Package 'fireSenseUtils' reference manual

Title:	Utilities for Working With the 'fireSense' Group of 'SpaDES' Modules
Description:	Utilities for working with the 'fireSense' group of 'SpaDES' modules.
Authors:	Jean Marchal [aut], Eliot J B McIntire [aut, cre] , Alex M Chubaty [aut] , Ian Eddy [aut]
Maintainer:	Eliot J B McIntire <[email protected]>
License:	GPL-3
Version:	0.0.5.9073
Built:	2024-11-01 03:12:24 UTC
Source:	https://github.com/PredictiveEcology/fireSenseUtils

`fireSenseUtils` package

Description

Utilities for working with the 'fireSense' group of 'SpaDES' modules.

Author(s)

Maintainer: Eliot J B McIntire [email protected] (ORCID)

Authors:

Jean Marchal [email protected]
Alex M Chubaty [email protected] (ORCID)
Ian Eddy [email protected] (ORCID)

Objective function when no piecewise model is used

Description

Objective function when no piecewise model is used

Usage

.objFunIgnition(params, linkinv, nll, sm, nx, mm, mod_env, offset)
.objFunIgnition(params, linkinv, nll, sm, nx, mm, mod_env, offset)

Arguments

`params`	DESCRIPTION NEEDED
`linkinv`	the link function
`nll`	the log-likelihood function
`sm`	scaling matrix
`nx`	number of covariates
`mm`	model matrix containing data
`mod_env`	the environment containing params - can be a data.frame
`offset`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

Function to pass to the optimizer - Piece-wise version

Description

Function to pass to the optimizer - Piece-wise version

Usage

.objFunIgnitionPW(
  params,
  formula,
  linkinv,
  nll,
  sm,
  updateKnotExpr,
  nx,
  mod_env,
  offset
)
.objFunIgnitionPW(
  params,
  formula,
  linkinv,
  nll,
  sm,
  updateKnotExpr,
  nx,
  mod_env,
  offset
)

Arguments

`params`	DESCRIPTION NEEDED
`formula`	DESCRIPTION NEEDED
`linkinv`	DESCRIPTION NEEDED
`nll`	DESCRIPTION NEEDED
`sm`	DESCRIPTION NEEDED
`updateKnotExpr`	DESCRIPTION NEEDED
`nx`	DESCRIPTION NEEDED
`mod_env`	the environment containing params - can be a data.frame
`offset`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

Objective function for `fireSense_spreadFit` module

Description

Objective function for fireSense_spreadFit module

Usage

.objfunSpreadFit(
  par,
  landscape,
  annualDTx1000,
  nonAnnualDTx1000,
  FS_formula,
  historicalFires,
  fireBufferedListDT,
  covMinMax = NULL,
  maxFireSpread = 0.28,
  minFireSize = 2,
  tests = "snll_fs",
  Nreps = 10,
  mutuallyExclusive = list(youngAge = c("class", "nf")),
  doAssertions = TRUE,
  plot.it = FALSE,
  objFunCoresInternal = 1,
  lanscape1stQuantileThresh = 0.265,
  thresh = 550,
  weighted = TRUE,
  verbose = TRUE
)
.objfunSpreadFit(
  par,
  landscape,
  annualDTx1000,
  nonAnnualDTx1000,
  FS_formula,
  historicalFires,
  fireBufferedListDT,
  covMinMax = NULL,
  maxFireSpread = 0.28,
  minFireSize = 2,
  tests = "snll_fs",
  Nreps = 10,
  mutuallyExclusive = list(youngAge = c("class", "nf")),
  doAssertions = TRUE,
  plot.it = FALSE,
  objFunCoresInternal = 1,
  lanscape1stQuantileThresh = 0.265,
  thresh = 550,
  weighted = TRUE,
  verbose = TRUE
)

Arguments

`par`	parameters
`landscape`	A SpatRaster with extent, res, proj used for SpaDES.tools::spread2
`annualDTx1000`	A list of data.table class objects. Each list element is data from a single calendar year, and whose name is "yearxxxx" where xxxx is the 4 number year. The columns in the data.table must integers, that are 1000x their actual values as this function will divide by 1000.
`nonAnnualDTx1000`	Like `annualDTx1000`, but with where each list element will be used for >1 year. The names of the list elements must be "yearxxxx_yearyyyy_yearzzzz" where the xxxx, yyyy, or zzzz represent the calendar years for which that list element should be used. The columns are variables that are used for more than 1 year.
`FS_formula`	Formula, put provided as a character string, not class `formula`. (if it is provided as a class `formula`, then it invariably will have an enormous amount of data hidden in the formula environment; this is bad for DEoptim)
`historicalFires`	DESCRIPTION NEEDED
`fireBufferedListDT`	DESCRIPTION NEEDED
`covMinMax`	This is a 2 row by multiple column data.frame indicating the minimum and maximum values of the original covariate data values. These will be used to rescale the covariates internally so that they are all between 0 and 1. It is important to not simply rescale internally here because only 1 year is run at a time; all years must be rescaled for a given covariate by the same amount.
`maxFireSpread`	A value for `spreadProb` that is considered impossible to go above. Default 0.28, which is overly generous unless there are many non-flammable pixels (e.g., lakes).
`minFireSize`	DESCRIPTION NEEDED
`tests`	One or more of `"mad"`, `"adTest"`, `"SNLL"`, or `"SNLL_FS"`. Default: `"mad"`.
`Nreps`	Integer. The number of replicates, per ignition, to run.
`mutuallyExclusive`	If there are any covariates, e.g,. youngAge, that should be considered mutually exclusive, i.e., "if youngAge is non-zero, should vegPC2 be set to zero", then this can be done here. A named list, where the name of the list element must be a single covariate column name in either `annualDTx1000` or `nonAnnualDTx1000`. The list content should be a "grep" pattern with which to match column names, e.g., `"vegPC"`. The values of all column names that match the grep value will be set to `0`, whenever the name of that list element is non-zero. Default is `list("youngAge" = list("vegPC"))`, meaning that all columns with `vegPC` in their name will be set to zero wherever `youngAge` is non-zero.
`doAssertions`	Logical. If `TRUE`, the default, the function will test a few minor things for consistency. This should be set to `FALSE` for operational situations, as the assertions take some small amount of time.
`plot.it`	DESCRIPTION NEEDED
`objFunCoresInternal`	Internally, this function can use `mcmapply` to run multiple parallel `spread` function calls. This should only be >1L if there are spare threads. It is highly likely that there won't be. However, sometimes the `DEoptim` is particularly inefficient, it starts X cores, and immediately several of them are stopped inside this function because the parameters are so bad, only 2 year are attempted. Then the core will stay idle until all other cores for the `DEoptim` iteration are complete. Similarly, if only physical cores are used for `DEoptim`, the additional use of hyperthreaded cores here, internally will speed things up (i.e., this maybe could be 2L or 3L).
`lanscape1stQuantileThresh`	A `spreadProb` value that represents a threshold for the 1st quantile of the `spreadProbs` on the landscape; if that quantile is above this number, then the `.objFunSpredFit` will bail because it is "too burny" a landscape. Default = `0.265`, meaning if only 25% of the pixels on the landscape are below this `spreadProb`, then it will bail.
`thresh`	Threshold multiplier used in SNLL fire size (SNLL_FS) test. Default 550. Lowering the threshold value will be more restrictive, but being too restrictive will result in `DEoptim` rejecting more tests and using the "fail value" of 10000. Too high a threshold, and more years will be run and it will take longer to find values.
`weighted`	Logical. Should empirical likelihood be weighted by log of the actual fire size? This will give large fires more influence on the SNLL.
`verbose`	DESCRIPTION NEEDED

Value

Attempting a weighted likelihood, https://stats.stackexchange.com/questions/267464/algorithms-for-weighted-maximum-likelihood-parameter-estimation. With log(fireSize) * likelihood for each fire.

