Package 'isocat'

Hierarchical clustering analysis of similarity matrix

Description

Function applies hierarchical clustering analysis to similarity matrix, such as one output by 'simmatrixMaker' function. Just a wrapper for pvclust. Output is a pvclust object.

Usage

clusterSimmatrix(
  simmatrix,
  dist_mthd = "correlation",
  hclust_mthd = "average",
  nBoot = 1000,
  nClusters = FALSE,
  r = seq(0.7, 1.4, by = 0.1)
)

Arguments

simmatrix	symmetric similarity matrix object.
dist_mthd	Distance measure to be used. Defaults to "correlation". See help(pvclust).
hclust_mthd	Method of clustering. Defaults to "average". See help(pvclust).
nBoot	number of bootstrap replications. Defaults to 1000. See help(pvclust).
nClusters	number of clusters to run in parallel using 'doParallel'. Defaults to FALSE (non-parallel).
r	Relative size of bootstrap replications.

Examples

# Create probability-of-origin maps to compare.
myiso <- rast(isoscape, type="xyz")
plot(myiso)
myiso_sd <- rast(isoscape_sd, type="xyz")
n <- 5
set.seed(42)
df <- data.frame(
         ID = LETTERS[1:n],
         isotopeValue = sample(-120:-40, n),
         SD_indv = rep(5, n)
         )
assignmentModels <- isotopeAssignmentModel(
                        ID = df$ID,
                        isotopeValue = df$isotopeValue,
                        SD_indv = df$SD_indv,
                        precip_raster = myiso,
                        precip_SD_raster = myiso_sd,
                        nClusters = FALSE
                        )
plot(assignmentModels)
# Build a similarity matrix with surfaceSimilarityMatrix.
mymatrix <- surfaceSimilarityMatrix(assignmentModels)
# Cluster similarity matrix.
clust_results <- clusterSimmatrix(mymatrix, dist_mthd = "correlation",
    hclust_mthd = "average", nBoot = 1000,  nClusters = FALSE,
    r = seq(.7,1.4,by=.1) )
clust_results

---

Cumulative sum at coordinates

Description

Function estimates cumulative sum of all values in a surface below the value at a specified longitude and latitude.

Usage

cumsumAtSamplingLocation(indivraster, Lat, Lon)

Arguments

indivraster	SpatRaster representing normalized probability of origin surface
Lat	Integer latitude
Lon	Integer longitude

See Also

makecumsumSurface

Examples

# Generate example probability surface.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
exampleSurface <- isotopeAssignmentModel(
         ID = "A",
         isotopeValue = -100,
         SD_indv = 5,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )
# Calculate cumulative sum at specific point.
set.seed(1)
x <- sample( which( !is.na(exampleSurface[]) ), size = 1)
pt <- terra::xyFromCell(exampleSurface, x)
cumsumAtSamplingLocation(indivraster = exampleSurface, Lat = pt[2], Lon = pt[1])

---

Cumulative sum below value

Description

Function that calculates the cumulative sum of values less than or equal to a given value.

Usage

cumsumbelow(vals)

Arguments

vals	Object of numeric or integer class.

Value

Returns list of values representing cumulative sum of 'val' values less than or equal to the input.

Examples

vals <- 1:10
cumsumbelow(vals)

---

Calculate model precision at given threshold values.

Description

Function that counts cells (number and proportion) above given values.

Usage

getPrecisionPar(rasterstack, checkVals, method = FALSE, nCluster = FALSE)

Arguments

rasterstack	SpatRaster of probability surfaces
checkVals	vector of numeric 'threshold' values against which to calculate precision
method	is FALSE by default. If character vector, appends a column recording 'method' used.
nCluster	Depreciated. Formerly the number of parallel clusters; retained for back-compatibility. A non-'FALSE' value now issues a message and proceeds serially.

Value

Returns a dataframe of precision values at given threshold.

Examples

myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
assignmentModels <- isotopeAssignmentModel(
         ID = LETTERS[1:4],
         isotopeValue = seq(-120, -25, length.out = 4),
         SD_indv = rep(5, 4),
         precip_raster = myiso,
         precip_SD_raster = myiso_sd
         )
getPrecisionPar(assignmentModels, checkVals = c(0.001, 0.005, 0.01))

---

Example monthly precipitation isoscape data

Description

Three months (July, August, September) of a d2H precipitation isoscape, provided to demonstrate makeMultiMonthIsoscape.

