| Title: | Species Distribution Modelling |
|---|---|
| Description: | An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>. |
| Authors: | Babak Naimi, Miguel B. Araujo |
| Maintainer: | Babak Naimi <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.2-46 |
| Built: | 2024-09-15 03:00:49 UTC |
| Source: | https://github.com/babaknaimi/sdm |
This function is an interface to extend the package. A user can define a new method and add it to the package. When the method is successfully added, it can be used along with other existing methods. The names of available methods in the package can be seen using the getmethodNames function. It is not limited only to the modelling methods, but can also be used for a replication method, or those used to generate pseudo-absences (backgrounds), etc.
You can get definitions for an existing method as an object using getmethod.
add(x,w,echo,...) getmethod(x,w,...) getmethodNames(w,...)add(x,w,echo,...) getmethod(x,w,...) getmethodNames(w,...)
x |
Either a |
w |
specify which group of methods the new method belongs to. "sdm" (default) can be used for modelling method |
echo |
logical (default=TRUE), determines whether a message should be printed to report if the adding is successful |
... |
additional arguments. see details |
These functions provide flexibility to extend the package by users through adding new methods to the package. It is also possible to add several instances of an existing method which, for example, edited to use the same method with different settings at the same time. Whatever the new method is, it can also be shared and used by other users.
getmethod gives an object of an appropriate class depending on w.
getmethodNames generates a list (if alt=TRUE is provided as an additional argument) containing the name of methods and all alternative names (aliases) specified for each method, or a character vector (if alt=FALSE) containing the main abbreviation names of the existing methods.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: getmethodNames('sdm') ## End(Not run)## Not run: getmethodNames('sdm') ## End(Not run)
If two sets of models fitted in two separate sdmModels objects, they can be merged into a single sdmModels object using '+'.
An object of class sdmModels.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp ~ b15 + NDVI, train = df) d #---- m1 <- sdm(sp ~ b15 + NDVI, data = d, methods = c('glm', 'gbm')) m1 m2 <- sdm(sp ~ b15 + NDVI, data = d, methods = c('svm')) m2 m <- m1 + m2 m ## End(Not run)## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp ~ b15 + NDVI, train = df) d #---- m1 <- sdm(sp ~ b15 + NDVI, data = d, methods = c('glm', 'gbm')) m1 m2 <- sdm(sp ~ b15 + NDVI, data = d, methods = c('svm')) m2 m <- m1 + m2 m ## End(Not run)
Converts a sdmdata object to a data.frame. By including additional arguments, it is possible to make a query on the dataset (see details).
## S4 method for signature 'sdmdata' as.data.frame(x, ...)## S4 method for signature 'sdmdata' as.data.frame(x, ...)
x |
sdmdata object |
... |
Additional arguments (optional, see details) |
The following additional arguments can optionally be used to get a subset of data by specifying record IDs; or make a query by specifying the name of species, and/or the name of data groups, and/or a range of time period (if time is available in data):
ind: an integer vector with record Ids;
sp: a character vector with the name of species;
grp: a character vector specifying groups of data (e.g., 'test', if independent test data is available)
time: a vector of times (an appropriate time class or a character that can be converted into a time format)
data.frame
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # a sdmdata object df <- as.data.frame(d) head(df) # only the records with rID == c(1,2,3): as.data.frame(d, ind=1:3) ## End(Not run)## Not run: file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # a sdmdata object df <- as.data.frame(d) head(df) # only the records with rID == c(1,2,3): as.data.frame(d, ind=1:3) ## End(Not run)
The function uses different methods to genrates background or Pseudo-absence records over an study area which is assumed to be the non-NA cells in the input Raster layer(s) in x.
background(x,n,method,bias,sp,setting)background(x,n,method,bias,sp,setting)
x |
an spatRaster or RasterStack or RasterBrick object with explanatory (predictor) variables that will be used to fit SDMs |
n |
size of background sample |
method |
a character, specifies the method of background generation; can be either of |
bias |
optional, a Raster object (SpatRaster or RasterLayer) with a single layer that specifies bias map which can ONLY be used by the method |
sp |
species presence locations (either as a SpatVector/SpatialPoints or a data.frame/matrix object); this argument is needed if the method is either |
setting |
optional, a list contains additional settings required by different methods (see details) |
The following methods are available:
- gRandom (random selection over geographical space): this method randomly select the non-missing pixels over the study area. Same weights are given to each pixel through the random selection of points unless the bias layer is introduced by a user which is a single raster layer that specifys a weighting scheme for background generation. A pixel with a greater value in the bias layer would have a higher chance to be selected as a background record. It has been shown by some studies that if the same bias in collecting the presence records (e.g., locations that are close to roads and residential areas have higher chance to be visited for recording species presence) is used to generate background records, it can improve performance of SDMs.
- eRandom (random selection over environmental spece): this method tries to collect a uniform (i.e., evenly distributed) distribution of records over environmental gradiants by sampling in environmental space.
- gDist (random sampling weighted by geographic distnce): This method uses a random selection of locations over geographical space but gives more weights to locations with larger distance to species presence locations.
- eDist (random sampling weighted by environmental distance): This method uses a random selection of locations over geographical space but gives more weights to locations with environmental conditions that are more dissimilar to the locations where species are observed.
a data.frame with spatial coordinates of background locations and the values of predictor variables extracted over the locations.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: ######### # Let's read raster dataset containing predictor variables for this study area: file <- system.file("external/predictors.tif", package="sdm") # path to a raster object r <- rast(file) r # a SpatRaster object including 2 rasters (covariates) plot(r) #---- file <- system.file("external/po_spatial_points.shp", package="sdm") # path to a shapefile po <- vect(file) # spatial points with presence-only records head(po) # it contains data for one species (sp4) and the dataset has only presence records! b1 <- background(r,n=20,method = 'gRandom') # you may specify the bias file (a raster object) head(b1) # background records generated using gRandom b2 <- background(r,n=20,method = 'eRandom') head(b2) # background records generated using eRandom b3 <- background(r,n=20,method = 'eDist',sp=po) head(b3) # background records generated using eDist b4 <- background(r,n=20,method = 'gDist') head(b4) # background records generated using gDist ## End(Not run)## Not run: ######### # Let's read raster dataset containing predictor variables for this study area: file <- system.file("external/predictors.tif", package="sdm") # path to a raster object r <- rast(file) r # a SpatRaster object including 2 rasters (covariates) plot(r) #---- file <- system.file("external/po_spatial_points.shp", package="sdm") # path to a shapefile po <- vect(file) # spatial points with presence-only records head(po) # it contains data for one species (sp4) and the dataset has only presence records! b1 <- background(r,n=20,method = 'gRandom') # you may specify the bias file (a raster object) head(b1) # background records generated using gRandom b2 <- background(r,n=20,method = 'eRandom') head(b2) # background records generated using eRandom b3 <- background(r,n=20,method = 'eDist',sp=po) head(b3) # background records generated using eDist b4 <- background(r,n=20,method = 'gDist') head(b4) # background records generated using gDist ## End(Not run)
Make a box plot of model evaluation data, i.e., the model predictions for known presence and absence points.
Arguments:
x Object of class sdmEvaluate
names Optional, the x-axis label for the group of data (e.g., 'Absence', 'Presence')
... Additional arguments that can be passed to boxplot
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
e <- evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) boxplot(e)e <- evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) boxplot(e)
evaluates for calibration
calibration(x,p,nbin,weight,...)calibration(x,p,nbin,weight,...)
x |
a numeric vector including the observed values; or a |
p |
a numeric vector including the predicted values |
nbin |
number of bins to discretize the predicted values into the specified bins (default: 10); instead, it can be the keyword of 'seek' to ask for seeking the best number |
weight |
logical, specifies whether a weight should be calculated based on the number of records at each bin. The weight will be used to summarize the calibration statistic |
... |
additional arguments (not implemented yet.) |
The output of this function can be used in the plot function to generate Calibration plot. The calibration statistic is calculated using a method developed by the authors of this package (the journal article is not published yet, but in preparation)
an object of class .sdmCalibration
Babak Naimi [email protected]
https://www.biogeoinformatics.org
Naimi, B., Niamir, A., Jimenez-Valverde, A., Araujo, M.B. (In preparation) Measuring calibration capacity of statistical models: a new statistic.
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
ca <- calibration(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) ca plot(ca)ca <- calibration(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) ca plot(ca)
Get or set spatial coordiates of a sdmdata object.
## S4 method for signature 'sdmdata' coords(obj,...) ## S4 replacement method for signature 'sdmdata' coords(object)<-value## S4 method for signature 'sdmdata' coords(obj,...) ## S4 replacement method for signature 'sdmdata' coords(object)<-value
obj |
speciesData (either of singleSpecies, multiple Species or SpeciesDataList) object |
object |
same as obj |
value |
spatial coordinates either a matrix, or data.frame, or as character to change the names of coordinates |
... |
Additional arguments |
matrix, or if the coordinates set, the sdmdata object is returned.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # a sdmdata object coords(d)file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # a sdmdata object coords(d)
Create a density plots of presence and absence data
A density plot. Presence data are in darkblue, and absence data are in red.
density(x, ...)
x |
Object of class 'sdmEvaluate' (or a numeric vector of observed presence/absence) | |
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
e <- evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) density(e)e <- evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) density(e)
Make a Raster object with a weighted averaging over all predictions from several fitted model in a sdmModel object.