Convert list of annual SpatRaster to `data.table`

Description

Convert list of annual SpatRaster to data.table

Usage

annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'SpatRaster'
annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'Raster'
annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'list'
annualStackToDTx1000(x, whNotNA, ...)
annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'SpatRaster'
annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'Raster'
annualStackToDTx1000(x, whNotNA, ...)

## S3 method for class 'list'
annualStackToDTx1000(x, whNotNA, ...)

Arguments

`x`	`RasterStack` or list of rasters to convert to `data.table` and multiply by 1000 to save space
`whNotNA`	Pixel indexes that should go through this process (i.e. not NA)
`...`	Not currently used

Value

data.table of the SpatRaster or the list

Examples

library(raster)
r1 <- raster(extent(0, 10, 0, 10), vals = 1:100)
r2 <- raster(extent(0, 10, 0, 10), vals = 100:1)
r3 <- raster(extent(0, 10, 0, 10), vals = 200:101)
r4 <- raster(extent(0, 10, 0, 10), vals = 300:201)

# list of Rasters
lRast <- list(r1, r2, r3)
lRast[[1]][5] <- NA
whNotNA <- setdiff(1:ncell(r1), 5)

# unnamed -- should error
try(out1 <- annualStackToDTx1000(lRast, whNotNA))

# named
names(lRast) <- c("OneToHun", "HunToOne", "TwoHunToOneHun")
out1 <- annualStackToDTx1000(lRast, whNotNA)

# RasterStack
out2 <- annualStackToDTx1000(raster::stack(lRast), whNotNA)

# List of RasterStacks
s1 <- raster::stack(r1, r2)
names(s1) <- names(lRast)[1:2]
s2 <- raster::stack(r4, r3)
names(s2) <- c(names(lRast)[3], "ThreeHunToTwoHun")
out3 <- annualStackToDTx1000(list(s1 = s1, s2 = s2), whNotNA) ## named list required

# With duplicated names -- to remove duplicates;
#  actually, this doesn't make sense: RasterStack can't have duplicated names
names(lRast) <- c("OneToHun", "OneToHun", "TwoHunToOneHun")
out4 <- annualStackToDTx1000(raster::stack(lRast), whNotNA)
library(raster)
r1 <- raster(extent(0, 10, 0, 10), vals = 1:100)
r2 <- raster(extent(0, 10, 0, 10), vals = 100:1)
r3 <- raster(extent(0, 10, 0, 10), vals = 200:101)
r4 <- raster(extent(0, 10, 0, 10), vals = 300:201)

# list of Rasters
lRast <- list(r1, r2, r3)
lRast[[1]][5] <- NA
whNotNA <- setdiff(1:ncell(r1), 5)

# unnamed -- should error
try(out1 <- annualStackToDTx1000(lRast, whNotNA))

# named
names(lRast) <- c("OneToHun", "HunToOne", "TwoHunToOneHun")
out1 <- annualStackToDTx1000(lRast, whNotNA)

# RasterStack
out2 <- annualStackToDTx1000(raster::stack(lRast), whNotNA)

# List of RasterStacks
s1 <- raster::stack(r1, r2)
names(s1) <- names(lRast)[1:2]
s2 <- raster::stack(r4, r3)
names(s2) <- c(names(lRast)[3], "ThreeHunToTwoHun")
out3 <- annualStackToDTx1000(list(s1 = s1, s2 = s2), whNotNA) ## named list required

# With duplicated names -- to remove duplicates;
#  actually, this doesn't make sense: RasterStack can't have duplicated names
names(lRast) <- c("OneToHun", "OneToHun", "TwoHunToOneHun")
out4 <- annualStackToDTx1000(raster::stack(lRast), whNotNA)

buffer ignition points to create non-ignitions for model

Description

buffer ignition points to create non-ignitions for model

Usage

bufferIgnitionPoints(ignitionPoints, rtm, bufferSize)
bufferIgnitionPoints(ignitionPoints, rtm, bufferSize)

Arguments

`ignitionPoints`	SpatialPolygonsDataFrame with year of ignition
`rtm`	a template raster
`bufferSize`	the size of the buffers

Value

a list of data.tables containing indices inside buffered area of each year's ignitions

Create buffers around polygons based on area target for buffer

Description

Create buffers around polygons based on area target for buffer

Usage

bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'list'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'SpatialPolygons'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'sf'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'list'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'SpatialPolygons'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

## S3 method for class 'sf'
bufferToArea(
  poly,
  rasterToMatch,
  areaMultiplier = 10,
  verb = FALSE,
  polyName = NULL,
  field = NULL,
  minSize = 500,
  cores = 1,
  ...
)

Arguments

`poly`	`sf` polygons or a list of `sf` containing polygons to buffer.
`rasterToMatch`	A `SpatRaster` with `res`, `origin`, `extent`, `crs` of desired outputted `pixelID` values.
`areaMultiplier`	Either a scalar that will buffer `areaMultiplier * fireSize` or a function of `fireSize.` Default is 1. See `multiplier()` for an example.
`verb`	Logical or numeric related to how much verbosity is printed. `FALSE` or `0` is none. `TRUE` or `1` is some. `2` is much more.
`polyName`	Optional character string of the polygon layer name (not the individual polygons on a `sf` polygon object)
`field`	Passed to `fasterize::fasterize`. If this is unique (such as polygon id), then each polygon will have its buffer calculated independently for each unique value in `field`
`minSize`	The absolute minimum size of the buffer & non-buffer together. This will be imposed after `areaMultiplier`.
`cores`	number of processor cores to use
`...`	passed to `fasterize::fasterize`

Value

A data.table (or list of data.tables if poly was a list) with 2 columns: buffer and pixelID. buffer is either 1 (the original polygon) or 0 (in the buffer).

create a variable sized buffer around a set of pixels belonging to the same fire ID

Description

create a variable sized buffer around a set of pixels belonging to the same fire ID

Usage

bufferToAreaRast(fireIDraster, areaMultiplier, minSize, flammableRTM, verb = 1)
bufferToAreaRast(fireIDraster, areaMultiplier, minSize, flammableRTM, verb = 1)

Arguments

`fireIDraster`	a `SpatRaster` with values representing distinct fires in a year
`areaMultiplier`	A scalar that will buffer `areaMultiplier * fireSize`
`minSize`	The absolute minimum size of the buffer & non-buffer together. This will be imposed after `areaMultiplier`.
`flammableRTM`	@template flammableRTM
`verb`	Logical or numeric related to how much verbosity is printed. `FALSE` or `0` is none. `TRUE` or `1` is some. `2` is much more.

