This function rearranges the user's input data to make sure they can be used within biomod2. The function allows to select pseudo-absences or background data in the case that true absences data are not available, or to add pseudo-asbence data to an existing set of absence (see details).
BIOMOD_FormatingData(resp.var,
expl.var,
resp.xy = NULL,
resp.name = NULL,
eval.resp.var = NULL,
eval.expl.var = NULL,
eval.resp.xy = NULL,
PA.nb.rep = 0,
PA.nb.absences = 1000,
PA.strategy = 'random',
PA.dist.min = 0,
PA.dist.max = NULL,
PA.sre.quant = 0.025,
PA.table = NULL,
na.rm = TRUE)a vector, SpatialPointsDataFrame (or SpatialPoints if you work with ‘only presences’ data) containing species data (a single species) in binary format (ones for presences, zeros for true absences and NA for indeterminated ) that will be used to build the species distribution models.
a matrix, data.frame, SpatialPointsDataFrame or RasterStack containing your explanatory variables that will be used to build your models.
optional 2 columns matrix containing the X and Y coordinates of resp.var (only consider if resp.var is a vector) that will be used to build your models.
a vector, SpatialPointsDataFrame your species data (a single species) in binary format (ones for presences, zeros for true absences and NA for indeterminated ) that will be used to evaluate the models with independant data (or past data for instance).
a matrix, data.frame, SpatialPointsDataFrame or RasterStack containing your explanatory variables that will be used to evaluate the models with independant data (or past data for instance).
opional 2 columns matrix containing the X and Y coordinates of resp.var (only consider if resp.var is a vector) that will be used to evaluate the modelswith independant data (or past data for instance).
response variable name (character). The species name.
number of required Pseudo Absences selection (if needed). 0 by Default.
number of pseudo-absence selected for each repetition (when PA.nb.rep > 0) of the selection (true absences included)
strategy for selecting the Pseudo Absences (must be ‘random’, ‘sre’, ‘disk’ or ‘user.defined’)
minimal distance to presences for ‘disk’ Pseudo Absences selection (in meters if the explanatory is a not projected raster (+proj=longlat) and in map units (typically also meters) when it is projected or when explanatory variables are stored within table )
maximal distance to presences for ‘disk’ Pseudo Absences selection(in meters if the explanatory is a not projected raster (+proj=longlat) and in map units (typically also meters) when it is projected or when explanatory variables are stored within table )
quantile used for ‘sre’ Pseudo Absences selection
a matrix (or a data.frame) having as many rows than resp.var values. Each column correspund to a Pseudo-absences selection. It contains TRUE or FALSE indicating which values of resp.var will be considered to build models. It must be used with ‘user.defined’ PA.strategy.
locical, if TRUE, all points having one or several missing value for environmental data will be removed from analyse
A 'data.formated.Biomod.object' for BIOMOD_Modeling.
It is strongly advised to check whether this formated data corresponds to what was expected. A summary is easily printed by simply tipping the name of the object. A generic plot function is also available to display the different dataset in the geographic space.
This function homogenises the initial data for making sure the modelling exercie will be completed with all the required data. It supports different kind of inputs.
IMPORTANT: When the explanatory data are given in rasterLayer or rasterStack objects, biomod2 will be extract the variables onto the XY coordinates of the presence (and absence is any) vector. Be sure to give the XY coordinates (‘resp.xy’) in the same projection system than the raster objects. Same for the evaluation data in the case some sort of independant (or past) data are available (‘eval.resp.xy’).
When the explanatory variables are given in SpatialPointsDataFrame, the same requirements are asked than for the raster objects. The XY coordinates must be given to make sure biomod2 can extract the explanatory variables onto the presence (absence) data
When the explanatory variables are stored in a data.frame, make sure there are in the same order than the response variable. biomod2 will simply merge the datasets without considering the XY coordinates.
When both presence and absence data are available, and there is enough absences: set sQuotePA.nb.rep to 0. No pseudo-absence will be extracted.
When no true absences are given or when there are not numerous enough. It's advise to make several pseudo absences selections. That way the influence of the pseudo-absence selection could then be estimated later on. If the user do not want to run several repetition, make sure to select a relatively high number pseudo-absence. Make sure the number of pseudo-absence data is not higher than the maximum number of potential pseudo-absence (e.g. do not select 10,000 pseudo-absence when the rasterStack or data.frame do not contain more than 2000 pixels or rows).