Usage

iso_monthly

Format

A data.frame with 43200 rows and 5 columns: x and y coordinates plus three monthly d2H layers d2h_07, d2h_08, and d2h_09. Convert to a multi-layer SpatRaster with rast(iso_monthly, type = "xyz").

References

Bowen G. J., West J.B., Miller C. C., Zhao L. and Zhang T. (2018) IsoMAP: Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

See Also

iso_monthly_se, makeMultiMonthIsoscape

Examples

iso <- rast(iso_monthly, type = "xyz")

---

Example monthly precipitation isoscape standard-error data

Description

Standard-error surfaces matching iso_monthly: three months (July, August, September) of the d2H precipitation isoscape prediction error.

Usage

iso_monthly_se

Format

A data.frame with 43200 rows and 5 columns: x and y coordinates plus three monthly standard-error layers d2h_se_07, d2h_se_08, and d2h_se_09. Convert to a multi-layer SpatRaster with rast(iso_monthly_se, type = "xyz").

References

Bowen G. J., West J.B., Miller C. C., Zhao L. and Zhang T. (2018) IsoMAP: Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

See Also

iso_monthly, makeMultiMonthIsoscape

Examples

iso_se <- rast(iso_monthly_se, type = "xyz")

---

Example isoscape data

Description

Example isoscape data

Usage

isoscape

Format

A data.frame with 43200 rows (x, y and a d2h_GS layer), derived from a cropped raster object.

References

Campbell C. J. (2018) IsoMAP job 66100, Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

Bowen G. J., West J.B., Miller C. C., Zhao L. and Zhang T. (2018) IsoMAP: Isoscapes Modeling, Analysis and Prediction (version 1.0). IsoMAP job 66100, Caitlin J. Campbell. Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

Examples

iso <- rast(isoscape, type="xyz")

---

Example isoscape standard deviation data

Description

Example isoscape standard deviation data

Usage

isoscape_sd

Format

A data.frame with 43200 rows (x, y and a d2h_se_GS layer), derived from a cropped raster object.

References

Campbell C. J. (2018) IsoMAP job 66100, Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

Bowen G. J., West J.B., Miller C. C., Zhao L. and Zhang T. (2018) IsoMAP: Isoscapes Modeling, Analysis and Prediction (version 1.0). IsoMAP job 66100, Caitlin J. Campbell. Isoscapes Modeling, Analysis and Prediction (version 1.0). The IsoMAP Project. http://isomap.org

Examples

iso_sd <- rast(isoscape_sd, type="xyz")

---

Isotope assignment model function

Description

Creates isotope assignment models projections of probable origin. Results returned as a SpatRaster, with layer names corresponding to individual ID.

Usage

isotopeAssignmentModel(
  ID,
  isotopeValue,
  SD_indv = NULL,
  precip_raster,
  precip_SD_raster,
  additionalModels = FALSE,
  additionalModel_name = "CombinedIsotope-OtherModelAssignments",
  savePath = FALSE,
  nClusters = FALSE
)

Arguments

ID	ID value or vector of values (for naming assignment model layers). If missing, will count from 1.
isotopeValue	Isotope precipitation value or vector of values.
SD_indv	error associated with transfer function fit. Value or vector of values. If NULL (default), no individual-level error is included (isoscape error only).
precip_raster	precipitation isoscape SpatRaster or raster.
precip_SD_raster	precipitation isoscape standard deviation SpatRaster or raster.
additionalModels	optional additional model raster object (e.g. an SDM, rasterized range map, or stack thereof). If specified, function will return isotope assignment rasters and the product of these additionalModels and each assignmentRaster.
additionalModel_name	optional filename for additionalModel .grd savepath
savePath	If specified, function will save results to this path as a '.grd' file.
nClusters	Depreciated. Formerly, integer of cores to run in parallel with doParallel. Default FALSE.