Value

a data.table with fire ID, buffer status, and pixelID

Modify cohortData with burn column

Description

Modify cohortData with burn column

Usage

buildCohortBurnHistory(cohortData, pixelGroupMap, firePolys, year)
buildCohortBurnHistory(cohortData, pixelGroupMap, firePolys, year)

Arguments

`cohortData`	either a `cohortData` object or list of `cohortData` objects named by year
`pixelGroupMap`	either a `SpatRaster` with `pixelGroups` or list of `SpatRasters` named by year
`firePolys`	the output of `fireSenseUtils::getFirePolys` with `YEAR` column
`year`	length-two vector giving temporal period used to subset firePolys. Closed interval

Value

cohortData modified with burn status

Generate, Summarize, Predict Burn Classes from Covariates

Description

Generate, Summarize, Predict Burn Classes from Covariates

Usage

burnClassGenerator(df, numClasses = 4:9, AUC = TRUE, plotAUC = FALSE)

burnClassSummary(mod)

burnClassPredict(mod, df)

burnProbFromClass(mod, df)
burnClassGenerator(df, numClasses = 4:9, AUC = TRUE, plotAUC = FALSE)

burnClassSummary(mod)

burnClassPredict(mod, df)

burnProbFromClass(mod, df)

Arguments

`df`	A `data.frame` (or `data.table`), with covariates, including "burned" (a binary 0, 1; not burned = 0, burned = 1), e.g., `timeSinceFire`, `biomassJackPine`, etc. that will be used to find fuel classes. This set of covariates must be available both during fitting and for prediction. These must be quantitative.
`numClasses`	A vector indicating how many classes should be attempted. The function will return the number of classes that best classify the data into homogeneous groups.
`AUC`	Logical. Should the Area Under the receiver operating Curve be returned?
`plotAUC`	Logical. Should the plot of the AUC be made.
`mod`	A model of class `Mclust`, e.g,. coming from `Mclust` or `burnClassGenerator`

Details

This was inspired by reading here: https://www.datanovia.com/en/blog/types-of-clustering-methods-overview-and-quick-start-r-code/ and here: https://www.datanovia.com/en/lessons/model-based-clustering-essentials/, with citation here: Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 205-233. https://journal.r-project.org/archive/2016/RJ-2016-021/RJ-2016-021.pdf

Value

A list with 2 elements, first the model, which comes from mclust::Mclust, and second the Area Under the Curve or AUC as an indicator of the overall goodness of fit.

The algorithm

The basic solution is to take all covariates, including the binary "not burned", "burned" (coded as 0 and 1, respectively), and do model-based clustering with the mclust R package. We can choose a fixed number of burn classes, or a finite range (see numClasses argument. This will make numClasses "homogeneous" groups, including whether they burned or not. From this, we can identify groups by looking at the mean values of "burned" to see what their burn tendency is as a "homogeneous" group.

Categorical data

For now, it is recommended to convert categorical data to dummy variables, 0 and 1. E.g., For land cover, wetland class can be converted to a column "wetland" with 1 for data points that are wetlands and 0 for non-wetland.

How much data to include

This has not been tested yet; however, I believe that having a relatively similar number of "burned" and "unburned" pixels (within 3x either way), is probably a good idea. In other words, if there are 100,000 burned data points, there should be between 30,000 and 300,000 unburned data points. If there are already buffers around the burned polygons that include unburned pixels, then these buffers can be used as part of the unburned content.

Author(s)

Eliot McIntire

Examples

## Not run: 
#################################
# Use own data; here is a generated set for reprex
library("data.table")
N <- 1e5
DT <- list()
for (i in c("train", "test")) {
  DT[[i]] <- data.table(burned = sample(c(0, 0, 0, 1), replace = TRUE, size = N))
  set(DT[[i]], NULL, "jp", rlnorm(N, mean = 4 + 0.5 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "bs", rlnorm(N, mean = 4 + 0.3 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "ws", rlnorm(N, mean = 4 + 0.2 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "age", rlnorm(N, mean = 4 - 0.2 * DT[[i]]$burned, sd = 0.25))
  DT[[i]][, c("jp", "bs", "ws", "age") := lapply(.SD, function(x) x / max(x) * 1000),
    .SDcols = c("jp", "bs", "ws", "age")
  ]
  DT[[i]][, c("age") := lapply(.SD, function(x) x / max(x) * 200), .SDcols = c("age")]
  summary(DT[[i]])
  boxplot(DT[[i]]$age ~ DT[[i]]$burned)
}

bc <- burnClassGenerator(DT[["train"]], 4:8)
# Show if the model is good at predicting burn state
(bc$AUC) # area under the curve

# print summary of mean values of each burn class
(summ <- burnClassSummary(bc$model))

# predict -- add Burn Class to object
set(DT[["test"]], NULL, "burnClass", burnClassPredict(bc$model, df = DT[["test"]]))
prob <- burnProbFromClass(bc$model, DT[["test"]])

## End(Not run)
## Not run: 
#################################
# Use own data; here is a generated set for reprex
library("data.table")
N <- 1e5
DT <- list()
for (i in c("train", "test")) {
  DT[[i]] <- data.table(burned = sample(c(0, 0, 0, 1), replace = TRUE, size = N))
  set(DT[[i]], NULL, "jp", rlnorm(N, mean = 4 + 0.5 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "bs", rlnorm(N, mean = 4 + 0.3 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "ws", rlnorm(N, mean = 4 + 0.2 * DT[[i]]$burned, sd = 0.25))
  set(DT[[i]], NULL, "age", rlnorm(N, mean = 4 - 0.2 * DT[[i]]$burned, sd = 0.25))
  DT[[i]][, c("jp", "bs", "ws", "age") := lapply(.SD, function(x) x / max(x) * 1000),
    .SDcols = c("jp", "bs", "ws", "age")
  ]
  DT[[i]][, c("age") := lapply(.SD, function(x) x / max(x) * 200), .SDcols = c("age")]
  summary(DT[[i]])
  boxplot(DT[[i]]$age ~ DT[[i]]$burned)
}

bc <- burnClassGenerator(DT[["train"]], 4:8)
# Show if the model is good at predicting burn state
(bc$AUC) # area under the curve

# print summary of mean values of each burn class
(summ <- burnClassSummary(bc$model))

# predict -- add Burn Class to object
set(DT[["test"]], NULL, "burnClass", burnClassPredict(bc$model, df = DT[["test"]]))
prob <- burnProbFromClass(bc$model, DT[["test"]])

## End(Not run)

Iteratively calculate `youngAge` column in FS covariates

Description

Iteratively calculate youngAge column in FS covariates

Usage

calcYoungAge(
  years,
  annualCovariates,
  standAgeMap,
  fireBufferedListDT,
  cutoffForYoungAge = 15
)
calcYoungAge(
  years,
  annualCovariates,
  standAgeMap,
  fireBufferedListDT,
  cutoffForYoungAge = 15
)

Arguments

`years`	the years over which to iterate
`annualCovariates`	list of data.table objects with pixelID
`standAgeMap`	template `SpatRaster`
`fireBufferedListDT`	data.table containing non-annual burn and buffer pixelIDs
`cutoffForYoungAge`	Numeric. Default is 15. This is the age below which the pixel is considered "young" –> `youngAge` column will be 1 if `age <= 15`

Value

a raster layer with unified standAge and time-since-disturbance values

preparing covariates for fitting modules

Description

preparing covariates for fitting modules

Usage

castCohortData(
  cohortData,
  pixelGroupMap,
  lcc,
  ageMap = NULL,
  missingLCC,
  year = NULL,
  cutoffForYoungAge = 15
)
castCohortData(
  cohortData,
  pixelGroupMap,
  lcc,
  ageMap = NULL,
  missingLCC,
  year = NULL,
  cutoffForYoungAge = 15
)