## S4 method for signature 'sdmModels' ensemble(x, newdata, filename="",setting,overwrite=FALSE,pFilename="",...)## S4 method for signature 'sdmModels' ensemble(x, newdata, filename="",setting,overwrite=FALSE,pFilename="",...)
x |
a sdmModels object |
newdata |
raster object or data.frame, can be either predictors or the results of the |
filename |
optional character, output file name (if newdata is raster object) |
setting |
list, contains the parameters that are used in the ensemble procedure; see details |
overwrite |
logical, whether existing filename is overwritten (if exists and filename is given) |
pFilename |
it is ignored if newdata is the output of |
... |
additional arguments pass to the |
ensemble function uses the fitted models in an sdmModels object to generate an ensemble/consensus of predictions by multiple individual models. Several ensemble methods are available and can be defined in the setting argument.
A list of settings can be introduced in the setting argument including:
- method: a character vector specifies which ensemble method(s) should be employed (multiple choice is possible). The details about the available methods are provided at the end of this page.
- stat: if the - method='weighted' is used, it specifies which evaluation metrics can be used as weight in the weighted averaging procedure. Alternatively, one may directly introduce weights (see the next argument).
- weights: an optional numeric vector (with a length equal to the models that are successfully fitted) to specify the weights for weighted averaging procedure (if the method='weighted' is specified).
- id: specifies the model IDs that should be considered in the ensemble procedure. If missing, all the models that are successfully fitted are considered.
- expr: A character or an expression specifies a condition to select models for the ensemble procedure. For example: expr='auc > 0.7' only use models with AUC accuracy greater than 0.7. OR expr='auc > 0.7 & tss > 0.5' subsets models based on both AUC and TSS metrics.
- wtest: specifies which test dataset ("training","test.dep","test.indep") should be used to extract the statistic (stat) values as weights (if a relevant method is specified)
- opt: if a thershold_based metric is used in is selected in stat or in expr, opt specifies the threshold selection criterion. The possible value can be between 1 to 14 for "sp=se", "max(se+sp)", "min(cost)", "minROCdist", "max(kappa)", "max(ppv+npv)", "ppv=npv", "max(NMI)", "max(ccr)", "prevalence", "P10", "P5", "P1", "P0" criteria, respectively.
- power: default: 1, a numeric value to which the weights are raised. Greater value than 1 affects weighting scheme (for the methods e.g., "weighted") to increase the weights for the models with greater weight. For example, if weights are c(0.2,0.2,0.2,0.4), raising them to power 2 would be resulted to new weights as c(0.1428571,0.1428571, 0.1428571, 0.5714286) that causes greater contribution of the models with greater performances to the ensemble output.
—> The available ensemble methods (to be specified in method) include:
– 'unweighted': unweighted averaging/mean.
– 'weighted': weighted averaging.
– 'median': median.
– 'pa': mean of predicted presence-absence values (predicted probabilities are first converted to presence-absence given a threshold (opt defines which threshold optimisation strategy should be used), then they are averaged).
– 'mean-weighted': A two step averaging, that can be used when several replications are available for each modelling methods (e.g., fitted through bootstrapping or cross-validation resampling); it first takes an unweighted mean over the predicted values of multiple replications for each method (within model averaging), then a weighted mean is employed to combine the probabilities of different methods (between models averaging).
– 'mean-unweighted': Same as the previous one, but an unweighted mean is also used for the second step (instead of weighted mean).
– 'median-weighted': Same as the 'mean-weighted, but the median is used in the first step.
– 'median-unweighted': another two-step method, median is used for the first step and unweighted mean is used for the second step.
—-> in addition to tne ensemble methods, some other methods are available to generate some outputs that can represent uncertainty:
– 'uncertainty' or 'entropy': this method generates the uncertainty among the models' predictions that can be interpreted as model-based uncertainty or inconsistency among different models. It ranges between 0 and 1, 0 means all the models predicted the same value (either presence or absence), and 1 referes to maximum uncertainy, e.g., half of the models predicted presence (or absence) and the other half predicted the oposite value.
– 'cv': Coefficient of variation of probabilities generated from multiple models
– 'stdev': Standard deviation of probabilities generated from multiple models
– 'ci': This generates confidence interval length (marginal error) which assigns the difference between upper and lower limits of confidence interval to each pixel (upper - lower). The default level of confidence interval is 95% (i.e., alpha = 0.05), unless a different alpha is defined in setting. In case two separate upper and lower rasters are needed, by using the following codes, the upper and lower limits can be calculated:
en <- ensemble(x, newdata, setting=list(method=c('mean','ci'))) # taking unweighted averaging and ci
# en[[1]] is the mean of all probabilities and en[[2]] is the ci
ci.upper <- en[[1]] + en[[2]] / 2 # adding marginal error (half of the generated ci) to mean
ci.lower <- en[[1]] - en[[2]] / 2 # subtracting marginal error from mean
plot(ci.upper,main='Upper limit of Confidence Interval - alpha = 0.05')
plot(ci.lower,main='Lower limit of Confidence Interval - alpha = 0.05')
- a Raster object if predictors is a Raster object
- a numeric vector (or a data.frame) if predictors is a data.frame object
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
#
## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit the models (5 methods, and 10 replications using bootstrapping procedure): m <- sdm(Occurrence~.,data=d,methods=c('rf','tree','fda','mars','svm'), replicatin='boot',n=10) # ensemble using weighted averaging based on AUC statistic: p1 <- ensemble(m, newdata=preds, filename='ens.img',setting=list(method='weighted',stat='AUC')) plot(p1) # ensemble using weighted averaging based on TSS statistic # and optimum threshold critesion 2 (i.e., Max(spe+sen)) : p2 <- ensemble(m, newdata=preds, filename='ens2.img',setting=list(method='weighted', stat='TSS',opt=2)) plot(p2) ## End(Not run)## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit the models (5 methods, and 10 replications using bootstrapping procedure): m <- sdm(Occurrence~.,data=d,methods=c('rf','tree','fda','mars','svm'), replicatin='boot',n=10) # ensemble using weighted averaging based on AUC statistic: p1 <- ensemble(m, newdata=preds, filename='ens.img',setting=list(method='weighted',stat='AUC')) plot(p1) # ensemble using weighted averaging based on TSS statistic # and optimum threshold critesion 2 (i.e., Max(spe+sen)) : p2 <- ensemble(m, newdata=preds, filename='ens2.img',setting=list(method='weighted', stat='TSS',opt=2)) plot(p2) ## End(Not run)
evaluates for accuracy
evaluates(x,p,...) getEvaluation(x,id,wtest,stat,opt,...) getReplication(x,id,replication,species,run,index,test)evaluates(x,p,...) getEvaluation(x,id,wtest,stat,opt,...) getReplication(x,id,replication,species,run,index,test)
x |
a numeric vector or a |
p |
a numeric vector or a |
id |
a single numeric value indicates the modelID |
wtest |
which test data should be used: "training", "test.dep", or "test.indep"? |
stat |
statistics that should be extracted from the |
opt |
a numeric value indicates which threshold optimisation criteria should be considered if a threshold-based statistic is selected in stat |
species |
optional; a character vector specifies the name of species for which the replication is returned (default is NULL) |
replication |
a character specifies the name of the replication method |
run |
a single numeric value specifies the replication ID |
index |
logical (default: FALSE); specifies whether the index or species data of drawn records should be returned |
test |
logical (default: TRUE); specifies whether the test partition should be returned or training partition |
... |
additional arguments (see details) |
Evaluates the preformance (accuracy) given the obsetved values, and the predicted values. As additional argument, the distribution of data can be specified (through distribution), that can be either of 'binomial', 'gaussian', 'laplase', or 'poisson'. If not specified, it will be guessed by the function!
getEvaluation can be used to get the evaluation results from a fitted model (sdmModels object that is output of the sdm function). Each model in sdmModels has a modelID, that can be specified in w argument. If w is not specified or more than a modelID is specified, then a data.frame is generated that contains the statistics specified in stat. For a single model (if length w is 1), stat can be 1 (threhold_independent statistics), or 2 (threshold_based statistics) or NULL (both groups). If more than a model is specified (w is either NULL or has a length greater than 1), stat can be the name of statistics such as 'AUC', 'COR', 'Deviance', 'obs.prevalence', 'threshold', 'sensitivity', 'specificity', 'TSS','MCC', 'Kappa', 'NMI', 'phi', 'ppv', 'npv', 'ccr', 'prevalence'.