Examples

myiso <- rast(isoscape, type="xyz")
plot(myiso)
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
         ID = paste0("Example.", 1:3),
         isotopeValue = c(-100, -80, -50),
         SD_indv = rep(5, 3)
         )
assignmentModels <- isotopeAssignmentModel(
                        ID = df$ID,
                        isotopeValue = df$isotopeValue,
                        SD_indv = df$SD_indv,
                        precip_raster = myiso,
                        precip_SD_raster = myiso_sd
                        )
plot(assignmentModels)

# Add additionalModels:
range_raster <- myiso
range_raster[] <- as.numeric( 1:ncell(myiso) %% 60 >= 10)
plot(range_raster)

sdm_raster <- myiso
sdm_raster[] <- (1:ncell(sdm_raster))^2
sdm_raster <- sdm_raster / terra::global(sdm_raster, "sum", na.rm = TRUE)[1, 1]
plot(sdm_raster)

extraModels <- c(range_raster, sdm_raster)
assignmentModels <- isotopeAssignmentModel(
                        ID = paste0("Combo.",df$ID),
                        isotopeValue = df$isotopeValue,
                        SD_indv = df$SD_indv,
                        precip_raster = myiso,
                        precip_SD_raster = myiso_sd,
                        additionalModels = extraModels
                        )
plot(assignmentModels)

---

Create cumulative sum probability surface

Description

Converts normalized probability surface (e.g. one layer output of isotopeAssignmentModel function) to cumulative sum surfaces, i.e., one where the new value of a given cell is equal to the sum of all old values less than or equal to the old value of the cell.

Usage

makecumsumSurface(indivraster, rescale = FALSE, rename = FALSE)

Arguments

indivraster	Normalized probability surface SpatRaster
rescale	Rescale between 0 and 1? Defaults to FALSE.
rename	Character value to append to raster name (e.g. "_odds"). Defaults to FALSE.

Value

Returns SpatRaster rescaled to Cumulative Sum values.

See Also

cumsumAtSamplingLocation

Examples

# Generate example probability surfaces. The isoscape is coarsened here so the
# example runs quickly; cumsumbelow() scales with the square of the cell count.
myiso    <- terra::aggregate(rast(isoscape,    type = "xyz"), 5)
myiso_sd <- terra::aggregate(rast(isoscape_sd, type = "xyz"), 5)
df <- data.frame(
        ID = c(-100, -80, -50),
        isotopeValue = c(-100, -80, -50),
        SD_indv = rep(5, 3)
        )
assignmentModels <- isotopeAssignmentModel(
        ID = df$ID,
        isotopeValue = df$isotopeValue,
        SD_indv = df$SD_indv,
        precip_raster = myiso,
        precip_SD_raster = myiso_sd,
        nClusters = FALSE
        )

# Convert to cumulative sum surface.
cumulative_sum_surface <- terra::rast(
     lapply( terra::as.list( assignmentModels ), makecumsumSurface )
     )
plot(cumulative_sum_surface)

---

Make mean multi-month isoscape and error surfaces.

Description

Combines a stack of monthly isoscape maps, monthly isoscape standard error maps, and optionally a precipitation (amount) stack into a single precipitation-weighted mean isoscape and a combined error surface. Each stack must contain layers corresponding to each of the target months.

Usage

makeMultiMonthIsoscape(iso_stack, iso_se_stack, precip_stack = NULL)

Arguments

iso_stack	A SpatRaster containing n layers corresponding to isoscape models for n months.
iso_se_stack	A SpatRaster containing n layers corresponding to isoscape standard error maps for n months.
precip_stack	Either a SpatRaster containing n layers corresponding to precipitation amounts for n months, or NULL (assumes equal precipitation amounts.)

Details

If precip_stack is NULL, equal precipitation amounts are assumed for each month.

Value

A list containing a precipitation-weighted mean isoscape ('mean_iso') and a root-sum-of-square combined error map ('iso_se').

