dataSDM {PointedSDMs}R Documentation

R6 class for creating a dataSDM object.

Description

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

Methods

Public methods

Method print()

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

Usage
dataSDM$print(...)
Arguments
...

Not used.

Method plot()

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

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

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

Species

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

Boundary

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

...

Not used.

Returns

A ggplot object.

Examples
 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}

Method addData()

Function used to add additional datasets to the dataSDM object. This function should be used if the user would like to add any additional datasets to the integrated model, but do not have the same standardized variable names as those added initially with intModel. Use ?intModel for a more comprehensive description on what each argument in this function is used for.

Usage
dataSDM$addData(
  ...,
  responseCounts,
  responsePA,
  trialsPA,
  markNames,
  markFamily,
  pointCovariates,
  trialsMarks,
  speciesName,
  temporalName,
  Coordinates,
  Offset
)
Arguments
...

The datasets to be added to the integrated model: should be either sf, data.frame or SpatialPoints* objects, or a list of objects from these classes.

responseCounts

The name of the response variable for the counts data.

responsePA

The name of the response variable for the presence absence data.

trialsPA

The name of the trials variable for the presence absence data.

markNames

The names of the marks found in the data.

markFamily

The associated distributions of the marks.

pointCovariates

The additional, non-spatial covariates describing the data.

trialsMarks

The name of the trials variable for the binomial marks.

speciesName

The name of the species variable included in the data. Used to make a stacked species distribution model.

temporalName

The name of the temporal variable in the datasets.

Coordinates

A vector of length 2 describing the names of the coordinates of the data.

Offset

Name of the offset column in the dataset

Examples
 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 
 #Only select eBird data
 ebird <- SolitaryTinamou$datasets$eBird
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- intModel(ebird, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj)
                             
 #Print summary
 organizedData
 
 #Add new dataset
 Parks = SolitaryTinamou$datasets$Parks
 organizedData$addData(Parks, responsePA = 'Present')
 
 #Print summary
 organizedData
   
 }

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

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

allPO

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

biasField

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

shareModel

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

temporalModel

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

Examples
 
 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')
 
#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
dataSDM$updateFormula(
  datasetName = NULL,
  Points = TRUE,
  speciesName = NULL,
  markName = NULL,
  Formula,
  allProcesses = FALSE,
  newFormula
)
Arguments
datasetName

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

Points

Logical: should the formula be changed for the points (or otherwise, a marked process). Defaults to TRUE. If FALSE, then markNames needs to be non-NULL in intModel.

speciesName

Name of the species (class character) to change the formula for. Defaults to NULL. If NULL and speciesName is non-NULL in intModel, will update the formula for all species within the dataset. Cannot be non-NULL if speciesName is NULL in intModel.

markName

Name of the mark (class character) to change the formula for. Defaults to NULL. If NULL and markNames is non-NULL in intModel, will update the formula for all marks within the dataset. Cannot be non-NULL if markNames is NULL in intModel.

Formula

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

allProcesses

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

newFormula

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

Returns

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

Examples
 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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
dataSDM$changeComponents(addComponent, removeComponent, print = TRUE)
Arguments
addComponent

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

removeComponent

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

print

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

Examples
\dontrun{

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 Forest <- SolitaryTinamou$covariates$Forest
 
 
 #Set model up
 organizedData <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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
dataSDM$priorsFixed(
  Effect,
  Species = NULL,
  datasetName = NULL,
  mean.linear = 0,
  prec.linear = 0.001
)
Arguments
Effect

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

Species

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

datasetName

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

mean.linear

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

prec.linear

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

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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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
dataSDM$specifySpatial(
  sharedSpatial = FALSE,
  datasetName,
  Species,
  Mark,
  Bias,
  PC = TRUE,
  Copy = NULL,
  Remove = FALSE,
  ...
)
Arguments
sharedSpatial

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

datasetName

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

Species

Name of which of the species' spatial field to be specified. Requires speciesName to be non-NULL in intModel.

Mark

Name of which of the mark' spatial field to be specified. Requires markNames to be non-NULL in intModel.

Bias

Name of the dataset for which the bias field to be specified.

PC

Logical: should the Matern model be specified with pc priors. Defaults to TRUE, which uses inla.spde2.pcmatern to specify the model; otherwise uses inla.spde2.matern.