If either of the thershold_based stats are selected, opt can be also specified to select one of the criteria for optimising the threshold. The possible value can be between 1 to 15 for "sp=se", "max(se+sp)", "min(cost)", "minROCdist", "max(kappa)", "max(ppv+npv)", "ppv=npv", "max(NMI)", "max(ccr)", "prevalence", "max(MCC)", "P10", "P5", "P1", "P0" criteria, respectively. P10, P5, P1 refer to 10, 5, and 1 percentile of presence records in the evaluation dataset, respectively for which the suitability value is used as the threshold. By choosing P0, the minimum suitability value across presence records is selected as the threshold.
getReplication returns portion of records randomly selected through data partitioning using one of the replication methods (e.g., 'cv', 'boot', 'sub').
an object of class sdmEvaluate from evaluates function
a list or data.frame from getEvaluation function
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
#
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) getModelInfo(m) # there are 4 models in the sdmModels objects # so let's take a look at all the results for the model with modelID 1 # evaluation using training data (both threshod_independent and threshold_based groups): getEvaluation(m,w=1,wtest='training') getEvaluation(m,w=1,wtest='training',stat=1) # stat=1 (threshold_independent) getEvaluation(m,w=1,wtest='test.dep',stat=2) # stat=2 (threshold_based) getEvaluation(m,w=1:3,wtest='test.dep',stat=c('AUC','TSS'),opt=2) getEvaluation(m,opt=1) # all models getEvaluation(m,stat=c('TSS','Kappa','AUC'),opt=1) # all models ############ #example for evaluation: evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) ############## # Example for getReplication: df <- read.csv(file) # load a csv file head(df) d <- sdmData(sp~b15+NDVI,train=df) # sdmdata object d #---- # fit SDMs using 2 methods and a subsampling replication method with 2 replications: m <- sdm(sp~b15+NDVI,data=d,methods=c('glmpoly','gbm'), replication='sub', test=30, n=2) m # randomly drawn species records for test data in the second replication (run) of subsampling: getReplication(m, replication='sub',run=2) getReplication(m, replication='sub',run=2,test=F) # drawn record in the training partition ind <- getReplication(m, replication='sub',run=2,index=T) # index of the selected test record head(ind) .df <- as.data.frame(m@data) # convert sdmdata object in the model to data.frame head(.df) .df <- .df[.df$rID %in% ind, ] # the full test dataset drawn (second replication) pr <- predict(m,.df) # predictions of all the methods for the test dataset pr <- predict(m,.df) # predictions of all the methods for the test dataset head(pr) e <- evaluates(.df$sp, pr[,1]) # evaluates for the first method using the selected test data e@statistics e@threshold_based ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) getModelInfo(m) # there are 4 models in the sdmModels objects # so let's take a look at all the results for the model with modelID 1 # evaluation using training data (both threshod_independent and threshold_based groups): getEvaluation(m,w=1,wtest='training') getEvaluation(m,w=1,wtest='training',stat=1) # stat=1 (threshold_independent) getEvaluation(m,w=1,wtest='test.dep',stat=2) # stat=2 (threshold_based) getEvaluation(m,w=1:3,wtest='test.dep',stat=c('AUC','TSS'),opt=2) getEvaluation(m,opt=1) # all models getEvaluation(m,stat=c('TSS','Kappa','AUC'),opt=1) # all models ############ #example for evaluation: evaluates(x=c(1,1,0,1,0,0,0,1,1,1,0), p=c(0.69,0.04,0.05,0.95,0.04,0.65,0.09,0.61,0.75,0.84,0.15)) ############## # Example for getReplication: df <- read.csv(file) # load a csv file head(df) d <- sdmData(sp~b15+NDVI,train=df) # sdmdata object d #---- # fit SDMs using 2 methods and a subsampling replication method with 2 replications: m <- sdm(sp~b15+NDVI,data=d,methods=c('glmpoly','gbm'), replication='sub', test=30, n=2) m # randomly drawn species records for test data in the second replication (run) of subsampling: getReplication(m, replication='sub',run=2) getReplication(m, replication='sub',run=2,test=F) # drawn record in the training partition ind <- getReplication(m, replication='sub',run=2,index=T) # index of the selected test record head(ind) .df <- as.data.frame(m@data) # convert sdmdata object in the model to data.frame head(.df) .df <- .df[.df$rID %in% ind, ] # the full test dataset drawn (second replication) pr <- predict(m,.df) # predictions of all the methods for the test dataset pr <- predict(m,.df) # predictions of all the methods for the test dataset head(pr) e <- evaluates(.df$sp, pr[,1]) # evaluates for the first method using the selected test data e@statistics e@threshold_based ## End(Not run)
This function extracts records of a sdmdata object and generates a new object of the same type (if drop=FALSE; otherwise a data.frame).
In sdmdata, rID is the unique ID for each record.
x[i]
Arguments
x |
a Raster* object | |
i |
an index: record id (rID) in sdmdata object | |
drop |
If TRUE, a data.frame is returned, otherwise a sdmdata object is returned. |
|
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) # see the number of records: d d2 <- d[1:10] d2 d3 <- d[1:10,drop=TRUE] d3file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) # see the number of records: d d2 <- d[1:10] d2 d3 <- d[1:10,drop=TRUE] d3
An S4 class contains the information of features used to fit a model
varsA character vector containing the name of variabes from the dataset used to generate the features
feature.typesA list containing the definition of features
response.specificNULL, or a list containing the definition of features that their definitions are according to the response variable (i.e. species)
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
When SDMs are fitted using the sdm function, a sdmModels object is generated containing all the information and objects created through fitting and evaluation procedures for all species and methods. To each model, a unique modelID is assigned. getModelInfo returns a data.frame summarising some information relevant to the fitted models including modelID, method name, whether the model is fitted successfully, whether and what replication procedure is used for data partitioning, etc. getModelInfo helps to get the unique model IDs for all or certain models given the parameters that users specify. getModelObject returns the fitted model object for a single model (specified through id, or other settings).
getModelId(x,success,species,method,replication, run) getModelInfo(x,...) getModelObject(x,id,species,method,replication, run)getModelId(x,success,species,method,replication, run) getModelInfo(x,...) getModelObject(x,id,species,method,replication, run)
x |
a |
success |
logical; specifies whether the info/ids should be returned only for the models that are successfully fitted or not (default is TRUE) |
species |
optional; a character vector specifies the name of species for which the info should be returned (default is NULL meaning for all species) |
method |
optional; a character vector specifies the name of methods for which the info should be returned (default is NULL meaning for all methods) |
replication |
optional; a character vector specifies the name of replication method for which the info should be returned (default is NULL meaning for all species) |
run |
optional; a numeric vector specifies for which replication runs the info should be returned (default is NULL meaning for all runs) |
id |
a single numeric value specifying the modelID |
... |
additional arguments. see details |
in getModelInfo, as additional arguments, you can use the arguments in the function getModelId to specify which records should be returned.
getModelInfo: data.frame getModelId: a numeric vector getModelObject: The fitted model object with a class depending on the method
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
#
file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) getModelInfo(m) # getModelId(m) # getModelId(m,method='brt') obj <- getModelInfo(m, id=3) # obj is the fitted BRT model (through the package of gbm) class(obj) # The class of the model object summary(obj)file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) getModelInfo(m) # getModelId(m) # getModelId(m,method='brt') obj <- getModelInfo(m, id=3) # obj is the fitted BRT model (through the package of gbm) class(obj) # The class of the model object summary(obj)
Calculates relative importance of different variables in the models using several approaches.
getVarImp(x,id,wtest,setting,...)getVarImp(x,id,wtest,setting,...)
x |
sdmModels object |
id |
numeric, specify the model (modelID) for which the variable importance values are extracted; OR it can be character with "ensemble" specifying that the variable importance should be calculated based on the ensemble of all the model objects |
wtest |
specifies which dataset ('training','test.dep','test.indep') should be used (if exist) to calculate the importance of variables |
setting |
an optional list with setting of ensemble function; it is only needed when id = 'ensemble' |
... |
additional arguments as for |
getVarImp function returns an object including different measures of variable importance, and if be put in plot function, a barplot is generated. If the ggplot2 package is installed on your machine, the plot is generated using ggplot (unless you turn gg = FALSE), otherwise, the standard barplot is used.
if id = "ensemble" is used in the function, the ensemble function is called to calculate the relative variable importance based on the ensemble prediction of all models. setting can be specified as an additional argument that will be passed to the ensemble function so check the ensemble function to see how can setting be specified!
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
## Not run: # if m is a sdmModels object (output of the sdm function) then: getVarImp(m,id=1) # variable importance vi <- getVarImp(m,id=1) vi plot(vi,'auc') plot(vi,'cor') ############# # You can get Mean variable importance (and confidence interval) for multiple models: vi <- getVarImp(m,id=1:10) # specify the modelIDs of the models vi plot(vi,'cor') #---- # you can use the getModelId function to find the id of the specific method, replication, etc. # or you may put the arguments of the getModelId in the getVarImp function: vi <- getVarImp(m, method='glm') # Mean variable importance for the method glm vi plot(vi) ################# ##### Variable Importance based on ENSEMBLE: # You can get variable importance based on the ensemble of multiple models: # setting is passed to the ensemble function vi <- getVarImp(m,id="ensemble",setting=list(method='weighted',stat='auc')) vi plot(vi,'cor') #---------------- # if you want the ensemble based on a subset of models, you can define # the id within the setting list: vi <- getVarImp(m,id="ensemble", setting=list(method='weighted',stat='auc',id=1:10)) vi plot(vi,'cor') plot(vi, gg = F) # R standard plot is used instead of ggplot ## End(Not run)## Not run: # if m is a sdmModels object (output of the sdm function) then: getVarImp(m,id=1) # variable importance vi <- getVarImp(m,id=1) vi plot(vi,'auc') plot(vi,'cor') ############# # You can get Mean variable importance (and confidence interval) for multiple models: vi <- getVarImp(m,id=1:10) # specify the modelIDs of the models vi plot(vi,'cor') #---- # you can use the getModelId function to find the id of the specific method, replication, etc. # or you may put the arguments of the getModelId in the getVarImp function: vi <- getVarImp(m, method='glm') # Mean variable importance for the method glm vi plot(vi) ################# ##### Variable Importance based on ENSEMBLE: # You can get variable importance based on the ensemble of multiple models: # setting is passed to the ensemble function vi <- getVarImp(m,id="ensemble",setting=list(method='weighted',stat='auc')) vi plot(vi,'cor') #---------------- # if you want the ensemble based on a subset of models, you can define # the id within the setting list: vi <- getVarImp(m,id="ensemble", setting=list(method='weighted',stat='auc',id=1:10)) vi plot(vi,'cor') plot(vi, gg = F) # R standard plot is used instead of ggplot ## End(Not run)
Provides the possibility of using functions in the package through an interactive graphical user interface (GUI). Depending on input, different GUIs are opened.
