A single fitted ELF now rescales its covariates with the covMinMax_spread of its ledger row, as several ELFs already did, not with sim$covMinMax_spread, which fireSense_spreadFit rebuilds in every run from that run’s tables (in a hindcast, from a formula rebuilt for another year, which can lack a fitted term). A row without ranges (an older ledger) still uses sim$covMinMax_spread. A fitted covariate with no range now stops, naming it; before, it silently stayed on its raw scale.
A single fitted ELF now predicts with the covariates its fitted parameters name, as several ELFs already did, not the terms of sim$fireSense_spreadFormula. A hindcast whose formula had a term the fit did not (e.g. treedWetland_agb) stopped with “not found in data objects”. Predictions where the formula matched the fit do not change. A fitted coefficient with no covariate still stops, naming it.
This release lets fire spread be forecast across study areas that contain several fire regions, each with its own fitted model, with predictions blended smoothly across the boundaries between them. Each simulation replicate now uses one complete set of fitted values instead of an average of all of them, so the spread of results across replicates reflects the uncertainty in the fit. The fitted year-to-year variation in fire spread is passed on to the fire simulation.
Forecasts now follow the same rules as the fit for young forest and for the types of models the fitting module can produce, and the module stops with a clear message when a fitted model and the forecast data do not match, instead of failing with a cryptic error. It is renamed fireSense_spreadPredict (lower-case “s”), so projects need to update the name they use for it.
lowerSpreadProb and maxFireSpread default to fireSenseUtils::spreadProbFloor (0.13) and fireSenseUtils::spreadProbCeiling (0.276; maxFireSpread was 0.28), the one floor and ceiling fireSense_spreadFit and the objective use. maxFireSpread does not change a prediction. Needs the fireSenseUtils change in PredictiveEcology/fireSenseUtils (floor to be set once it has a version).A ledger row from a fit with an intercept (a parameter named fireSenseUtils::spreadInterceptTxt, from fireSense_dataPrepFit’s spreadIntercept) is predicted with it: spreadProbOneELF() adds a column of 1s for it after rescaling and centring, and subtracts the row’s stored covariate centres (fireSenseUtils::spreadFitCovCentreTxt, ledgerCovCentre()) from the rescaled covariates, as the fit did. With several ELFs each row uses its own. A row without them, such as every row written so far, predicts exactly as before. Needs fireSenseUtils >= 0.2.3.9083 (PredictiveEcology/fireSenseUtils#131).
Predictions no longer average parameter sets (spreadProbOneELF() took rowMeans() of one map per row, and yearSpreadSDOf() the mean yearSpreadSD, which broke the pairing of each set’s yearSpreadSD with its own coefficients). New parameter .rep (integer, default 1): replicate .rep uses the one whole parameter set ((rep - 1) %% number of sets) + 1 of each ELF, and fireSense_SpreadSD is that set’s yearSpreadSD. .rep is a SpaDES-aware parameter: SpaDES.project::setupProject() sets .globals$.rep from the experiment’s .rep (PredictiveEcology/SpaDES.project#190), so nothing needs wiring by hand.
reqdPkgs now lists data.table, which the module calls (copy, data.table, setDT) but did not list. Version 1.1.2.
spreadProbOneELF() (fireSense_spreadPredict.R:266-323 pre-fix) went on when a fitted coefficient had no covariate column, and died in rowMeans(spreadProbMat) with “‘x’ must be an array of at least two dimensions” (a predict-only run whose fit had nfLCC_100_60 and nfLCC_40_50_80 but whose covariates had a single nf). It now stops at once, naming the coefficients without a covariate and the covariates available, and says the non-forest groups / fuel classes differ from the fit’s. Version 1.1.1.
Renamed from fireSense_SpreadPredict to fireSense_spreadPredict (module naming convention <model>_<camelCaseComponent>); projects must rename the module and its params key. Version 1.1.0.
spreadProbOneELF() (fireSense_SpreadPredict.R:282 pre-fix) called fireSenseUtils::spreadProbFromIntegerCovs() with mutuallyExclusive = NULL, so a young pixel’s fuel biomass and non-forest land-cover columns reached the logistic unchanged instead of being zeroed with youngAge, as the fit requires. Prediction now derives the same youngAge-exclusivity rule the fit uses, via the new fireSenseUtils::youngAgeExclusiveCols(). Requires fireSenseUtils@development (>= 0.2.3.9048). Version 1.0.0.9006.
New parameter .studyAreaName (default NA), the name PredictiveEcology modules use for the study area. This module does not use it yet.
The fitted per-year random effect (yearSpreadSD, fireSense_SpreadFit / fireSenseUtils >= 0.2.3.9041) is no longer read as a covariate coefficient: with it, a single ELF treated it as a fourth logistic parameter and several ELFs stopped with “‘yearSpreadSD’ not found”. It becomes the new output fireSense_SpreadSD, which fireSense scales one draw per year by: one number with one ELF (the mean over retained parameter sets), a raster blended with the spread probabilities’ weights with several. 0 when the fit has none.
Several fitted ELFs in one study area. Every pixel gets a spread probability: each ELF’s model (its parameter sets, its covMinMax_spread and only the covariates it was fitted with, from its ledger row) predicts its own pixels and those within ELFblendWidth (default 20 km) of them, and overlapping predictions are averaged with weights falling linearly from 1 inside an ELF to 0 at ELFblendWidth outside it. Each pixel’s ELF comes from the new input rasterToMatchLargeELF (fireSense_ELFs with a studyAreaLarge). One ELF works as before.
A fit made with fireSense_SpreadFit’s link = "logistic3pUpper" stores upperTail1; prediction uses the upper-tail link for it, chosen by the parameter’s name (fireSenseUtils::logisticAll()). Needs fireSenseUtils >= 0.2.3.9038.
First release from development since master was last updated (2021-01-27). Full history: https://github.com/PredictiveEcology/fireSense_SpreadPredict/compare/a5b41f9…v1.0.0
dataFireSense_SpreadPredict (RasterLayer, RasterStack).spreadPredictedProbability (list).fireSense_SpreadPredicted is now SpatRaster (was RasterLayer, RasterStack).data, mapping, modelObjName, typesOfFuel.