Package 'ppmData'

Convert a ppmData object to an owin from the SpatStat package.

Description

Convert a ppmData object to an owin from the SpatStat package.

Usage

## S3 method for class 'ppmData'
as.owin(W, ..., fatal = TRUE)

Arguments

W	Window A ppmData object. Uses the saved window in the ppmData object.
...	Unused.
fatal	Unused.

---

Function to covert a spatstat quadrature

Description

Function to covert a spatstat quadrature

Usage

## S3 method for class 'ppmData'
as.ppp(X, ..., fatal = TRUE)

Arguments

X	A ppmData object
fatal	Logical value specifying what to do if the data cannot be converted. See Details.
...	Ignored

Examples

## Not run: 
library(ppmData)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
preds <- rast(lst)
window <- preds[[1]]
presences <- subset(snails,SpeciesID %in% "Tasmaphena sinclairi")
object <- ppmData(npoints = 10000, presences=presences, window = window, covariates = preds)
X <- as.ppp(object)

## End(Not run)

---

Function to covert a spatstat quadrature and spatstat data (covariates)

Description

This function will convert the ppmData to a spatstat object that can be used in the spatstat library. This currenly only works for unmarked point process

Usage

as.spatstat(object, ...)

Arguments

object	A ppmData object
...	Ignored

Examples

## Not run: 
library(ppmData)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
preds <- rast(lst)
window <- preds[[1]]
presences <- subset(snails,SpeciesID %in% "Tasmaphena sinclairi")
object <- ppmData(npoints = 10000, presences=presences, window = window, covariates = preds)
ss.object <- as.spatstat(object)

## now you should be able to fit a ppm from spatstat
ss.form <- ss.object$Q~1+poly(min_temp_coldest_month,2)+poly(annual_mean_precip,2)
ppmfit <- ppm(ss.form,data=ss.object$covariates,Poisson())
pred <- predict(ppmfit, covariates = ss.object$covariates,type="trend")

## End(Not run)

---

dirTess

Description

Computes the Dirichlet tessellation using the dual-graph of the Delaunay triangulation. The Delaunay triangulation is calculated internally for this function using a sweep algorithm.

Usage

dirTess(coords, bbox = NULL)

Arguments

coords	The coordinates of the points to triangulate.
bbox	Optional - doesn't actually do anything anymore :)

Examples

coords <- matrix(runif(2000),ncol=2)
tess <- dirTess(coords)

---

gridQuad

Description

A relatively quick way to generate psuedo-random quadrature set for ppm models.

Usage

gridQuad(npoints, window, coord = c("X", "Y"), control)

Arguments

npoints	The number of background points to use. The default is 1000, but it is generally recommended to use more when fitting a ppm.
window	A SpatRaster from terra package which will represent the extent and resolution of the point process model.
coord	The names of the coordinates. Default is c("X","Y").
control	A list of control options passed from ppmData

Author(s)

Skipton Woolley

Examples

library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
window <- terra::rast(lst[1])
res <- gridQuad(10000,window,control=list(quiet=FALSE))
plot(window)
points(res[,1:2],pch=".")

---

gridWeights

Description

Quick way to generate psuedo-random points using the terria package. Weights are taken as the the area(window)/npoints. The default unit is km^2, but other units can be used such as meters squared "m" or hectars "ha". There is an internal check to see if the input data is in lon/lat and if so, it returns the area of the cell in which the point lies.

Usage

gridWeights(
  presences,
  quadrature,
  window,
  coord = c("X", "Y"),
  mark.id,
  unit = c("geo", "m", "km", "ha")
)

Arguments

presences	The observed presence locations
quadrature	The quadrature locations
window	A SpatRaster from terra package which will represent the extent and resolution of the point process model.
coord	The names of the coordinates. Default is c("X","Y").
mark.id	character Name of the species ID column
unit	The scale of the area weights, default is kilometers squared "km" but meters squared "m" or hectars "ha" can be used.

Author(s)

Skipton Woolley

Examples

library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
window <- terra::rast(lst[1])
presences <- subset(snails,SpeciesID %in% "Tasmaphena sinclairi")[,1:2]
quadrature <- gridQuad(10000,window,control=list(quiet=FALSE))
res <- gridWeights(presences,quadrature,window)
plot(window)
points(res[res$presence==1,1:2],pch=16,cex=0.5,col='blue')
points(res[res$presence==0,1:2],pch='.')