## S4 method for signature 'sdmModels' gui(x,...)## S4 method for signature 'sdmModels' gui(x,...)
x |
a sdm* object |
... |
not implemented yet. |
When x is missing, a GUI is opened to facilitate all the steps required to create sdmData, specify the settings for the different steps, and fit sdm models.
Specifying x would be useful to interact with sdm* object. For example, if x is a sdmModels (that is generated by sdm function), a user can interactively explore the results (e.g., to see different plots of model evaluation results).
A HTML page in browser is opened.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) m gui(m) ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) m gui(m) ## End(Not run)
This function facilitates to install the required packages that some functions are dependent on in the sdm package. It first checks whether the package is already installed, and if not, it installs the packages. If update=TRUE is used, the packages re-installed if they were already installed.
installAll(pkgs,update,...)installAll(pkgs,update,...)
pkgs |
optional. the user provided list of packages (not required for the purpose of this function) |
update |
logical (default=FALSE), specifies whether the packages re-installed if they are already installed on the machine |
... |
Additional arguments passed to the |
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
#
## Not run: installAll() ## End(Not run)## Not run: installAll() ## End(Not run)
Get or set the names of the species of a sdmdata object
## S4 method for signature 'sdmdata' names(x) ## S4 replacement method for signature 'sdmdata' names(x)<-value## S4 method for signature 'sdmdata' names(x) ## S4 replacement method for signature 'sdmdata' names(x)<-value
x |
A sdm data object ( |
value |
character (vector) |
For names, a character
For names<-, the updated object.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d names(d) # returns the names of speciesfile <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d names(d) # returns the names of species
This function maps the species data (either presence/absence or probability of occurrence/habitat suitability) into a two-dimensional environmental space (i.e., based on two environmental variables) to characterise ecological niche based on the specified environmental variables.
niche(x,h,n,.size,plot,out,...)niche(x,h,n,.size,plot,out,...)
x |
A |
h |
A |
n |
A character vector specifying the names of environmental variables (two names) that should be used to map the ecological niche; if |
.size |
optional; a numeric value (default: 1e6) specifies the size of the maximum number of records should be used to generate the ecological niche map; would be useful when the |
plot |
logical, specifies whether the generated niche should be plotted |
out |
logical, specifies whether the niche should be returned by the function; it will be |
... |
additional arguments includingthe argument |
As an additional argument, a user may specify gg which is logical, specifies whether the plot should be generated using the ggplot2 package (if the package is installed), otherwise, the raster package is used to generate the plot.
- ...: additional arguments for the plot function (e.g., xlab, ylab, main, col, ...) can be used with the function
an object of class .nicheRaster that contains some information about the environmental variable, and a RasterLayer (100x100) that represents the two-dimensional ecological niche.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object names(preds) # 4 environmental variables are used! d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit models: m <- sdm(Occurrence~.,data=d,methods=c('rf','glm','brt')) # ensemble using weighted averaging based on AUC statistic: p1 <- ensemble(m, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p1, main='Habitat Suitability in Geographic Space') # Mapping Ecological Niche using selected two variables niche(x=preds, h=p1, c('precipitation','temperature')) niche(x=preds, h=p1, c('vegetation','temperature')) # in case if you do not have the habitat suitability map but species data: niche(x=preds, h=species, c('vegetation','temperature','Occurrence')) niche(x=preds, h=d, n=c('vegetation','temperature','Occurrence'), rnd=2) # rnd is the argument specifies the decimal degrees to which the values on axis rounded. ## End(Not run)## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object names(preds) # 4 environmental variables are used! d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit models: m <- sdm(Occurrence~.,data=d,methods=c('rf','glm','brt')) # ensemble using weighted averaging based on AUC statistic: p1 <- ensemble(m, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p1, main='Habitat Suitability in Geographic Space') # Mapping Ecological Niche using selected two variables niche(x=preds, h=p1, c('precipitation','temperature')) niche(x=preds, h=p1, c('vegetation','temperature')) # in case if you do not have the habitat suitability map but species data: niche(x=preds, h=species, c('vegetation','temperature','Occurrence')) niche(x=preds, h=d, n=c('vegetation','temperature','Occurrence'), rnd=2) # rnd is the argument specifies the decimal degrees to which the values on axis rounded. ## End(Not run)
Compute multiple niche similarity (overlap) statistics between two rasters with probability of occurrence (habitat suitability) values (e.g., outputs of the predict/ensemble functions). The statistics range between 0 (no similarity) and 1 (maximum similarity; identical). The calculations can be done either in geographic space (when x and y are raster maps representing geographical distributions of species) or in environmental (niche) space (when x and y are the output of niche function).
nicheSimilarity(x,y,stat,...)nicheSimilarity(x,y,stat,...)
x |
habitat suitabiliy of the first species in geographic or niche space: a single-layer |
y |
habitat suitabiliy of the second species in geographic or niche space: a |
stat |
A character vector specifying the names of niche similarity statistics that can be one or multiple items from c("Imod","Icor","D","O","BC","R"); "all" (or NULL) for all statistics |
... |
not implemented. |
Six metrics are implemented to quantify niche overlap (similarity) between two species (or two separate populations of the same species) including:
- D: Schoener's D
- Imod: Modified Hellinger distance
- Icor: Corrected Modified Hellinger distance
- R: Horn's R
- O: Pianka's O
- BC: Bray-Curtis distance
The equations for these metrics are described in Rodder & Engler (2011).
The probability raster maps (geographic distributions) of the two species can be provided in x and y (so, nlyr(x) = nlyr(y) = 1 should be valid), or both rasters can be provided in x when y is missing (then, nlyr(x) = 2 should be valid).
Alternatively, the niche similarity can be calculated in environmental space given the object generated by the niche function for each species. Of course the niche for both species should be generated based on the same set of predictors. Given that the niche function generates the niche raster based on only two predictors, the niche similarity calculation may be repeated for different combinations of predictors, or all the predictor variables can be first transformed and reduced into two components (using priciple component analysis; pca), then the niche for each species can be generated based on the first two components (see example.)
a numeric vector with values of niche similarity for different metrics.
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
Rodder, D., & Engler, J. O. (2011). Quantitative metrics of overlaps in Grinnellian niches: advances and possible drawbacks. Global Ecology and Biogeography, 20(6), 915-927.
## Not run: file <- system.file("external/sp1.shp", package="sdm") # get the path to the species data sp1 <- vect(file) # read the shapefile for species 1 file <- system.file("external/sp2.shp", package="sdm") sp2 <- vect(file) # read the shapefile for species 2 path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list of predictor filenames lst preds <- rast(lst) # making a raster object names(preds) # 4 environmental variables are used! d1 <- sdmData(formula=Occurrence~., train=sp1, predictors=preds) d1 d2 <- sdmData(formula=Occurrence~., train=sp2, predictors=preds) d2 # fit models for species 1 m1 <- sdm(Occurrence~.,data=d1,methods=c('rf','glm','brt'), replication='sub',test.p=30) m1 # fit models for species 2: m2 <- sdm(Occurrence~.,data=d2,methods=c('rf','glm','brt'), replication='sub',test.p=30) m2 # ensemble using weighted averaging based on AUC statistic (species 1): p1 <- ensemble(m1, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p1, main='Habitat Suitability in Geographic Space (species 1)') # ensemble for species 2: p2 <- ensemble(m2, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p2, main='Habitat Suitability in Geographic Space (species 2)') # maps together: plot(c(p1,p2), main=c('species 1','species 2')) # calculating niche similarity (all metrics) in geographic space: nicheSimilarity(p1,p2) nicheSimilarity(p1,p2, stat=c('Icor','Imod')) ###################################### # calculating niche similarity in environmental space: # Mapping Ecological Niche using selected two variables n1 <- niche(x=preds, h=p1, c('precipitation','temperature'),out = T) n2 <- niche(x=preds, h=p2, c('precipitation','temperature'),out=T) nicheSimilarity(n1,n2) ################### #### Alternatively, predictors can be transformed to two components using PCA pc <- pca(preds) pc # niche for first species based on the first two components of PCA: n1 <- niche(pc@data,p1,c("Comp.1","Comp.2"),out=T) n2 <- niche(pc@data,p2,c("Comp.1","Comp.2"),out=T) nicheSimilarity(n1,n2) ## End(Not run)## Not run: file <- system.file("external/sp1.shp", package="sdm") # get the path to the species data sp1 <- vect(file) # read the shapefile for species 1 file <- system.file("external/sp2.shp", package="sdm") sp2 <- vect(file) # read the shapefile for species 2 path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list of predictor filenames lst preds <- rast(lst) # making a raster object names(preds) # 4 environmental variables are used! d1 <- sdmData(formula=Occurrence~., train=sp1, predictors=preds) d1 d2 <- sdmData(formula=Occurrence~., train=sp2, predictors=preds) d2 # fit models for species 1 m1 <- sdm(Occurrence~.,data=d1,methods=c('rf','glm','brt'), replication='sub',test.p=30) m1 # fit models for species 2: m2 <- sdm(Occurrence~.,data=d2,methods=c('rf','glm','brt'), replication='sub',test.p=30) m2 # ensemble using weighted averaging based on AUC statistic (species 1): p1 <- ensemble(m1, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p1, main='Habitat Suitability in Geographic Space (species 1)') # ensemble for species 2: p2 <- ensemble(m2, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(p2, main='Habitat Suitability in Geographic Space (species 2)') # maps together: plot(c(p1,p2), main=c('species 1','species 2')) # calculating niche similarity (all metrics) in geographic space: nicheSimilarity(p1,p2) nicheSimilarity(p1,p2, stat=c('Icor','Imod')) ###################################### # calculating niche similarity in environmental space: # Mapping Ecological Niche using selected two variables n1 <- niche(x=preds, h=p1, c('precipitation','temperature'),out = T) n2 <- niche(x=preds, h=p2, c('precipitation','temperature'),out=T) nicheSimilarity(n1,n2) ################### #### Alternatively, predictors can be transformed to two components using PCA pc <- pca(preds) pc # niche for first species based on the first two components of PCA: n1 <- niche(pc@data,p1,c("Comp.1","Comp.2"),out=T) n2 <- niche(pc@data,p2,c("Comp.1","Comp.2"),out=T) nicheSimilarity(n1,n2) ## End(Not run)
For many applications, the predicted probability of occurrence (habitat suitability) should be transformed to presence-absence given a threshold which can be selected based on some criteria through model evaluation. pa facilitates this transformation based on either the threshold specified by a user or extracted from the models (or ensemble of the mdoels).
