datasetOut function

datasetOut: function that removes a dataset out of the main model, and calculates some cross-validation score.

This function calculates the difference in covariate values between a full integrated model and a model with one dataset left out, as well as some cross-validation score, which is used to obtain a score of the relative importance of the dataset in the full model. The score is calculated as follows:

1. Running a new model with one less dataset (from the main model) -- resulting in a reduced model,
2. predicting the intensity function at the locations of the left-out dataset with the reduced model,
3. using the predicted values as an offset in a new model,
4. finding the difference between the marginal-likelihood of the main model (ie the model with all the datasets considered) and the marginal-likelihood of the offset model.

datasetOut(model, dataset, predictions = TRUE)

Arguments

• model: Model of class modISDM run with multiple datasets.
• dataset: Names of the datasets to leave out. If missing, will run for all datasets used in the full model.
• predictions: Will new models be used for predictions. If TRUE returns marginals and bru_info in model. Defaults to TRUE.

Returns

A list of inlabru models with the specified dataset left out. If predictions is FALSE, these objects will be missing their bru_info and call lists.

Examples

## Not run:

 if (requireNamespace('INLA')) {
   
 #Get Data
 data("SolitaryTinamou")
 proj <- "+proj=longlat +ellps=WGS84"
 data <- SolitaryTinamou$datasets
 mesh <- SolitaryTinamou$mesh
 mesh$crs <- proj
 
 #Set model up
 organizedData <- startISDM(data, Mesh = mesh,
                            Projection = proj, 
                            responsePA = 'Present')
 
  ##Run the model
  modelRun <- fitISDM(organizedData,
              options = list(control.inla = list(int.strategy = 'eb')))
   
  #Choose dataset to leave out
  eBirdOut <- datasetOut(modelRun, dataset = 'eBird')
  
  #Print datasetOut summary
  eBirdOut
  
  
   
 }
## End(Not run)