Response variable encoding
BIOMOD_FormatingData concerns a single species at a time so resp.var must be a uni-dimentional object.
Response variable must be a vector or a one column data.frame/matrix/SpatialPointsDataFrame ( SpatialPoints are also allowed if you work with ‘only presences’ data) object.
As most of biomod2 models need Presences AND Absences data, the response variable must contain some absences (if there are not, make sure to select pseudo-absence). In the input resp.var argument, the data should be coded in the following way :
Presences : 1
True Absesnces : 0 (if any)
No Information : NA (if any, might latter be used for pseudo-absence)
If resp.var is a non-spatial object (vector, matrix/data.frame) and that some models requiring spatial data are being used (e.g. MAXENT.Phillips) and/or pseudo absences spatialy dependent (i.e 'disk'), make sure to give the XY coordinates of the sites/rows (‘resp.xy’).
Explanatory variables encoding
Explanatory variables must be stored together in a multidimentional object. It may be a matrix, a data.frame, a SpatialPointsDataFrame or a rasterStack object. Factorial variables are allowed here even if that can lead to some models omissions.
Evaluation Data
If you have data enough, we strongly recommand to split your dataset into 2 part : one for training/calibrating and testing the models and another to evaluate it. If you do it, fill the eval.resp.var, eval.expl.var and optionally the eval.resp.xy arguments whith this data. The advantage of working with a specific dataset for evaluating your models is that you will be able to evaluate more properly your ‘ensemble modeled’ models. That being said, this argument is optional and you may prefer only to test (kind of evaluation) your models only with a ‘cross-validation’ procedure (see Models function). The best practice is to use one set of data for training/calibrating, one set of testing and one for evaluating. The calibration and testing of the data can be done automaticaly in biomod2 in the Models function. The dataset for evaluation must be entered in BIOMOD_FormatingData.
Pseudo Absences selection
The PA.xxx's arguments let you parametrise your pseudo absences selection if you want some. It's an optional step.
Pseudo absences will be selected within the ‘background data’ and might be constrained by a defined ‘strategy’.
background data
‘Background data’ represents data there is no information whether the species of interest occurs or not. It is defined by the ‘No Information’ data of your resp.var if you give some. If not, (i.e Only presences data or all cells with a define presence or absence state) the backgroud will be take into your expl.var object if it's a RasterStack.
strategy
The strategy allows to constrain the choice of pseudo-absence within the ‘background data’.
3 ways are currently implemented to select the pseudo-absences candidate cells (PA.strategy argument):
‘random’: all cell of initial background are Pseudo absences candidates. The choice is made randomly given the number of pseudo-absence to select PA.nb.absences.
‘disk’: you may define a minimal (PA.dist.min), respectively a maximal (PA.dist.max) distance to presences points for selecting your pseudo absences candidates. That may be usefull if you don't want to select pseudo-absences too close to your presences (same niche and to avoid pseudo-replication), respectively too far from your presences (localised sampling startegy).
‘sre’: Pseudo absences candidates have to be selected in condition that differs from a defined proportion (PA.sre.quant) of presences data. It forces pseudo absences to be selected outside of the broadly defined environemental conditions for the species. It means that a surface range envelop model (sre, similar the BIOCLIM) is first carried out (using the specified quantile) on the species of interest, and then the pseudo-absence data are extracted outside of this envelop. This particular case may lead to over optimistic models evaluations.
‘user.defined’: In this case, pseudo absences selection should have been done in a previous step. This pseudo absences have to be reference into a well formated data.frame (e.g. PA.table argument)
# NOT RUN {
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",
package="biomod2"), row.names = 1)
head(DataSpecies)
# the name of studied species
myRespName <- 'GuloGulo'
# the presence/absences data for our species
myResp <- as.numeric(DataSpecies[,myRespName])
# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]
# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = stack( system.file( "external/bioclim/current/bio3.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio4.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio7.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio11.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio12.grd",
package="biomod2"))
# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
myBiomodData
plot(myBiomodData)
# }
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