pa(x,y,id,opt,...)pa(x,y,id,opt,...)
x |
a SpatRaster object contains predicted probability of occurrence generated by the predict or ensemble functions |
y |
eithe a sdmModels (outcome of the sdm function) or a numeric vector with threshold values |
id |
when |
opt |
when |
... |
not implemented. |
if id is numeric, the number of layers in x (SpatRaster) should be the same as the length of id, but if id = "ensemble", nlyr(x) should be 1.
a SpatRaster with presence-absence values
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: # let's first fit a set of models and generate prediction and ensemble maps: # get the path to the species data file <- system.file("external/sp1.shp", package="sdm") sp <- vect(file) # read the species records path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list of predictor filenames preds <- rast(lst) # making a SpatRaster object (predictors) d <- sdmData(formula=Occurrence~., train=sp, predictors=preds) d # fit two models: m <- sdm(Occurrence~.,data=d,methods=c('glmp','brt'),replication='boot',n=1) m # predictions: pr <- predict(m, newdata=preds) plot(pr) # ensemble:: en <- ensemble(m, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(en) ######################### # let's convert probabilities to Presence/absences: # threshold is extracted for both models based on opt=2 (max[se+sp]) pr.pa <- pa(pr, m, opt=2) plot(pr.pa) # if only one of them was needed: pr.pa1 <- pa(pr[[1]],m, id=1,opt=2) plot(pr.pa1) #--------------- # if you have threshold values, you can directly use them in y: th <- getEvaluation(m,stat='threshold',opt=1) # get threshold values th pr.pa <- pa(pr, y=th[,2]) plot(pr.pa) #-------------- # to convert to PA based on "ensemble": en.pa <- pa(en, m, id="ensemble", opt=2) plot(en.pa) ## End(Not run)## Not run: # let's first fit a set of models and generate prediction and ensemble maps: # get the path to the species data file <- system.file("external/sp1.shp", package="sdm") sp <- vect(file) # read the species records path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list of predictor filenames preds <- rast(lst) # making a SpatRaster object (predictors) d <- sdmData(formula=Occurrence~., train=sp, predictors=preds) d # fit two models: m <- sdm(Occurrence~.,data=d,methods=c('glmp','brt'),replication='boot',n=1) m # predictions: pr <- predict(m, newdata=preds) plot(pr) # ensemble:: en <- ensemble(m, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(en) ######################### # let's convert probabilities to Presence/absences: # threshold is extracted for both models based on opt=2 (max[se+sp]) pr.pa <- pa(pr, m, opt=2) plot(pr.pa) # if only one of them was needed: pr.pa1 <- pa(pr[[1]],m, id=1,opt=2) plot(pr.pa1) #--------------- # if you have threshold values, you can directly use them in y: th <- getEvaluation(m,stat='threshold',opt=1) # get threshold values th pr.pa <- pa(pr, y=th[,2]) plot(pr.pa) #-------------- # to convert to PA based on "ensemble": en.pa <- pa(en, m, id="ensemble", opt=2) plot(en.pa) ## End(Not run)
pca performs a principal components analysis (using princomp function from stats package) on the given numeric data matrix and returns the results as an object of class princomp.
## S4 method for signature 'sdmdata' pca(x,scale,filename,...) ## S4 method for signature 'data.frame' pca(x,scale,filename,...) ## S4 method for signature 'RasterStackBrick' pca(x,scale,filename,...) ## S4 method for signature 'SpatRaster' pca(x,scale,filename,...)## S4 method for signature 'sdmdata' pca(x,scale,filename,...) ## S4 method for signature 'data.frame' pca(x,scale,filename,...) ## S4 method for signature 'RasterStackBrick' pca(x,scale,filename,...) ## S4 method for signature 'SpatRaster' pca(x,scale,filename,...)
x |
sdmdata object, or a data.frame, or a Raster (either RasterStackBrick or SpatRaster) object |
scale |
logical; specifies whether the input data should be scaled (by subtracting the variable's mean, then dividing it by its standard deviation) |
filename |
optional character; specifies a filename that should be either a CSV file when |
... |
additional arguments pass to |
pca analysis can be considered as a way to deal with multicollinearity problem and/or reduction of the data dimention. It returns two items in a list including data, and pca. The data contains the transoformed data into priciple components (the number of components is the same as the number of variables in the input data). You can check the pca item to see how many components (e.g., first 3) should be selected (e.g., by checking loadings). For more information on the calculation, see the princomp function.
a list including data (a data.frame or a RasterStack depending on the type of x), and pca results (output of the princomp function)
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
filename <- system.file('external/predictors.tif',package='sdm') r <- rast(filename) p <- pca(r) # p is a .pcaObject p plot(p@pcaObject) # or biplot(p@pcaObject) plot(p@data)filename <- system.file('external/predictors.tif',package='sdm') r <- rast(filename) p <- pca(r) # p is a .pcaObject p plot(p@pcaObject) # or biplot(p@pcaObject) plot(p@data)
Make a Raster or matrix object (depending on input dataset) with predictions from one or several fitted models in sdmModels object.
## S4 method for signature 'sdmModels' predict(object, newdata, filename="", id=NULL,species=NULL ,method=NULL,replication=NULL,run=NULL,mean=FALSE, overwrite=TRUE,parallelSetting, ...)## S4 method for signature 'sdmModels' predict(object, newdata, filename="", id=NULL,species=NULL ,method=NULL,replication=NULL,run=NULL,mean=FALSE, overwrite=TRUE,parallelSetting, ...)
object |
sdmModels object |
newdata |
SpatRaster object, or data.frame |
filename |
character, output filename, if missing, a name starts with sdm_prediction will be generated |
id |
numeric (optional), specifies which model(s) should be used if the object contains several models; with NULL all models are considered |
species |
character (optional), specifies which species should be used if the object contains models for multiple species; with NULL all species are used |
method |
character, names of fitted models, e.g., glm, brt, etc. |
replication |
character (optional), specifies the names of replication method,if NULL, all available replications are considered |
run |
numeric (optional), works if replication with multiple runs are used |
mean |
logical, works if replication with multiple runs are used to fit the models, and specifies whether a mean should be calculated over all predictions of a replication method (e.g., bootstrapping) for each modelling method. |
overwrite |
logical, whether the filename should be overwriten it it does exist |
parallelSetting |
default is NULL; a list contains setting items for parallel processing. The items in parallel setting include: ncore, method, type, hosts, doParallel, fork, and strategy; see details for more information. |
... |
additional arguments, as for |
predict uses the fitted models in the sdmModels object to generate the predictions given newdata. A SpatRaster object (if the newdata is Raster) or a data.frame (if newdata is data.frame) will be returned.
The predictions can be generated for some of models in the sdmModels object by specifying id (modelIDs) or explicitely specifying the names of species, or method, replication or run (replications ID).
For each prediction, a name is assigned which is an abbreviation representing the names of species, method, replication method, and run (replication ID). If the output is a SpatRaster object, metags function can be used to get full names of raster layers.
For parallel processing, a list of items can be passed to parallelSetting, including:
ncore: defines the number of cores (it can also be specified outside of this list
method: defines the parallelising engine. Currently, three options are available including 'parallel', 'foreach', and 'future'. default is 'parallel'
doParallel: Optional, definition to register for a backend for parallel processing (needed when method='foreach'). It should be provided as an R expression like the following example:
expression(registerDoParallel(parallelSetting@cl))
The above example is based on the function available in doParallel package. Other packages can also be used to provide and register backend technologies (e.g., doMC)
cluster: Optional; in case a cluster is created and available (e.g., using cl <- parallel::makeCluster(2)), the cluster object can be introduced here to be used as the parallel processing engine, otherwise, it is handled by the sdm package.
hosts: Optional; To use remote machines/clusters in the parallel processing, a character vector with the addresses (names or IPs) of the accessible (on the network) remote clusters can be provided here to be registered and used in parallel processing (still under development so it may not work appropriately!)
fork: Logical, Available for non-windows operating system and specifies whether a fork solution should be used for the parallelisation. Default is TRUE for non-windows OS and FALSE for windows.
strategy: character (default='auto'), specifies the parallelisation strategy that can be either 'data' (split data across multiple parallel cores) or 'model' (predict for different models in parallel); if 'auto' is selected, it is decided by the function depending on the size of dataset and number of models.
