specifySpecies: R6 class for creating a 'startSpecies' object.
In PhilipMostert/inlabruSDMs: Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

specifySpecies

R Documentation

R6 class for creating a `startSpecies` object.

Description

A data object containing the data and the relevant information about the integrated model. The function startSpecies acts as a wrapper in creating one of these objects. The output of this object has additional functions within the object which allow for further specification and customization of the integrated model.

Methods

Method `help()`

Function to provide documentation for a specifySpecies object.

Usage

specifySpecies$help(...)

Arguments

...: Not used

Returns

Documentation.

Method `print()`

Prints the datasets, their data type and the number of observations, as well as the marks and their respective families.

Usage

specifySpecies$print(...)

Arguments

...: Not used.

Method `plot()`

Makes a plot of the points surrounded by the boundary of the region where they were collected.

Usage

specifySpecies$plot(datasetNames, Species = TRUE, Boundary = TRUE, ...)

Arguments

datasetNames: Name of the datasets to plot. If this argument is missing, the function will plot all the data available to the model.
Species: Logical: should the points be plotted based on the species name. Defaults to TRUE.
Boundary: Logical: should a boundary (created using the Mesh object) be used in the plot. Defaults to TRUE.
...: Not used.

Returns

A ggplot object.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 library(ggplot2)
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               Projection = proj, 
                               responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}
}

Method `addBias()`

Function used to add additional spatial fields (called bias fields) to a selected dataset present in the integrated model. Bias fields are typically used to account for sampling biases in opportunistic citizen science data in the absence of any covariate to do such.

Usage

specifySpecies$addBias(
  datasetNames = NULL,
  allPO = FALSE,
  biasField = NULL,
  copyModel = TRUE,
  shareModel = FALSE,
  temporalModel = list(model = "ar1")
)

Arguments

datasetNames: A vector of dataset names (class character) for which a bias field needs to be added to. If NULL (default), then allPO has to be TRUE.
allPO: Logical: should a bias field be added to all datasets classified as presence only in the integrated model. Defaults to FALSE.
biasField: An inla.spde object used to describe the bias field. Defaults to NULL which uses inla.spde2.matern to create a Matern model for the field.
copyModel: Create copy models for all the of the datasets specified with either datasetNames or allPO. The first dataset in the vector will have its own spatial effect, and the other datasets will "copy" the effect with shared hyperparameters. Defaults to TRUE.
shareModel: Share a bias field across the datasets specified with datasetNames. Defaults to FALSE.
temporalModel: List of model specifications given to the control.group argument in the time effect component. Defaults to list(model = 'ar1'); see control.group from the INLA package for more details. temporalName needs to be specified in intModel prior.

Returns

A bias field to the model.

Examples

 \dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               Projection = proj, 
                               responsePA = 'Present')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}
}

Method `updateFormula()`

Function used to update the formula for a selected observation model. The function is designed to work similarly to the generic update formula, and should be used to thin terms out of a process from the full model specified in intModel. The function also allows the user to add their own formula to the model, such that they can include non-linear components in the model. The function can also be used to print out the formula for a process by not specifying the Formula or newFormula arguments.

Usage

specifySpecies$updateFormula(
  datasetName = NULL,
  speciesName = NULL,
  Formula,
  processLevel = FALSE,
  newFormula
)

Arguments

datasetName: Name of the dataset (class character) for which the formula needs to be changed.
speciesName: Name of the species for which to change a formula for. Defaults to NULL which chnages the formula for all species present in datasetName.
Formula: An updated formula to give to the process. The syntax provided for the formula in this argument should be identical to the formula specification as in base R. Should be used to thin terms out of a formula but could be used to add terms as well. If adding new terms not specified in intModel, remember to add the associated component using .$changeComponents as well.
processLevel: Logical argument: if TRUE changes the formulas for all of the processes in a dataset. Defaults to FALSE.
newFormula: Completely change the formula for a process – primarily used to add non-linear components into the formula. Note: all terms need to be correctly specified here.

