Package 'PointedSDMs' reference manual

Title:	Fit Models Derived from Point Processes to Species Distributions using 'inlabru'
Description:	Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
Authors:	Philip Mostert [aut, cre], Bob O'hara [aut]
Maintainer:	Philip Mostert <[email protected]>
License:	GPL (>= 3)
Version:	2.1.2.9000
Built:	2024-11-26 09:30:26 UTC
Source:	https://github.com/philipmostert/pointedsdms

Dataset of setophaga caerulescens obtained from the Pennsylvania Atlas of Breeding Birds.

Description

Dataset of setophaga caerulescens obtained from the Pennsylvania Atlas of Breeding Birds.

References

https://ebird.org/atlaspa/home

Dataset of setophaga caerulescens obtained from the North American Breeding Bird survey across Pennsylvania state.

Description

Dataset of setophaga caerulescens obtained from the North American Breeding Bird survey across Pennsylvania state.

References

https://www.pwrc.usgs.gov/bbs

Dataset of Colinus Virginianus obtained from the North American Breeding Bird survey across Alabama state.

Description

Dataset of Colinus Virginianus obtained from the North American Breeding Bird survey across Alabama state.

References

https://www.pwrc.usgs.gov/bbs

blockedCV: run spatial blocked cross-validation on the integrated model.

Description

This function is used to perform spatial blocked cross-validation with regards to model selection for the integrated model. It does so by leaving out a block of data in the full model, running a model with the remaining data, and then calculating the deviance information criteria (DIC) as a score of model fit.

Usage

blockedCV(
  data,
  options = list(),
  method = "DIC",
  predictName = NULL,
  datasetCombs = NULL
)
blockedCV(
  data,
  options = list(),
  method = "DIC",
  predictName = NULL,
  datasetCombs = NULL
)

Arguments

`data`	An object produced by either `startISDM` of `startSpecies`. Requires the slot function, `.$spatialBlock` to be run first in order to specify how the data in the model is blocked.
`options`	A list of INLA or inlabru options to be used in the model. Defaults to `list()`.
`method`	Which cross-validation method to perform. Must be one of `'DIC'` or `'Predict'`. If `'DIC'` then the DIC values for each block are obtained. If `'Predict'` then predictions are made on a dataset in the left out block. For this to work, please specify the argument `methodOptions`.
`predictName`	Name of the dataset to predict onto if `method = 'Predict'`.
`datasetCombs`	A list of vectors containing dataset combinations to include in each model run if `method = 'Prediction'`. If `NULL` then all combinations of the dataset will be estimated.

Value

An object of class blockedCV, which is essentially a list of DIC values obtained from each iteration of the model.

Examples


## 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 <- startISDM(data, Mesh = mesh,
                            responsePA = 'Present',
                            Projection = proj)
 
 #Set up spatial block
 organizedData$spatialBlock(k = 2, rows = 2, cols = 1)
 
 #Run spatial block cross-validation
 blocked <- blockedCV(organizedData)
 
 #Print summary
 blocked
   
 }

## End(Not run)

## 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 <- startISDM(data, Mesh = mesh,
                            responsePA = 'Present',
                            Projection = proj)
 
 #Set up spatial block
 organizedData$spatialBlock(k = 2, rows = 2, cols = 1)
 
 #Run spatial block cross-validation
 blocked <- blockedCV(organizedData)
 
 #Print summary
 blocked
   
 }

## End(Not run)

Export class blockedCV

Description

Export class blockedCV

Export class blockedCVpred

Description

Export class blockedCVpred

Export class predict_bru_sdm

Description

Export class predict_bru_sdm

Export bru_sdm class

Description

Export bru_sdm class

changeCoords: function used to change coordinate names.

Description

An internal function used to change the coordinate names for datasets.

Usage

changeCoords(data, oldcoords, newcoords)
changeCoords(data, oldcoords, newcoords)

Arguments

`data`	A list of datasets.
`oldcoords`	The old coordinate names.
`newcoords`	The new coordinate names.

Value

A list of data.frame or spatial objects with the coordinate names changed.

checkCoords: function used to check coordinate names.

Description

An internal function used to check if all the coordinates are the same.

Usage

checkCoords(data, coords)
checkCoords(data, coords)

Arguments

`data`	A list of datasets.
`coords`	A vector of length 2 of the coordinate names.

Value

A logical variable.

checkVar: Function used to check variable names.

Description

Internal function used to check if variable is in dataset.

Usage

checkVar(data, var)
checkVar(data, var)

Arguments

`data`	A list of datasets.
`var`	A variable name.

Value

A logical variable

data2ENV: function used to move objects from one environment to another.

Description

Internal function: used to assign objects specified in bruSDM to the dataSDM/blockedCV function environments.

Usage

data2ENV(data, env)
data2ENV(data, env)

Arguments

`data`	bruSDM data file to be used in the integrated model.
`env`	Environment where the objects should be assigned.

Value

Assignment of the relevant spatial fields to the specified environment.

R6 class to assist in reformatting the data to be used in dataSDM.

Description

Internal functions used to temporarily store data and other information before adding to dataSDM.

Methods

Method `makeData()`

Usage

dataOrganize$makeData(
  datapoints,
  datanames,
  coords,
  proj,
  marktrialname,
  paresp,
  countsresp,
  trialname,
  speciesname,
  marks,
  pointcovnames,
  markfamily,
  temporalvar,
  offsetname
)

Method `makeSpecies()`

Usage

dataOrganize$makeSpecies(speciesname, repl = FALSE)

Method `makeMultinom()`

Usage

dataOrganize$makeMultinom(multinomVars, return, oldVars, repl = FALSE)

Method `makeFormulas()`

Usage

dataOrganize$makeFormulas(
  spatcovs,
  speciesname,
  paresp,
  countresp,
  marks,
  marksspatial,
  speciesintercept,
  speciesenvironment,
  spatial,
  intercept,
  temporalname,
  speciesindependent,
  markintercept,
  pointcovs,
  speciesspatial,
  biasformula,
  covariateformula
)

Method `makeComponents()`

Usage

dataOrganize$makeComponents(
  spatial,
  intercepts,
  datanames,
  marks,
  speciesname,
  multinomnames,
  pointcovariates,
  covariatenames,
  covariateclass,
  marksspatial,
  marksintercept,
  temporalname,
  speciesspatial,
  speciesenvironment,
  speciesintercept,
  numtime,
  temporalmodel,
  offsetname,
  copymodel,
  speciesindependent,
  biasformula,
  covariateformula,
  marksCopy
)

Method `makeLhoods()`

Usage

dataOrganize$makeLhoods(
  mesh,
  ips,
  paresp,
  ntrialsvar,
  markstrialsvar,
  speciesname
)

Method `clone()`

The objects of this class are cloneable with this method.

Usage

dataOrganize$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Internal function used to standardize datasets, as well as assign metadata.

Description

Internal function used to assist in structuring the data.

Usage

dataSet(
  datapoints,
  datanames,
  coords = c("CoordLoc1", "CoordLoc2"),
  proj,
  pointcovnames,
  paresp,
  countsresp,
  trialname,
  speciesname,
  marks,
  marktrialname,
  markfamily,
  temporalvar,
  offsetname
)
dataSet(
  datapoints,
  datanames,
  coords = c("CoordLoc1", "CoordLoc2"),
  proj,
  pointcovnames,
  paresp,
  countsresp,
  trialname,
  speciesname,
  marks,
  marktrialname,
  markfamily,
  temporalvar,
  offsetname
)

Arguments

`datapoints`	A list of datasets as sf objects
`datanames`	A vector of the names of the datasets.
`coords`	Names of the coordinates used in the model.
`proj`	The projection reference system used in the model.
`pointcovnames`	Name of the point covariates used in the model.
`paresp`	Name of the response variable used by the presence absence datasets.
`countsresp`	Name of the response variable used by the counts data.
`trialname`	Name of the trial variable used by the presence absence datasets.
`speciesname`	Name of the species name variable.
`marks`	Name of the marks considered in the model.
`marktrialname`	Name of the trial variable used by the binomial marks.
`markfamily`	A vector describing the distribution of the marks.
`temporalvar`	Name of the temporal variable.
`offsetname`	Name of the offset column in the datasets.