NOTE: Only use parallelSetting when you deal with a big dataset or large number of models otherwise, it make the procedure slower rather than faster if the procedure is quick without parallelising!
a SpatRaster object or data.frame
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
#
## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit the models (5 methods, and 10 replications using bootstrapping procedure): m <- sdm(Occurrence~.,data=d,methods=c('rf','tree','fda','mars','svm'), replicatin='boot',n=10) # predict for all the methods and replications: p1 <- predict(m, newdata=preds, filename='preds.tif') plot(p1) # predict for all the methods but take the mean over all replications for each replication method: p2 <- predict(m, newdata=preds, filename='preds.img',mean=T) plot(p2) # for parallel processing p3 <- predict(m, newdata=preds, filename='preds.tif',parallelSetting=list(ncore=2)) ## End(Not run)## Not run: file <- system.file("external/species.shp", package="sdm") # get the location of the species data species <- vect(file) # read the shapefile path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files # stack is a function in the raster package, to read/create a multi-layers raster dataset preds <- rast(lst) # making a raster object d <- sdmData(formula=Occurrence~., train=species, predictors=preds) d # fit the models (5 methods, and 10 replications using bootstrapping procedure): m <- sdm(Occurrence~.,data=d,methods=c('rf','tree','fda','mars','svm'), replicatin='boot',n=10) # predict for all the methods and replications: p1 <- predict(m, newdata=preds, filename='preds.tif') plot(p1) # predict for all the methods but take the mean over all replications for each replication method: p2 <- predict(m, newdata=preds, filename='preds.img',mean=T) plot(p2) # for parallel processing p3 <- predict(m, newdata=preds, filename='preds.tif',parallelSetting=list(ncore=2)) ## End(Not run)
Calculate the response of species to the range of values in each predictor variable based on the fitted models in a sdmModels object.
rcurve(x,n,id,mean,fun,confidence,gg,...) getResponseCurve(x,id,...)rcurve(x,n,id,mean,fun,confidence,gg,...) getResponseCurve(x,id,...)
x |
A |
id |
specifies the modelIDs corresponding to the models in the sdmModels object for which the response curves should be generated |
n |
A vector with the name of variables for which the response curve should be generated |
mean |
logical, specifies whether a mean should be calculated over responses to a variable when multiple models are specified in ids |
fun |
character or function, (default="mean") specifies what function should be used to calculate the value of the variables over the presence locations (except the variable of interest) |
confidence |
logical, specifies whether a confidence interval should be added to the curve when the mean response curve is calculated based on multiple models |
gg |
logical, specifies whether the plot should be generated using the ggplot2 package (if the package is installed) |
... |
additional arguments passed to plot function |
getResponseCurve calculates the responses for the models that are specified in id argument, and put the results in a .responseCurve object. This object can be used as an input in the plot function, or rcurve function.
If you just need the response curve graphs (plots), you can put a sdmModels object directly in the rcurve function, and do not need to first use getResponseCurve function.
In getResponseCurve function (or in rcurve when x is sdmModels), there are some additional arguments:
- size: a numeric value; default is 100. Specifies the size of the variable sequence that is used as the x-axis in the response curve plot. Greater number results to a smoother curve.
- includeTest: a logical value; default is FALSE; when a data object based on which a sdmModels is created containing independent test data; it specifies whether those records should be included into the response curve generation or not.
- ...: additional arguments for the plot function (e.g., xlab, ylab, main, col, lwd, lty)
an object of class .responseCurve or a series of graphs
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) rcurve(m) rcurve(m,id=1) # for the first model rcurve(m, id=1:2) rcurve(m,method = 'glm',smooth = T) # only for models fitted using glm method & with smoothed curve ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) rcurve(m) rcurve(m,id=1) # for the first model rcurve(m, id=1:2) rcurve(m,method = 'glm',smooth = T) # only for models fitted using glm method & with smoothed curve ## End(Not run)
Read an sdm object from a file, or write it to a file.
read.sdm(filename,...) write.sdm(x,filename,overwrite,...)read.sdm(filename,...) write.sdm(x,filename,overwrite,...)
filename |
Filename (character) |
x |
a sdm object (e.g., sdmModels,sdmdata or sdmSetting) |
overwrite |
Logical. If |
... |
additional arguments |
read.sdm function reads any files that has been writed by write.sdm. These functions use saveRDS and readRDS to write and read the sdm objects. Additional arguments ... pass to these functions. An sdmModels object is saved to a file with an extension of ".sdm". The file extensions for sdmdata and sdmSetting object are ".sdd", and "sds", respectively.
Babak Naimi
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # can be used to read sdm models (sdmModels) and sdmSettings as well. write.sdm(d,'dataset') # extension is created for data, model and settings as .sdd, .sds, and .sdm respectively. list.files(pattern='dataset') ## End(Not run)## Not run: file <- system.file("external/data.sdd", package="sdm") d <- read.sdm(file) d # can be used to read sdm models (sdmModels) and sdmSettings as well. write.sdm(d,'dataset') # extension is created for data, model and settings as .sdd, .sds, and .sdm respectively. list.files(pattern='dataset') ## End(Not run)
Plot the Receiver Operating Characteristics (ROC) curve with AUC statistic in the legend.
roc(x,p=NULL,species=NULL,method=NULL,replication=NULL,run=NULL, wtest=NULL,smooth=FALSE,legend=TRUE,...) getRoc(x,p,...)roc(x,p=NULL,species=NULL,method=NULL,replication=NULL,run=NULL, wtest=NULL,smooth=FALSE,legend=TRUE,...) getRoc(x,p,...)
x |
Either |
p |
if x is sdmModels, p is an optional vector with model ID number(s) that should be plotted (NULL (default means all models)); if x is a numeric vector, p is a vector with the same length including the predicted values |
species |
the name of species should be specified (required if x is |
method |
a character vector with the name of modelling methods that one need to get the roc plot for (if NULL [default], all methods in the object are considered); only if x is |
replication |
a character vector with the name of replication methods (i.e., 'sub','cv','boot') that one need to get the roc plot for |
run |
if x is |
wtest |
evaluation for which test datasets are required, maximum 2 names from 'training', 'test.dep', 'test.indep' (i.e., evaluation for training data, dependent test dataset, and independent test dataset, respectively) |
smooth |
logical, specified whether the ROC curves should be smoothed through a spline procedure |
legend |
logical, specified whether a legend including AUC statistic is required on the plot |
... |
additional arguments passed to plot function |
roc generates the plots of roc curves, and getRoc generate the values of ROC
an object of class matrix
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) roc(m) roc(m,1) # for the first model roc(m, 1:2) roc(m,method = 'glm',smooth = T) # only for models fitted using glm method & with smoothed curve ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # a sdmModels Object (fitted using sdm function) roc(m) roc(m,1) # for the first model roc(m, 1:2) roc(m,method = 'glm',smooth = T) # only for models fitted using glm method & with smoothed curve ## End(Not run)
Fits sdm for single or multiple species using single or multiple methods specified by a user in methods argument, and evaluates their performance.
sdm(formula, data, methods,...)sdm(formula, data, methods,...)
formula |
Specifies the structure of the model, types of features, etc. |
data |
a |
methods |
Character. Specifies the methods, used to fit the models |
... |
additional arguments |
sdm fits multiple models and can be used to generate multiple runs (replicates) of each method through partitioning (using one or several partitioning methods including: subsampling, cross-validation, and bootstrapping.
Each model is evaluated against training data, and if available, splitted data (through partitioning; called dependent test data as well, i.e., "dep.test") and/or indipendent test data ("indep.test").
User should make sure that the methods are available and the required packages for them are installed before putting their names in the function, otherwise, the methods that cannot be run for any reason, are excluded by the function. It is a good practice to call installAll function (just one time when the sdm is installed), that tries to install all the packages that may be needed somewhere in the sdm package.
A new method can be adopted and added to the package by a user using add function. It is also possible to get an instance of an existing method, override the setting and definition, and then add it with a new name (e.g., my.glm).
The output would be a single object (sdmModels) that can be read/reproduced everywhere (e.g., on a new machine). A setting object can also be taken (exported) out of the output sdmModels object, that can be used to reproduce the same practice but given new conditions (i.e., new dataset, area. etc.)
For speed up the model fitting, you may use parallel processing (a high-performance computing solution) by providing a list of items can be passed to parallelSetting argument. The items in the list includes:
ncore: defines the number of cores (it can also be specified outside of this list
method: defines the parallelising engine. Currently, three options are available including 'parallel', 'foreach', and 'future'. default is 'parallel'
doParallel: Optional, definition to register for a backend for parallel processing (needed when method='foreach'). It should be provided as an R expression like the following example:
expression(registerDoParallel(parallelSetting@cl))
The above example is based on the function available in doParallel package. Other packages can also be used to provide and register backend technologies (e.g., doMC)
cluster: Optional; in case a cluster is created and available (e.g., using cl <- parallel::makeCluster(2)), the cluster object can be introduced here to be used as the parallel processing engine, otherwise, it is handled by the sdm package.
hosts: Optional; To use remote machines/clusters in the parallel processing, a character vector with the addresses (names or IPs) of the accessible (on the network) remote clusters can be provided here to be registered and used in parallel processing (still under development so it may not work appropriately!)
fork: Logical, Available for non-windows operating system and specifies whether a fork solution should be used for the parallelisation. Default is TRUE for non-windows OS and FALSE for windows.
