create_aliens: Simulate datasets for calibrations or assignments

Description

This function allows to simulate data so to provide examples for the calibration and for the assignment procedure. We name the simulated individuals 'Aliens' so to make it clear that the data we use to illustrate our package are not real data.

Usage

create_aliens(
  calib_fn = list(intercept = 3, slope = 0.5, resid_var = 5),
  isoscape = NULL,
  coordinates = NA,
  raster = NULL,
  n_sites = NA,
  min_n_samples = 1,
  max_n_samples = 10
)

Value

This functions returns a data.frame (see example for column names)

Arguments

calib_fn: A list containing the parameter values describing the relationship between the isotope values in the environment and those in the simulated organisms. This list must contain three parameters: the intercept, the slope, and the residual variance.
isoscape: The output of the function isoscape
coordinates: An optional data.frame with columns site_ID, long and lat
raster: A SpatRaster containing an elevation raster
n_sites: The number of sites from which the simulated organisms originate (integer)
min_n_samples: The minimal number of observations (integer) per site
max_n_samples: The maximal number of observations (integer) per site

Details

The isostopic values for the organisms are assumed to be linearly related to the one from the environment. The linear function can be parametrized using the first argument of the function (calib_fn). With this function the user can simulate data for different sites.

The number and locations of sites can be controlled in two ways. A first possibility is to use the argument n_sites. The sites will then be selected randomly among the locations present in the isoscape (argument isoscape) provided to this function. An alternative possibility is to provide a data frame containing three columns (site_ID, long and lat) to input the coordinate of the sampling site manually.

Irrespective of how locations are chosen, a random number of observations will be drawn, at each site, according to a uniform distribution bounded by the values of the argument min_n_samples and max_n_samples.

From the selected coordinates, the isotope values for the environment are directly extracted from the corresponding point predictions stored in the isoscape object. No uncertainty is considered during this process. Then the linear calibration defines the means of the isotope values for the simulated organisms. The actual values is then drawn from a Gaussian distribution centred around such mean and a variance defined by the residual variance (resid_var) input within the list calib_fn.

Examples

Run this code


## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. options_IsoriX(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)

if (getOption_IsoriX("example_maxtime") > 30) {
  ## We fit the models for Germany
  GNIPDataDEagg <- prepsources(data = GNIPDataDE)

  GermanFit <- isofit(data = GNIPDataDEagg)

  ## We build the isoscapes
  GermanScape <- isoscape(raster = ElevRasterDE, isofit = GermanFit)

  ## We create a simulated dataset with 25 sites and 5 observations per site
  Aliens <- create_aliens(
    calib_fn = list(intercept = 3, slope = 0.5, resid_var = 5),
    isoscape = GermanScape,
    raster = ElevRasterDE,
    n_sites = 25,
    min_n_samples = 5,
    max_n_samples = 5
  )

  ## We display the simulated dataset
  Aliens

  ## We plot the relationship between the environmental isotope values
  ## and those from the simulated organisms
  plot(sample_value ~ source_value, data = Aliens, ylab = "Tissue", xlab = "Environment")
  abline(3, 0.5, col = "blue") ## the true relationship

  ## We create a simulated dataset with 2 sites imputing coordinates manually
  Aliens2 <- create_aliens(
    calib_fn = list(intercept = 3, slope = 0.5, resid_var = 5),
    isoscape = GermanScape,
    coordinates = data.frame(
      site_ID = c("Berlin", "Bielefeld"),
      long = c(13.52134, 8.49914),
      lat = c(52.50598, 52.03485)
    ),
    raster = ElevRasterDE,
    min_n_samples = 5,
    max_n_samples = 5
  )

  Aliens2
}