Returns

An updated formula.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Remove Forest from eBird
 organizedData$updateFormula(datasetName = 'eBird', Formula = ~ . - Forest)
 
 #Add some scaling to Forest for Parks
 organizedData$updateFormula(datasetName ='Parks', newFormula = ~ I(. +(Forest+1e-6)*scaling))
 
 #Now dd scaling to components
 organizedData$changeComponents(addComponent = 'scaling') 
 
}
}

Method `changeComponents()`

Function to add and specify custom components to model, which are required by inlabru. The main purpose of the function is to re-specify or completely change components already in the model, however the user can also add completely new components to the model as well. In this case, the components need to be added to the correct formulas in the model using the .$updateFormula function. If addComponent and removeComponent are both missing, the function will print out the components to be supplied to inlabru's bru function.

Usage

specifySpecies$changeComponents(addComponent, removeComponent, print = TRUE)

Arguments

addComponent: Component to add to the integrated model. Note that if the user is re-specifying a component already present in the model, they do not need to remove the old component using removeComponent.
removeComponent: Component (or just the name of a component) present in the model which should be removed.
print: Logical: should the updated components be printed. Defaults to TRUE.

Returns

An updated components list.

Examples

\dontrun{

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               spatialCovariates = Forest,
                               Projection = proj, 
                               responsePA = 'Present')

 #Remove Forest from components
 organizedData$changeComponents(removeComponent = 'speciesSpatial')

}

}

Method `priorsFixed()`

Function to change priors for the fixed (and possibly random) effects of the model.

Usage

specifySpecies$priorsFixed(
  Effect,
  Species = NULL,
  datasetName = NULL,
  mean.linear = 0,
  prec.linear = 0.001
)

Arguments

Effect: Name of the fixed effect covariate to change the prior for. Can take on 'intercept', which will change the specification for an intercept (specified by one of species or datasetName).
Species: Name of the species (class character) for which the prior should change. Defaults to NULL which will change the prior for all species added to the model.
datasetName: Name of the dataset for which the prior of the intercept should change (if fixedEffect = 'intercept'). Defaults to NULL which will change the prior effect of the intercepts for all the datasets in the model.
mean.linear: Mean value for the prior of the fixed effect. Defaults to 0.
prec.linear: Precision value for the prior of the fixed effect. Defaults to 0.001.

Returns

New priors for the fixed effects.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Add prior to Forest
 organizedData$priorsFixed(Effect = 'Forest', mean.linear = 2, prec.linear = 0.1)

}
}

Method `specifySpatial()`

Function to specify random fields in the model using penalizing complexity (PC) priors for the parameters.

Usage

specifySpecies$specifySpatial(
  sharedSpatial = FALSE,
  datasetName,
  Species,
  Bias,
  PC = TRUE,
  Remove = FALSE,
  ...
)

Arguments

sharedSpatial: Logical: specify the shared spatial field in the model. Requires pointsSpatial == 'shared' in intModel. Defaults to FALSE.
datasetName: Name of which of the datasets' spatial fields to be specified. Requires pointsSpatial = 'individual' in intModel.
Species: Name of the species to change the spatial effect for. If TRUE then changes the spatial effect for the shared species field.
Bias: Logical: specify the spatial field for the bias effect. If seperate fields are specified for different fields, the argument may be the name of the dataset for which the bias field to be specified. #' @param PC Logical: should the Matern model be specified with pc priors. Defaults to TRUE, which uses inla.spde2.pcmatern to specify the model; otherwise uses inla.spde2.matern.
Remove: Logical: should the chosen spatial field be removed. Requires one of sharedSpatial, species, mark or bias to be non-missing, which chooses which field to remove.
...: Additional arguments used by INLA's inla.spde2.pcmatern or inla.spde2.matern function, dependent on the value of PC.