NOTE: Only use parallelSetting when you deal with a big dataset or large number of models otherwise, it make the procedure slower rather than faster if the procedure is quick without parallelising!
an object of class sdmModels
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) d #---- # Example 1: fit using 3 models, and no evaluation (evaluation based on training dataset): m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm')) m # Example 3: fit using 5 models, and # evaluates using 10 runs of subsampling replications taking 30 percent as test: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm','svm','rf'), replication='sub',test.percent=30,n=10) m # Example 3: fits using 5 models, and # evaluates using 10 runs of both 5-folds cross-validation and bootsrapping replication methods m <- sdm(sp~.,data=d,methods=c('gbm','tree','mars','mda','fda'), replication=c('cv','boot'),cv.folds=5,n=10) m # Example 4: fit using 3 models; evaluate the models using subsampling, # and override the default settings for the method brt: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','brt'),test.p=30, modelSettings=list(brt=list(n.trees=500,train.fraction=0.8))) m ## End(Not run)## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) d #---- # Example 1: fit using 3 models, and no evaluation (evaluation based on training dataset): m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm')) m # Example 3: fit using 5 models, and # evaluates using 10 runs of subsampling replications taking 30 percent as test: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm','svm','rf'), replication='sub',test.percent=30,n=10) m # Example 3: fits using 5 models, and # evaluates using 10 runs of both 5-folds cross-validation and bootsrapping replication methods m <- sdm(sp~.,data=d,methods=c('gbm','tree','mars','mda','fda'), replication=c('cv','boot'),cv.folds=5,n=10) m # Example 4: fit using 3 models; evaluate the models using subsampling, # and override the default settings for the method brt: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','brt'),test.p=30, modelSettings=list(brt=list(n.trees=500,train.fraction=0.8))) m ## End(Not run)
The structure of the sdmdata and sdmModels classes were slightly changed in the new version of the package (> 1.2-X). If an sdmdata or sdmModels object is created and saved in an old version of the package (e.g., 1.1-8), using the sdmAdapt function, its structure is modified and adapted to the new version.
sdmAdapt(x)sdmAdapt(x)
x |
an object of |
an object with the same class as x
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
#
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # an sdmModels Object (fitted using old version of sdm) m <- sdmAdapt(m) m ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) # an sdmModels Object (fitted using old version of sdm) m <- sdmAdapt(m) m ## End(Not run)
An S4 class representing sdm dataset
nameModelling method name
aliasesAlternative names for the method
dataArgument.namesA list keeps the name of data agruments in both fit and predict functions
packagesThe required external package by the method
modelTypesSpecifies whether the model is presence-absence, presence-only, abundance, or multinomial
fitParamsa list of parameters needed by the method
fitSettingsa list of setting parameters for the method
settingRulesa function that adjust the setting parameters according to data
fitFunctionThe main function use for fitting the model
tuneParamsa list of parameters to be tuned before the final fitting
predictParamsa list of parameters needed by predict function
predictSettingsa list of setting parameters for prediction
predictFunctionThe main predict function
metadataa metadata object containing the information about who creates the object, date, etc.
.temp.envan environment object containing the functions defined by a user that is not from a package
Creates an sdmdata object that holds (single or multiple) species records and explanatory variates. In addition, more information such as spatial coordinates, time, grouping variables, and metadata (e.g., author, date, reference, etc.) can be included.
sdmData(formula,train,predictors,test,bg,filename,crs,impute,metadata,...)sdmData(formula,train,predictors,test,bg,filename,crs,impute,metadata,...)
formula |
Specifies which species and explanatory variables should be taken from the input data. Other information (e.g., spatial coordinates, grouping variables, time, etc.) can be determined as well |
train |
Training data containing species observations as a |
test |
Independent test data with the same structure as the train data |
predictors |
explanatory variables (predictors), defined as a raster object ( |
bg |
Background data (pseudo-absence), as a data.frame. It can also be a list contains the settings to generate background data (a Raster object is required in the predictors argument) |
filename |
filename of the sdm data object to store in the disk |
crs |
optional, coordinate reference system |
impute |
logical or character, specifies whether missing values for predictor variables should be imputed. It can be a character specifies imputation method. default is "neighbor" |
metadata |
Additional arguments (optional) that are used to create a metadata object. See details |
... |
Not implemented yet. |
sdmData creates a data object, for single or multiple species. It can automatically detect the variables containing species data (if a data.frame is provided in train), but it is recommended to use formula through which all species (in the left hand side, e.g., sp1+sp2+sp3 ~ .), and the explanatory variables (in the right hand side) can be determined. If there are additional information such as spatial coordinates, time, or some variables based on which the observation can be grouped, they can be determined in the right hand side of the formula in a flexsible way (e.g., ~ . + coords(x+y) + g(var); This right hand side formula, simply determines all variables (.) + x and y as spatial coordinates + grouping observations based on the variable var; for grouping, the variable (var in this example) should be categorical, i.e., factor ).
Additional items can be provided as a list in the metadata argument including:
author, website, citation, help, description, date, and license
an object of class sdmdata
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
## Not run: # Example 1: a data.frame containing records for a species (sp) and two predictors (b15 & NDVI): file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) d # or simply: d <- sdmData(sp~.,train=df) d #-------- # if formula is not specified, function tries to detect species and covariates, it works well only # if dataset contains no additional columns but species and covariates! d <- sdmData(train=df) d # # only right hand side of the formula is specified (one covariate), so function detects species: d <- sdmData(~NDVI,train=df) d #---------- ########### # Example 2: a data.frame containing presence-absence records for 1 species, 4 covariates, and # x, y coordinates: file <- system.file("external/pa_df_with_xy.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI+categoric1+categoric2+coords(x+y),train=df) d #---- # categoric1 and categoric2 are categorical variables (factors), if not sure the data.frame has # them as factor, it can be specified in the formula: d <- sdmData(sp~b15+NDVI+f(categoric1)+f(categoric2)+coords(x+y),train=df) d # more simple forms of the formula: d <- sdmData(sp~.+coords(x+y),train=df) d d <- sdmData(~.+coords(x+y),train=df) # function detects the species d ############## # Example 3: a data.frame containing presence-absence records for 10 species: file <- system.file("external/multi_pa_df.csv", package="sdm") df <- read.csv(file) head(df) # in the following formula, spatial coordinates columns are specified, and the rest is asked to # be detected by the function: d <- sdmData(~.+coords(x+y),train=df) d #--- or it can be customized wich species and which covariates are needed: d <- sdmData(sp1+sp2+sp3~b15+NDVI+f(categoric1) + coords(x+y),train=df) d # 3 species, 3 covariates, and coordinates # just be careful that if you put "." in the right hand side, while not all species columns or # additional columns (e.g., coordinates, time) are specified in the formula, then it takes those # columns as covariates which is NOT right! ######### # Example 4: Spatial data: file <- system.file("external/pa_spatial_points.shp", package="sdm") # path to a shapefile # use the vect function in terra to read the shapefile: p <- vect(file) class(p) # a "SpatVector" plot(p) head(p) # it contains data for 3 species # presence-absence plot for the first species (i.e., sp1) plot(p[p$sp1 == 1,], col = 'blue', pch=16, main = 'Presence-Absence for sp1') points(p[p$sp1 == 0,],col='red',pch=16) # Let's read raster dataset containing predictor variables for this study area: file <- system.file("external/predictors.tif", package="sdm") # path to a raster object r <- rast(file) r # a SpatRaster object including 2 rasters (covariates) plot(r) # now, we can use the species points and predictor rasters in sdmData function: d <- sdmData(sp1 + sp2 + sp3 ~ b15 + NDVI, train = p, predictors = r) d ################## # Example 5: presence-only records: file <- system.file("external/po_spatial_points.shp", package="sdm") # path to a shapefile po <- vect(file) head(po) # it contains data for one species (sp4) and the dataset has only presence records! d <- sdmData(sp4 ~ b15 + NDVI, train = po, predictors = r) d # as you see in the type, the data is Presence-Only ### we can add another argument (i.e., bg) to generate background (pseudo-absence) records: #------ in bg, we are going to provide a list containing the setting to generate background #------ the setting includes n (number of background records), method (the method used for #------ background generation; gRandom refers to random in geographic space), and remove (whether #------ points located in presence sites should be removed). d <- sdmData(sp4 ~ b15 + NDVI, train = po, predictors = r, bg = list(n=1000,method = 'gRandom')) d # as you see in the type, the data is Presence-Background # you can alternatively, put a data.frame including background records in bg! ## End(Not run)## Not run: # Example 1: a data.frame containing records for a species (sp) and two predictors (b15 & NDVI): file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) d # or simply: d <- sdmData(sp~.,train=df) d #-------- # if formula is not specified, function tries to detect species and covariates, it works well only # if dataset contains no additional columns but species and covariates! d <- sdmData(train=df) d # # only right hand side of the formula is specified (one covariate), so function detects species: d <- sdmData(~NDVI,train=df) d #---------- ########### # Example 2: a data.frame containing presence-absence records for 1 species, 4 covariates, and # x, y coordinates: file <- system.file("external/pa_df_with_xy.