = "latent" will fit an approximate latent Gaussian field to account for the bias.
pa.data	an optional data frame. When fitting an integrated data model use this to pass in the presence/absence data.
coord.names	a vector of character strings describing the column names of the coordinates in both data frames.
quad.weights.name	a charater string of the column name of quadrature weights in the data.
approx.type	a character string indicating the type of model to be used. May be one of 'laplace' or 'variational' for models involving spatial random effects, OR 'not_sre' for a fixed effect model.
model.type	a character string indicating the type of data to be used. May be one of 'PO' (for a presence-only PPM) or 'PA' (for a presence/absence Binary GLM) or 'IDM' (for an integrated data model).
basis.functions	an optional object of class 'Basis' created by FRK::auto_basis() or 'bf.df' created by scampr::simple_basis(). Either object describes a set of basis functions for approximating the latent Gaussian field. If NULL the model will use default FRK::auto_basis() with max_basis = 0.25 * # of points.
bf.matrix.type	a character string, one of 'sparse' or 'dense' indicating whether to use sparse or dense matrix computations for the basis functions created.
starting.pars	an optional named list or scampr model object that gives warm starting values for the parameters of the model.
latent.po.biasing	a logical indicating whether biasing in the presence-only data should be accounted for via an additional latent field. Applies to IDM only.
po.biasing.basis.functions	an optional extra set of basis functions that can be used when latent.po.biasing = TRUE, otherwise basis.functions are used.
prune.bfs	an integer indicating the number of presence-only records required within a basis function's radius for it NOT to be pruned. Applies to the PO and IDM model (additionally, within the presence-only biasing basis functions in the IDM case) to assist with stability in model convergence. Default is zero, i.e. no pruning.

Value

list of elements required for TMB::MakeADFun

Examples

# Get the flora data for one of the species
dat_po <- flora$po$sp1
dat_pa <- flora$pa

# obtain a sample of 10,000 quadrature points for the point process model
set.seed(1)
quad.pts <- flora$quad[sample(1:nrow(flora$quad), 10000, replace = F), ]
set.seed(NULL)

# Attach the quadrature points to the presence-only data
dat_po <- rbind.data.frame(dat_po, quad.pts)

# Ensure the "response" variable in each data set shares the same name
dat_po$presence <- dat_po$pres
dat_pa$presence <- dat_pa$sp1

# Get the TMB data lists for a combined data model without latent field
tmb.input <- scampr:::get.TMB.data.input(presence ~ MNT, bias.formula ~ D.Main, po.data = dat_po, pa.data = dat_pa)
str(tmb.input)

ElliotDovers/scampr documentation built on March 17, 2024, 3:27 p.m.