Arguments

`cohortData`	A `data.table` with columns: `pixelGroup`, `ecoregionGroup`, `speciesCode`, and optionally `age`, `B`, `mortality`, `aNPPAct`, and `sumB`.
`pixelGroupMap`	A `RasterLayer` with pixel values equal to a pixel group number that corresponds exactly to `pixelGroup` column in `cohortData`.
`lcc`	data.table of dummified landcover
`ageMap`	a stand age map to assign ages to non-forest LCC used during predict
`missingLCC`	LCC class to assign forested pixels absent from `cohortData` must be a character matching a `nonForestedLCC` group, e.g. '`⁠nonForest_highFlam'⁠`
`year`	numeric representing the year represented by `cohortData`
`cutoffForYoungAge`	Numeric. Default is 15. This is the age below which the pixel is considered "young" –> `youngAge` column will be 1 if `age <= 15`

Value

a trimmed cohortData with wide-layout and rows for every pixel in lcc

Data checks and assertions for `spreadFitRun`

Description

Data checks and assertions for spreadFitRun

Usage

chk_duplicatedStartPixels(cells, size)

.doDataChecks(moduleName, envir, attribs, fml)
chk_duplicatedStartPixels(cells, size)

.doDataChecks(moduleName, envir, attribs, fml)

Arguments

`cells`	DESCRIPTION NEEDED
`size`	DESCRIPTION NEEDED
`moduleName`	DESCRIPTION NEEDED
`envir`	DESCRIPTION NEEDED
`attribs`	DESCRIPTION NEEDED
`fml`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

Ensure fire points are located on flammable pixels inside a fire polygon Intended to be run using `Map`

Description

Ensure fire points are located on flammable pixels inside a fire polygon Intended to be run using Map

Usage

cleanUpSpreadFirePoints(firePoints, bufferDT, flammableRTM)
cleanUpSpreadFirePoints(firePoints, bufferDT, flammableRTM)

Arguments

`firePoints`	a `sf` points object representing annual ignitions
`bufferDT`	a data.table of burned cells, output from `bufferToArea`
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data

Value

a list of harmonized points and polygons

Converts stacks of climate rasters to data.table and optionally subsets to index

Description

Converts stacks of climate rasters to data.table and optionally subsets to index

Usage

climateRasterToDataTable(historicalClimateRasters, Index = NULL)
climateRasterToDataTable(historicalClimateRasters, Index = NULL)

Arguments

`historicalClimateRasters`	named list of `SpatRaster` objects
`Index`	optional list of `data.table` objects named by fireYear and containing fire buffer indices

Value

a long-layout data.table of climate values in each pixel and year

Classify `pixelGroups` by flammability

Description

Classify pixelGroups by flammability

Usage

cohortsToFuelClasses(
  cohortData,
  pixelGroupMap,
  flammableRTM,
  landcoverDT = NULL,
  sppEquiv,
  sppEquivCol,
  cutoffForYoungAge,
  fuelClassCol = "FuelClass"
)
cohortsToFuelClasses(
  cohortData,
  pixelGroupMap,
  flammableRTM,
  landcoverDT = NULL,
  sppEquiv,
  sppEquivCol,
  cutoffForYoungAge,
  fuelClassCol = "FuelClass"
)

Arguments

`cohortData`	A `data.table` with columns: `pixelGroup`, `ecoregionGroup`, `speciesCode`, and optionally `age`, `B`, `mortality`, `aNPPAct`, and `sumB`.
`pixelGroupMap`	A `RasterLayer` with pixel values equal to a pixel group number that corresponds exactly to `pixelGroup` column in `cohortData`.
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`landcoverDT`	Optional table of nonforest landcovers and pixel indices. It will override pixel values in `cohortData`, if supplied.
`sppEquiv`	table with species name equivalencies between the kNN and final naming formats. See `data("sppEquivalencies_CA", "LandR")`. Only necessary if `mixedType == 2`. If not provided and `mixedType == 2`, will attempt to use `data("sppEquivalencies_CA", "LandR")`.
`sppEquivCol`	the column name to use from `sppEquiv`.
`cutoffForYoungAge`	age at and below which pixels are considered 'young'
`fuelClassCol`	the column in `sppEquiv` that describes unique fuel classes

Value

a SpatRaster of biomass by fuel class as determined by fuelClassCol and cohortData

Download and prepare fire data from National Fire Database

Description

Download and prepare fire data from National Fire Database

Usage

compareMDC(
  historicalMDC,
  projectedMDC,
  flammableRTM = NULL,
  Ylimits = c(80, 220),
  firstHistoricalYear = 2001,
  firstProjectedYear = 2011
)
compareMDC(
  historicalMDC,
  projectedMDC,
  flammableRTM = NULL,
  Ylimits = c(80, 220),
  firstHistoricalYear = 2001,
  firstProjectedYear = 2011
)

Arguments

`historicalMDC`	raster stack of historical MDC
`projectedMDC`	raster stack of projected MDC
`flammableRTM`	an optional raster of flammable pixels to subset data
`Ylimits`	the upper and lower MDC range for the plot
`firstHistoricalYear`	the earliest year of historical data
`firstProjectedYear`	the earliest year of projected data

Value

a ggplot object

Examples

## Not run: 
compareMDC(
  historicalMDC = simOutPreamble$historicalClimateRasters$MDC,
  projectedMDC = simOutPreamble$projectedClimateRasters$MDC,
  flammableRTM = fSsimDataPrep$flammableRTM
)

## End(Not run)
## Not run: 
compareMDC(
  historicalMDC = simOutPreamble$historicalClimateRasters$MDC,
  projectedMDC = simOutPreamble$projectedClimateRasters$MDC,
  flammableRTM = fSsimDataPrep$flammableRTM
)

## End(Not run)

Replace `NA`s in a `data.table` with zeros

Description

Replace NAs in a data.table with zeros

Usage

dtReplaceNAwith0(DT, colsToUse = NULL)
dtReplaceNAwith0(DT, colsToUse = NULL)

Arguments

`DT`	DESCRIPTION NEEDED
`colsToUse`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

Extract the elements of the special terms, i.e. the variable and the knot value

Description

Extract the elements of the special terms, i.e. the variable and the knot value

Usage

extractSpecial(v, k)
extractSpecial(v, k)

Arguments

`v`	DESCRIPTION NEEDED
`k`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

Get Fire `SpatialPoints` from Canadian Fire Database

Description

Get Fire SpatialPoints from Canadian Fire Database

Usage

getFirePoints_NFDB(
  url = NULL,
  studyArea = NULL,
  rasterToMatch = NULL,
  redownloadIn = 1,
  years = 1991:2017,
  fireSizeColName = "SIZE_HA",
  NFDB_pointPath
)
getFirePoints_NFDB(
  url = NULL,
  studyArea = NULL,
  rasterToMatch = NULL,
  redownloadIn = 1,
  years = 1991:2017,
  fireSizeColName = "SIZE_HA",
  NFDB_pointPath
)

Arguments

`url`	Passed to `prepInputs`
`studyArea`	A `SpatialPolygons*` object used as the principle study region, passed to `prepInputs`.
`rasterToMatch`	A `RasterLayer` objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).
`redownloadIn`	Numeric Time in YEARS that we tolerate the data to be "old" i.e. 0.5 would mean "redownload data older than 6 months"
`years`	Numeric vector of consecutive years to fetch.
`fireSizeColName`	Character describing the name of the column containing fire size information.
`NFDB_pointPath`	Passed to `destinationPath` in `prepInputs`

Value

A sf spatial points object.