Examples

myiso <- rast(isoscape, type = "xyz")
myiso_sd <- rast(isoscape_sd, type = "xyz")
# Two example "months" derived from the bundled isoscape.
iso_stack    <- c(myiso, myiso + 5)
iso_se_stack <- c(myiso_sd, myiso_sd)
precip_stack <- c(myiso * 0 + 1, myiso * 0 + 3)   # relative weights 1:3
result <- makeMultiMonthIsoscape(iso_stack, iso_se_stack, precip_stack)
plot(result$mean_iso)
plot(result$iso_se)

---

Convert probability surface to odds-ratio surface

Description

Converts normalized probability surface (e.g. one layer output of isotopeAssignmentModel function) to Odds Ratio surfaces.

Usage

makeOddsSurfaces(probabilitySurface, rename = FALSE)

Arguments

probabilitySurface	Normalized probability surface SpatRaster
rename	Character value to append to raster name (e.g. "_odds"). Defaults to FALSE.

Value

Returns SpatRaster rescaled to Odds Ratio values.

See Also

oddsAtSamplingLocation

Examples

# Generate example probability surfaces.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
         ID = c(-100, -80, -50),
         isotopeValue = c(-100, -80, -50),
         SD_indv = rep(5, 3)
         )
assignmentModels <- isotopeAssignmentModel(
         ID = df$ID,
         isotopeValue = df$isotopeValue,
         SD_indv = df$SD_indv,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )

# Convert to odds ratio surfaces.
odds_ratio_surface <- terra::rast(
   lapply( terra::as.list(assignmentModels), makeOddsSurfaces )
   )
plot(odds_ratio_surface)

---

Create quantile-simulation surfaces

Description

Converts normalized probability surfaces (e.g. one layer output of isotopeAssignmentModel function) to quantile surfaces.

Usage

makeQuantileSimulationSurface(
  probabilitySurface,
  ValidationQuantiles,
  rename = FALSE,
  rescale = TRUE
)

Arguments

probabilitySurface	Normalized probability surface SpatRaster.
ValidationQuantiles	Vector of quantile values from known-origin individuals, against which to compare each value within the probability surface. Each value must be between 0 and 1.
rename	Character value to append to raster name (e.g. "_quantileSimulation"). Defaults to FALSE.
rescale	If rescale = TRUE, returns surface showing proportion of times each surface cell value fell within the validation quantiles distribution. If rescale = FALSE, returns discrete number of times the cell fell within the distribution.

Value

Returns SpatRaster rescaled to quantile values.

Examples

# Generate example probability surfaces.
library(isocat)
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
         ID = c(-100, -80, -50),
         isotopeValue = c(-100, -80, -50),
         SD_indv = rep(5, 3)
         )
assignmentModels <- isotopeAssignmentModel(
         ID = df$ID,
         isotopeValue = df$isotopeValue,
         SD_indv = df$SD_indv,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd
         )

# Example known-origin quantile data.
q <- rweibull(20000, 6, .98)
q <- sample( q[ q >=0 & q <= 1 ], 10000, replace = TRUE)
hist(q)

# Convert to quantile surfaces.
quantileSimulation_surface <-  terra::rast(
                  lapply(
                            terra::as.list(assignmentModels),
                            makeQuantileSimulationSurface,
                            ValidationQuantiles = q)
                        )
plot(quantileSimulation_surface)

---

Convert probability surface to probability-quantile surface

Description

Converts normalized probability surface (e.g. one layer output of isotopeAssignmentModel function) to quantile surfaces.

Usage

makeQuantileSurfaces(probabilitySurface, rename = FALSE)

Arguments

probabilitySurface	Normalized probability surface SpatRaster
rename	Character value to append to raster name (e.g. "_quantile"). Defaults to FALSE.

Value

Returns SpatRaster rescaled to quantile values.

See Also

quantileAtSamplingLocation

Examples

# Generate example probability surfaces.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
         ID = c(-100, -80, -50),
         isotopeValue = c(-100, -80, -50),
         SD_indv = rep(5, 3)
         )
assignmentModels <- isotopeAssignmentModel(
         ID = df$ID,
         isotopeValue = df$isotopeValue,
         SD_indv = df$SD_indv,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )

# Convert to quantile surfaces.
quantile_surface <-  terra::rast( lapply( terra::as.list(assignmentModels), makeQuantileSurfaces) )
plot(quantile_surface)

---

Create mean aggregate probability-of-origin surfaces for each cluster.

