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The goal ofPointedSDMs is to simplify the construction of integratedspecies distribution models (ISDMs) for large collections ofheterogeneous data. It does so by building wrapper functions aroundinlabru,which uses theINLAmethodologyto estimate a class of latent Gaussian models.
You can install the development version of PointedSDMs fromGitHub with:
# install.packages("devtools")devtools::install_github("PhilipMostert/PointedSDMs")
or directly through CRAN using:
install.packages('PointedSDMs')PointedSDMs includes a selection of functions used to streamline theconstruction of ISDMs as well and perform model cross-validation. Thecore functions of the package are:
| Function name | Function description |
|---|---|
startISDM() | Initialize and specify the components used in the integrated model. |
startSpecies() | Initialize and specify the components used in the multi-species integrated model. |
blockedCV() | Perform spatial blocked cross-validation. |
fitISDM() | Estimate and preform inference on the integrated model. |
datasetOut() | Perform dataset-out cross-validation, which calculates the impact individual datasets have on the full model. |
The functionintModel() produces anR6object, and as a result there are variousslot functions available tofurther specify the components of the model. Theseslot functionsinclude:
intModel() slot function | Function description |
|---|---|
`.$help()` | Show documentation for each of the slot functions. |
`.$plot()` | Used to create a plot of the available data. The output of this function is an object of classgg. |
`.$addBias()` | Add an additional spatial field to a dataset to account for sampling bias in unstructured datasets. |
`.$updateFormula()` | Used to update a formula for a process. The idea is to start specify the full model withstartISDM(), and then thin components per dataset with this function. |
`.$updateComponents()` | Change or add new components used byinlabru in the integrated model. |
`.$priorsFixed()` | Change the specification of the prior distribution for the fixed effects in the model. |
`.$specifySpatial()` | Specify the spatial field in the model using penalizing complexity (PC) priors. |
`.$spatialBlock()` | Used to specify how the points are spatially blocked. Spatial cross-validation is subsequently performed usingblockedCV(). |
`.$addSamplers()` | Function to add an integration domain for the PO datasets. |
`.$specifyRandom()` | Specify the priors for the random effects in the model. |
`.$changeLink()` | Change the link function of a process. |
This is a basic example which shows you how to specify and run anintegrated model, using three disparate datasets containing locations ofthe solitary tinamou (Tinamus solitarius).
library(PointedSDMs)library(ggplot2)library(terra)
bru_options_set(inla.mode="experimental")#Load data indata("SolitaryTinamou")projection<-"+proj=longlat +ellps=WGS84"species<-SolitaryTinamou$datasetscovariates<-terra::rast(system.file('extdata/SolitaryTinamouCovariates.tif',package="PointedSDMs"))mesh<-SolitaryTinamou$mesh
Setting up the model is done easily withstartISDM(), where we specifythe required components of the model:
#Specify model -- here we run a model with one spatial covariate and a shared spatial fieldmodel<- startISDM(species,spatialCovariates=covariates,Projection=projection,Mesh=mesh,responsePA='Present')
We can also make a quick plot of where the species are located using`.$plot()`:
region<-SolitaryTinamou$regionmodel$plot(Boundary=FALSE)+ geom_sf(data= st_boundary(region))
To improve stability, we specify priors for the intercepts of the modelusing`.$priorsFixed()`
model$priorsFixed(Effect='Intercept',mean.linear=0,prec.linear=1)
AndPC priors for the spatial field using`.$specifySpatial()`:
model$specifySpatial(sharedSpatial=TRUE,prior.range= c(0.2,0.1),prior.sigma= c(0.1,0.1))
We can then estimate the parameters in the model using thefitISDM()function:
modelRun<- fitISDM(model,options=list(control.inla=list(int.strategy='eb'),safe=TRUE))summary(modelRun)#> Summary of 'modISDM' object:#>#> inlabru version: 2.12.0#> INLA version: 24.06.27#>#> Types of data modelled:#>#> eBird Present only#> Parks Present absence#> Gbif Present only#> Time used:#> Pre = 1.16, Running = 21.3, Post = 0.274, Total = 22.7#> Fixed effects:#> mean sd 0.025quant 0.5quant 0.975quant mode kld#> Forest 0.054 0.006 0.042 0.054 0.066 0.054 0#> NPP 0.000 0.000 0.000 0.000 0.000 0.000 0#> Altitude -0.002 0.001 -0.003 -0.002 -0.001 -0.002 0#> eBird_intercept -5.419 0.435 -6.271 -5.419 -4.567 -5.419 0#> Parks_intercept -4.783 0.485 -5.734 -4.783 -3.832 -4.783 0#> Gbif_intercept -6.128 0.412 -6.936 -6.128 -5.320 -6.128 0#>#> Random effects:#> Name Model#> eBird_spatial SPDE2 model#> Parks_spatial Copy#> Gbif_spatial Copy#>#> Model hyperparameters:#> mean sd 0.025quant 0.5quant 0.975quant mode#> Range for eBird_spatial 3.615 0.912 2.460 3.421 5.946 2.92#> Stdev for eBird_spatial 2.514 0.427 1.924 2.438 3.567 2.21#> Beta for Parks_spatial 0.137 0.100 -0.043 0.133 0.348 0.11#> Beta for Gbif_spatial 0.714 0.067 0.585 0.713 0.848 0.71#>#> Deviance Information Criterion (DIC) ...............: 259.79#> Deviance Information Criterion (DIC, saturated) ....: 253.61#> Effective number of parameters .....................: -840.82#>#> Watanabe-Akaike information criterion (WAIC) ...: 2441.50#> Effective number of parameters .................: 686.08#>#> Marginal log-Likelihood: -1330.87#> is computed#> Posterior summaries for the linear predictor and the fitted values are computed#> (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')
PointedSDMs also includes generic predict and plot functions:
predictions<- predict(modelRun,mesh=mesh,mask=region,spatial=TRUE,fun='linear')plot(predictions,variable= c('mean','sd'))
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Wrapper function for 'inlabru' for modeling species distribution models from disparate datasets.
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