# Format input data, and select pseudo-absences if wanted, for usage in `biomod2` This function gathers together all input data needed (**xy, presences/absences, explanatory variables, and the same for evaluation data if available**) to run `biomod2` models. It allows to select pseudo-absences if no absence data is available, with different strategies (see Details). ```r BIOMOD_FormatingData( resp.name, resp.var, expl.var, dir.name = ".", resp.xy = NULL, eval.resp.var = NULL, eval.expl.var = NULL, eval.resp.xy = NULL, PA.nb.rep = 0, PA.nb.absences = 1000, PA.strategy = NULL, PA.dist.min = 0, PA.dist.max = NULL, PA.sre.quant = 0.025, PA.fact.aggr = NULL, PA.user.table = NULL, na.rm = TRUE, filter.raster = FALSE, seed.val = NULL ) ``` ## Arguments - `resp.name`: a `character` corresponding to the species name - `resp.var`: a `vector`, a `SpatVector` without associated data (**if presence-only**), or a `SpatVector` object containing binary data (`0` : absence, `1` : presence, `NA` : indeterminate) for a single species that will be used to build the species distribution model(s) **Note that old format from `sp` are still supported such as `SpatialPoints` (if presence-only) or `SpatialPointsDataFrame` object containing binary data.** - `expl.var`: a `matrix`, `data.frame`, `SpatVector` or `SpatRaster` object containing the explanatory variables (in columns or layers) that will be used to build the species distribution model(s) **Note that old format from `raster` and `sp` are still supported such as `RasterStack` and `SpatialPointsDataFrame` objects.** - `dir.name`: (**optional, default** `.`) A `character` corresponding to the modeling folder - `resp.xy`: (**optional, default** `NULL`) If `resp.var` is a `vector`, a 2-columns `matrix` or `data.frame` containing the corresponding `X` and `Y` coordinates that will be used to build the species distribution model(s) - `eval.resp.var`: (**optional, default** `NULL`) A `vector`, a `SpatVector` without associated data (**if presence-only**), or a `SpatVector` object containing binary data (`0` : absence, `1` : presence, `NA` : indeterminate) for a single species that will be used to evaluate the species distribution model(s) with independent data **Note that old format from `sp` are still supported such as `SpatialPoints` (if presence-only) or `SpatialPointsDataFrame` object containing binary data.** - `eval.expl.var`: (**optional, default** `NULL`) A `matrix`, `data.frame`, `SpatVector` or `SpatRaster` object containing the explanatory variables (in columns or layers) that will be used to evaluate the species distribution model(s) with independent data. **Note that old format from `raster` and `sp` are still supported such as `RasterStack` and `SpatialPointsDataFrame` objects.** - `eval.resp.xy`: (**optional, default** `NULL`) If `resp.var` is a `vector`, a 2-columns `matrix` or `data.frame` containing the corresponding `X` and `Y` coordinates that will be used to evaluate the species distribution model(s) with independent data - `PA.nb.rep`: (**optional, default** `0`) If pseudo-absence selection, an `integer` corresponding to the number of sets (repetitions) of pseudo-absence points that will be drawn - `PA.nb.absences`: (**optional, default** `0`) If pseudo-absence selection, and `PA.strategy = 'random'` or `PA.strategy = 'sre'` or `PA.strategy = 'disk'`, an `integer` corresponding to the number of pseudo-absence points that will be selected for each pseudo-absence repetition (true absences included). It can also be a `vector` of the same length as `PA.nb.rep` containing `integer` values corresponding to the different numbers of pseudo-absences to be selected - `PA.strategy`: (**optional, default** `NULL`) If pseudo-absence selection, a `character` defining the strategy that will be used to select the pseudo-absence points. Must be `random`, `sre`, `disk` or `user.defined` (see Details) - `PA.dist.min`: (**optional, default** `0`) If pseudo-absence selection and `PA.strategy = 'disk'`, a `numeric` defining the minimal distance to presence points used to make the `disk` pseudo-absence selection (in the same projection system units as `resp.xy` and `expl.var`, see Details) - `PA.dist.max`: (**optional, default** `0`) If pseudo-absence selection and `PA.strategy = 'disk'`, a `numeric` defining the maximal distance to presence points used to make the `disk` pseudo-absence selection (in the same projection system units as `resp.xy` and `expl.var`, see Details) - `PA.sre.quant`: (**optional, default** `0`) If pseudo-absence selection and `PA.strategy = 'sre'`, a `numeric` between `0` and `0.5` defining the half-quantile used to make the `sre` pseudo-absence selection (see Details) - `PA.fact.aggr`: (**optional, default** `NULL`) If `strategy = 'random'` or `strategy = 'disk'`, a `integer` defining the factor of aggregation to reduce the resolution - `PA.user.table`: (**optional, default** `NULL`) If pseudo-absence selection and `PA.strategy = 'user.defined'`, a `matrix` or `data.frame` with as many rows as `resp.var` values, as many columns as `PA.nb.rep`, and containing `TRUE` or `FALSE` values defining which points will be used to build the species distribution model(s) for each repetition (see Details) - `na.rm`: (**optional, default** `TRUE`) A `logical` value defining whether points having one or several missing values for explanatory variables should be removed from the analysis or not - `filter.raster`: (**optional, default** `FALSE`) If `expl.var` is of raster type, a `logical` value defining whether `resp.var` is to be filtered when several points occur in the same raster cell - `seed.val`: (**optional, default** `NULL`) An `integer` value corresponding to the new seed value to be set ## Returns A `BIOMOD.formated.data` object that can be used to build species distribution model(s) with the `BIOMOD_Modeling` function. [list("print/show")](https://biomodhub.github.io/biomod2/reference/BIOMOD.formated.data.html), [list("plot")](https://biomodhub.github.io/biomod2/reference/plot.html) and [list("summary")](https://biomodhub.github.io/biomod2/reference/summary.html) functions are available to have a summary of the created object. ## Details This function gathers and formats all input data needed to run `biomod2` models. It supports different kind of inputs (e.g. `matrix`, `SpatVector`, `SpatRaster`) and provides different methods to select pseudo-absences if needed. Concerning explanatory variables and XY coordinates : * if `SpatRaster`, `RasterLayer` or `RasterStack` provided for `expl.var` or `eval.expl.var`, `biomod2` will extract the corresponding values from XY coordinates provided : * either through `resp.xy` or `eval.resp.xy` respectively * or `resp.var` or `eval.resp.var`, if provided as `SpatVector` or `SpatialPointsDataFrame` **Be sure to give the objects containing XY coordinates in the same projection system than the raster objects !** * if `data.frame` or `matrix` provided for `expl.var` or `eval.expl.var`, `biomod2` will simply merge it (`cbind`) with `resp.var` without considering XY coordinates. **Be sure to give explanatory and response values in the same row order !** Concerning pseudo-absence selection (see ‘bm_PseudoAbsences’) : * if both presence and absence data are available, and there is enough absences : set `PA.nb.rep = 0` and no pseudo-absence will be selected. * if no absence data is available, several pseudo-absence repetitions are recommended (to estimate the effect of pseudo-absence selection), as well as high number of pseudo-absence points. **Be sure not to select more pseudo-absence points than maximum number of pixels in the studied area !** * it is possible now to create several pseudo-absence repetitions with different number of points, BUT with the same sampling strategy. - **Response variable**: `biomod2` models single species at a time (no multi-species). Hence, `resp.var` must be a uni-dimensional object (either a `vector`, a one-column `matrix`, `data.frame`, a `SpatVector` (**without associated data - if presence-only**), a `SpatialPoints` (**if presence-only**), a `SpatialPointsDataFrame` or `SpatVector` object), containing values among : * `1` : presences * `0` : true absences (if any) * `NA` : no information point (might be used to select pseudo-absences if any) If no true absences are available, pseudo-absence selection must be done. If `resp.var` is a non-spatial object (`vector`, `matrix` or `data.frame`), XY coordinates must be provided through `resp.xy`. If pseudo-absence points are to be selected, `NA` points must be provided in order to select pseudo-absences among them. - **Explanatory variables**: Factorial variables are allowed, but might lead to some pseudo-absence strategy or models omissions (e.g. `sre`). - **Evaluation data**: Although `biomod2` provides tools to automatically divide dataset into calibration and validation parts through the modeling process (see `CV.[..]