Description

Subset probability-of-origin surfaces by cluster assignment and find mean aggregate probability-of-origin surface for each clustered group.

Usage

meanAggregateClusterProbability(indivIDs, clusters, surfaces, nClust = FALSE)

Arguments

indivIDs	Vector of individual ID variables corresponding to surface names.
clusters	Vector of cluster IDs, in an order corresponding to 'indivIDs'.
surfaces	SpatRaster of probability-of-origin surfaces for all individuals.
nClust	Depreciated. Formerly enabled multi-core processing; retained for back-compatibility. A non-'FALSE' value now issues a message and proceeds serially.

Examples

# Create and cluster example assignment surfaces.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
        ID = LETTERS[1:9],
        isotopeValue = seq(-120,-25,length.out = 9),
        SD_indv = rep(5, 9)
        )
assignmentModels <- isotopeAssignmentModel(
         ID = df$ID,
         isotopeValue = df$isotopeValue,
         SD_indv = df$SD_indv,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )
mySimilarityMatrix <- surfaceSimilarityMatrix(assignmentModels)
cS <- clusterSimmatrix(
         simmatrix = mySimilarityMatrix,
         r = seq(.7,1.4,by=.1)
         )
# Cut clusters.
myheight <- 0.25
df$cluster <- dendextend::cutree(cS$hclust, h = myheight)
# Create mean aggregate surfaces.r p
meanSurfaces <- meanAggregateClusterProbability(
         indivIDs = df$ID,
         clusters = df$cluster,
         surfaces = assignmentModels,
         nClust = FALSE
         )

---

Odds ratio at coordinates function

Description

Function estimates percentile of each non-NA value within a RasterLayer using the empirical cumulative distribution function, and extracts value at location specified. For more information, see help(ecdf).

Usage

oddsAtSamplingLocation(indivraster, Lat, Lon)

Arguments

indivraster	SpatRaster representing normalized probability of origin surface
Lat	Integer latitude
Lon	Integer longitude

See Also

makeOddsSurfaces

Examples

# Generate example probability surface.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
exampleSurface <- isotopeAssignmentModel(
         ID = "A",
         isotopeValue = -100,
         SD_indv = 5,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )
# Calculate odds ratio at specific point.
set.seed(1)
x <- sample( which( !is.na(exampleSurface[]) ), size = 1)
pt <- terra::xyFromCell(exampleSurface, x)
oddsAtSamplingLocation(exampleSurface, Lat = pt[2], Lon = pt[1])

---

Project probability-of-origin surfaces into one summary surface.

Description

Create a summary surface showing which RasterLayer in a Stack has the highest value at a given location. For each cell in a RasterStack, this function returns the identity of the RasterLayer with the highest value at that cell. This surface is intended as a visual summary of common origins, not a basis for quantitative analysis.

Usage

projectSummaryMaxSurface(surfaces, nClust = FALSE)

Arguments

surfaces	SpatRaster where each layer represents a probability-of-origin surface
nClust	Depreciated. Formerly enabled multi-core processing; retained for back-compatibility. A non-'FALSE' value now issues a message and proceeds serially.

Examples

# Create and cluster example assignment surfaces.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
assignmentModels <- isotopeAssignmentModel(
        ID = LETTERS[1:4],
        isotopeValue = seq(-120,-25,length.out = 4),
        SD_indv = rep(5,4),
        precip_raster = myiso,
        precip_SD_raster = myiso_sd,
        nClusters = FALSE
        )
plot(assignmentModels)

# Project mean aggregate surfaces into space.
summaryMap <- projectSummaryMaxSurface(
        surfaces = assignmentModels,
        nClust = FALSE
        )
plot(summaryMap)

---

Quantile at coordinates function

Description

Function estimates percentile of each non-NA value within a RasterLayer using the empirical cumulative distribution function, and extracts value at location specified. For more information, see help(ecdf).

