title	fireSense_spreadFit Manual
subtitle	v.1.2.0
date	Last updated: 2026-10-08
output	bookdown::html_document2 toc toc_float theme number_sections df_print keep_md true true sandstone false paged true
editor_options	chunk_output_type console
bibliography	citations/references_fireSense_spreadFit.bib
link-citations	true
always_allow_html	true

fireSense_spreadFit Module

(ref:fireSense-spreadFit) fireSense_spreadFit

Authors:

Eliot McIntire eliot.mcintire@nrcan-rncan.gc.ca [aut, cre], Tati Micheletti tati.micheletti@gmail.com [aut], Ian Eddy ian.eddy@nrcan-rncan.gc.ca [aut], Jean Marchal jean.d.marchal@gmail.com [aut], Alex M. Chubaty achubaty@for-cast.ca [ctb]

Module Overview

Module summary

Fit statistical models that can be used to parameterize the fire spread component of simulation models (e.g., fireSense [@Marchal:2017a; @Marchal:2017b; @Marchal:2019]). This module implement a Pattern Oriented Modelling (POM) approach to derive spread probabilities from final fire sizes. Spread probabilities can vary between pixels, and thus reflect local heterogeneity in environmental conditions.

The fit is a differential evolution search (DEoptim, run by fireSenseUtils::runDEoptim() on a cluster built by the clusters package). Each candidate parameter set is scored by simulating the historical fires and comparing simulated with observed fire sizes (fireSenseUtils::.objfunSpreadFit()). The 5 best parameter sets, and the covariate ranges used to rescale the covariates, are written as one row per polygon to a shared "fit ledger" on Google Drive (spreadFitFilename in spreadFitGoogleDriveFolder), keyed by .ELFind. If the ledger already holds a row for the polygon, the module does nothing unless refitExisting = TRUE. By default (stopIfNoPreRunFit = TRUE) the module stops rather than start a fit; set it to FALSE to fit.

refitExisting

refitExisting = TRUE forces a fit for this polygon even when the ledger already holds a row for it. Use it when the fit's INPUTS have changed -- new land cover, new vegetation parameters, a new objective function -- so the stored row is stale and the polygon must be fitted again.

refitExisting overrides stopIfNoPreRunFit: with refitExisting = TRUE, init schedules the fit rather than stopping, whatever stopIfNoPreRunFit is set to. Setting stopIfNoPreRunFit = FALSE is only needed when the polygon has no ledger row.

refitExisting is intended for developers who have access to at least 40 cores: it triggers a full DEoptim run (see cores and nCoresNeeded), which is not practical on a small machine.

Module inputs and parameters

The covariate tables, fire buffers, fire points and formula are made by fireSense_dataPrepFit. fireSense_spreadFormula must be supplied; .ELFind defaults to .runName.

Table @ref(tab:moduleInputs-fireSense-spreadFit) shows the full list of module inputs.

(\#tab:moduleInputs-fireSense-spreadFit)(\#tab:moduleInputs-fireSense-spreadFit)List of (ref:fireSense-spreadFit) input objects and their description.

objectName	objectClass	desc	sourceURL
.runName	character	Some descriptive, short name for this fitting, e.g., ELF14.1	NA
.ELFind	character	Identifier of the polygon being fit, e.g. '6.1.1'. This becomes the `polygonID` of the row this module writes to the shared cloud fit ledger (`spreadFitFilename` in `spreadFitGoogleDriveFolder`), which `fireSense_dataPrepFit` matches against the polygon ids carried by `rasterToMatchELF`. It must therefore be the polygon's identity, not a run label: `.runName` encodes the whole scenario (climate period, GCM, SSP, rep) in some projects, and keying the ledger on it writes rows no other run can find and trips dataPrepFit's id match. Defaults to `.runName` for backwards compatibility.	NA
fireBufferedListDT	list	list of data.tables with fire id, pixelID, and buffer status	NA
fireSense_annualSpreadFitCovariates	data.table	table of climate and/or veg covariates, burn status, polyID, and pixelID	NA
fireSense_nonAnnualSpreadFitCovariates	data.table	table of veg covariates, burn status, polyID, and pixelID	NA
spreadFitAdditionalColNames	character	Names of the list-columns of the ledger row. Reset to `fireSenseUtils::spreadFitAdditionalColNamesTxt` if different.	NA
fireSense_spreadFormula	character	a formula that contains the annual and non-annual covariates e.g. `~ 0 + MDC + class2 + class3 + youngAge`.	NA
parsKnown	numeric	Optional vector of known parameters, e.g., from a previous `DEoptim` run. If this is supplied, then 'mode' will be automatically converted to 'debug'	NA
rasterToMatch	SpatRaster	template raster for study area	NA
spreadFirePoints	sf	list of `sf` points, one element per year, of fire ignition locations	NA
studyAreaWithSpreadParams	sf	Rows of the shared fit ledger, set by `fireSense_dataPrepFit`; `init` checks them for a fit of this polygon. Declared as an input so the event's cache key includes it, otherwise a cache hit restores an older copy over the current rows.	NA
studyArea	sf	Polygon being fit; its geometry and crs go in the ledger row. Defaults to NWT.	https://drive.google.com/open?id=1LUxoY2-pgkCmmNH5goagBp3IMpj6YrdU