Get Fire `SpatialPoints` from Canadian Fire Database

Description

Get Fire SpatialPoints from Canadian Fire Database

Usage

getFirePoints_NFDB_V2(
  url = NULL,
  studyArea = NULL,
  redownloadIn = 1,
  years = 1991:2017,
  fireSizeColName = "SIZE_HA",
  NFDB_pointPath = NULL,
  plot = FALSE
)
getFirePoints_NFDB_V2(
  url = NULL,
  studyArea = NULL,
  redownloadIn = 1,
  years = 1991:2017,
  fireSizeColName = "SIZE_HA",
  NFDB_pointPath = NULL,
  plot = FALSE
)

Arguments

`url`	Passed to `prepInputs`
`studyArea`	A `SpatialPolygons*` object used as the principle study region, passed to `prepInputs`.
`redownloadIn`	Numeric Time in YEARS that we tolerate the data to be "old" i.e. 0.5 would mean "re-download data older than 6 months"
`years`	Numeric vector of consecutive years to fetch.
`fireSizeColName`	Character describing the name of the column containing fire size information.
`NFDB_pointPath`	Passed to `destinationPath` in `prepInputs`
`plot`	logical indicating whether to produce plot of fire points. Default FALSE.

Value

A sf spatial points object.

Download and prepare fire data from National Fire Database

Description

Download and prepare fire data from National Fire Database

Usage

getFirePolygons(years, useInnerCache = FALSE, ...)
getFirePolygons(years, useInnerCache = FALSE, ...)

Arguments

`years`	years to filter fire polygons by
`useInnerCache`	logical indicating whether to cache the `prepInputs` call
`...`	additional arguments passed to `prepInputs()`

Value

list of fire polygons by year

Cleaning up the polygon points

Description

Mostly this is about 2 things:

remove fires that were so small that they take less than 1 pixel so they are not in the buff object but are in the cent object.
the centroid cell is in a buffer or otherwise nonburnable cell (e.g., water). For 1) remove these from the centroid data. For 2) this function will search in the neighbourhood for the next closest pixel that has at least 7 available neighbours that can burn. If not, remove these.

Usage

harmonizeBufferAndPoints(cent, buff, ras, idCol = "FIRE_ID")
harmonizeBufferAndPoints(cent, buff, ras, idCol = "FIRE_ID")

Arguments

`cent`	List of points as `SpatialPointsDataFrame`
`buff`	List of `data.table` objects with 3 columns, "buffer" which is 1 (in the fire) or 0 (in a buffer), `ids` which are the fire ids which MUST match the ids in the `cent`.
`ras`	The raster that created the `pixelIDs` in the `buff`.
`idCol`	The column name as a character string with the fire ids. Defaults to `"FIRE_ID"`.

Outer wrapper on spread fire polygon data munging that does several things:

ensure buffered fires are entirely in studyArea

ensure every fire has a corresponding ignition point, and vice versa

ensure these points are flammable

Description

Outer wrapper on spread fire polygon data munging that does several things:

ensure buffered fires are entirely in studyArea
ensure every fire has a corresponding ignition point, and vice versa
ensure these points are flammable

Usage

harmonizeFireData(
  firePolys,
  flammableRTM,
  spreadFirePoints,
  areaMultiplier,
  minSize,
  pointsIDcolumn = "FIRE_ID"
)
harmonizeFireData(
  firePolys,
  flammableRTM,
  spreadFirePoints,
  areaMultiplier,
  minSize,
  pointsIDcolumn = "FIRE_ID"
)

Arguments

`firePolys`	the semi-processed fire polys, with field matching pointsIDcolumn
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`spreadFirePoints`	the ignition points corresponding to `firePolys`
`areaMultiplier`	Either a scalar that will buffer `areaMultiplier * fireSize` or a function of `fireSize`. See `?fireSenseUtils::bufferToArea`.
`minSize`	an alternative to areaMultiplier, typically used when fires are small
`pointsIDcolumn`	the name of the column denoting fire ids in both spreadFirePoints and firePolys

Four- and five-parameter logistic functions

Description

Four- and five-parameter logistic functions

Usage

logistic4p(x, par)

logistic5p(x, par)

logistic3p(x, par, par1 = 0.1)

logistic2p(x, par, par1 = 0.1, par4 = 0.5)
logistic4p(x, par)

logistic5p(x, par)

logistic3p(x, par, par1 = 0.1)

logistic2p(x, par, par1 = 0.1, par4 = 0.5)

Arguments

`x`	DESCRIPTION NEEDED
`par`	DESCRIPTION NEEDED
`par1`	DESCRIPTION NEEDED
`par4`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

identify each year's individual fires and buffer them accordingly

Description

identify each year's individual fires and buffer them accordingly

Usage

makeFireIDs(
  year,
  fireRaster,
  flammableRTM,
  bufferForFireRaster,
  areaMultiplier,
  minSize = 5000,
  verb = 1
)
makeFireIDs(
  year,
  fireRaster,
  flammableRTM,
  bufferForFireRaster,
  areaMultiplier,
  minSize = 5000,
  verb = 1
)

Arguments

`year`	numeric fire year
`fireRaster`	a `SpatRaster` with values representing fire years
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`bufferForFireRaster`	buffer size used to group discrete patches of burned pixels as belonging to the same fire
`areaMultiplier`	A scalar that will buffer `areaMultiplier * fireSize`
`minSize`	The absolute minimum size of the buffer & non-buffer together. This will be imposed after `areaMultiplier`.
`verb`	Logical or numeric related to how much verbosity is printed. `FALSE` or `0` is none. `TRUE` or `1` is some. `2` is much more.

Value

a data.table with fire ID, buffer status, and pixelID

Create landcoverDT object to classify and track non-forest lcc

Description

Create landcoverDT object to classify and track non-forest lcc

Usage

makeLandcoverDT(rstLCC, flammableRTM, forestedLCC, nonForestedLCCGroups)
makeLandcoverDT(rstLCC, flammableRTM, forestedLCC, nonForestedLCCGroups)

Arguments

`rstLCC`	landcover raster
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`forestedLCC`	vector of values representing forested landcover classes in `rstLCC`
`nonForestedLCCGroups`	a named list of non-forested flammable landcover groups

Value

a data.table with columns for pixelID and binary presence of landcover

Convert a list of `SpatialPointsDataFrame` object to a list of `data.table` objects

Description

Must supply a raster so that points can be converted to the cells on a raster. It is assumed that the sizeCol is accurate. If not, it should be recalculated before this function call.

Usage

makeLociList(
  ras,
  pts,
  idsCol = "FIRE_ID",
  dateCol = "YEAR",
  sizeCol = "POLY_HA",
  sizeColUnits = "ha"
)
makeLociList(
  ras,
  pts,
  idsCol = "FIRE_ID",
  dateCol = "YEAR",
  sizeCol = "POLY_HA",
  sizeColUnits = "ha"
)

Arguments

`ras`	A raster that will be the template for cells (pixel ids)
`pts`	A list of `sf` point objects
`idsCol`	Character string identifying column name in `pts` that has unique id per event (i.e., fire)
`dateCol`	Character string identifying column name in `pts` that has year
`sizeCol`	Character string identifying column name in `pts` that has size of individual event. Can be in hectares or metres squared. Should set `sizeColUnits`
`sizeColUnits`	Character string. Either `"ha"` or `"m2"`.

