Type: Package
Title: Fit Models Derived from Point Processes to Species Distributions using 'inlabru'
Version: 2.1.3
Maintainer: Philip Mostert <philip.s.mostert@ntnu.no>
URL: https://github.com/PhilipMostert/PointedSDMs
BugReports: https://github.com/PhilipMostert/PointedSDMs/issues
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>.
Depends: R (≥ 4.1), stats, sf, inlabru (≥ 2.12.0), R6 (≥ 2.5), methods,
Imports: terra, ggplot2, fmesher, raster, sp (≥ 1.4-5), R.devices, blockCV (≥ 3.0.0), FNN
Suggests: testthat (≥ 3.0.0), sn, INLA (≥ 21.08.31), rasterVis, ggmap, RColorBrewer, cowplot, knitr, kableExtra, rmarkdown, spocc, covr
Additional_repositories: https://inla.r-inla-download.org/R/testing
RoxygenNote: 7.3.2
License: GPL (≥ 3)
VignetteBuilder: knitr
Config/testthat/edition: 3
LazyData: false
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2025-01-13 17:16:55 UTC; philism
Author: Philip Mostert [aut, cre], Bob O'hara [aut]
Repository: CRAN
Date/Publication: 2025-01-13 17:50:22 UTC

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/

data.frame object containing solitary tinamou observations from Gbif

Description

data.frame object containing solitary tinamou observations from Gbif

Source

https://www.gbif.org/

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.

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/

List of all data objects used for the Setophaga vignette.

Description

List of all data objects used for the Setophaga vignette.

spatRaster object containing covariate values

Description

spatRaster object containing covariate values

Source

https://github.com/oharar/PointedSDMs

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

Description

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

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
)

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)

Export class blockedCV

Description

Export class blockedCV

Export class blockedCVpred

Description

Export class blockedCVpred

Export bru_sdm class

Description

Export bru_sdm class

Export class predict_bru_sdm

Description

Export class predict_bru_sdm

changeCoords: function used to change coordinate names.

Description

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

Usage

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)

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)

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)

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

Public 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
)

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:

  1. Running a new model with one less dataset (from the main model) – resulting in a reduced model,

  2. predicting the intensity function at the locations of the left-out dataset with the reduced model,

  3. using the predicted values as an offset in a new model,

  4. 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)

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)

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())

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)

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)
)

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')
                      
 }

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)

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
)

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 modISDM class

Description

Export modISDM class

Export class predict.modISDM

Description

Export class predict.modISDM

Export modMarks class

Description

Export modMarks class

Export class predict_modMarks

Description

Export class predict_modMarks

Export modSpecies class

Description

Export modSpecies class

Export class predict_modSpecies

Description

Export class predict_modSpecies

nameChanger: function to change a variable name.

Description

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

Usage

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, ...)

Arguments

pts

The species location data

r

The raster file to extract the covariates at.

...

Extra arguments for knnx.index.

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, ...)

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.

A ggplot2 object.

A ggplot2 object.

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)

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",
  ...
)

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.

A list of inlabru predict objects.

A list of inlabru predict objects.

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)

Print function for blockedCV.

Description

Print for blockedCV

Usage

## 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, ...)

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, ...)

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, ...)

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, ...)

Arguments

x

datasetOut object.

...

Unused argument.

Generic print function for modISDM.

Description

Print method for modISDM

Usage

## 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, ...)

Arguments

x

modMarks object.

...

Not used.

Generic print function for modSpecies.

Description

Print method for modSpecies

Usage

## 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)

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)

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())

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)

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

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)

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

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()

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
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()

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
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()

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
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()

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
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()

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
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)

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

Method help()

Function to provide documentation for a specifySpecies object.

Usage
specifySpecies$help(...)
Arguments
...

Not used

Returns

Documentation.

Method print()

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

Usage
specifySpecies$print(...)
Arguments
...

Not used.

Method plot()

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

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

Name of the datasets to plot. If this argument is missing, the function will plot all the data available to the model.

Species

Logical: should the points be plotted based on the species name. Defaults to TRUE.

Boundary

Logical: should a boundary (created using the Mesh object) be used in the plot. Defaults to TRUE.

...

Not used.

Returns

A ggplot object.

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

}
}

Method addBias()

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

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

A vector of dataset names (class character) for which a bias field needs to be added to. If NULL (default), then allPO has to be TRUE.

allPO

Logical: should a bias field be added to all datasets classified as presence only in the integrated model. Defaults to FALSE.

biasField

An inla.spde object used to describe the bias field. Defaults to NULL which uses inla.spde2.matern to create a Matern model for the field.

copyModel

Create copy models for all the of the datasets specified with either datasetNames or allPO. The first dataset in the vector will have its own spatial effect, and the other datasets will "copy" the effect with shared hyperparameters. Defaults to TRUE.

shareModel

Share a bias field across the datasets specified with datasetNames. Defaults to FALSE.

temporalModel

List of model specifications given to the control.group argument in the time effect component. Defaults to list(model = 'ar1'); see control.group from the INLA package for more details. temporalName needs to be specified in intModel prior.