Returns

A new model for the spatial effects.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

 #Specify the shared spatial field
 organizedData$specifySpatial(sharedSpatial = TRUE, PC = TRUE, 
                       prior.range = c(1,0.001),
                       prior.sigma = c(1,0.001))

} 
}

Method `changeLink()`

Function used to change the link function for a given process.

Usage

specifySpecies$changeLink(datasetName, Link, ...)

Arguments

datasetName: Name of the dataset for which the link function needs to be changed.
Link: Name of the link function to add to the process. If missing, will print the link function of the specified dataset.

Returns

A new link function for a process.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

 #Specify the shared spatial field
 organizedData$changeLink('Parks', 'logit')
 
 
} 
}

Method `spatialBlock()`

Function to spatially block the datasets, which will then be used for model cross-validation with blockedCV. See the spatialBlock function from blockCV for how the spatial blocking works and for further details on the function's arguments.

Usage

specifySpecies$spatialBlock(k, rows_cols, plot = FALSE, seed = 1234, ...)

Arguments

k: Integer value reflecting the number of folds to use.
rows_cols: Integer value by which the area is divided into longitudinal and latitudinal bins.
plot: Plot the cross-validation folds as well as the points across the boundary. Defaults to FALSE.
seed: Seed used by blockCV's spatialBlock to make the spatial blocking reproducible across different models. Defaults to 1234.
...: Additional arguments used by blockCV's spatialBlock.

Returns

If plot = TRUE, a plot of the grid.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Specify the spatial block
 organizedData$spatialBlock(k = 2, rows = 2, cols = 1, plot = FALSE)

} 
}

Method `addSamplers()`

Function to add an integration domain for the PO datasets.

Usage

specifySpecies$addSamplers(datasetName, Samplers)

Arguments

datasetName: Name of the dataset for the samplers.
Samplers: A Spatial* object representing the integration domain.

Returns

New samplers for a process.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                              speciesName = 'speciesName',
                              Projection = proj, responsePA = 'Present')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}
}

Method `specifyRandom()`

Function to specify the models and priors for the random effects included in the model.

Usage

specifySpecies$specifyRandom(
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  copyBias = list(beta = list(fixed = FALSE)),
  speciesCopy = list(beta = list(fixed = FALSE)),
  speciesIntercepts = list(prior = "loggamma", param = c(1, 5e-05)),
  speciesGroup = list(model = "iid", hyper = list(prec = list(prior = "loggamma", param =
    c(1, 5e-05))))
)

Arguments

temporalModel: List of model specifications given to the control.group argument in the time effect component. Defaults to list(model = 'ar1'); see control.group from the INLA package for more details.
copyModel: List of model specifications given to the hyper parameters for the "copy" model. Defaults to list(beta = list(fixed = FALSE)).
copyBias: List of model specifications given to the hyper parameters for the "copy" bias model. Defaults to list(beta = list(fixed = FALSE)).
speciesCopy: List of model specifications given to the hyper parameters for the species "copy" model. Defaults to list(beta = list(fixed = FALSE)).
speciesIntercepts: Prior distribution for precision parameter for the random species intercept term. Defaults to INLA's default choice.
speciesGroup: Prior distribution for the precision parameter for the iid group model. Defaults to INLA's default. #' @return An updated component list.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh,
                           speciesName = 'speciesName',
                           Projection = proj, 
                           responsePA = 'Present',
                           pointsSpatial = copy)
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}
}

Method `new()`

Usage

specifySpecies$new(
  data,
  projection,
  Inlamesh,
  initialnames,
  responsecounts,
  responsepa,
  pointcovariates,
  speciesintercept,
  trialspa,
  spatial,
  intercepts,
  spatialcovariates,
  boundary,
  ips,
  temporal,
  temporalmodel,
  offset,
  copymodel,
  formulas,
  speciesindependent,
  speciesname,
  speciesenvironment,
  speciesspatial
)