---

plot a Dirichlet tessellation

Description

plot a Dirichlet tessellation

Usage

## S3 method for class 'dirTess'
plot(x, ...)

Arguments

x	dirTess object
...	Additional plotting arguments

---

Plot a ppmData object

Description

Plot a ppmData object

Usage

## S3 method for class 'ppmData'
plot(
  x,
  main = "Quadrature Scheme",
  rcols = gray(0.5, 0.75),
  pch.cols = c("#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#E6AB02", "#A6761D",
    "#666666"),
  pchs = 15:20,
  plt.legend = TRUE,
  ...
)

Arguments

x	A ppmData object.
main	Title of the plot, default is 'Quadrature Scheme'
rcols	Vector of colours or colour codes for raster
pch.cols	Vector of point shapes
pchs	Vector of pch codes
plt.legend	boolean plot the legend or not, default is TRUE.
...	Ignored

---

Convert the Dirichlet tessellation to polygons and clip if so desired.

Description

Convert the Dirichlet tessellation to polygons and clip if so desired.

Usage

polygonise(
  x,
  window = NULL,
  clippy = TRUE,
  crs = sf::st_crs("EPSG:4326"),
  unit,
  ...
)

Arguments

x	Dirichlet tessellation object
window	polygon A polygon to clip the dirichlet tessellation - intersection between boundary polygons and polygon
clippy	Logical Clip to the bounding box or polgon if polyclip = TRUE.
crs	CRS Projection of coordinates default is "EPSG:4326" (lon/lat wgs84)
unit	Character The type of area to return. The default is "geo" and returns the area based on the euclidean distance between geographic coordinates. This will default to the values of the raster and presence coordinate system. Alternatively, meters squared "m", kilometers squared "km", or hectares "ha" can be used.
...	Additional arguments for a polygonise function

Examples

library(sf)
coords <- matrix(runif(2000),ncol=2)
tess <- dirTess(coords)
tess.polys <- polygonise(tess)
plot(st_geometry(tess.polys$polygons), col=hcl.colors(100))
points(coords,col='tomato',pch=16, cex=.3)

---

Develop a quadrature scheme using quasi-random sampling for spatial point processes.

Description

ppmData is a package for setting up quadrature to implement spatial Poisson Point process models and extensions. The approach uses quasi- random sampling (Grafston & Tille, 2013, Foster et al., 2018) to generate a quadrature scheme for numerical approximation of a Poisson point process model (Berman & Turner 1992; Warton & Shepard 2010). Quasi-random sampling quadrature are form of spatially-balanced survey design or point stratification that aims to reduce the frequency of placing samples close to each other (relative to pseudo-random or grid designs). A quasi-random quadrature design improves efficiency of background point sampling (and subsequent modelling) by reducing the amount of spatial auto-correlation between data implying that each sample is providing as much unique information as possible (Grafston & Tille, 2013, Foster et al., 2018) and thus reducing low errors for geostatistical prediction (Diggle & Ribeiro, 2007). Because the quasi-random design is not on a regular grid we use Dirichlet tessellation to generate polygons for each point in the quadrature scheme. Areal weights are then derived from these polygons.

Usage

ppmData(
  presences,
  window = NULL,
  covariates = NULL,
  npoints = NULL,
  coord = c("X", "Y"),
  mark.id = "SpeciesID",
  quad.method = c("quasi.random", "pseudo.random", "grid"),
  interpolation = c("simple", "bilinear"),
  unit = c("geo", "m", "km", "ha"),
  na.rm = FALSE,
  control = list()
)

