Package 'PointedSDMs'

Description

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

References

https://ebird.org/atlaspa/home

Description

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

References

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

Description

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

References

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

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

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)

Description

Export class blockedCV

Description

Export class blockedCVpred

Description

Export class predict_bru_sdm

Description

Export bru_sdm class

Description

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

Usage

changeCoords(data, oldcoords, newcoords)

Arguments

Value

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

Description

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

Usage

checkCoords(data, coords)

Arguments

Value

A logical variable.

Description

Internal function used to check if variable is in dataset.

Usage

checkVar(data, var)

Arguments

Value

A logical variable

Description

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

Usage

data2ENV(data, env)

Arguments

Value

Assignment of the relevant spatial fields to the specified environment.

Description

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

Methods

Public methods

• dataOrganize$makeData()

• dataOrganize$makeSpecies()

• dataOrganize$makeMultinom()

• dataOrganize$makeFormulas()

• dataOrganize$makeComponents()

• dataOrganize$makeLhoods()

• dataOrganize$clone()

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,
  spatcovclass,
  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

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

Value

A list of relevant metadata

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

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)

Description

Export class bru_sdm_leave_one_out

Description

data.frame object containing solitary tinamou observations from eBird

References

http://ebird.org/

Description

Raster object containing the elevation across Pennsylvania state.

References

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

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

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)

Description

data.frame object containing solitary tinamou observations from Gbif

Source

https://www.gbif.org/

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

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
 
                      
 }

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.

Description

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

Usage

makeFormulaComps(form, species, speciesnames, type)

Arguments

Value

A vector of components required for inlabru.

Description

Function to make the datasets into likelihoods.

Usage

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

Arguments

Description

Export class predict.modISDM

Description

Export modISDM class

Description

Export class predict_modMarks

Description

Export modMarks class

Description

Export class predict_modSpecies

Description

Export modSpecies class

Description

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

Usage

nameChanger(data, oldName, newName)

Arguments

Value

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

Description

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

Usage

nearestValue(pts, r, ...)

Arguments

Description

Raster object containing the canopy cover across Pennsylvania state.

References

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

Description

data.frame object containing solitary tinamou observations from Parks

Source

https://www.gbif.org/

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

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)

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

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)

Description

Print for blockedCV

Usage

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

Arguments

Description

Print for blockedCVpred

Usage

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

Arguments

Description

Print method for bru_sdm

Usage

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

Arguments

Description

Generic print function for bru_sdm_predict.

Usage

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

Arguments

Description

Print for bru_sdm_leave_one_out

Usage

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

Arguments

Description

Print method for modISDM

Usage

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

Arguments

Description

Print method for modMarks

Usage

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

Arguments

Description

Print method for modSpecies

Usage

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

Arguments

Description

reduceComps: Reduce the components of the model.

Usage

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

Arguments

Value

New components.

Description

sf object containing the boundary region for solitary tinamouc

Source

https://github.com/oharar/PointedSDMs

Description

formulaChanger: Internal function used to remove formula terms.

Usage

removeFormula(formulaRemove, oldFormula)

Arguments

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

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)

Description

List of all data objects used for the Setophaga vignette.

Description

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

Description

spatRaster object containing covariate values

Source

https://github.com/oharar/PointedSDMs

Description

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

Methods

Public methods

• specifyISDM$help()

• specifyISDM$print()

• specifyISDM$plot()

• specifyISDM$addBias()

• specifyISDM$updateFormula()

• specifyISDM$changeComponents()

• specifyISDM$priorsFixed()

• specifyISDM$specifySpatial()

• specifyISDM$specifyFamily()

• specifyISDM$spatialBlock()

• specifyISDM$addSamplers()

• specifyISDM$specifyRandom()

• specifyISDM$new()

• specifyISDM$samplingBias()

Method help()

Function to provide documentation for a specifyISDM object.

Usage

specifyISDM$help(...)

Arguments

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

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

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

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

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

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

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

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 specifyFamily()

Function used to specify the family properties of a dataset.

Usage

specifyISDM$specifyFamily(datasetName, Family, Link)

Arguments

Returns

A new link function of family 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

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

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

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$specifyFamily`
## ------------------------------------------------

## 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)

Description

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

Methods

Public methods

• specifyMarks$help()

• specifyMarks$print()

• specifyMarks$plot()

• specifyMarks$addBias()

• specifyMarks$updateFormula()

• specifyMarks$changeComponents()

• specifyMarks$priorsFixed()

• specifyMarks$specifySpatial()

• specifyMarks$changeLink()

• specifyMarks$spatialBlock()

• specifyMarks$addSamplers()

• specifyMarks$specifyRandom()

• specifyMarks$new()

• specifyMarks$samplingBias()

Method help()

Function to provide documentation for a specifyMarks object.

Usage

specifyMarks$help(...)

Arguments

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

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

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

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

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

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

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

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

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

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

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

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)

Description

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

Methods

Public methods

• specifySpecies$help()

• specifySpecies$print()

• specifySpecies$plot()

• specifySpecies$addBias()

• specifySpecies$updateFormula()

• specifySpecies$changeComponents()

• specifySpecies$priorsFixed()

• specifySpecies$specifySpatial()

• specifySpecies$changeFamily()

• specifySpecies$spatialBlock()

• specifySpecies$addSamplers()

• specifySpecies$specifyRandom()

• specifySpecies$new()

• specifySpecies$samplingBias()

Method help()

Function to provide documentation for a specifySpecies object.

Usage

specifySpecies$help(...)

Arguments

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

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

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

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

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

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

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

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 changeFamily()

Function used to specify the family properties of a dataset.

Usage

specifySpecies$changeFamily(datasetName, Family, Link)

Arguments

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

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

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$specifyRandom(
  temporalModel = list(model = "ar1"),
  copyModel = list(beta = list(fixed = FALSE)),
  copyBias = list(beta = list(fixed = FALSE)),
  speciesCopy = list(beta = list(fixed = FALSE)),
  speciesIntercepts = list(prec = list(fixed = TRUE, initial =
    log(INLA::inla.set.control.fixed.default()$prec))),
  speciesGroup = list(model = "iid", hyper = list(prec = list(prior = "loggamma", param =
    c(1, 5e-05))))
)

Arguments

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$changeFamily`
## ------------------------------------------------

## 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)

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

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)

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

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')
                      
 }

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 = "stack",
  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

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)

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