Method `samplingBias()`

Usage

specifySpecies$samplingBias(datasetName, Samplers)

Examples


## ------------------------------------------------
## Method `specifySpecies$plot`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 library(ggplot2)
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               Projection = proj, 
                               responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$addBias`
## ------------------------------------------------

 ## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               Projection = proj, 
                               responsePA = 'Present')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$updateFormula`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Remove Forest from eBird
 organizedData$updateFormula(datasetName = 'eBird', Formula = ~ . - Forest)
 
 #Add some scaling to Forest for Parks
 organizedData$updateFormula(datasetName ='Parks', newFormula = ~ I(. +(Forest+1e-6)*scaling))
 
 #Now dd scaling to components
 organizedData$changeComponents(addComponent = 'scaling') 
 
}

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$changeComponents`
## ------------------------------------------------

## Not run: 

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                               speciesName = 'speciesName',
                               spatialCovariates = Forest,
                               Projection = proj, 
                               responsePA = 'Present')

 #Remove Forest from components
 organizedData$changeComponents(removeComponent = 'speciesSpatial')

}


## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$priorsFixed`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Add prior to Forest
 organizedData$priorsFixed(Effect = 'Forest', mean.linear = 2, prec.linear = 0.1)

}

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$specifySpatial`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

 #Specify the shared spatial field
 organizedData$specifySpatial(sharedSpatial = TRUE, PC = TRUE, 
                       prior.range = c(1,0.001),
                       prior.sigma = c(1,0.001))

} 

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$changeLink`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

 #Specify the shared spatial field
 organizedData$changeLink('Parks', 'logit')
 
 
} 

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$spatialBlock`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- terra::rast(
 system.file(
 'extdata/SolitaryTinamouCovariates.tif', 
 package = "PointedSDMs"))$Forest
 
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                           speciesName = 'speciesName',
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

 #Specify the spatial block
 organizedData$spatialBlock(k = 2, rows = 2, cols = 1, plot = FALSE)

} 

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$addSamplers`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh, 
                              speciesName = 'speciesName',
                              Projection = proj, responsePA = 'Present')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}

## End(Not run)

## ------------------------------------------------
## Method `specifySpecies$specifyRandom`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startSpecies(data, Mesh = mesh,
                           speciesName = 'speciesName',
                           Projection = proj, 
                           responsePA = 'Present',
                           pointsSpatial = copy)
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}

## End(Not run)

PhilipMostert/inlabruSDMs documentation built on April 14, 2025, 11:39 a.m.

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PhilipMostert/inlabruSDMs Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

specifySpecies: R6 class for creating a 'startSpecies' object. In PhilipMostert/inlabruSDMs: Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

R6 class for creating a startSpecies object.

Description

Methods

Public methods

Method help()

Usage

Arguments

Returns

Method print()

Usage

Arguments

Method plot()

Usage

Arguments

Returns

Examples

Method addBias()

Usage

Arguments

Returns

Examples

Method updateFormula()

Usage

Arguments

Returns

Examples

Method changeComponents()

Usage

Arguments

Returns

Examples

Method priorsFixed()

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Method specifySpatial()

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Method changeLink()

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Method spatialBlock()

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Method addSamplers()

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Method specifyRandom()

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Method new()

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Method samplingBias()

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PhilipMostert/inlabruSDMs
Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

specifySpecies: R6 class for creating a 'startSpecies' object.
In PhilipMostert/inlabruSDMs: Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

R6 class for creating a `startSpecies` object.

Method `help()`

Method `print()`

Method `plot()`

Method `addBias()`

Method `updateFormula()`

Method `changeComponents()`

Method `priorsFixed()`

Method `specifySpatial()`

Method `changeLink()`

Method `spatialBlock()`

Method `addSamplers()`

Method `specifyRandom()`

Method `new()`

Method `samplingBias()`