Value

A list of relevant metadata

datasetOut: function that removes a dataset out of the main model, and calculates some cross-validation score.

Description

This function calculates the difference in covariate values between a full integrated model and a model with one dataset left out, as well as some cross-validation score, which is used to obtain a score of the relative importance of the dataset in the full model. The score is calculated as follows:

Running a new model with one less dataset (from the main model) – resulting in a reduced model,
predicting the intensity function at the locations of the left-out dataset with the reduced model,
using the predicted values as an offset in a new model,
finding the difference between the marginal-likelihood of the main model (ie the model with all the datasets considered) and the marginal-likelihood of the offset model.

Usage

datasetOut(model, dataset, predictions = TRUE)
datasetOut(model, dataset, predictions = TRUE)

Arguments

`model`	Model of class modISDM run with multiple datasets.
`dataset`	Names of the datasets to leave out. If missing, will run for all datasets used in the full model.
`predictions`	Will new models be used for predictions. If `TRUE` returns marginals and bru_info in model. Defaults to `TRUE`.

Value

A list of inlabru models with the specified dataset left out. If predictions is FALSE, these objects will be missing their bru_info and call lists.

Examples


## 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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData,
              options = list(control.inla = list(int.strategy = 'eb')))
   
  #Choose dataset to leave out
  eBirdOut <- datasetOut(modelRun, dataset = 'eBird')
  
  #Print datasetOut summary
  eBirdOut
  
  
   
 }

## End(Not run)
## 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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData,
              options = list(control.inla = list(int.strategy = 'eb')))
   
  #Choose dataset to leave out
  eBirdOut <- datasetOut(modelRun, dataset = 'eBird')
  
  #Print datasetOut summary
  eBirdOut
  
  
   
 }

## End(Not run)

Export class bru_sdm_leave_one_out

Description

Export class bru_sdm_leave_one_out

data.frame object containing solitary tinamou observations from eBird

Description

data.frame object containing solitary tinamou observations from eBird

References

http://ebird.org/

Raster object containing the elevation across Pennsylvania state.

Description

Raster object containing the elevation across Pennsylvania state.

References

https://cran.r-project.org/package=elevatr

fitISDM: function used to run the integrated model.

Description

This function takes a intModel object and produces an inlabru model object with additional lists and meta-data added.

Usage

fitISDM(data, options = list())
fitISDM(data, options = list())

Arguments

`data`	A intModel object to be used in the integrated model.
`options`	A list of INLA options used in the model. Defaults to `list()`.

Value

An inlabru model with additional lists containing some more metadata attached.

Examples


## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, 
  options = list(control.inla = list(int.strategy = 'eb')))
   
  #Print summary of model
  modelRun
   
 }

## End(Not run)

## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, 
  options = list(control.inla = list(int.strategy = 'eb')))
   
  #Print summary of model
  modelRun
   
 }

## End(Not run)

data.frame object containing solitary tinamou observations from Gbif

Description

data.frame object containing solitary tinamou observations from Gbif

Source

https://www.gbif.org/

intModel: Function used to initialize the integrated species distribution model.

Description

This function is depreciated. Please use one of startISDM or startSpecies.

Usage

intModel(
  ...,
  spatialCovariates = NULL,
  Coordinates,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  speciesSpatial = "copy",
  speciesIndependent = FALSE,
  markNames = NULL,
  markFamily = NULL,
  pointCovariates = NULL,
  pointsIntercept = TRUE,
  marksIntercept = TRUE,
  speciesEffects = list(randomIntercept = FALSE, Environmental = TRUE),
  Offset = NULL,
  pointsSpatial = "copy",
  marksSpatial = TRUE,
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  trialsMarks = NULL,
  speciesName = NULL,
  temporalName = NULL,
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)
intModel(
  ...,
  spatialCovariates = NULL,
  Coordinates,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  speciesSpatial = "copy",
  speciesIndependent = FALSE,
  markNames = NULL,
  markFamily = NULL,
  pointCovariates = NULL,
  pointsIntercept = TRUE,
  marksIntercept = TRUE,
  speciesEffects = list(randomIntercept = FALSE, Environmental = TRUE),
  Offset = NULL,
  pointsSpatial = "copy",
  marksSpatial = TRUE,
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  trialsMarks = NULL,
  speciesName = NULL,
  temporalName = NULL,
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)

Arguments

`...`	The datasets to be used in the model. May come as either `sf`, `data.frame` or `SpatialPoints*` objects, or as a list of objects with these classes. The classes of the datasets do not necessarily need to be standardized, however the variable names within them often have to be.
`spatialCovariates`	The spatial covariates used in the model. These covariates must be measured at every location (pixel) in the study area, and must be a `Raster*`, `SpatialPixelsDataFrame` or `SpatialRaster` object. Can be either `numeric`, `factor` or `character` data.
`Coordinates`	A vector of length 2 containing the names (class `character`) of the coordinate variables used in the model.
`Projection`	The coordinate reference system used by both the spatial points and spatial covariates. Must be of class `character`.
`Mesh`	An `fm_mesh_2d` object required for the spatial random fields and the integration points in the model (see `fm_mesh_2d_inla` from the fmesher package for more details).
`IPS`	The integration points to be used in the model (that is, the points on the map where the intensity of the model is calculated). See `fm_int` from the fmesher package for more details regarding these points; however defaults to `NULL` which will create integration points from the `Mesh` object.
`Boundary`	A `sf` object of the study area. If not missing, this object is used to help create the integration points.
`speciesSpatial`	Argument to specify if each species should have their own spatial effect with different hyperparameters to be estimated using INLA's "replicate" feature, of if a the field's should be estimated per species copied across datasets using INLA's "copy" feature. Possible values include: `'replicate'`, `'copy'`, `'shared'` or `NULL` if no species-specific spatial effects should be estimated.
`speciesIndependent`	Logical argument: Should species effects be made independent of one another. Defaults to `FALSE` which creates effects for each species independently.
`markNames`	A vector with the mark names (class `character`) to be included in the integrated model. Marks are variables which are used to describe the individual points in the model (for example, in the field of ecology the size of the species or its feeding type could be considered). Defaults to `NULL`, however if this argument is non-`NULL`, the model run will become a marked point process. The marks must be included in the same data object as the points.
`markFamily`	A vector with the statistical families (class `character`) assumed for the marks. Must be the same length as markNames, and the position of the mark in the vector `markName` is associated with the position of the family in `markFamily`. Defaults to `NULL` which assigns each mark as "Gaussian".
`pointCovariates`	The non-spatial covariates to be included in the integrated model (for example, in the field of ecology the distance to the nearest road or time spent sampling could be considered). These covariates must be included in the same data object as the points.
`pointsIntercept`	Logical argument: should the points be modeled with intercepts. Defaults to `TRUE`. Note that if this argument is non-`NULL` and `pointsIntercepts` is missing, `pointsIntercepts` will be set to `FALSE`.
`marksIntercept`	Logical argument: should the marks be modeled with intercepts. Defaults to `TRUE`.
`speciesEffects`	List specifying if intercept terms and environments effects should be made for the species. Defaults to `list(randomIntercept = FALSE, Environmental = TRUE)`. `randomIntercept` may take on three values: `TRUE` which creates a random intercept for each species, `FALSE` which creates fixed intercepts for each species, of `NULL` which removes all species level intercepts. Note that if `randomIntercept = NULL` and `pointsIntercept = TRUE`, dataset specific intercept terms will be created.
`Offset`	Name of the offset variable (class `character`) in the datasets. Defaults to `NULL`; if the argument is non-`NULL`, the variable name needs to be standardized across datasets (but does not need to be included in all datasets). The offset variable will be transformed onto the log-scale in the integrated model.
`pointsSpatial`	Argument to determine whether the spatial field is shared between the datasets, or if each dataset has its own unique spatial field. The datasets may share a spatial field with INLA's "copy" feature if the argument is set to `copy`. May take on the values: `"shared"`, `"individual"`, `"copy"`, `"correlation"` or `NULL` if no spatial field is required for the model. Defaults to `"copy"`.
`marksSpatial`	Logical argument: should the marks have their own spatial field. Defaults to `TRUE`.
`responseCounts`	Name of the response variable in the counts/abundance datasets. This variable name needs to be standardized across all counts datasets used in the integrated model. Defaults to `'counts'`.
`responsePA`	Name of the response variable (class `character`) in the presence absence/detection non-detection datasets. This variable name needs to be standardized across all present absence datasets. Defaults to `'present'`.
`trialsPA`	Name of the trials response variable (class `character`) for the presence absence datasets. Defaults to `NULL`.
`trialsMarks`	Name of the trials response variable (class `character`) for the binomial marks (if included). Defaults to `NULL`.
`speciesName`	Name of the species variable name (class `character`). Specifying this argument turns the model into a stacked species distribution model, and calculates covariate values for the individual species, as well as a species group model in the shared spatial field. Defaults to `NULL`. Note that if this argument is non-`NULL` and `pointsIntercepts` is missing, `pointsIntercepts` will be set to `FALSE`.
`temporalName`	Name of the temporal variable (class `character`) in the model. This variable is required to be in all the datasets. Defaults to `NULL`.
`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))`.
`Formulas`	A named list with two objects. The first one, `covariateFormula`, is a formula for the covariates and their transformations for the distribution part of the model. Defaults to `NULL` which includes all covariates specified in `spatialCovariates` into the model. The second, `biasFormula`, specifies which covariates are used for the PO datasets. Defaults to `NULL` which includes no covariates for the PO datasets.