Summary of user-visible parameters (Table @ref(tab:moduleParams-fireSense-spreadFit))

(\#tab:moduleParams-fireSense-spreadFit)(\#tab:moduleParams-fireSense-spreadFit)List of (ref:fireSense-spreadFit) parameters and their description.

paramName	paramClass	default	min	max	paramDesc
.plots	characte....		NA	NA	Plot types passed to `Plots()`, e.g. 'png' or 'screen'; NULL or NA for none.
.plotInterval	numeric	25	NA	NA	DEoptim generations between DEoptim progress figures; the final figures are always drawn. Passed to `fireSenseUtils::runDEoptim()` as `plotEvery`.
.plotSize	list	1600, 2000	NA	NA	List specifying height and width of plotting device (in pixels) used to plot DEoptim histograms when `visualizeDEoptim` is TRUE.
.runInitialTime	numeric	0	NA	NA	when to start this module? By default, the start time of the simulation.
.studyAreaName	character	NA	NA	NA	Human-readable name for the study area used.
.useCache	logical,....	init	NA	NA	Should this entire module be run with caching activated? This is generally intended for data-type modules, where stochasticity and time are not relevant.
cores	integer	1	NA	NA	Passed to `cores` in `fireSenseUtils::runDEoptim()`: a number of local cores, or a character vector of machine names, one element per core wanted on that machine.
DEoptimTests	character	adTest, ....	NA	NA	Currently either `'SNLL_FS'` or `'adTest'` or a length 2 character vector of both. Passed to `tests` in `fireSenseUtils::.objfunSpreadFit()`.
doObjFunAssertions	logical	TRUE	NA	NA	Passed to `fireSenseUtils::.objfunSpreadFit()`; TRUE runs diagnostics but is slower; FALSE for operational runs
initialpop	numeric		NA	NA	A numeric matrix of dimensions `NCOL = length(lower)` and `NROW = NP`. This will be passed into DEoptim through `control$initialpop = P(sim)$initialpop` if it is not NULL
iterDEoptim	integer	5000	NA	NA	integer defining the maximum number of iterations allowed (DEoptim optimizer). A ceiling: clusters (>= 0.0.46) stops the fit earlier, once the population's median value has stopped improving.
iterThresh	integer	96	NA	NA	Number of random parameter sets tried when calibrating `SNLL_FS_thresh`.
thresholdMargin	numeric	2	NA	NA	When calibrating `SNLL_FS_thresh`, the threshold is this multiple of the best usable trial's first-block average annual SNLL (trials run with no early stop; a trial that saturates spreadProb is not usable). Must be >= 1.
libPathDEoptim	character	/home/ru....	NA	NA	Absolute path specifying R package directory location to use when running DEotpim. NOTE: this path must be read/write accessible on ALL machines used for fitting (identified in cores). Therefore, it's best use a directory in your user's `~` directory. If the directory does not exist at this path, will attempt to create it.
lower	numeric	NA	NA	NA	see `?DEoptim`. Lower limits for the logistic function parameters (maxAsymptote, then upperTail1 if `link` is 'logistic3pUpper') and the statistical model parameters (named in the order they appear in the formula). Do not include `hillSlope1` or `inflectionPoint1`: they are fixed at 1, not fitted (see `estimateSpreadParams()`); supplying either is an error.
maxFireSpread	numeric	0.276	NA	NA	optional. Maximum fire spread average to be passed to the `.objFun`; default `fireSenseUtils::spreadProbCeiling`, also the upper bound of `maxAsymptote`. `maxAsymptote` is the spread-probability ceiling in a typical year. The year random effect (`yearSpreadSD`) is added on the logit of the final spread probability, after the link, so in a given year p can exceed `maxAsymptote` or fall below `lowerSpreadProb`; that is intended, and there is no absolute cap because `spreadCpp` does not need one. `maxAsymptote` is bounded because runaway fires are slow to simulate and wasted if the parameters are wrong.
link	character	logistic3p	NA	NA	The spread link. 'logistic3p', or 'logistic3pUpper': the same curve with Stukel's upper tail, one more parameter `upperTail1` that changes only how the curve approaches its ceiling (`fireSenseUtils::logistic3pUpper()`). Its default bounds are `upperTailBounds`.