Value

A list of data.table objects, each with 4 columns, "size" (in pixels), "date", "ids" from idsCol, and "cells", which are the pixel indices of the pts points.

guarantees mutually exclusive values in a data table

Description

guarantees mutually exclusive values in a data table

Usage

makeMutuallyExclusive(dt, mutuallyExclusiveCols = list(youngAge = c("vegPC")))
makeMutuallyExclusive(dt, mutuallyExclusiveCols = list(youngAge = c("vegPC")))

Arguments

dt

a data.table with columns that should be mutually exclusive

mutuallyExclusiveCols

A named list, where the name of the list element must be a single covariate column name in dt. The list content should be a "grep" pattern with which to match column names, e.g., "vegPC". The values of all column names that match the grep value will be set to 0, whenever the name of that list element is non-zero. Default is list("youngAge" = list("vegPC")), meaning that all columns with vegPC in their name will be set to zero wherever youngAge is non-zero.

Value

a data.table with relevant columns made mutually exclusive

Put `cohortData` back into a `SpatRaster` with some extra details

Description

Put cohortData back into a SpatRaster with some extra details

Usage

makeRastersFromCD(class, cohortData, flammableRTM, pixelGroupMap)
makeRastersFromCD(class, cohortData, flammableRTM, pixelGroupMap)

Arguments

`class`	fuelClass from `sppEquiv`
`cohortData`	A `data.table` with columns: `pixelGroup`, `ecoregionGroup`, `speciesCode`, and optionally `age`, `B`, `mortality`, `aNPPAct`, and `sumB`.
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`pixelGroupMap`	A `RasterLayer` with pixel values equal to a pixel group number that corresponds exactly to `pixelGroup` column in `cohortData`.

Value

a SpatRaster with values equal to class biomass (B)

preparing a time since disturbance map from stand age and fire data

Description

preparing a time since disturbance map from stand age and fire data

Usage

makeTSD(
  year,
  firePolys = NULL,
  fireRaster = NULL,
  standAgeMap,
  lcc,
  cutoffForYoungAge = 15
)
makeTSD(
  year,
  firePolys = NULL,
  fireRaster = NULL,
  standAgeMap,
  lcc,
  cutoffForYoungAge = 15
)

Arguments

`year`	the year represented by `standAge`
`firePolys`	list of `spatialPolygon` objects comprising annual fires. fireRaster will supersede firePolys if provided
`fireRaster`	a `RasterLayer` with values representing fire years
`standAgeMap`	initial stand age map
`lcc`	`data.table` with landcover values - `landcoverDT`
`cutoffForYoungAge`	Numeric. Default is 15. This is the age below which the pixel is considered "young" –> `youngAge` column will be 1 if `age <= 15`

Value

a SpatRaster with values representing time since disturbance

multiplier

Description

DESCRIPTION NEEDED

Usage

multiplier(size, minSize = 1000, baseMultiplier = 5)
multiplier(size, minSize = 1000, baseMultiplier = 5)

Arguments

`size`	DESCRIPTION NEEDED
`minSize`	DESCRIPTION NEEDED
`baseMultiplier`	DESCRIPTION NEEDED

`objNlminb`

Description

Wrapper around stats::nlminb

Usage

objNlminb(x, objective, lower, upper, control, hvPW, ...)
objNlminb(x, objective, lower, upper, control, hvPW, ...)

Arguments

`x`	DESCRIPTION NEEDED
`objective`	objective function
`lower`	lower bounds on coefficients
`upper`	upper bounds on coefficients
`control`	DESCRIPTION NEEDED
`hvPW`	logical indicating whether the formula is piece-wise #IE added
`...`	additional arguments passed to objective function

Value

DESCRIPTION NEEDED

Order of Magnitude

Description

Order of Magnitude

Usage

oom(x)
oom(x)

Arguments

x

a numeric

Value

the order of magnitude

Plot burn summary

Description

Create plot with subplots showing: a) area burned; b) number of fires; c) mean fire size.

Usage

plotBurnSummary(studyAreaName, climateScenario, outputDir, Nreps)
plotBurnSummary(studyAreaName, climateScenario, outputDir, Nreps)

Arguments

`studyAreaName`	character string giving the study area name
`climateScenario`	character string specifying the name of a CIMP6 climate scenario, including SSP, formatted as in `ClimateNA`, using underscores as separator (e.g., 'CanESM5_SSP370').
`outputDir`	Path specifying the directory to which outputs figures/objects should be saved.
`Nreps`	the number of simulation replicates/run used to produce summary figures. NOTE: `mclapply` is used internally, so you should set `options(mc.cores = nReps)` to take advantage of parallel processing.

Value

list of file names corresponding to the figures and/or objects written to disk

Plot cumulative burn maps

Description

Plot cumulative burn maps

Usage

plotCumulativeBurns(
  studyAreaName,
  climateScenario,
  outputDir,
  Nreps,
  rasterToMatch
)
plotCumulativeBurns(
  studyAreaName,
  climateScenario,
  outputDir,
  Nreps,
  rasterToMatch
)

Arguments

`studyAreaName`	character string giving the study area name
`climateScenario`	character string specifying the name of a CIMP6 climate scenario, including SSP, formatted as in `ClimateNA`, using underscores as separator (e.g., 'CanESM5_SSP370').
`outputDir`	Path specifying the directory to which outputs figures/objects should be saved.
`Nreps`	the number of simulation replicates/run used to produce summary figures. NOTE: `mclapply` is used internally, so you should set `options(mc.cores = nReps)` to take advantage of parallel processing.
`rasterToMatch`	A `RasterLayer` objects to use as the template for all subsequent raster operations (i.e., the one used throughout the simulation).

Value

list of file names corresponding to the figures and/or objects written to disk

a file path corresponding to the images and/or objects written to disk

Plot historic ignitions, escapes, and area burned

Description

Plot historic ignitions, escapes, and area burned

Usage

plotHistoricFires(
  climateScenario,
  studyAreaName,
  outputDir,
  firePolys,
  ignitionPoints
)
plotHistoricFires(
  climateScenario,
  studyAreaName,
  outputDir,
  firePolys,
  ignitionPoints
)

Arguments

`climateScenario`	character string specifying the name of a CIMP6 climate scenario, including SSP, formatted as in `ClimateNA`, using underscores as separator (e.g., 'CanESM5_SSP370').
`studyAreaName`	character string giving the study area name
`outputDir`	Path specifying the directory to which outputs figures/objects should be saved.
`firePolys`	A `sf` spatial polygons of historic fire burn areas, from the Canadian National Fire Database.
`ignitionPoints`	A `sf` spatial points of historic fire ignitions, from the Canadian National Fire Database.