Value

A specifyISDM object (class R6). Use ?specifyISDM to get a comprehensive description of the slot functions associated with this object.

Note

The idea with this function is to describe the full model: that is, all the covariates and spatial effects will appear in all the formulas for the datasets and species. If some of these terms should not be included in certain observation models in the integrated model, they can be thinned out using the .$updateFormula function. Note: the point covariate and mark terms will only be included in the formulas for where they are present in a given dataset, and so these terms do not need to be thinned out if they are not required by certain observation models.

Examples


 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 
 indSpat <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, pointsSpatial = 'individual', responsePA = 'Present')
                     
 #Model with offset variable
 offSet <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, Offset = 'area', responsePA = 'Present')
                     
 #Assume area as a mark
 markModel <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, markNames = 'area', markFamily = 'gamma',
                     responsePA = 'Present')
                      
 }

if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 
 indSpat <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, pointsSpatial = 'individual', responsePA = 'Present')
                     
 #Model with offset variable
 offSet <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, Offset = 'area', responsePA = 'Present')
                     
 #Assume area as a mark
 markModel <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                     Projection = proj, markNames = 'area', markFamily = 'gamma',
                     responsePA = 'Present')
                      
 }

Dataset of Eucalyptus globulus (common name: blue gum) sightings collected across the Koala conservation reserve on Phillip island (Australia) between 1993 and 2004. Two marks are considered from this dataset: "koala" which describes the number of koala visits to each tree, and "food" which is some index of the palatability of the leaves.

Description

Dataset of Eucalyptus globulus (common name: blue gum) sightings collected across the Koala conservation reserve on Phillip island (Australia) between 1993 and 2004. Two marks are considered from this dataset: "koala" which describes the number of koala visits to each tree, and "food" which is some index of the palatability of the leaves.

Format

eucTrees is a data.frame object (1284 observations; 6 variables) with the following columns:

E: Latitude of the observation.
N: Longitude of the observation.
FOOD: Some value of the palatability of the leaves of the trees.
koala: The number of koala sightings per tree.
nitrogen: Total nitrogen (mg.g-1 DM).
dbh: Diameter at breast hight of the trees (cm).

References

Moore, B.D., Lawler, I.R., Wallis, I.R., Collin, B.M. and Foley, W.J. (2010). Palatability mapping: a koala’s eye view of spatial variation in habitat quality. Ecology 91 (11): 3165-3176.

makeFormulaComps: function to make components for the covariate and bias Formulas.

Description

An internal function used to make the formula components required for inlabru.

Usage

makeFormulaComps(form, species, speciesnames, type)
makeFormulaComps(form, species, speciesnames, type)

Arguments

`form`	The formula which needs to be changed into a component.
`species`	Logical indicating if species occur in the model.
`speciesnames`	The names of the species occurring in the model.
`type`	What type of component is being created: one for the covariate formula or the bias formula.

Value

A vector of components required for inlabru.

makeLhoods: function to make likelihoods.

Description

Function to make the datasets into likelihoods.

Usage

makeLhoods(
  data,
  formula,
  family,
  mesh,
  ips,
  paresp,
  ntrialsvar,
  markstrialsvar,
  speciesname,
  speciesindex,
  samplers,
  pointcovs = NULL
)
makeLhoods(
  data,
  formula,
  family,
  mesh,
  ips,
  paresp,
  ntrialsvar,
  markstrialsvar,
  speciesname,
  speciesindex,
  samplers,
  pointcovs = NULL
)

Arguments

`data`	A list of sf objects containing the datasets for which likelihoods need to be constructed.
`formula`	A list of formulas to add to the likelihoods.
`family`	A list of vectors containing the families within each dataset.
`mesh`	An `fm_mesh_2d` object.
`ips`	Integration points used.
`paresp`	The response variable name for the presence absence datasets.
`ntrialsvar`	The trials variable name for the presence absence datasets.
`markstrialsvar`	The trial variable name for the binomial marks.
`speciesname`	The name of the species variable used.
`speciesindex`	A vector containing the numeric index of where the species occurs in the data
`samplers`	A list of integration domains for the datasets.
`pointcovs`	A vector of the point covariates used in the model.

Export class predict.modISDM

Description

Export class predict.modISDM

Export modISDM class

Description

Export modISDM class

Export class predict_modMarks

Description

Export class predict_modMarks

Export modMarks class

Description

Export modMarks class

Export class predict_modSpecies

Description

Export class predict_modSpecies

Export modSpecies class

Description

Export modSpecies class

nameChanger: function to change a variable name.

Description

An internal function used to change the name of a variable.

Usage

nameChanger(data, oldName, newName)
nameChanger(data, oldName, newName)

Arguments

`data`	A list of datasets.
`oldName`	The old variable name.
`newName`	The new variable name.

Value

A list of data.frame or spatial objects with the name of the variable changes.

nearestValue: Match species location data to environmental raster layers

Description

Obtain the nearest covariate value at each of the species locations.

Usage

nearestValue(pts, r, ...)
nearestValue(pts, r, ...)

Arguments

`pts`	The species location data
`r`	The raster file to extract the covariates at.
`...`	Extra arguments for knnx.index.

Raster object containing the canopy cover across Pennsylvania state.

Description

Raster object containing the canopy cover across Pennsylvania state.

References

https://cran.r-project.org/package=FedData

data.frame object containing solitary tinamou observations from Parks

Description

data.frame object containing solitary tinamou observations from Parks

Source

https://www.gbif.org/

Generic plot function for `predict_bru_sdm`.

Description

Plot for predict_bru_sdm

Plot for modISDM_predict

Plot for modMarks_predict

Plot for modSpecies_predict

Usage

## S3 method for class 'bruSDM_predict'
plot(
  x,
  whattoplot = c("mean"),
  cols = NULL,
  layout = NULL,
  colourLow = NULL,
  colourHigh = NULL,
  plot = TRUE,
  ...
)

## S3 method for class 'modISDM_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modMarks_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modSpecies_predict'
plot(x, variable = "mean", plot = TRUE, ...)
## S3 method for class 'bruSDM_predict'
plot(
  x,
  whattoplot = c("mean"),
  cols = NULL,
  layout = NULL,
  colourLow = NULL,
  colourHigh = NULL,
  plot = TRUE,
  ...
)

## S3 method for class 'modISDM_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modMarks_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modSpecies_predict'
plot(x, variable = "mean", plot = TRUE, ...)