mode	character	fit	NA	NA	Options: debug, fit, visualize, validate. Can use multiples. 'debug' runs the objective function with visuals instead of DEoptim; 'fit' runs DEoptim; 'visualize' adds the `debug` and `plot` events after the fit; 'validate' adds `crossValidate`, two more fits, each on half the years, predicting the other half (`sim$spreadFitHeldOut`). Validation never writes the ledger, but does write `sim$spreadFitHeldOut` to `outputPath(sim)`, since a batch run typically stops after `crossValidate` and the simList is discarded. See `heldOutFold` to run a single fold as its own job instead of both folds together.
heldOutFold	integer	NA	NA	NA	NA (default): unchanged behaviour, governed by `mode`. `1` or `2`: run ONLY that cross-validation fold, as its own job. `init` then schedules `spreadFitPrepare`, `estimateThreshold` and `crossValidate` -- never `run`, so the full fit and the ledger write never happen, and the ledger (`stopIfNoPreRunFit`/`refitExisting`) is not consulted. `crossValidate` fits on the OTHER fold's years and scores this fold's held-out years (`cvFolds()` in `R/fitSpread.R`), and writes `spreadFitHeldOut_<.runName>_fold<heldOutFold>.rds` instead of `spreadFitHeldOut_<.runName>.rds`. A run script stops after `crossValidate`: `events = list(.stopAfter = list(fireSense_spreadFit = "crossValidate"))`. Any other value is an error.
profileReps	integer	10	NA	NA	After the fit, each covariate coefficient in turn is set to 0 and to 5 values across the final population, the others held at the best member, and each point is evaluated this many times (`fireSenseUtils::profileCoefficients()`). About `6 nCoefficients profileReps` evaluations, on the fit's workers. It decides which coefficients are identified in isolation (`sim$spreadFitIdentifiability`). 0 skips it.
mutuallyExclusiveCols	list	c("class....	NA	NA	a named list of mutually exclusive covariates - see `fireSenseUtils::makeMutuallyExclusive`
nCoresNeeded	integer		NA	NA	How many workers to request for the DEoptim cluster. This IS the population size: `clusters::clusterSetup()` sets NP to the workers it builds. `NULL` leaves `fireSenseUtils::runDEoptim()`'s default of 10 per estimated parameter. A generation costs the slowest of NP evaluations and that barely falls as NP falls, so a smaller NP buys throughput by allowing more fits at once rather than by shortening generations (measured 2026-09-16).
simulateMembers	integer	10	NA	NA	After the fit, this many best members simulate the observed fires, for `sim$spreadFitSizes` and `sim$spreadFitLinkSaturation`; also the members each `crossValidate` fold predicts with. 0 skips it after the fit.
sizeLik	character	t	NA	NA	Likelihood of fire size in the objective, 'kde' or 't', passed to `fireSenseUtils::runDEoptim()`. 't' with `weighted = FALSE` predicted held-out years best in the 2026-09-21 cross-validation.
escapeSizeHa	numeric	50	NA	NA	Size (ha) a fire must reach to count as escaped. The spread model is fitted to escaped fires only, and each simulated fire first grows to this size with its own spread probabilities (its burning cells stay active until it gets there), then spreads normally. `NULL` or `NA` gives the old fit (any fire over 1 pixel). Passed to `fireSenseUtils::runDEoptim()`.
sizeLikDf	numeric	5	NA	NA	Degrees of freedom of the 't' size likelihood.
weighted	logical&#124;....	FALSE	NA	NA	Weight of each fire in the size likelihood: FALSE (none), TRUE (log size) or 'sqrt'. Passed to `fireSenseUtils::runDEoptim()`.
adWeight	characte....	auto	NA	NA	Weight of the Anderson-Darling term against the size likelihood; 'auto' is `fireSenseUtils::adWeightAuto()`.
yearAreaWeight	numeric&#124;....	auto	NA	NA	Weight of the annual-area term in the objective: each fit year's observed area burned is scored against that year's simulated totals (one per replicate) with the size likelihood. 0 leaves it out; 'auto' (default) is (number of fitted fires) / (number of fit years), so the year view and the per-fire view weigh the same. Passed to `fireSenseUtils::runDEoptim()`.