Value

list of file names corresponding to the figures and/or objects written to disk

Predictions from ignition model

Description

Predictions from ignition model

Usage

predictIgnition(
  model,
  data,
  coefs,
  rescaleFactor,
  lambdaRescaleFactor,
  linkinv
)
predictIgnition(
  model,
  data,
  coefs,
  rescaleFactor,
  lambdaRescaleFactor,
  linkinv
)

Arguments

`model`	formula of fitted model (`sim$fireSense_IgnitionFitted[["formula"]][-2]`)
`data`	data for prediction
`coefs`	model coefficients (`sim$fireSense_IgnitionFitted$coef`)
`rescaleFactor`	spatial rescaling factor when predicted and fitted data are at different scales. Calculaed as: (predResolution/fitResolution)^2
`lambdaRescaleFactor`	If the data for fitting has been sampled for pseudo-absences, this imposes a new baseline probability of fire occurrences, hence predictions need to be adjusted. If the original fire prob. is (total no. fires)/(total no. fires + total no. absences), and the fire probability imposed by sampling is (total no. fires)/(total no. fires + no. sampled pseudo-absences), to adjust predicted values, one needs to multiply them by (total no. fires + no. sampled pseudo-absences/(total no. fires + total no. absences)
`linkinv`	family link function (`sim$fireSense_IgnitionFitted$family$linkinv`)

Value

vector of predicted values.

Handling piecewise terms in a formula

Description

Handling piecewise terms in a formula

Usage

pw(variable, knot)
pw(variable, knot)

Arguments

`variable`	DESCRIPTION NEEDED
`knot`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

this is a wrapper to simplify caching of `lapply` with `bufferForFireRaster`. Years are iteratively processed by `makeFireID`.

Description

this is a wrapper to simplify caching of lapply with bufferForFireRaster. Years are iteratively processed by makeFireID.

Usage

rasterFireBufferDT(
  years,
  fireRaster,
  flammableRTM,
  bufferForFireRaster,
  areaMultiplier,
  minSize = 5000,
  verb = 1,
  cores = 1
)
rasterFireBufferDT(
  years,
  fireRaster,
  flammableRTM,
  bufferForFireRaster,
  areaMultiplier,
  minSize = 5000,
  verb = 1,
  cores = 1
)

Arguments

`years`	numeric fire years
`fireRaster`	a `SpatRaster` with values representing fire years
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data
`bufferForFireRaster`	buffer size used to group discrete patches of burned pixels as belonging to the same fire
`areaMultiplier`	A scalar that will buffer `areaMultiplier * fireSize`
`minSize`	The absolute minimum size of the buffer & non-buffer together. This will be imposed after `areaMultiplier`.
`verb`	Logical or numeric related to how much verbosity is printed. `FALSE` or `0` is none. `TRUE` or `1` is some. `2` is much more.
`cores`	number of processor cores to use

Value

a list of data.tables named by year, with cols ids, buffer, and pixelID

create a list of annual ignition points based on fire raster

Description

create a list of annual ignition points based on fire raster

Usage

rasterFireSpreadPoints(fireBufferDT, flammableRTM)
rasterFireSpreadPoints(fireBufferDT, flammableRTM)

Arguments

`fireBufferDT`	a `data.table` with columns `buffer` (1 = burned), `id` (unique fire ID), and `pixelID`
`flammableRTM`	@template flammableRTM

Value

a list of sf point objects

Generate random beta variates between 2 values and a mean

Description

Generate random beta variates between 2 values and a mean

Usage

rbetaBetween(n, l, u, m, shape1, shape2 = NULL)
rbetaBetween(n, l, u, m, shape1, shape2 = NULL)

Arguments

`n`	number of observations. If `length(n) > 1`, the length is taken to be the number required.
`l`	scalar numeric for the lower bound
`u`	scalar numeric for the upper bound
`m`	scalar numeric for the mean
`shape1`	non-negative parameter of the Beta distribution
`shape2`	If provided, passed to `rbeta`. If not, `m` must be (i.e., the mean)

remove buffered fires in fireBufferedListDT that are outside RTM

Description

remove buffered fires in fireBufferedListDT that are outside RTM

Usage

removeBufferedFiresOutsideRTM(fireBufferedDT, flammableRTM)
removeBufferedFiresOutsideRTM(fireBufferedDT, flammableRTM)

Arguments

`fireBufferedDT`	data.table containing indices for buffered annual fires
`flammableRTM`	a `rasterToMatch` with binary values where 1 represents flammable pixels, 0 nonflammable, and NA no data

Value

fireBufferedDT excluding fires with indices (burned or unburned) outside flammableRTM

rescale function no.2

Description

rescale function no.2

Usage

rescaleKnown2(x, minNew, maxNew, minOrig, maxOrig)
rescaleKnown2(x, minNew, maxNew, minOrig, maxOrig)

Arguments

`x`	a vector to be rescaled
`minNew`	the minimum of the new range
`maxNew`	the max of the new range
`minOrig`	the minimum of the original data
`maxOrig`	the maximum of the original data

Value

the rescaled vector

Wrapper around `DEoptim` call

Description

Does the multiple cluster connections. This will only work if ssh keys are correctly made between machines (if using multiple machines).

Usage

runDEoptim(
  landscape,
  annualDTx1000,
  nonAnnualDTx1000,
  fireBufferedListDT,
  historicalFires,
  itermax,
  initialpop = NULL,
  NP = NULL,
  trace,
  strategy,
  cores = NULL,
  libPath = .libPaths()[1],
  logPath = tempfile(sprintf("fireSense_SpreadFit_%s_", format(Sys.time(),
    "%Y-%m-%d_%H%M%S")), fileext = ".log"),
  doObjFunAssertions = getOption("fireSenseUtils.assertions", TRUE),
  cachePath,
  iterStep = 25,
  lower,
  upper,
  mutuallyExclusive,
  FS_formula,
  objFunCoresInternal,
  covMinMax = covMinMax,
  tests = c("SNLL", "adTest"),
  maxFireSpread,
  Nreps,
  thresh = 550,
  .verbose,
  visualizeDEoptim,
  .plotSize = list(height = 1600, width = 2000)
)

DEoptimIterative(
  itermax,
  lower,
  upper,
  control,
  FS_formula,
  covMinMax,
  tests = c("SNLL", "adTest"),
  objFunCoresInternal,
  maxFireSpread,
  Nreps,
  visualizeDEoptim,
  cachePath,
  mutuallyExclusive,
  doObjFunAssertions = getOption("fireSenseUtils.assertions", TRUE),
  iterStep = 25,
  thresh = 550,
  .verbose,
  .plotSize = list(height = 1600, width = 2000)
)
runDEoptim(
  landscape,
  annualDTx1000,
  nonAnnualDTx1000,
  fireBufferedListDT,
  historicalFires,
  itermax,
  initialpop = NULL,
  NP = NULL,
  trace,
  strategy,
  cores = NULL,
  libPath = .libPaths()[1],
  logPath = tempfile(sprintf("fireSense_SpreadFit_%s_", format(Sys.time(),
    "%Y-%m-%d_%H%M%S")), fileext = ".log"),
  doObjFunAssertions = getOption("fireSenseUtils.assertions", TRUE),
  cachePath,
  iterStep = 25,
  lower,
  upper,
  mutuallyExclusive,
  FS_formula,
  objFunCoresInternal,
  covMinMax = covMinMax,
  tests = c("SNLL", "adTest"),
  maxFireSpread,
  Nreps,
  thresh = 550,
  .verbose,
  visualizeDEoptim,
  .plotSize = list(height = 1600, width = 2000)
)

