inst/doc/PennsylvaniaWarbler.R

In intSDM: Reproducible Integrated Species Distribution Models Across Norway using 'INLA'

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE,
  fig.width=8, 
  fig.height=5,
  warning = FALSE,
  message = FALSE)

## ----setup--------------------------------------------------------------------
# 
# library(intSDM)
# library(USAboundaries)
# 

## ----get data-----------------------------------------------------------------
# 
# data("SetophagaData")
# BBA <- SetophagaData$BBA
# BBA$Species_name <- paste0('Setophaga_', BBA$Species_name)
# BBS <- SetophagaData$BBS
# BBS$Species_name <- paste0('Setophaga_', BBS$Species_name)
# 

## ----startWorkflow------------------------------------------------------------
# 
# workflow <- startWorkflow(Richness = FALSE,
#   Projection = "+proj=utm +zone=17 +datum=WGS84 +units=km",
#   Species = c("Setophaga_caerulescens"),
#               #"Setophaga_fusca", "Setophaga_magnolia"),
#   saveOptions = list(projectName =  'Setophaga'), Save = FALSE
# )
# 

## ----addArea------------------------------------------------------------------
# 
# workflow$addArea(Object = USAboundaries::us_states(states = "Pennsylvania"))
# 

## ----download Data------------------------------------------------------------
# 
# workflow$addGBIF(datasetName = 'eBird', datasetType = 'PO', limit = 5000,
#                  datasetKey = '4fa7b334-ce0d-4e88-aaae-2e0c138d049e',
#                  year = '2005,2009')
# 
# workflow$addStructured(dataStructured = BBS, datasetType = 'Counts',
#                        responseName = 'Counts',
#                        speciesName = 'Species_name')
# 
# workflow$addStructured(dataStructured = BBA, datasetType = 'PA',
#                        responseName = 'NPres',
#                        speciesName = 'Species_name')
# 
# workflow$plot(Species = TRUE)
# 

## ----addCovariates------------------------------------------------------------
# 
# covariates <- scale(terra::rast(system.file('extdata/SetophagaCovariates.tif',
#                                       package = "PointedSDMs")))
# names(covariates) <- c('elevation', 'canopy')
# 
# workflow$addCovariates(Object = covariates)
# 
# workflow$plot(Covariates = TRUE)
# 

## ----biasFields---------------------------------------------------------------
# 
# workflow$addMesh(cutoff = 0.2 * 5,
#                  max.edge = c(0.1, 0.24) * 80,
#                  offset = c(0.1, 0.4) * 100)
# 
# workflow$plot(Mesh = TRUE)
# 

## ----speciyRandom-------------------------------------------------------------
# 
# workflow$specifySpatial(prior.range = c(30, 0.1),
#                         prior.sigma = c(1, 0.1))
# 
# workflow$biasFields(datasetName = 'eBird',
#                     prior.range = c(15, 0.1),
#                     prior.sigma = c(1, 0.1))
# 
# workflow$specifyPriors(effectNames = 'Intercept',
#                        Mean = 0,
#                        Precision = 1)
# 

## ----outcomes-----------------------------------------------------------------
# 
# workflow$workflowOutput(c('Model', 'Cross-validation'))
# 
# workflow$crossValidation(Method = 'spatialBlock',
#                          blockOptions = list(k = 4,
#                                              rows_cols = c(20, 20),
#                                              plot = TRUE, seed = 123),
#                          blockCVType = "Predict")
# 

## ----sdmWorkflow--------------------------------------------------------------
# 
# Model <- sdmWorkflow(Workflow = workflow,
#                       inlaOptions = list(control.inla=list(int.strategy = 'eb',
#                                                     diagonal = 0.1,
#                                                     cmin = 0),
#                                   safe = TRUE,
#                                   verbose = TRUE,
#                                   inla.mode = 'experimental'))
# 

## ----plot int-----------------------------------------------------------------
# 
# Model[[1]]$Model
# 

## ----plot bias----------------------------------------------------------------
# 
# Model[[1]]$spatialBlock
#