` parameters in `BIOMOD_Modeling` function ; or `bm_CrossValidation function`), it is also possible (and strongly advised) to directly provide two independent datasets, one for calibration/validation and one for evaluation - **Pseudo-absence selection (see `bm_PseudoAbsences`)**: If no true absences are available, pseudo-absences must be selected from the **background data**, meaning data there is no information whether the species of interest occurs or not. It corresponds either to the remaining pixels of the `expl.var` (if provided as a `SpatRaster` or `RasterSatck`) or to the points identified as `NA` in `resp.var` (if `expl.var` provided as a `matrix` or `data.frame`). Several methods are available to do this selection : - **random**: all points of initial background are pseudo-absence candidates. `PA.nb.absences` are drawn randomly, for each `PA.nb.rep` requested. - **sre**: pseudo-absences have to be selected in conditions (combination of explanatory variables) that differ in a defined proportion (`PA.sre.quant`) from those of presence points. A **Surface Range Envelop** model is first run over the species of interest (see `bm_SRE`), and pseudo-absences are selected outside this envelop. **This case is appropriate when all the species climatic niche has been sampled, otherwise it may lead to over-optimistic model evaluations and predictions !** - **disk**: pseudo-absences are selected within circles around presence points defined by `PA.dist.min` and `PA.dist.max` distance values (in the same projection system units as `coord` and `expl.var`). It allows to select pseudo-absence points that are not too close to (avoid same niche and pseudo-replication) or too far (localized sampling strategy) from presences. - **user.defined**: pseudo-absences are defined in advance and given as `data.frame` through the `PA.user.table` parameter. ## Examples ```r library(terra) # Load species occurrences (6 species available) data(DataSpecies) head(DataSpecies) # Select the name of the studied species myRespName <- 'GuloGulo' # Get corresponding presence/absence data myResp <- as.numeric(DataSpecies[, myRespName]) # Get corresponding XY coordinates myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')] # Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12) data(bioclim_current) myExpl <- terra::rast(bioclim_current) # ---------------------------------------------------------------# # Format Data with true absences myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName) myBiomodData summary(myBiomodData) plot(myBiomodData) # ---------------------------------------------------------------# # # Transform true absences into potential pseudo-absences # myResp.PA <- ifelse(myResp == 1, 1, NA) # # # Format Data with pseudo-absences : random method # myBiomodData.r <- BIOMOD_FormatingData(resp.var = myResp.PA, # expl.var = myExpl, # resp.xy = myRespXY, # resp.name = myRespName, # PA.nb.rep = 4, # PA.nb.absences = 1000, # PA.strategy = 'random') # # # Format Data with pseudo-absences : disk method # myBiomodData.d <- BIOMOD_FormatingData(resp.var = myResp.PA, # expl.var = myExpl, # resp.xy = myRespXY, # resp.name = myRespName, # PA.nb.rep = 4, # PA.nb.absences = 500, # PA.strategy = 'disk', # PA.dist.min = 5, # PA.dist.max = 35) # # # Format Data with pseudo-absences : SRE method # myBiomodData.s <- BIOMOD_FormatingData(resp.var = myResp.PA, # expl.var = myExpl, # resp.xy = myRespXY, # resp.name = myRespName, # PA.nb.rep = 4, # PA.nb.absences = 1000, # PA.strategy = 'sre', # PA.sre.quant = 0.025) # # # Format Data with pseudo-absences : user.defined method # myPAtable <- data.frame(PA1 = ifelse(myResp == 1, TRUE, FALSE), # PA2 = ifelse(myResp == 1, TRUE, FALSE)) # for (i in 1:ncol(myPAtable)) myPAtable[sample(which(myPAtable[, i] == FALSE), 500), i] = TRUE # myBiomodData.u <- BIOMOD_FormatingData(resp.var = myResp.PA, # expl.var = myExpl, # resp.xy = myRespXY, # resp.name = myRespName, # PA.strategy = 'user.defined', # PA.user.table = myPAtable) # # myBiomodData.r # myBiomodData.d # myBiomodData.s # myBiomodData.u # plot(myBiomodData.r) # plot(myBiomodData.d) # plot(myBiomodData.s) # plot(myBiomodData.u) # ---------------------------------------------------------------# # # Select multiple sets of pseudo-absences # # # Transform true absences into potential pseudo-absences # myResp.PA <- ifelse(myResp == 1, 1, NA) # # # Format Data with pseudo-absences : random method # myBiomodData.multi <- BIOMOD_FormatingData(resp.var = myResp.PA, # expl.var = myExpl, # resp.xy = myRespXY, # resp.name = myRespName, # PA.nb.rep = 4, # PA.nb.absences = c(1000, 500, 500, 200), # PA.strategy = 'random') # myBiomodData.multi # summary(myBiomodData.multi) # plot(myBiomodData.multi) ``` ## See Also `bm_PseudoAbsences`, `BIOMOD_Modeling` Other Main functions: `BIOMOD_EnsembleForecasting()`, `BIOMOD_EnsembleModeling()`, `BIOMOD_LoadModels()`, `BIOMOD_Modeling()`, `BIOMOD_Projection()`, `BIOMOD_RangeSize()` ## Author(s) Damien Georges, Wilfried Thuiller

BIOMOD_FormatingData function