DEoptimIterative(
  itermax,
  lower,
  upper,
  control,
  FS_formula,
  covMinMax,
  tests = c("SNLL", "adTest"),
  objFunCoresInternal,
  maxFireSpread,
  Nreps,
  visualizeDEoptim,
  cachePath,
  mutuallyExclusive,
  doObjFunAssertions = getOption("fireSenseUtils.assertions", TRUE),
  iterStep = 25,
  thresh = 550,
  .verbose,
  .plotSize = list(height = 1600, width = 2000)
)

Arguments

`landscape`	A `RasterLayer` which has the correct metadata associated with the `pixelID` and cells of other objects in this function call
`annualDTx1000`	A list of data.table objects. Each list element will be from 1 year, and it must be the same length as `fireBufferedListDT` and `historicalFires`. All covariates must be integers, and must be 1000x their actual values.
`nonAnnualDTx1000`	A list of data.table objects. Each list element must be named with a concatenated sequence of names from `names(annualDTx1000)`, e.g., `⁠1991_1992_1993⁠`. It should contain all the years in `names(annualDTx1000)`. All covariates must be integers, and must be 1000x their actual values.
`fireBufferedListDT`	A list of data.table objects. It must be same length as `annualDTx1000`, with same names. Each element is a `data.table` with columns: `buff`...TODO: INCOMPLETE
`historicalFires`	DESCRIPTION NEEDED
`itermax`	Passed to `DEoptim.control`
`initialpop`	DESCRIPTION NEEDED
`NP`	DESCRIPTION NEEDED
`trace`	Passed to `DEoptim.control`
`strategy`	Passed to `DEoptim.control`
`cores`	A numeric (for running on localhost only) or a character vector of machine names (including possibly "localhost"), where the length of the vector indicates how many cores should be used on that machine.
`libPath`	A character string indicating an R package library directory. This location must exist on each machine, though the function will make sure it does internally.
`logPath`	A character string indicating what file to write logs to. This `dirname(logPath)` must exist on each machine, though the function will make sure it does internally.
`doObjFunAssertions`	logical indicating whether to do assertions.
`cachePath`	The `cachePath` to store cache in. Should likely be `cachePath(sim)`
`iterStep`	Integer. Must be less than `itermax`. This will cause `DEoptim` to run the `itermax` iterations in `ceiling(itermax / iterStep)` steps. At the end of each step, this function will plot, optionally, the parameter histograms (if `visualizeDEoptim` is `TRUE`)
`lower`	Passed to `DEoptim`
`upper`	Passed to `DEoptim`
`mutuallyExclusive`	If there are any covariates, e.g,. youngAge, that should be considered mutually exclusive, i.e., "if youngAge is non-zero, should vegPC2 be set to zero", then this can be done here. A named list, where the name of the list element must be a single covariate column name in either `annualDTx1000` or `nonAnnualDTx1000`. The list content should be a "grep" pattern with which to match column names, e.g., `"vegPC"`. The values of all column names that match the grep value will be set to `0`, whenever the name of that list element is non-zero. Default is `list("youngAge" = list("vegPC"))`, meaning that all columns with `vegPC` in their name will be set to zero wherever `youngAge` is non-zero.
`FS_formula`	Passed to `DEoptim`
`objFunCoresInternal`	DESCRIPTION NEEDED
`covMinMax`	Passed to `fireSenseUtils::.objfunSpreadFit`
`tests`	Passed to `fireSenseUtils::.objfunSpreadFit`
`maxFireSpread`	Passed to `fireSenseUtils::.objfunSpreadFit`
`Nreps`	Passed to `fireSenseUtils::.objfunSpreadFit`
`thresh`	Threshold multiplier used in SNLL fire size (SNLL_FS) test. Default 550.
`.verbose`	Passed to `fireSenseUtils::.objfunSpreadFit`
`visualizeDEoptim`	Logical. If `TRUE`, then histograms will be made of `DEoptim` outputs.
`.plotSize`	List specifying plot `height` and `width`, in pixels.
`control`	DESCRIPTION NEEDED

Value

DESCRIPTION NEEDED

prepare covariate table with ignition year, fuel class, climate value, and land cover

Description

prepare covariate table with ignition year, fuel class, climate value, and land cover

Usage

stackAndExtract(years, fuel, LCC, climate, fires)
stackAndExtract(years, fuel, LCC, climate, fires)

Arguments

`years`	character vector of fire years with FS notation e.g. year2002
`fuel`	raster brick of aggregated fuel classes
`LCC`	raster brick of aggregated LCC classes
`climate`	list of raster layers named by climate variable with raster layer names matching `years`
`fires`	list of spatial points representing annual ignitions

Value

a data.frame with cell numbers, ignitions, and covariates for each year

Update name of layers in a climate raster stack

Description

Update name of layers in a climate raster stack

Usage

updateStackYearNames(annualDataStack, desiredYears)
updateStackYearNames(annualDataStack, desiredYears)

Arguments

`annualDataStack`	`RasterStack`
`desiredYears`	character

Make histograms of `DEoptim` object `pars`

Description

Make histograms of DEoptim object pars

Usage

visualizeDE(DE, cachePath)
visualizeDE(DE, cachePath)

Arguments

`DE`	An object from a `DEoptim` call
`cachePath`	A `cacheRepo` to pass to `showCache` and `loadFromCache` if `DE` is missing.

Package 'fireSenseUtils'

Help Index

fireSenseUtils package

Description

Author(s)

See Also

Objective function when no piecewise model is used

Description

Usage

Arguments

Value

Function to pass to the optimizer - Piece-wise version

Description

Usage

Arguments

Value

Objective function for fireSense_spreadFit module

Description

Usage

Arguments

Value

Convert list of annual SpatRaster to data.table

Description

Usage

Arguments

Value

Examples

buffer ignition points to create non-ignitions for model

Description

Usage

Arguments

Value

Create buffers around polygons based on area target for buffer

Description

Usage

Arguments

Value

create a variable sized buffer around a set of pixels belonging to the same fire ID

Description

Usage

Arguments

Value

Modify cohortData with burn column

Description

Usage

Arguments

Value

Generate, Summarize, Predict Burn Classes from Covariates

Description

Usage

Arguments

Details

Value

The algorithm

Categorical data

How much data to include

Author(s)

Examples

Iteratively calculate youngAge column in FS covariates

Description

Usage

Arguments

Value

preparing covariates for fitting modules

Description

Usage

Arguments

Value

Data checks and assertions for spreadFitRun

Description

Usage

Arguments

Value

Ensure fire points are located on flammable pixels inside a fire polygon Intended to be run using Map

Description

Usage

Arguments

Value

Converts stacks of climate rasters to data.table and optionally subsets to index

Description

`fireSenseUtils` package

Objective function for `fireSense_spreadFit` module

Convert list of annual SpatRaster to `data.table`

Iteratively calculate `youngAge` column in FS covariates

Data checks and assertions for `spreadFitRun`

Ensure fire points are located on flammable pixels inside a fire polygon Intended to be run using `Map`

Classify `pixelGroups` by flammability

Replace `NA`s in a `data.table` with zeros

Get Fire `SpatialPoints` from Canadian Fire Database

Get Fire `SpatialPoints` from Canadian Fire Database

Outer wrapper on spread fire polygon data munging that does several things:

ensure buffered fires are entirely in studyArea

ensure every fire has a corresponding ignition point, and vice versa

ensure these points are flammable

Convert a list of `SpatialPointsDataFrame` object to a list of `data.table` objects