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Distribution Modeling","monthly_downloads":320,"total_dependent_repos":1,"total_dependent_packages":1,"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# PointedSDMs\n\n\u003c!-- badges: start --\u003e\n\n[![R-CMD-check](https://github.com/PhilipMostert/PointedSDMs/actions/workflows/R-CMD-check.yaml/badge.svg?branch=main)](https://github.com/PhilipMostert/PointedSDMs/actions/workflows/R-CMD-check.yaml)[![Codecov test coverage](https://codecov.io/gh/PhilipMostert/PointedSDMs/branch/main/graph/badge.svg)](https://app.codecov.io/gh/PhilipMostert/PointedSDMs?branch=ChangingToR6) [![DOI](https://zenodo.org/badge/368823136.svg)](https://zenodo.org/badge/latestdoi/368823136)\n\n\u003c!-- badges: end --\u003e\n\nThe goal of *PointedSDMs* is to simplify the construction of integrated species distribution models (ISDMs) for large collections of heterogeneous data. It does so by building wrapper functions around [inlabru](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13168), which uses the [INLA methodology](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9868.2008.00700.x) to estimate a class of latent Gaussian models.\n\n## Installation\n\nYou can install the development version of PointedSDMs from [GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"PhilipMostert/PointedSDMs\")\n```\n\nor directly through CRAN using:\n\n``` r\ninstall.packages('PointedSDMs')\n```\n\n## Package functionality\n\n*PointedSDMs* includes a selection of functions used to streamline the construction of ISDMs as well and perform model cross-validation. The core functions of the package are:\n\n| Function name | Function description |\n|-------------------|-----------------------------------------------------|\n| `startISDM()` | Initialize and specify the components used in the integrated model. |\n| `startSpecies()` | Initialize and specify the components used in the multi-species integrated model. |\n| `blockedCV()` | Perform spatial blocked cross-validation. |\n| `fitISDM()` | Estimate and preform inference on the integrated model. |\n| `datasetOut()` | Perform dataset-out cross-validation, which calculates the impact individual datasets have on the full model. |\n\nThe function `intModel()` produces an [R6](https://github.com/r-lib/R6) object, and as a result there are various *slot functions* available to further specify the components of the model. These *slot functions* include:\n\n| `intModel()` slot function | Function description |\n|--------------------|----------------------------------------------------|\n| `` `.$help()` `` | Show documentation for each of the slot functions. |\n| `` `.$plot()` `` | Used to create a plot of the available data. The output of this function is an object of class [`gg`](https://github.com/tidyverse/ggplot2). |\n| `` `.$addBias()` `` | Add an additional spatial field to a dataset to account for sampling bias in unstructured datasets. |\n| `` `.$updateFormula()` `` | Used to update a formula for a process. The idea is to start specify the full model with `startISDM()`, and then thin components per dataset with this function. |\n| `` `.$updateComponents()` `` | Change or add new components used by [inlabru](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13168) in the integrated model. |\n| `` `.$priorsFixed()` `` | Change the specification of the prior distribution for the fixed effects in the model. |\n| `` `.$specifySpatial()` `` | Specify the spatial field in the model using penalizing complexity (PC) priors. |\n| `` `.$spatialBlock()` `` | Used to specify how the points are spatially blocked. Spatial cross-validation is subsequently performed using `blockedCV()`. |\n| `` `.$addSamplers()` `` | Function to add an integration domain for the PO datasets. |\n| `` `.$specifyRandom()` `` | Specify the priors for the random effects in the model. |\n| `` `.$changeLink()` `` | Change the link function of a process. |\n\n## Example\n\nThis is a basic example which shows you how to specify and run an integrated model, using three disparate datasets containing locations of the solitary tinamou (*Tinamus solitarius)*.\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(warning = FALSE, message = FALSE) \n```\n\n```{r example}\n\nlibrary(PointedSDMs)\nlibrary(ggplot2)\nlibrary(terra)\n\n```\n\n```{r data}\n\nbru_options_set(inla.mode = \"experimental\")\n\n#Load data in\n\ndata(\"SolitaryTinamou\")\n\nprojection \u003c- \"+proj=longlat +ellps=WGS84\"\n\nspecies \u003c- SolitaryTinamou$datasets\n\ncovariates \u003c- terra::rast(system.file('extdata/SolitaryTinamouCovariates.tif', \n                                      package = \"PointedSDMs\"))\n\nmesh \u003c- SolitaryTinamou$mesh\n\n\n```\n\nSetting up the model is done easily with `startISDM()`, where we specify the required components of the model:\n\n```{r intModel, message = FALSE, warning = FALSE}\n\n#Specify model -- here we run a model with one spatial covariate and a shared spatial field\n\nmodel \u003c- startISDM(species, spatialCovariates = covariates,\n                 Projection = projection, Mesh = mesh, responsePA = 'Present')\n\n```\n\nWe can also make a quick plot of where the species are located using `` `.$plot()` ``:\n\n```{r plot, warning = FALSE, message = FALSE, fig.width=8, fig.height=5}\n\nregion \u003c- SolitaryTinamou$region\n\nmodel$plot(Boundary = FALSE) + \n  geom_sf(data = st_boundary(region))\n\n```\n\nTo improve stability, we specify priors for the intercepts of the model using `` `.$priorsFixed()` ``\n\n```{r specifyPriors}\n\nmodel$priorsFixed(Effect = 'Intercept',\n                  mean.linear = 0, \n                  prec.linear = 1)\n\n```\n\nAnd *PC* priors for the spatial field using `` `.$specifySpatial()` ``:\n\n```{r specifySpatial}\n\nmodel$specifySpatial(sharedSpatial = TRUE,\n                     prior.range = c(0.2, 0.1),\n                     prior.sigma = c(0.1, 0.1))\n\n```\n\nWe can then estimate the parameters in the model using the `fitISDM()` function:\n\n```{r fitISDM, warning = FALSE, message = FALSE}\n\nmodelRun \u003c- fitISDM(model, options = list(control.inla = \n                                            list(int.strategy = 'eb'), \n                                          safe = TRUE))\nsummary(modelRun)\n\n```\n\n*PointedSDMs* also includes generic predict and plot functions:\n\n```{r predict_and_plot, warning = FALSE, message = FALSE, fig.width=8, fig.height=5}\n\npredictions \u003c- predict(modelRun, mesh = mesh,\n                       mask = region, \n                       spatial = TRUE,\n                       fun = 'linear')\n\nplot(predictions, variable = c('mean', 'sd'))\n\n```\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/302197","html_url":"https://ost.ecosyste.ms/projects/302197"}