This helper function summarizes a given object, including DistributionModel, PredictorDataset or PriorList objects and others. This can be a helpful way to summarize what is contained within and the values of specified models or objects.
When unsure, it is usually a good strategy to run summary on any object.
Usage
# S3 method for distribution
summary(object, ...)
# S3 method for DistributionModel
summary(object, ...)
# S3 method for PredictorDataset
summary(object, ...)
# S3 method for BiodiversityScenario
summary(object, ...)
# S3 method for PriorList
summary(object, ...)
# S3 method for Settings
summary(object, ...)
Examples
if (FALSE) {
# Example with a trained model
x <- distribution(background) |>
# Presence-absence data
add_biodiversity_poipa(surveydata) |>
# Add predictors and scale them
add_predictors(env = predictors) |>
# Use glmnet and lasso regression for estimation
engine_glmnet(alpha = 1)
# Train the model
mod <- train(x)
summary(mod)
# Example with a prior object
p1 <- BREGPrior(variable = "forest", hyper = 2, ip = NULL)
p2 <- BREGPrior(variable = "cropland", hyper = NULL, ip = 1)
pp <- priors(p1,p2)
summary(pp)
}