Arguments

`x`	A modSpecies_predict object.
`whattoplot`	One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"
`cols`	Number of columns required for the plotting. Used by inlabru's multiplot function.
`layout`	Layout of the plots. Used by inlabru's multiplot function.
`colourLow`	Colour for the low values in the predictions (see ?scale_colour_gradient from `ggplot2`). Defaults to `NULL`. If non-`NULL`, `colourHigh` is required.
`colourHigh`	Colour for the high values in the predictions (see ?scale_colour_gradient from `ggplot2`). Defaults to `NULL`. If non-`NULL`, `colourLow` is required.
`plot`	Should the plots be printed, defaults to `TRUE`. If `FALSE` will produce a list of ggplot objects.
`...`	Argument not used
`variable`	One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"

Value

A ggplot2 object.

Examples

## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

## 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 <- startISDM(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

## 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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  ##Run the model
 modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                                     diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions)
 
 }

## End(Not run)

## 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, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

## 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 <- startISDM(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

## 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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  ##Run the model
 modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                                     diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions)
 
 }

## End(Not run)

## 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, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions, colourHigh = 'red', colourLow = 'orange')
 
 }

## End(Not run)

Generic predict function for `bru_SDM` objects.

Description

Predict function for the object produced by fitISDM. Should act identically to inlabru's generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since intModel creates variable names which might not be directly apparent to the user.

Predict function for the object produced by fitISDM. Should act identically to inlabru's generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since startISDM creates variable names which might not be directly apparent to the user.

Predict function for the object produced by fitISDM. Should act identically to inlabru's generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since startMarks creates variable names which might not be directly apparent to the user.

Predict function for the object produced by fitISDM. Should act identically to inlabru's generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since startSpecies creates variable names which might not be directly apparent to the user.

Usage

## S3 method for class 'bruSDM'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  temporal = FALSE,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  species = NULL,
  marks = NULL,
  biasfield = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  format = "sf",
  ...
)

## S3 method for class 'modISDM'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)

## S3 method for class 'modMarks'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  marks = NULL,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)

## S3 method for class 'modSpecies'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  species,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)
## S3 method for class 'bruSDM'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  temporal = FALSE,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  species = NULL,
  marks = NULL,
  biasfield = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  format = "sf",
  ...
)

## S3 method for class 'modISDM'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)

## S3 method for class 'modMarks'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  marks = NULL,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)

## S3 method for class 'modSpecies'
predict(
  object,
  data = NULL,
  formula = NULL,
  mesh = NULL,
  mask = NULL,
  covariates = NULL,
  spatial = FALSE,
  intercepts = FALSE,
  datasets = NULL,
  species,
  bias = FALSE,
  biasnames = NULL,
  predictor = FALSE,
  fun = "linear",
  ...
)

Arguments

`object`	A `modSpecies` object.
`data`	Data containing points of the map with which to predict on. May be `NULL` if one of `mesh` or `mask` is `NULL`.
`formula`	Formula to predict. May be `NULL` if other arguments: `covariates`, `spatial`, `intercepts` are not `NULL`.
`mesh`	An `fm_mesh_2d` object.
`mask`	A mask of the study background. Defaults to `NULL`.
`temporal`	Make predictions for the temporal component of the model.
`covariates`	Name of covariates to predict.
`spatial`	Logical: include spatial effects in prediction. Defaults to `FALSE`.
`intercepts`	Logical: include intercept terms in prediction. Defaults to `FALSE`.
`datasets`	Names of the datasets to include intercept and spatial term.
`species`	Names of the species to predict. Default of `NULL` results in all species being predicted.
`marks`	Names of the marks to include intercept and spatial term.
`biasfield`	Logical include bias field in prediction. Defaults to `FALSE`.
`biasnames`	Names of the datasets to include bias term. Defaults to `NULL`. Note: the chosen dataset needs to be run with a bias field first; this can be done using `.$addBias` with the object produced by `startSpecies`.
`predictor`	Should all terms (except the bias terms) included in the linear predictor be used in the predictions. Defaults to `FALSE`.
`fun`	Function used to predict. Set to `'linear'` if effects on the linear scale are desired.
`format`	Class of the data for which to predict on. Must be one of `'sp'`, `'sf'` or `'terra'`. Defaults to `'sf'`.
`...`	Additional arguments used by the inlabru `predict` function.
`bias`	Logical include bias field in prediction. Defaults to `FALSE`.

Details

Predict for bru_sdm

Predict for modISDM

Predict for modMarks

Predict for modSpecies

Value

A list of inlabru predict objects.

Examples


## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)


## 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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)


## Not run: 
 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 data <- lapply(data, function(x) {x$mark = runif(nrow(x));x})
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh, markNames = 'mark',
                             markFamily = 'gaussian',
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                    diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, marks = 'mark', fun = 'linear')
   
 }

## End(Not run)


## 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')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                    diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)

## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)


## 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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)


## Not run: 
 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 data <- lapply(data, function(x) {x$mark = runif(nrow(x));x})
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh, markNames = 'mark',
                             markFamily = 'gaussian',
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                    diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, marks = 'mark', fun = 'linear')
   
 }

## End(Not run)


## 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')
 
  ##Run the model
  modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                    diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
 }

## End(Not run)

Print function for `blockedCV`.

Description

Print for blockedCV

Usage

## S3 method for class 'blockedCV'
print(x, ...)
## S3 method for class 'blockedCV'
print(x, ...)

Arguments

`x`	A blockedCV object.
`...`	Unused argument.

Print function for `blockedCV`.

Description

Print for blockedCVpred

Usage

## S3 method for class 'blockedCVpred'
print(x, ...)
## S3 method for class 'blockedCVpred'
print(x, ...)

Arguments

`x`	A blockedCV object.
`...`	Unused argument.

Generic print function for `bruSDM`.

Description

Print method for bru_sdm

Usage

## S3 method for class 'bruSDM'
print(x, ...)
## S3 method for class 'bruSDM'
print(x, ...)

Arguments

`x`	bruSDM object.
`...`	Un used argument.

Generic print function for `bru_sdm_predict`.

Description

Generic print function for bru_sdm_predict.

Usage

## S3 method for class 'bruSDM_predict'
print(x, ...)
## S3 method for class 'bruSDM_predict'
print(x, ...)

Arguments

`x`	bruSDM_predict object
`...`	Not used.

Generic print function for `datasetOut`.

Description

Print for bru_sdm_leave_one_out

Usage

## S3 method for class 'datasetOut'
print(x, ...)
## S3 method for class 'datasetOut'
print(x, ...)

Arguments

`x`	datasetOut object.
`...`	Unused argument.

Generic print function for `modISDM`.

Description

Print method for modISDM

Usage

## S3 method for class 'modISDM'
print(x, ...)
## S3 method for class 'modISDM'
print(x, ...)

Arguments

`x`	modISDM object.
`...`	Not used.

Generic print function for `modMarks`.

Description

Print method for modMarks

Usage

## S3 method for class 'modMarks'
print(x, ...)
## S3 method for class 'modMarks'
print(x, ...)

Arguments

`x`	modMarks object.
`...`	Not used.

Generic print function for `modSpecies`.

Description

Print method for modSpecies

Usage

## S3 method for class 'modSpecies'
print(x, ...)
## S3 method for class 'modSpecies'
print(x, ...)

Arguments

`x`	modSpecies object.
`...`	Not used.

reduceComps: Reduce the components of the model.

Description

reduceComps: Reduce the components of the model.

Usage

reduceComps(componentsOld, pointsCopy, biasCopy, datasetName, reducedTerms)
reduceComps(componentsOld, pointsCopy, biasCopy, datasetName, reducedTerms)

Arguments

`componentsOld`	The old components in the model.
`pointsCopy`	Logical: is the spatial model for the points copy.
`biasCopy`	Logical: is the bias model copy.
`datasetName`	Name of the dataset of interest.
`reducedTerms`	Terms to include in the new components.

Value

New components.

sf object containing the boundary region for solitary tinamouc

Description

sf object containing the boundary region for solitary tinamouc

Source

https://github.com/oharar/PointedSDMs

removeFormula: Function to remove term from a formula.

Description

formulaChanger: Internal function used to remove formula terms.

Usage

removeFormula(formulaRemove, oldFormula)
removeFormula(formulaRemove, oldFormula)

Arguments

`formulaRemove`	The formula with all the components to remove.
`oldFormula`	The formula which needs to change.

runModel: function used to run the integrated model. Note that this function is depreciated, and will be removed in a later version of the package.