areaDistWeight	numeric&#124;....	auto	NA	NA	Weight of the area-weighted size-distribution term: simulated and observed fires compared by the share of area burned that fires up to each size make up (`fireSenseUtils::areaWeightedCvM()`). 0 leaves it out; 'auto' (default) uses the Anderson-Darling term's weight (`fireSenseUtils::adWeightAuto()`).
penaliseRunaways	logical	TRUE	NA	NA	A simulated fire that burns its buffer's outer edge (see `runawayEdgeFrac`) is a runaway, censored in the size likelihood: it has no density at the observed size. The Anderson-Darling, annual-area and area-distribution terms score the size it burned. Fires are not capped at a size; spread is bounded by the buffers. FALSE scores the simulated size in the likelihood too. Passed to `fireSenseUtils::runDEoptim()`; the threshold calibration uses the same setting.
runawayEdgeFrac	numeric	0.01	NA	NA	A simulated fire is a runaway when it burns at least `max(runawayEdgeMin, ceiling(runawayEdgeFrac n))` of the `n` pixels of the edge ring of its own buffer (never more than `n`); one touched pixel is luck. Passed to `fireSenseUtils::runDEoptim()`. Not part of the DEoptim cache key.
runawayEdgeMin	integer	3	NA	NA	The least number of edge-ring pixels that makes a fire a runaway; see `runawayEdgeFrac`.
jumpTries	numeric	20	NA	NA	With `escapeSizeHa`: how many attempts a simulated fire that is still below the escape size, with no burnable neighbour left, may make to jump to burnable land nearby (`SpaDES.tools::spreadCpp()`). Default 20; 0 is off.
jumpMeanDist	numeric	3	NA	NA	Mean jump distance (pixels) for `jumpTries`; distances are exponential, truncated to 1.5-20 pixels. No effect while `jumpTries` is 0.
objFunCoresInternal	integer	1	NA	NA	Integer defining the number of cores to pass to `mcmapply(mc.cores = ...)` This will fork this many to do the years loop internally. This would be in addition to `cores` and is effecively a multiplier. The computer needs to have `cores objFunCoresInternal` threads or it will stall.
objfunFireReps	integer	50	NA	NA	integer defining the number of replicates the objective function will attempt each fire.
.rep	integer	1	NA	NA	An optional integer indicating which replicate run this represents. This is used to identify unique runs of `runDEoptim`, from a Cache perspective. For example, if this module is run twice with all the same data, Cache will think that the second run should recover the cache result, unless this `.rep` is modified. A SpaDES-aware parameter: `SpaDES.project::setupProject()` sets `.globals$.rep` from the experiment's `.rep`.
.c	numeric	0	NA	NA	the `c` argument passed to DEoptim.control. `iterStep` is hard-coded to 1, so DEoptim's adaptation restarts every generation, and `c` has no effect either way.
DEoptimControl	list	0.1	NA	NA	Further `DEoptim.control()` settings, e.g. `list(CR = 0.7, F = 0.6)`, passed through `fireSenseUtils::runDEoptim()` to DEoptim. Names must be `DEoptim.control()` arguments. `strategy`, `trace`, `initialpop` and `.c` have their own parameters; `NP` is the number of workers the cluster gets. The default `p = 0.1` is for `strategy = 6`.
rescaleAll	logical	TRUE	NA	NA	rescale covariates for `DEOptim`
spreadFitGoogleDriveFolder	character	https://....	NA	NA	Google Drive folder url holding the shared fit ledger (`spreadFitFilename`).
spreadFitFilename	character	latest	NA	NA	File name of the shared fit ledger: an `sf` object with one row of fitted parameters per polygon. `"latest"` (the default) writes to the file named for this fit's fire years and model, `fireSenseUtils::spreadFitFilenameFor()`, e.g. `fireSenseParams_1985-2024_linearFuel.rds`; readers then find it with `fireSenseUtils::latestSpreadFits()`.
strategy	integer	6	NA	NA	Passed to `DEoptim.control`. 6 (DE/current-to-p-best/1) with `p = 0.1` did best in a settings study (see NEWS).
SNLL_FS_thresh	integer		NA	NA	Threshold multiplier used in objective function SNLL fire size test.