Usage

quantileAtSamplingLocation(indivraster, Lat, Lon)

Arguments

indivraster	A SpatRaster representing normalized probability of origin surface
Lat	Integer latitude
Lon	Integer longitude

See Also

makeQuantileSurfaces

Examples

# Generate example probability surface.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
exampleSurface <- isotopeAssignmentModel(
         ID = "A",
         isotopeValue = -100,
         SD_indv = 5,
         precip_raster = myiso,
         precip_SD_raster = myiso_sd,
         nClusters = FALSE
         )
# Calculate quantile probability value at specific point.
set.seed(1)
x <- sample( which( !is.na(exampleSurface[]) ), size = 1)
pt <- terra::xyFromCell(exampleSurface, x)
quantileAtSamplingLocation(exampleSurface, Lat = pt[2], Lon = pt[1])

---

Calculates Schoener's D-value between two RasterLayers.

Description

RasterLayers must have identical resolutions and extents. The function will automatically normalize surfaces to sum to 1.

Usage

schoenersD(rast1, rast2)

Arguments

rast1	First object of class SpatRaster
rast2	Second object of class SpatRaster

Details

Calculates similarity value of two RasterLayers using Schoener's D-metric.

Examples

# Generate example probability surfaces.
myiso <- rast(isoscape, type="xyz")
myiso_sd <- rast(isoscape_sd, type="xyz")
df <- data.frame(
        ID = c(-100, -80, -50),
        isotopeValue = c(-100, -80, -50),
        SD_indv = rep(5, 3)
        )
assignmentModels <- isotopeAssignmentModel(
        ID = df$ID,
        isotopeValue = df$isotopeValue,
        SD_indv = df$SD_indv,
        precip_raster = myiso,
        precip_SD_raster = myiso_sd,
        nClusters = FALSE
        )

# Calculate Schoener's D-metric of spatial similarity between two of the
# example probability surfaces.
schoenersD(assignmentModels[[1]], assignmentModels[[2]])
## 0.969156

---

Generates similarity matrix for RasterStack

Description

Legacy function that runs on raster::stack. Applies pairwise comparisons of Schoener's D-metric between each RasterLayer in a RasterStack to populate a similarity matrix.

Usage

simmatrixMaker(assignmentRasters, nClusters = FALSE, csvSavePath = FALSE)

Arguments

assignmentRasters	Input RasterStack
nClusters	Clusters to create run in parallel using 'doParallel'. Defaults to FALSE.
csvSavePath	Optional savepath to write similarity matrix to csv file. Defaults to FALSE, will not create csv.

Examples

# simmatrixMaker() is a legacy function for raster::stack input;
# for SpatRaster input use surfaceSimilarityMatrix() instead.
if (requireNamespace("raster", quietly = TRUE)) {
  myiso <- rast(isoscape, type = "xyz")
  myiso_sd <- rast(isoscape_sd, type = "xyz")
  set.seed(42)
  assignmentModels <- isotopeAssignmentModel(
    ID = LETTERS[1:5],
    isotopeValue = sample(-120:-40, 5),
    SD_indv = rep(5, 5),
    precip_raster = myiso,
    precip_SD_raster = myiso_sd
  )
  # Coerce to a RasterStack for the legacy interface.
  simmatrixMaker(raster::stack(assignmentModels))
}

---

Generate a similarity matrix for the layers of a SpatRaster

Description

Populates a similarity matrix from pairwise Schoener's D comparisons between the layers of a SpatRaster.

Usage

surfaceSimilarityMatrix(spatrast)

Arguments

spatrast

Input SpatRaster

Examples

# Create probability-of-origin maps to compare.
myiso <- rast(isoscape, type="xyz")
plot(myiso)
myiso_sd <- rast(isoscape_sd, type="xyz")
n <- 5
set.seed(42)
df <- data.frame(
         ID = LETTERS[1:n],
         isotopeValue = sample(-120:-40, n),
         SD_indv = rep(5, n)
         )
assignmentModels <- isotopeAssignmentModel(
                        ID = df$ID,
                        isotopeValue = df$isotopeValue,
                        SD_indv = df$SD_indv,
                        precip_raster = myiso,
                        precip_SD_raster = myiso_sd,
                        nClusters = FALSE
                        )
plot(assignmentModels)
# Compare maps with surfaceSimilarityMatrix.
surfaceSimilarityMatrix(assignmentModels)

---