Description

This function takes a intModel object and produces an inlabru model object with additional lists and meta-data added.

Usage

runModel(data, options = list())
runModel(data, options = list())

Arguments

`data`	A intModel object to be used in the integrated model.
`options`	A list of INLA options used in the model. Defaults to `list()`.

Value

An inlabru model with additional lists containing some more metadata attached.

Examples


## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- runModel(organizedData, 
  options = list(control.inla = list(int.strategy = 'eb')))
   
  #Print summary of model
  modelRun
   
 }

## End(Not run)

## 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  ##Run the model
  modelRun <- runModel(organizedData, 
  options = list(control.inla = list(int.strategy = 'eb')))
   
  #Print summary of model
  modelRun
   
 }

## End(Not run)

List of all data objects used for the Setophaga vignette.

Description

List of all data objects used for the Setophaga vignette.

List of all data objects used for the solitary tinamou vignette.

Description

List of all data objects used for the solitary tinamou vignette.

spatRaster object containing covariate values

Description

spatRaster object containing covariate values

Source

https://github.com/oharar/PointedSDMs

R6 class for creating a `startISDM` object.

Description

A data object containing the data and the relevant information about the integrated model. The function startISDM 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

Public methods

specifyISDM$help()
specifyISDM$print()
specifyISDM$plot()
specifyISDM$addBias()
specifyISDM$updateFormula()
specifyISDM$changeComponents()
specifyISDM$priorsFixed()
specifyISDM$specifySpatial()
specifyISDM$changeLink()
specifyISDM$spatialBlock()
specifyISDM$addSamplers()
specifyISDM$specifyRandom()
specifyISDM$new()
specifyISDM$samplingBias()

Method `help()`

Function to provide documentation for a specifyISDM object.

Usage

specifyISDM$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

specifyISDM$print(...)

Arguments

...: Not used.

Method `plot()`

Makes a plot of the points surrounded by the boundary of the region where they were collected. The points may either be plotted based on which dataset they come from, or which species group they are part of (if speciesName is non-NULL in intModel).

Usage

specifyISDM$plot(datasetNames, 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.
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 <- startISDM(data, Mesh = mesh,
                            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

specifyISDM$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 <- startISDM(data, Mesh = mesh,
                             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

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

Arguments

datasetName: Name of the dataset (class character) for which the formula needs to be changed.
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.
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.
processLevel: Logical argument: if TRUE changes the formulas for all of the processes in a dataset. Defaults to FALSE.

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 <- startISDM(data, Mesh = mesh,
                           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('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

specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

}

}

Method `priorsFixed()`

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

Usage

specifyISDM$priorsFixed(
  Effect,
  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).
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 <- startISDM(data, Mesh = mesh,
                           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

specifyISDM$specifySpatial(
  sharedSpatial = FALSE,
  datasetName,
  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.
Bias: Name of the dataset for which the bias field to be specified.
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 <- startISDM(data, Mesh = mesh,
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

 #Specify the shared spatial field
 organizedData$specifySpatial(sharedSpatial = 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

specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           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

specifyISDM$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

 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 <- startISDM(data, Mesh = mesh,
                           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

specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           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

specifyISDM$specifyRandom(
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  copyBias = list(beta = list(fixed = FALSE))
)

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)).

Returns

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 <- startISDM(data, Mesh = mesh,
                           Projection = proj, 
                           responsePA = 'Present',
                           pointsSpatial = copy)
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}
}

Method `new()`

Usage

specifyISDM$new(
  data,
  projection,
  Inlamesh,
  initialnames,
  responsecounts,
  responsepa,
  pointcovariates,
  trialspa,
  spatial,
  intercepts,
  spatialcovariates,
  boundary,
  ips,
  temporal,
  temporalmodel,
  offset,
  copymodel,
  formulas
)

Method `samplingBias()`

Usage

specifyISDM$samplingBias(datasetName, Samplers)

Examples


## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                             Projection = proj, 
                             responsePA = 'Present')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           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('Parks', newFormula = ~ I(. +(Forest+1e-6)*scaling))
 
 #Now dd scaling to components
 organizedData$changeComponents(addComponent = 'scaling') 
 
}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

}


## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           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 `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$spatialBlock`
## ------------------------------------------------

 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 <- startISDM(data, Mesh = mesh,
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

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


## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           Projection = proj, 
                           responsePA = 'Present')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           Projection = proj, 
                           responsePA = 'Present',
                           pointsSpatial = copy)
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}

## End(Not run)
## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                             Projection = proj, 
                             responsePA = 'Present')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           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('Parks', newFormula = ~ I(. +(Forest+1e-6)*scaling))
 
 #Now dd scaling to components
 organizedData$changeComponents(addComponent = 'scaling') 
 
}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

}


## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           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 `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh, 
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$spatialBlock`
## ------------------------------------------------

 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 <- startISDM(data, Mesh = mesh,
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

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


## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           Projection = proj, 
                           responsePA = 'Present')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}

## End(Not run)

## ------------------------------------------------
## Method `specifyISDM$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 <- startISDM(data, Mesh = mesh,
                           Projection = proj, 
                           responsePA = 'Present',
                           pointsSpatial = copy)
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}

## End(Not run)

R6 class for creating a `specifyMarks` object.

Description

A data object containing the data and the relevant information about the integrated model. The function startMarks 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

Public methods

specifyMarks$help()
specifyMarks$print()
specifyMarks$plot()
specifyMarks$addBias()
specifyMarks$updateFormula()
specifyMarks$changeComponents()
specifyMarks$priorsFixed()
specifyMarks$specifySpatial()
specifyMarks$changeLink()
specifyMarks$spatialBlock()
specifyMarks$addSamplers()
specifyMarks$specifyRandom()
specifyMarks$new()
specifyMarks$samplingBias()

Method `help()`

Function to provide documentation for a specifyMarks object.

Usage

specifyMarks$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

specifyMarks$print(...)

Arguments

...: Not used.

Method `plot()`

Usage

specifyMarks$plot(datasetNames, 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.
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 organizedData up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  #Create plot of data
  organizedData$plot()

}
}

Method `addBias()`

Usage

specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}
}

Method `updateFormula()`

Usage

specifyMarks$updateFormula(
  datasetName = NULL,
  Points = TRUE,
  Mark = NULL,
  Formula,
  newFormula
)

Arguments

datasetName: Name of the dataset (class character) for which the formula needs to be changed.
Points: Logical: should the formula be changed for the points (or otherwise, a marked process). Defaults to TRUE.
Mark: Name of the mark (class character) to change the formula for. Defaults to NULL.
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.
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

If Formula and newFormula are missing, will print out the formula for the specified processes.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Remove Forest from eBird
   organizedData$updateFormula(datasetName = 'eBird', Mark = 'speciesName', Formula = ~ . - Forest)
 
}
}

Method `changeComponents()`

Usage

specifyMarks$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.

Examples

\dontrun{

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

}

}

Method `priorsFixed()`

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

Usage

specifyMarks$priorsFixed(
  Effect,
  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).
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 <- startMarks(data, Mesh = mesh, marksIntercept = FALSE,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Add prior to Forest
 organizedData$priorsFixed(Effect = 'Intercept', 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

specifyMarks$specifySpatial(
  sharedSpatial = FALSE,
  datasetName,
  Mark,
  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.
Mark: Name of the marks to specify the spatial field for. If TRUE changes the spatial effect for all marks.
Bias: Name of the dataset for which the bias field to be specified.
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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Specify the shared spatial field
 organizedData$specifySpatial(sharedSpatial = 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

specifyMarks$changeLink(datasetName, Mark, Link, ...)

Arguments

datasetName: Name of the dataset for which the link function needs to be changed.
Mark: Name of the mark 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.
...: Not used
Species: Name of the species for which the link function needs to be changed.

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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Specify the shared spatial field
 organizedData$changeLink(datasetName = 'Parks', 
                          Mark = 'speciesName',
                          Link = 'logit')
 
 
} 
}

Method `spatialBlock()`

Usage

specifyMarks$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.