refitExisting	logical	FALSE	NA	NA	FOR DEVELOPERS ONLY: a re-fit is a full DEoptim run and is only practical with access to at least 40 cores. Fit this polygon even when the ledger already holds parameters for it. A ledger row normally means the fit is done, and the run event skips it. Set this when the fit's INPUTS have changed -- new land cover, new vegetation parameters, a new objective -- so the stored row is stale and the polygon must be fitted again. When TRUE it OVERRIDES `stopIfNoPreRunFit`: `init` schedules the fit instead of stopping.
stopIfNoPreRunFit	logical	TRUE	NA	NA	If TRUE, `init` stops with an error when this polygon would have to be fitted, instead of fitting it. Ignored when `refitExisting` is TRUE.
trace	numeric	1	NA	NA	non-negative integer. If > 0, tracing information on the progress of the optimization are printed every `trace` iteration. Default is 1, i.e. every iteration. Setting to 0 turns off tracing.
upper	numeric	NA	NA	NA	see `?DEoptim`. Upper limits for the logistic function parameters (maxAsymptote, then upperTail1 if `link` is 'logistic3pUpper') and the statistical model parameters (named in the order they appear in the formula). Do not include `hillSlope1` or `inflectionPoint1`: they are fixed at 1, not fitted (see `estimateSpreadParams()`); supplying either is an error.
useCache_DE	logical	TRUE	NA	NA	should `DEoptim` use `Cache`? to do multiple independent runs, use FALSE
verbose	numeric	1	NA	NA	optional. With increasing number, more verbosity. Level 1 is normal reproducible (e.g., Cache), level 2 includes objective function e.g., print median of spreadProb during calculations
visualizeDEoptim	Path	/tmp/Rtm....	NA	NA	Directory where `runDEoptim` saves parameter plots every `.plotInterval` generations. Reset to `figurePath(sim)` unless its last folder is the module name.
covFixedRange	list	c(0, 100....	NA	NA	Named list of `c(min, max)`: the FIXED range every climate covariate is rescaled with, not the range of this polygon's data. Default `fireSenseUtils::climateCovRanges`, the one table of climate ranges, which documents each variable's units and says its values are provisional. `CMDsm = c(0, 100)` makes the covariate CMDsm / 100 in every polygon. With the data's range, 1 meant a CMDsm of 104 in one polygon and 297 in another, so the coefficient could not be compared across polygons, and a polygon that never gets dry stretched its small range over [0, 1]. A climate covariate with no entry stops the fit; there is no fallback to the data's range. `youngAge`, the `nfLCC_ ` groups and the `treedWetland` indicator are always `c(0, 1)` (`fireSenseUtils::spreadIndicatorRanges()`). `fireSense_spreadPredict` rescales with the stored `covMinMax_spread`, so it follows.
yearSpreadSDBounds	numeric	0, 1	NA	NA	Bounds of `yearSpreadSD`, the sd of a per-year random effect on logit spread probability (`fireSenseUtils::.objfunSpreadFit()`), when `lower`/`upper` are not supplied. A seasonal departure: each year draws one eps, so all of a year's fires burn hotter or cooler together, which widens the simulated fire-size distribution. `NA` turns it off.
upperTailBounds	numeric	-1, 1	NA	NA	Bounds of `upperTail1` when `link` is 'logistic3pUpper' and `lower`/`upper` are not supplied.
upperAndLowerVal	numeric	50	NA	NA	Bound given to each covariate coefficient (`upper` = this, `lower` = minus this) when `upper` or `lower` is not supplied. Bounds should be wide enough that they do not influence the fitted value; only the sign of drought-index, `youngAge` and fuel biomass terms is constrained (see `estimateSpreadParams()`). A held-out experiment (7 ELFs x 2 folds) found estimates up to 25.7 and youngAge medians down to -23.0 with the previous default of 9.
upperAndLowerValFuel	numeric	100	NA	NA	As `upperAndLowerVal`, for the fuel biomass covariates. They are biomass / 1e4, so their coefficients are larger than those of covariates rescaled to [0, 1]. The same held-out experiment found fuel estimates up to 54.5 (29 of 82 above 25) with the previous default of 60.