Remove

Logical: should the chosen spatial field be removed. Requires one of sharedSpatial, species, mark or bias to be non-missing, which chooses which field to remove.

...

Additional arguments used by INLA's inla.spde2.pcmatern or inla.spde2.matern function, dependent on the value of PC.

Examples
 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

}

Method changeLink()

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

Usage
dataSDM$changeLink(datasetName, Species, Mark, Link, ...)
Arguments
datasetName

Name of the dataset for which the link function needs to be changed.

Species

Name of the species for which the link function needs to be changed.

Mark

Name of the mark for which the link function needs to be changed.

Link

Name of the link function to add to the process. If missing, will print the link function of the specified dataset.

...

Not used

Examples
\dontrun{

#Create data object
dataObj <- intModel(...)

#Print link function for a process

dataObj$changeLink(Dataset = Dataset, Species = Species)

#Change link function
dataObj$changeLink(Dataset = Dataset, Species = Species,
                   Link = "log")

}

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

Integer value reflecting the number of folds to use.

rows_cols

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

plot

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

seed

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

...

Additional arguments used by blockCV's spatialBlock.

Examples
 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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
dataSDM$addSamplers(datasetName, Samplers)
Arguments
datasetName

Name of the dataset for the samplers.

Samplers

A Spatial* object representing the integration domain.

Method new()

Usage
dataSDM$new(
  coordinates,
  projection,
  Inlamesh,
  initialnames,
  responsecounts,
  responsepa,
  speciesindependent,
  marksnames,
  marksfamily,
  pointcovariates,
  speciesintercept,
  trialspa,
  trialsmarks,
  speciesname,
  marksspatial,
  speciesenvironment,
  spatial,
  intercepts,
  spatialcovariates,
  marksintercepts,
  boundary,
  ips,
  temporal,
  temporalmodel,
  speciesspatial,
  offset,
  copymodel
)

Method samplingBias()

Usage
dataSDM$samplingBias(datasetName, Samplers)

Note

The arguments of this function may be missing (ie not provided) if they have already been specified in intModel, and do not need changing. Therefore this function is useful if there are some variable names not standardized across the datasets; this function will thus standardize the variable names to those provided initially in intModel.

Examples


## ------------------------------------------------
## Method `dataSDM$plot`
## ------------------------------------------------


 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj, responsePA = 'Present')
 
  #Create plot of data
  organizedData$plot()

}

## ------------------------------------------------
## Method `dataSDM$addData`
## ------------------------------------------------

 
 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 
 #Only select eBird data
 ebird <- SolitaryTinamou$datasets$eBird
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- intModel(ebird, Mesh = mesh, Coordinates = c('X', 'Y'),
                             Projection = proj)
                             
 #Print summary
 organizedData
 
 #Add new dataset
 Parks = SolitaryTinamou$datasets$Parks
 organizedData$addData(Parks, responsePA = 'Present')
 
 #Print summary
 organizedData
   
 }

## ------------------------------------------------
## Method `dataSDM$addBias`
## ------------------------------------------------

 
 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')
 
#Add bias field to eBird records
organizedData$addBias(datasetNames = 'eBird')

}

## ------------------------------------------------
## Method `dataSDM$updateFormula`
## ------------------------------------------------


 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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 `dataSDM$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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present')

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

}


## End(Not run)

## ------------------------------------------------
## Method `dataSDM$priorsFixed`
## ------------------------------------------------

 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

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

}

## ------------------------------------------------
## Method `dataSDM$specifySpatial`
## ------------------------------------------------

 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           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 `dataSDM$changeLink`
## ------------------------------------------------

## Not run: 

#Create data object
dataObj <- intModel(...)

#Print link function for a process

dataObj$changeLink(Dataset = Dataset, Species = Species)

#Change link function
dataObj$changeLink(Dataset = Dataset, Species = Species,
                   Link = "log")


## End(Not run)

## ------------------------------------------------
## Method `dataSDM$spatialBlock`
## ------------------------------------------------

 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 <- intModel(data, Mesh = mesh, Coordinates = c('X', 'Y'),
                           spatialCovariates = Forest,
                           Projection = proj, responsePA = 'Present',
                           pointsSpatial = 'individual')

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

}
[Package PointedSDMs version 1.3.2 Index]