Arguments

presences	a three column matrix or data.frame object giving the coordinates of each species' presence in (should be a matrix of nsites * 3) with the three columns being c("X","Y","SpeciesID"), where X is longitude, Y is latitude and SpeciesID is a factor which associated each occurrence to a species.
window	SpatRaster a raster object giving the region where to generate the quadrature scheme. Windows with NA cells are ignored and masked out of returned data. If NULL, a rectangle bounding the extent of presences will be used as the default window.
covariates	SpatRaster A terra raster object containing covariates for modelling the point process. These layers should match the resolution and extent of the window provided. If NULL, only the coordinates of the presences and quadrature points are returned for the ppmData object.
npoints	Integer The number of quadrature points to generate. If NULL, the number of quadrature points is calculated based on linear scaling. In reality, the number of quadrature points needed to approximate the log-likelihood function will depend on the data and likelihood function being approximated. Typically, the more quadrature the better the estimate, but there is a trade off between computational efficiency and accuracy. See Warton & Shepard (2010) or Renner et al., 2015 for useful discussions on the location and number of quadrature points required to converge a ppm likelihood.
coord	Character These are the users name of site coordinates. The default is c('X','Y'). This should match the name of the coordinates in the presences data set.
mark.id	Character This is the column name of the mark ID. The default is "SpeciesID". But this should be changed to match the user's data. If this column contains multiple "species" then a marked quadrature scheme will be created.
quad.method	Character The quadrature generation method. Default is "quasi.random" for quasi-random, "pseudo.random" for pseudo-random (regular random) and "grid" for a regular grid at a set resolution (with respect to the original window resolution).
interpolation	Character The interpolation method to use when extracting covariate data at a presence or quadrature location. Default is "bilinear", can also use "simple", this is based on the terra package extract.
unit	Character The type of area to return. The default is "geo" and returns the area based on the euclidean distance between geographic coordinates. This will default to the values of the raster and presence coordinate system. Alternatively, meters squared "m", kilometers squared "km" , or hectares "ha" can be used.
na.rm	Boolean Remove NA data from covariates. Only works for single species models.
control	list A list of control parameters for using ppmData. See details for uses of control parameters.

Details

The approach uses quasi-random sampling to generate a quadrature scheme based (e.g Berman & Turner 1992; Warton & Shepard 2010; Foster et al, 2017). The weights each quasi-random point in the quadrature scheme is calculated using a Dirichlet tessellation (Turner 2020). To improve computational efficiency we have rewritten the Delaunay triangulation and Dirichlet tessellation in c++ using a sweep algorithm. The control has a bunch of parameters you can use to tweek the ppmData object.

• quasirandom.samples integer This sets the total number of samples to consider in the BAS step (rejection sampling). The default is set to NULL and the function internally generates 10 times the total number of quadrature points needed. This means if 10000 quadrature points are required for ppmData, then a halton sequence of 100000 quasi-random numbers are drawn and then thinned according to the inclusion probabilities. The more quasirandom.samples selected the slower the quasi-random quadrature scheme will be to generate.

• buffer.NA boolean If extract from extract returns NA for point extract, do you want us to attempt to user buffer to calculate cells which are NA.

• buffer.size numeric If you call 'buffer.NA' what is the range of the buffer in meters.

• mc.cores integer The number of cores to use in the processing. default is parallel::detectCores()-1

• quiet boolean If TRUE, do not print messages. Default is FALSE.

Author(s)

Skipton Woolley <[email protected]> & Scott Foster <[email protected]>

References

Diggle, P. J., P. J. Ribeiro, Model-based Geostatistics. Springer Series in Statistics. Springer, 2007.

Foster, S.D., Monk, J., Lawrence, E., Hayes, K.R., Hosack, G.R. and Przeslawski, R., 2018. Statistical considerations for monitoring and sampling. Field manuals for marine sampling to monitor Australian waters, pp.23-41.

Grafstrom, Anton, and Yves Tille. Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics 24.2 (2013): 120-131.

Warton, D. I., and L. C. Shepherd. Poisson point process models solve the pseudo-absence problem for presence-only data #'in ecology. The Annals of Applied Statistics 4.3 (2010): 1383-1402.

Examples

## Not run: 
library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
preds <- rast(lst)
window <- preds[[1]]
presences <- subset(snails,SpeciesID %in% "Tasmaphena sinclairi")
quad <- ppmData(npoints = 1000, presences=presences, window = window, covariates = preds)

## End(Not run)

---

Print a summary of ppmData object.

Description

Print a summary of ppmData object.

Usage

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

Arguments

x	A ppmData object.
...	Ignored

---

psuedoRandomQuad

Description

A relatively quick way to generate psuedo-random quadrature set for ppm models.

Usage

pseudoRandomQuad(npoints, window, coord = c("X", "Y"), control)

Arguments

npoints	The number of background points to use. The default is 1000, but it is generally recommended to use more when fitting a ppm.
window	A SpatRaster from terra package which will represent the extent and resolution of the point process model.
coord	The names of the coordinates. Default is c("X","Y").
control	A list of control options passed from ppmData

Author(s)

Skipton Woolley

Examples

library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
window <- rast(lst[1])
res <- pseudoRandomQuad(10000,window,control=list(quiet=FALSE))
plot(window)
points(res[,1:2],pch=".")