Examples

\dontrun{
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #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

specifyMarks$addSamplers(datasetName, Samplers)

Arguments

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

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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#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

specifyMarks$specifyRandom(
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  copyBias = list(beta = list(fixed = FALSE))
)

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)).

Returns

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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}
}

Method `new()`

Usage

specifyMarks$new(
  data,
  coordinates,
  projection,
  Inlamesh,
  initialnames,
  responsecounts,
  responsepa,
  marksnames,
  marksfamily,
  pointcovariates,
  trialspa,
  trialsmarks,
  marksspatial,
  spatial,
  intercepts,
  spatialcovariates,
  marksintercepts,
  boundary,
  ips,
  temporal,
  temporalmodel,
  offset,
  copymodel,
  formulas
)

Method `samplingBias()`

Usage

specifyMarks$samplingBias(datasetName, Samplers)

Examples


## ------------------------------------------------
## Method `specifyMarks$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 organizedData up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  #Create plot of data
  organizedData$plot()

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$updateFormula`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Remove Forest from eBird
   organizedData$updateFormula(datasetName = 'eBird', Mark = 'speciesName', Formula = ~ . - Forest)
 
}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$changeComponents`
## ------------------------------------------------

## Not run: 

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

}


## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh, marksIntercept = FALSE,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Specify the shared spatial field
 organizedData$changeLink(datasetName = 'Parks', 
                          Mark = 'speciesName',
                          Link = 'logit')
 
 
} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$spatialBlock`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}

## End(Not run)
## ------------------------------------------------
## Method `specifyMarks$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 organizedData up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  #Create plot of data
  organizedData$plot()

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$updateFormula`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Remove Forest from eBird
   organizedData$updateFormula(datasetName = 'eBird', Mark = 'speciesName', Formula = ~ . - Forest)
 
}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$changeComponents`
## ------------------------------------------------

## Not run: 

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

}


## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh, marksIntercept = FALSE,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

 #Specify the shared spatial field
 organizedData$changeLink(datasetName = 'Parks', 
                          Mark = 'speciesName',
                          Link = 'logit')
 
 
} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$spatialBlock`
## ------------------------------------------------

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')

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

} 

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add integration domain for the eBird records
organizedData$addSamplers(datasetName = 'eBird', Samplers = SolitaryTinamou$region)

}

## End(Not run)

## ------------------------------------------------
## Method `specifyMarks$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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
#Add integration domain for the eBird records
organizedData$specifyRandom(copyModel =  list(beta = list(fixed = TRUE)))

}

## End(Not run)

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

Public methods

specifySpecies$help()
specifySpecies$print()
specifySpecies$plot()
specifySpecies$addBias()
specifySpecies$updateFormula()
specifySpecies$changeComponents()
specifySpecies$priorsFixed()
specifySpecies$specifySpatial()
specifySpecies$changeLink()
specifySpecies$spatialBlock()
specifySpecies$addSamplers()
specifySpecies$specifyRandom()
specifySpecies$new()
specifySpecies$samplingBias()

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()`

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()`

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()`

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: Name of the dataset for which the bias field to be specified.
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()`

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)
## ------------------------------------------------
## 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)

startISDM: Function used to initialize the integrated species distribution model.

Description

This function is used to create an object containing all the data, metadata and relevant components required for the integrated species distribution model and INLA to work. As a result, the arguments associated with this function are predominantly related to describing variable names within the datasets that are relevant, and arguments related to what terms should be included in the formula for the integrated model. The output of this function is an R6 object, and so there are a variety of public methods within the output of this function which can be used to further specify the model (see ?specifyISDM or .$help() for a comprehensive description of these public methods).

Usage

startISDM(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  pointCovariates = NULL,
  Offset = NULL,
  pointsIntercept = TRUE,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)
startISDM(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  pointCovariates = NULL,
  Offset = NULL,
  pointsIntercept = TRUE,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)

Arguments

`...`	The datasets to be used in the model. Must come as either `sf` objects, or as a list of named `sf` objects.
`spatialCovariates`	The spatial covariates used in the model. These covariates must be measured at every location (pixel) in the study area, and must be a `SpatialRaster` object. Can be either `numeric`, `factor` or `character` data. Defaults to `NULL` which includes no spatial effects in the model.
`Projection`	The coordinate reference system used by both the spatial points and spatial covariates. Must be of class `character`.
`Mesh`	An `fm_mesh_2d` object required for the spatial random fields and the integration points in the model (see `fm_mesh_2d_inla` from the fmesher package for more details).
`IPS`	The integration points to be used in the model (that is, the points on the map where the intensity of the model is calculated). See `fm_int` from the fmesher package for more details regarding these points; however defaults to `NULL` which will create integration points from the `Mesh` and `Boundary` objects.
`Boundary`	A `sf` object of the study area. If not missing, this object is used to help create the integration points.
`pointCovariates`	The non-spatial covariates to be included in the integrated model (for example, in the field of ecology the distance to the nearest road or time spent sampling could be considered). These covariates must be included in the same data object as the points, and do not necessarily need to be present in all datasets.
`Offset`	Name of the offset variable (class `character`) in the datasets. Defaults to `NULL`; if the argument is non-`NULL`, the variable name needs to be standardized across datasets (but does not need to be included in all datasets). The offset variable will be transformed onto the log-scale in the integrated model.
`pointsIntercept`	Logical argument: should the points be modeled with intercepts. Defaults to `TRUE`.
`pointsSpatial`	Argument to determine whether the spatial field is shared between the datasets, or if each dataset has its own unique spatial field. The datasets may share a spatial field with INLA's "copy" feature if the argument is set to `copy`. May take on the values: `"shared"`, `"individual"`, `"copy"` or `NULL` if no spatial field is required for the model. Defaults to `"copy"`.
`responseCounts`	Name of the response variable in the counts/abundance datasets. This variable name needs to be standardized across all counts datasets used in the integrated model. Defaults to `'counts'`.
`responsePA`	Name of the response variable (class `character`) in the presence absence/detection non-detection datasets. This variable name needs to be standardized across all present absence datasets. Defaults to `'present'`.
`trialsPA`	Name of the trials response variable (class `character`) for the presence absence datasets. Defaults to `NULL`.
`temporalName`	Name of the temporal variable (class `character`) in the model. This variable is required to be in all the datasets. Defaults to `NULL`.
`Formulas`	A named list with two objects. The first one, `covariateFormula`, is a formula for the covariates and their transformations for the distribution part of the model. Defaults to `NULL` which includes all covariates specified in `spatialCovariates` into the model. The second, `biasFormula`, specifies which covariates are used for the PO datasets. Defaults to `NULL` which includes no covariates for the PO datasets.

Value

A specifyISDM object (class R6). Use ?specifyISDM to get a comprehensive description of the slot functions associated with this object.

Note

The idea with this function is to describe the full model: that is, all the covariates and spatial effects will appear in all the formulas for the datasets and species. If some of these terms should not be included in certain observation models in the integrated model, they can be thinned out using the .$updateFormula function. Note: the point covariate will only be included in the formulas for where they are present in a given dataset, and so these terms do not need to be thinned out if they are not required by certain observation models.

Examples


## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startISDM(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 indSpat <- startISDM(data, Mesh = mesh,
                     Projection = proj, pointsSpatial = 'individual', responsePA = 'Present')
                     
 #Model with offset variable
 offSet <- startISDM(data, Mesh = mesh,
                     Projection = proj, Offset = 'area', responsePA = 'Present')
 
 #Add own formula
 quadModel <- startISDM(data, Mesh = mesh, 
                        Projection = proj, pointsSpatial = 'copy', responsePA = 'Present',
                        Formulas = list(covariateFormula = ~ NPP + I(NPP^2)))                    
                      
 }
 
## End(Not run)

## Not run: 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startISDM(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 indSpat <- startISDM(data, Mesh = mesh,
                     Projection = proj, pointsSpatial = 'individual', responsePA = 'Present')
                     
 #Model with offset variable
 offSet <- startISDM(data, Mesh = mesh,
                     Projection = proj, Offset = 'area', responsePA = 'Present')
 
 #Add own formula
 quadModel <- startISDM(data, Mesh = mesh, 
                        Projection = proj, pointsSpatial = 'copy', responsePA = 'Present',
                        Formulas = list(covariateFormula = ~ NPP + I(NPP^2)))                    
                      
 }
 
## End(Not run)

startMarks: Function used to initialize a marked-point process model.