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI+categoric1+categoric2+coords(x+y),train=df) d #---- # categoric1 and categoric2 are categorical variables (factors), if not sure the data.frame has # them as factor, it can be specified in the formula: d <- sdmData(sp~b15+NDVI+f(categoric1)+f(categoric2)+coords(x+y),train=df) d # more simple forms of the formula: d <- sdmData(sp~.+coords(x+y),train=df) d d <- sdmData(~.+coords(x+y),train=df) # function detects the species d ############## # Example 3: a data.frame containing presence-absence records for 10 species: file <- system.file("external/multi_pa_df.csv", package="sdm") df <- read.csv(file) head(df) # in the following formula, spatial coordinates columns are specified, and the rest is asked to # be detected by the function: d <- sdmData(~.+coords(x+y),train=df) d #--- or it can be customized wich species and which covariates are needed: d <- sdmData(sp1+sp2+sp3~b15+NDVI+f(categoric1) + coords(x+y),train=df) d # 3 species, 3 covariates, and coordinates # just be careful that if you put "." in the right hand side, while not all species columns or # additional columns (e.g., coordinates, time) are specified in the formula, then it takes those # columns as covariates which is NOT right! ######### # Example 4: Spatial data: file <- system.file("external/pa_spatial_points.shp", package="sdm") # path to a shapefile # use the vect function in terra to read the shapefile: p <- vect(file) class(p) # a "SpatVector" plot(p) head(p) # it contains data for 3 species # presence-absence plot for the first species (i.e., sp1) plot(p[p$sp1 == 1,], col = 'blue', pch=16, main = 'Presence-Absence for sp1') points(p[p$sp1 == 0,],col='red',pch=16) # Let's read raster dataset containing predictor variables for this study area: file <- system.file("external/predictors.tif", package="sdm") # path to a raster object r <- rast(file) r # a SpatRaster object including 2 rasters (covariates) plot(r) # now, we can use the species points and predictor rasters in sdmData function: d <- sdmData(sp1 + sp2 + sp3 ~ b15 + NDVI, train = p, predictors = r) d ################## # Example 5: presence-only records: file <- system.file("external/po_spatial_points.shp", package="sdm") # path to a shapefile po <- vect(file) head(po) # it contains data for one species (sp4) and the dataset has only presence records! d <- sdmData(sp4 ~ b15 + NDVI, train = po, predictors = r) d # as you see in the type, the data is Presence-Only ### we can add another argument (i.e., bg) to generate background (pseudo-absence) records: #------ in bg, we are going to provide a list containing the setting to generate background #------ the setting includes n (number of background records), method (the method used for #------ background generation; gRandom refers to random in geographic space), and remove (whether #------ points located in presence sites should be removed). d <- sdmData(sp4 ~ b15 + NDVI, train = po, predictors = r, bg = list(n=1000,method = 'gRandom')) d # as you see in the type, the data is Presence-Background # you can alternatively, put a data.frame including background records in bg! ## End(Not run)
An S4 class representing sdm dataset sdmdata
species.namesThe names of species
speciesContains the species data
features.nameThe names of predictor variables
featuresA data.frame containing predictor variables
factorsThe names of categorical variables (if any)
infoOther information such as coordinates, metadata, etc.
groupsA list including information on groups in the dataset
sdmFormulaAn object of class sdmFormula containing the formula and its' terms defined by user
errorLogReports on errors in the data raised throgh data cleaning (e.g., NA, duplications, etc.)
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
An S4 class to keep all the information of fitted models as well as their evaluations.
Slots for sdmModels objects:
dataa sdmdata object
recordIDsContains the species data
settingA data.frame containing predictor variables
run.infoa data.frame containing info on runs
replicatesThe names of categorical variables (if any)
modelsa list contains all fitted objects and relevant information (e.g., evaluation)
Slots for sdmEvaluate objects:
observeda numeric vector of observed values
predicteda numeric vector of predicted values
statisticsa list of threshold-independent statistics
threshold_baseda data.frame of threshold-based statistics
Slots for sdmFormula objects:
formulainput formula
varscharacter, name of variables
model.termsthe formula terms used in model fitting
data.termsthe formula terms used to manipulate data
Babak Naimi
https://www.biogeoinformatics.org/
Creates sdmSetting object that holds settings to fit and evaluate the models. It can be used to reproduce a study.
sdmSetting(formula,data,methods,interaction.depth=1,n=1,replication=NULL,cv.folds=NULL, test.percent=NULL,bg=NULL,bg.n=NULL,var.importance=NULL,response.curve=TRUE, var.selection=FALSE,modelSettings=NULL,seed=NULL,parallelSetting=NULL,...)sdmSetting(formula,data,methods,interaction.depth=1,n=1,replication=NULL,cv.folds=NULL, test.percent=NULL,bg=NULL,bg.n=NULL,var.importance=NULL,response.curve=TRUE, var.selection=FALSE,modelSettings=NULL,seed=NULL,parallelSetting=NULL,...)
formula |
specify the structure of the model |
data |
sdm data object or data.frame including species and feature data |
methods |
character, name of the algorithms |
interaction.depth |
level of interactions between predictors |
n |
number of replicates (run) |
replication |
replication method (e.g., 'subsampling', 'bootstrapping', 'cv') |
cv.folds |
number of folds if cv (cross-validation) is in the selected replication methods |
test.percent |
test percentage if subsampling is in the selected replication methods |
bg |
method to generate background |
bg.n |
number of background records |
var.importance |
logical, whether variable importance should be calculated |
response.curve |
method to calculate variable importance |
var.selection |
logical, whether variable selection should be considered |
modelSettings |
optional list; settings for modelling methods can be specified by users |
seed |
default is NULL; either logical specify whether a seed for random number generator should be considered, or a numerical to specify the exact seed number |
parallelSetting |
default is NULL; a list include setting items for parallel processing. The items in parallel setting include: ncore, method, type, hosts, doParallel, and fork; see details for more information. |
... |
additional arguments |
using sdmSetting, the feature types, interaction.depth and all settings of the model can be defined. This function generate a sdmSetting object that can be specifically helpful for reproducibility. The object can be shared by a user that may be used for other studies.
If a user aims to reproduce the same results for every time the code is running with the same data and settings, a seed number should be specified. Through the seed argument, a user can specify NULL, means a seed should not be set (if a random sampling is incorporated in the modelling procedure, for different runs the results would be different); TRUE, means a seed should be set (the seed number is randomly selected and used everytime the same setting is incorporated); a number, means the seed will be set to the number specified by the user.
For parallel processing, a list of items can be passed to parallelSetting, including:
ncore: defines the number of cores (it can also be specified outside of this list
method: defines the parallelising engine. Currently, three options are available including 'parallel', 'foreach', and 'future'. default is 'parallel'
doParallel: Optional, definition to register for a backend for parallel processing (needed when method='foreach'). It should be provided as an R expression like the following example:
expression(registerDoParallel(parallelSetting@cl))
The above example is based on the function available in doParallel package. Other packages can also be used to provide and register backend technologies (e.g., doMC)
cluster: Optional; in case a cluster is created and available (e.g., using cl <- parallel::makeCluster(2)), the cluster object can be introduced here to be used as the parallel processing engine, otherwise, it is handled by the sdm package.
hosts: Optional; To use remote machines/clusters in the parallel processing, a character vector with the addresses (names or IPs) of the accessible (on the network) remote clusters can be provided here to be registered and used in parallel processing (still under development so it may not work appropriately!)
fork: Logical, Available for non-windows operating system and specifies whether a fork solution should be used for the parallelisation. Default is TRUE for non-windows OS and FALSE for windows.
NOTE: Only use parallelSetting when you deal with a big dataset or large number of models otherwise, it make the procedure slower rather than faster if the procedure is quick without parallelising!
an object of class sdmSettings
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) # generate sdmSettings object: s <- sdmSetting(sp~., methods=c('glm','gam','brt','svm','rf'), replication='sub',test.percent=30,n=10,modelSettings=list(brt=list(n.trees=500))) s ## End(Not run)## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) # generate sdmSettings object: s <- sdmSetting(sp~., methods=c('glm','gam','brt','svm','rf'), replication='sub',test.percent=30,n=10,modelSettings=list(brt=list(n.trees=500))) s ## End(Not run)
This function extracts a subset of models from a sdmModels object. In generates a new object of the same typeas the origical object. In sdmModels, modelID provides the unique IDs.
Instead of using the subset function, double brackes '[[ ]]' can be used.
#
sdmModels object
subset(x, subset, drop=TRUE, ...)
x[[i,...]]
Arguments:
x - sdmModels object
i- integer. Indicates the index/id of the models (modelID) should be extracted from sdmModels object
subset - Same as i
drop - If TRUE, new modelIDs are generated, otherwise, the original modelIDs are kept in the new object.
... - additional arguments (not impplemented yet!)
Babak Naimi [email protected]
https://www.biogeoinformatics.org/
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) m getModelInfo(m) m1 <- m[[3:4]] m1 getModelInfo(m1) m2 <- m[[3:4,drop=FALSE]] m2 getModelInfo(m2) #---- the following is the same as previous: m2 <- subset(m,3:4,drop=FALSE) m2 getModelInfo(m2) ## End(Not run)## Not run: file <- system.file("external/model.sdm", package="sdm") m <- read.sdm(file) m getModelInfo(m) m1 <- m[[3:4]] m1 getModelInfo(m1) m2 <- m[[3:4,drop=FALSE]] m2 getModelInfo(m2) #---- the following is the same as previous: m2 <- subset(m,3:4,drop=FALSE) m2 getModelInfo(m2) ## End(Not run)