---

random_background_weights

Description

Quick way to generate psuedo-random points using the terria package. Weights are taken as the the area(window)/npoints. The default unit is km^2, but other units can be used such as meters squared "m" or hectars "ha". There is an internal check to see if the input data is in lon/lat and if so, it returns the area of the cell in which the point lies.

Usage

pseudoRandomWeights(
  presences,
  quadrature,
  window,
  coord,
  mark.id,
  unit = c("geo", "m", "km", "ha")
)

Arguments

presences	The observed presence locations
quadrature	The quadrature locations
window	A SpatRaster from terra package which will represent the extent and resolution of the point process model.
coord	The names of the coordinates. Default is c("X","Y").
mark.id	character Name of the species ID column
unit	The scale of the area weights, default is kilometers squared "km" but meters squared "m" or hectars "ha" can be used.

Author(s)

Skipton Woolley

Examples

library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
window <- terra::rast(lst[1])
presences <- subset(snails,SpeciesID %in% "Tasmaphena sinclairi")[,1:2]
quadrature <- pseudoRandomQuad(1000,window,control=list(quiet=FALSE))
res <- pseudoRandomWeights(presences,quadrature,window,unit="geo")
plot(window)
points(res[res$presence==1,1:2],pch=16,cex=0.5,col='blue')
points(res[res$presence==0,1:2],pch='.')

---

quasiSampFromHyperRect

Description

Copied from MBHdesign as it wasn't an exported function.

Usage

quasiSampFromHyperRect(nSampsToConsider, randStartType = 2, designParams)

Arguments

nSampsToConsider	Number of samples to consider
randStartType	Random start type, default is 2.
designParams	A list of design parameters for the sampler.

Author(s)

Scott Foster

---

Convert a terra SpatRaster* object to an sf object Convert a terra SpatRaster* object to an sf object. This creates a binary mask for the raster NA vs data and then converts it to POLYGONS. For use internally to turn a rast object in to a mask for polygon clipping.

Description

Convert a terra SpatRaster* object to an sf object Convert a terra SpatRaster* object to an sf object. This creates a binary mask for the raster NA vs data and then converts it to POLYGONS. For use internally to turn a rast object in to a mask for polygon clipping.

Usage

rast_to_sf(x, ...)

Arguments

x	rast* to be converted into an object class sf
...	further specifications, see st_as_sf

---

Raster data set of environmental and social covariates clipped to Tasmania

Description

Raster data set of environmental and social covariates clipped to Tasmania

Details

The a subset of the ANUCLIM v6 layers are included in the package, but have been clipped to the Tasmanian extent. A distance from roads layer is also included which is calculated as the distance from main roads, based on the road network dataset from OpenStreetMap (OSM) across the geographic area of Tasmania

• precip_of_driest_month.tif Precipitation of the driest month

• min_temp_coldest_month.tif Minimum temperature of the coldest month

• max_temp_coldest_month.tif Maximum temperature of the warmest month

• annual_mean_temp.tif Annual mean temperature

• annual_mean_precip.tif Annual mean precipitation

• distance_from_main_roads.tif Distance from main roads

References

Xu, Tingbao, and Michael Hutchinson. "ANUCLIM version 6.1 user guide." The Australian National University, Fenner School of Environment and Society, Canberra 90 (2011).

OpenStreetMap, (2020): OpenStreetMap - Road Network (Australia) 2020; accessed from AURIN.

Examples

## Not run: 
## This is how you would load these data
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
covariates <- rast(lst)

## End(Not run)

---

Presence only observations of snails from the family Rhytididae located in Tasmania, Australia.

Description

Presence only observations of snails from the family Rhytididae located in Tasmania, Australia.

Usage

snails

Format

A data frame with columns:

X

Longitude.

Y

Latitude.

SpeciesID

Species taxonomic name.

Source

Atlas of Living Australia. http://www.ala.org/

Examples

## Not run: 
 snails

## End(Not run)

---

convert at terra raster to a spatstat image taken from maptools and converted to terra

Description

convert at terra raster to a spatstat image taken from maptools and converted to terra

Usage

terra2im(rast, factor.col.name = NULL)

Arguments

rast	rast A terra raster to convert to a spatstat image
factor.col.name	string Names of factors in the raster to convert to window

Examples

library(ppmData)
library(terra)
path <- system.file("extdata", package = "ppmData")
lst <- list.files(path=path,pattern='*.tif',full.names = TRUE)
covariates <- rast(lst[1])
im.r <- terra2im(covariates)

plot(im.r)

---