Description

This function is used to create an object containing all the data, metadata and relevant components required for the integrated species distribution model and INLA to work. As a result, the arguments associated with this function are predominantly related to describing variable names within the datasets that are relevant, and arguments related to what terms should be included in the formula for the integrated model. The output of this function is an R6 object, and so there are a variety of public methods within the output of this function which can be used to further specify the model (see ?specifyMarks for a comprehensive description of these public methods).

Usage

startMarks(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  markNames = NULL,
  markFamily = NULL,
  marksSpatial = TRUE,
  pointCovariates = NULL,
  pointsIntercept = TRUE,
  marksIntercept = TRUE,
  Offset = NULL,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  trialsMarks = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)
startMarks(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  IPS = NULL,
  Boundary = NULL,
  markNames = NULL,
  markFamily = NULL,
  marksSpatial = TRUE,
  pointCovariates = NULL,
  pointsIntercept = TRUE,
  marksIntercept = TRUE,
  Offset = NULL,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  trialsMarks = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)

Arguments

`...`	The datasets to be used in the model. Must come as either `sf` objects, or as a list of named `sf` objects.
`spatialCovariates`	The spatial covariates used in the model. These covariates must be measured at every location (pixel) in the study area, and must be a `SpatialRaster` object. Can be either `numeric`, `factor` or `character` data. Defaults to `NULL` which includes no spatial effects in the model.
`Projection`	The coordinate reference system used by both the spatial points and spatial covariates. Must be of class `character`.
`Mesh`	An `fm_mesh_2d` object required for the spatial random fields and the integration points in the model (see `fm_mesh_2d_inla` from the fmesher package for more details).
`IPS`	The integration points to be used in the model (that is, the points on the map where the intensity of the model is calculated). See `fm_int` from the fmesher package for more details regarding these points; however defaults to `NULL` which will create integration points from the `Mesh` and `Boundary` objects.
`Boundary`	A `sf` object of the study area. If not missing, this object is used to help create the integration points.
`markNames`	A vector with the mark names (class `character`) to be included in the integrated model. Marks are variables which are used to describe the individual points in the model (for example, in the field of ecology the size of the species or its feeding type could be considered). Defaults to `NULL`, however if this argument is non-`NULL`, the model run will become a marked point process. The marks must be included in the same data object as the points.
`markFamily`	A vector with the statistical families (class `character`) assumed for the marks. Must be the same length as markNames, and the position of the mark in the vector `markName` is associated with the position of the family in `markFamily`. Defaults to `NULL` which assigns each mark as "Gaussian".
`marksSpatial`	Logical argument: should the marks have their own spatial field. Defaults to `TRUE`.
`pointCovariates`	The non-spatial covariates to be included in the integrated model (for example, in the field of ecology the distance to the nearest road or time spent sampling could be considered). These covariates must be included in the same data object as the points.
`pointsIntercept`	Logical argument: should the points be modeled with intercepts. Defaults to `TRUE`. Note that if this argument is non-`NULL` and `pointsIntercepts` is missing, `pointsIntercepts` will be set to `FALSE`.
`marksIntercept`	Logical argument: should the marks be modeled with intercepts. Defaults to `TRUE`.
`Offset`	Name of the offset variable (class `character`) in the datasets. Defaults to `NULL`; if the argument is non-`NULL`, the variable name needs to be standardized across datasets (but does not need to be included in all datasets). The offset variable will be transformed onto the log-scale in the integrated model.
`pointsSpatial`	Argument to determine whether the spatial field is shared between the datasets, or if each dataset has its own unique spatial field. The datasets may share a spatial field with INLA's "copy" feature if the argument is set to `copy`. May take on the values: `"shared"`, `"individual"`, `"copy"` or `NULL` if no spatial field is required for the model. Defaults to `"shared"`.
`responseCounts`	Name of the response variable in the counts/abundance datasets. This variable name needs to be standardized across all counts datasets used in the integrated model. Defaults to `'counts'`.
`responsePA`	Name of the response variable (class `character`) in the presence absence/detection non-detection datasets. This variable name needs to be standardized across all present absence datasets. Defaults to `'present'`.
`trialsPA`	Name of the trials response variable (class `character`) for the presence absence datasets. Defaults to `NULL`.
`trialsMarks`	Name of the trials response variable (class `character`) for the binomial marks (if included). Defaults to `NULL`.
`temporalName`	Name of the temporal variable (class `character`) in the model. This variable is required to be in all the datasets. Defaults to `NULL`.
`Formulas`	A named list with two objects. The first one, `covariateFormula`, is a formula for the covariates and their transformations for the distribution part of the model. Defaults to `NULL` which includes all covariates specified in `spatialCovariates` into the model. The second, `biasFormula`, specifies which covariates are used for the PO datasets. Defaults to `NULL` which includes no covariates for the PO datasets.

Value

A specifyMarks object (class R6). Use ?specifyMarks of .$help() to get a comprehensive description of the slot functions associated with this object.

Note

Examples


 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
                      
 }

if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
                      
 }

startSpecies: Function used to initialize a multi-species integrated species distribution model.

Description

This function is used to create an object containing all the data, metadata and relevant components required for the multi-species integrated species distribution model and INLA to work. As a result, the arguments associated with this function are predominantly related to describing variable names within the datasets that are relevant, and arguments related to what terms should be included in the formula for the integrated model. The output of this function is an R6 object, and so there are a variety of public methods within the output of this function which can be used to further specify the model (see ?specifySpecies or .$help() for a comprehensive description of these public methods).

Usage

startSpecies(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  speciesSpatial = "replicate",
  speciesIntercept = TRUE,
  speciesEnvironment = TRUE,
  speciesName,
  IPS = NULL,
  Boundary = NULL,
  pointCovariates = NULL,
  Offset = NULL,
  pointsIntercept = TRUE,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)
startSpecies(
  ...,
  spatialCovariates = NULL,
  Projection,
  Mesh,
  speciesSpatial = "replicate",
  speciesIntercept = TRUE,
  speciesEnvironment = TRUE,
  speciesName,
  IPS = NULL,
  Boundary = NULL,
  pointCovariates = NULL,
  Offset = NULL,
  pointsIntercept = TRUE,
  pointsSpatial = "copy",
  responseCounts = "counts",
  responsePA = "present",
  trialsPA = NULL,
  temporalName = NULL,
  Formulas = list(covariateFormula = NULL, biasFormula = NULL)
)