Events

• init: schedules spreadFitPrepare and, if the polygon needs a fit, estimateThreshold then run (or debug when mode includes "debug"; debug and plot after run when it includes "visualize");
• spreadFitPrepare: sets default lower/upper, computes covMinMax_spread and lociList, converts covariates to integers (x 1000);
• estimateThreshold: uses SNLL_FS_thresh, or calibrates it from iterThresh random parameter sets (cached, with a seed derived from .ELFind);
• run: runs DEoptim (cached per generation) and writes the polygon's row to the ledger;
• debug: evaluates the objective function without DEoptim;
• plot: histograms of the final population;

Plotting

With .plots set, histograms of the annual and non-annual covariates. During the fit, runDEoptim() saves parameter histograms and trace plots to visualizeDEoptim after each generation.

Saving

The fit's row in the cloud ledger, and the DEoptim cache entries. Nothing else is saved.

Module outputs

Description of the module outputs (Table @ref(tab:moduleOutputs-fireSense-spreadFit)).

(\#tab:moduleOutputs-fireSense-spreadFit)(\#tab:moduleOutputs-fireSense-spreadFit)List of (ref:fireSense-spreadFit) outputs and their description.

objectName	objectClass	desc
covMinMax_spread	data.table	`data.table` of covariates min and max
covCentre_spread	list	Named list, the mean of each rescaled covariate over the fitting data (`fireSenseUtils::spreadCovCentre()`), subtracted from it in the fit; `NULL` unless the formula has an intercept. Stored in the ledger row as `fireSenseUtils::spreadFitCovCentreTxt`, so `fireSense_spreadPredict` centres alike.
DE	data.table	list of `DEoptim` objects, one per generation, ordered by best objective value
studyAreaWithSpreadParams	sf	Rows of the shared fit ledger that intersect `studyArea`, including the row this fit writes: `studyArea` geometry, `polygonID`, and list-columns named by `spreadFitAdditionalColNames` (the 5 best parameter sets are in `params`).
fsSpreadFit_hists	ggplot	histograms of each parameter used in `DEoptim` fitting.
lociList	list	per-year `data.table`s of fire start cells and sizes, from `fireSenseUtils::makeLociList()`
spreadFitConvergence	data.table	The objective across the fit's generations (`fireSenseUtils::fitConvergence()`).
spreadFitRescore	data.table	The final population, one row per member, with the mean and sd of its replicated re-scores (`reMean`, `reSD`). The ledger's parameter sets are the best of these.
spreadFitIdentifiability	data.table	One row per covariate coefficient: how tightly the population pins it (`fireSenseUtils::coefIdentifiability()`) and, with `profileReps > 0`, whether dropping it worsens the fit; `identified` = identified in isolation (`fireSenseUtils::identifiedInIsolation()`).
spreadFitProfile	data.table	The one-at-a-time profile around the best member (`fireSenseUtils::profileCoefficients()`).
spreadFitSizes	data.table	Observed against simulated fire sizes of the fitted years (`fireSenseUtils::scoreFireSizes()`): bias, error, quantiles.
spreadFitLinkSaturation	data.table	Per member, the share of pixel-years at the spread-probability ceiling and the quantiles of spread probability (`fireSenseUtils::linkSaturation()`).
spreadFitHeldOut	list	mode 'validate', or `heldOutFold` in `1:2`: `sims`, the held-out years simulated from the fit to the other years (column `fold`), and `score`, from `fireSenseUtils::scoreFireSizes()`. With `heldOutFold`, `sims` holds only that fold, and the list also has `fit` (the fold's fitted parameters as a one-row ledger `sf` object, the same columns the `run` event writes, with all `simulateMembers` members in `params`), `heldOutFold`, `fitYears`, `heldOutYears`, `formula` (`fireSense_spreadFormula`) and `link`, so `fireSense_spreadPredict` can predict with the fold's fit. Also written to `file.path(outputPath(sim), currentModule(sim), "spreadFitHeldOut_<.runName>.rds")` (mode 'validate') or `"...spreadFitHeldOut_<.runName>_fold<heldOutFold>.rds"` (`heldOutFold`).

Links to other modules

Inputs come from fireSense_dataPrepFit. fireSense_spreadPredict uses studyAreaWithSpreadParams (the ledger rows) to predict spread probabilities.

Usage

## in a project that also runs fireSense_dataPrepFit
params <- list(fireSense_spreadFit = list(
  stopIfNoPreRunFit = FALSE,          # allow a fit to start
  cores = rep(c("hostA", "hostB"), each = 20), # host name repeated once per worker; or a number for localhost
  nCoresNeeded = 40,                  # = DEoptim population size (NP)
  iterDEoptim = 500,
  mode = "fit"
))
objects <- list(.ELFind = "6.1.1")    # polygon id used as the ledger key

Getting help

• https://github.com/PredictiveEcology/fireSense_spreadFit/issues

References