Returns

A bias field to the model.

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

}
}

Method updateFormula()

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

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

Name of the dataset (class character) for which the formula needs to be changed.

speciesName

Name of the species for which to change a formula for. Defaults to NULL which chnages the formula for all species present in datasetName.

Formula

An updated formula to give to the process. The syntax provided for the formula in this argument should be identical to the formula specification as in base R. Should be used to thin terms out of a formula but could be used to add terms as well. If adding new terms not specified in intModel, remember to add the associated component using .$changeComponents as well.

processLevel

Logical argument: if TRUE changes the formulas for all of the processes in a dataset. Defaults to FALSE.

newFormula

Completely change the formula for a process – primarily used to add non-linear components into the formula. Note: all terms need to be correctly specified here.

Returns

An updated formula.

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

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

Method changeComponents()

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

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

Component to add to the integrated model. Note that if the user is re-specifying a component already present in the model, they do not need to remove the old component using removeComponent.

removeComponent

Component (or just the name of a component) present in the model which should be removed.

print

Logical: should the updated components be printed. Defaults to TRUE.

Returns

An updated components list.

Examples
\dontrun{

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

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

}

}

Method priorsFixed()

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

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

Name of the fixed effect covariate to change the prior for. Can take on 'intercept', which will change the specification for an intercept (specified by one of species or datasetName).

Species

Name of the species (class character) for which the prior should change. Defaults to NULL which will change the prior for all species added to the model.

datasetName

Name of the dataset for which the prior of the intercept should change (if fixedEffect = 'intercept'). Defaults to NULL which will change the prior effect of the intercepts for all the datasets in the model.

mean.linear

Mean value for the prior of the fixed effect. Defaults to 0.

prec.linear

Precision value for the prior of the fixed effect. Defaults to 0.001.

Returns

New priors for the fixed effects.

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

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

}
}

Method specifySpatial()

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

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

Logical: specify the shared spatial field in the model. Requires pointsSpatial == 'shared' in intModel. Defaults to FALSE.

datasetName

Name of which of the datasets' spatial fields to be specified. Requires pointsSpatial = 'individual' in intModel.

Species

Name of the species to change the spatial effect for. If TRUE then changes the spatial effect for the shared species field.

Bias

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()

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

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

Integer value reflecting the number of folds to use.

rows_cols

Integer value by which the area is divided into longitudinal and latitudinal bins.

plot

Plot the cross-validation folds as well as the points across the boundary. Defaults to FALSE.

seed

Seed used by blockCV's spatialBlock to make the spatial blocking reproducible across different models. Defaults to 1234.

...

Additional arguments used by blockCV's spatialBlock.

Returns

If plot = TRUE, a plot of the grid.

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

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

} 
}

Method addSamplers()

Function to add an integration domain for the PO datasets.

Usage
specifySpecies$addSamplers(datasetName, Samplers)
Arguments
datasetName

Name of the dataset for the samplers.

Samplers

A Spatial* object representing the integration domain.

Returns

New samplers for a process.

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

}
}

Method specifyRandom()

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

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

List of model specifications given to the control.group argument in the time effect component. Defaults to list(model = 'ar1'); see control.group from the INLA package for more details.

copyModel

List of model specifications given to the hyper parameters for the "copy" model. Defaults to list(beta = list(fixed = FALSE)).

copyBias

List of model specifications given to the hyper parameters for the "copy" bias model. Defaults to list(beta = list(fixed = FALSE)).

speciesCopy

List of model specifications given to the hyper parameters for the species "copy" model. Defaults to list(beta = list(fixed = FALSE)).

speciesIntercepts

Prior distribution for precision parameter for the random species intercept term. Defaults to INLA's default choice.

speciesGroup

Prior distribution for the precision parameter for the iid group model. Defaults to INLA's default. #' @return An updated component list.

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

}
}

Method new()

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

Method samplingBias()

Usage
specifySpecies$samplingBias(datasetName, Samplers)

Examples


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

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

}

## End(Not run)

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

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

}

## End(Not run)

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

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

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

## End(Not run)

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

## Not run: 

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

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

}


## End(Not run)

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

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

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

}

## End(Not run)

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

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

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

} 

## End(Not run)

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

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

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

## End(Not run)

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

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

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

} 

## End(Not run)

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

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

}

## End(Not run)

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

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

}

## End(Not run)

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)
)

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)

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)
)

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

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 <- 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)
)

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)

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, ...)

Arguments

object

modMarks object.

...

Not used argument