Arguments

`...`	The datasets to be used in the model. Must come as either `sf` objects, or as a list of named `sf` objects.
`spatialCovariates`	The spatial covariates used in the model. These covariates must be measured at every location (pixel) in the study area, and must be a `SpatialRaster` object. Can be either `numeric`, `factor` or `character` data. Defaults to `NULL` which includes no spatial effects in the model.
`Projection`	The coordinate reference system used by both the spatial points and spatial covariates. Must be of class `character`.
`Mesh`	An `fm_mesh_2d` object required for the spatial random fields and the integration points in the model (see `fm_mesh_2d_inla` from the fmesher package for more details).
`speciesSpatial`	Argument to specify if each species should have their own spatial effect with different hyperparameters to be estimated using INLA's "replicate" feature, of if a the field's should be estimated per species copied across datasets using INLA's "copy" feature. Possible values include: `'replicate'`, `'copy'`, `'shared'` or `NULL` if no species-specific spatial effects should be estimated.
`speciesIntercept`	Argument to control the species intercept term. Defaults to `TRUE` which creates a random intercept term, `FALSE` creates a fixed intercept term, and `NULL` removes the intercept term.
`speciesEnvironment`	Argument to control the species environmental term. Defaults to `TRUE` which creates species level environental effects. To create shared effects across the species, use `FALSE`.
`speciesName`	Name of the species variable name (class `character`). Specifying this argument turns the model into a stacked species distribution model, and calculates covariate values for the individual species, as well as a species group model in the shared spatial field. Defaults to `NULL`. Note that if this argument is non-`NULL` and `pointsIntercepts` is missing, `pointsIntercepts` will be set to `FALSE`.
`IPS`	The integration points to be used in the model (that is, the points on the map where the intensity of the model is calculated). See `fm_int` from the fmesher package for more details regarding these points; however defaults to `NULL` which will create integration points from the `Mesh` object.
`Boundary`	A `sf` object of the study area. If not missing, this object is used to help create the integration points.
`pointCovariates`	The non-spatial covariates to be included in the integrated model (for example, in the field of ecology the distance to the nearest road or time spent sampling could be considered). These covariates must be included in the same data object as the points.
`Offset`	Name of the offset variable (class `character`) in the datasets. Defaults to `NULL`; if the argument is non-`NULL`, the variable name needs to be standardized across datasets (but does not need to be included in all datasets). The offset variable will be transformed onto the log-scale in the integrated model.
`pointsIntercept`	Logical argument: should the points be modeled with intercepts. Defaults to `TRUE`. Note that if this argument is non-`NULL` and `pointsIntercepts` is missing, `pointsIntercepts` will be set to `FALSE`.
`pointsSpatial`	Argument to determine whether the spatial field is shared between the datasets, or if each dataset has its own unique spatial field. The datasets may share a spatial field with INLA's "copy" feature if the argument is set to `copy`. May take on the values: `"shared"`, `"individual"`, `"copy"`, `"correlate"` or `NULL` if no spatial field is required for the model. Defaults to `"copy"`.
`responseCounts`	Name of the response variable in the counts/abundance datasets. This variable name needs to be standardized across all counts datasets used in the integrated model. Defaults to `'counts'`.
`responsePA`	Name of the response variable (class `character`) in the presence absence/detection non-detection datasets. This variable name needs to be standardized across all present absence datasets. Defaults to `'present'`.
`trialsPA`	Name of the trials response variable (class `character`) for the presence absence datasets. Defaults to `NULL`.
`temporalName`	Name of the temporal variable (class `character`) in the model. This variable is required to be in all the datasets. Defaults to `NULL`.
`Formulas`	A named list with two objects. The first one, `covariateFormula`, is a formula for the covariates and their transformations for the distribution part of the model. Defaults to `NULL` which includes all covariates specified in `spatialCovariates` into the model. The second, `biasFormula`, specifies which covariates are used for the PO datasets. Defaults to `NULL` which includes no covariates for the PO datasets.

Value

A specifySpecies object (class R6). Use ?specifySpecies to get a comprehensive description of the slot functions associated with this object.

Note

The idea with this function is to describe the full model: that is, all the covariates and spatial effects will appear in all the formulas for the datasets and species. If some of these terms should not be included in certain observation models in the integrated model, they can be thinned out using the .$updateFormula function. Note: the point covariate will only be included in the formulas for where they are present in a given dataset, and so these terms do not need to be thinned out if they are not required by certain observation models.

Examples

 
 ## Not run: 
 if (requireNamespace('INLA')) {
 
 ##REDO WITH OTHER DATA
   
 #Get Data
 data("SolitaryTinamou")
 
 
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startSpecies(data, Mesh = mesh,
                           Projection = proj, responsePA = 'Present',
                           speciesName = 'speciesName')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 
 indSpat <- startSpecies(data, Mesh = mesh,
                         Projection = proj, pointsSpatial = 'individual', 
                         responsePA = 'Present', speciesName = 'speciesName')
                     
 #Model with offset variable
 offSet <- startSpecies(data, Mesh = mesh,
                     Projection = proj, 
                     Offset = 'area', 
                     responsePA = 'Present',
                     speciesName = 'speciesName')
                     
 #Non-random effects for the species
 speciesInt <- startSpecies(data, Mesh = mesh,
                        Projection = proj, 
                        speciesIntercept = FALSE,
                        responsePA = 'Present',
                        speciesName = 'speciesName')
                        
#Turn off species level field

 speciesInt <- startSpecies(data, Mesh = mesh,
                        Projection = proj, 
                        speciesSpatial = NULL,
                        responsePA = 'Present',
                        speciesName = 'speciesName')
                     
 }
 
## End(Not run)

## Not run: 
 if (requireNamespace('INLA')) {
 
 ##REDO WITH OTHER DATA
   
 #Get Data
 data("SolitaryTinamou")
 
 
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set base model up
 baseModel <- startSpecies(data, Mesh = mesh,
                           Projection = proj, responsePA = 'Present',
                           speciesName = 'speciesName')
 
 #Print summary
 baseModel
 
 #Set up model with dataset specific spatial fields
 
 indSpat <- startSpecies(data, Mesh = mesh,
                         Projection = proj, pointsSpatial = 'individual', 
                         responsePA = 'Present', speciesName = 'speciesName')
                     
 #Model with offset variable
 offSet <- startSpecies(data, Mesh = mesh,
                     Projection = proj, 
                     Offset = 'area', 
                     responsePA = 'Present',
                     speciesName = 'speciesName')
                     
 #Non-random effects for the species
 speciesInt <- startSpecies(data, Mesh = mesh,
                        Projection = proj, 
                        speciesIntercept = FALSE,
                        responsePA = 'Present',
                        speciesName = 'speciesName')
                        
#Turn off species level field

 speciesInt <- startSpecies(data, Mesh = mesh,
                        Projection = proj, 
                        speciesSpatial = NULL,
                        responsePA = 'Present',
                        speciesName = 'speciesName')
                     
 }
 
## End(Not run)

Generic summary function for `bruSDM`.

Description

Summary for bru_sdm

Summary for modSpecies

Summary for modISDM

Summary for modMarks

Usage

## S3 method for class 'bruSDM'
summary(object, ...)

## S3 method for class 'modSpecies'
summary(object, ...)

## S3 method for class 'modISDM'
summary(object, ...)

## S3 method for class 'modMarks'
summary(object, ...)
## S3 method for class 'bruSDM'
summary(object, ...)

## S3 method for class 'modSpecies'
summary(object, ...)

## S3 method for class 'modISDM'
summary(object, ...)

## S3 method for class 'modMarks'
summary(object, ...)

Arguments

`object`	modMarks object.
`...`	Not used argument

Package 'PointedSDMs'

Help Index

Dataset of setophaga caerulescens obtained from the Pennsylvania Atlas of Breeding Birds.

Description

References

Dataset of setophaga caerulescens obtained from the North American Breeding Bird survey across Pennsylvania state.

Description

References

Dataset of Colinus Virginianus obtained from the North American Breeding Bird survey across Alabama state.

Description

References

blockedCV: run spatial blocked cross-validation on the integrated model.

Description

Usage

Arguments

Value

Examples

Export class blockedCV

Description

Export class blockedCVpred

Description

Export class predict_bru_sdm

Description

Export bru_sdm class

Description

changeCoords: function used to change coordinate names.

Description

Usage

Arguments

Value

checkCoords: function used to check coordinate names.

Description

Usage

Arguments

Value

checkVar: Function used to check variable names.

Description

Usage

Arguments

Value

data2ENV: function used to move objects from one environment to another.

Description

Usage

Arguments

Value

R6 class to assist in reformatting the data to be used in dataSDM.

Description

Methods

Public methods

Method makeData()

Usage

Method makeSpecies()

Usage

Method makeMultinom()

Usage

Method makeFormulas()

Usage

Method makeComponents()

Usage

Method makeLhoods()

Usage

Method clone()

Usage

Arguments

Internal function used to standardize datasets, as well as assign metadata.

Description

Usage

Arguments

Value

datasetOut: function that removes a dataset out of the main model, and calculates some cross-validation score.

Description

Usage

Arguments

Value

Examples

Export class bru_sdm_leave_one_out

Description

data.frame object containing solitary tinamou observations from eBird

Description

References

Method `makeData()`

Method `makeSpecies()`

Method `makeMultinom()`

Method `makeFormulas()`

Method `makeComponents()`

Method `makeLhoods()`

Method `clone()`

Generic plot function for `predict_bru_sdm`.

Generic predict function for `bru_SDM` objects.