Observation Process

This function simulates an observation process. It takes sim_results object generated by sim function and uses Virtual Ecologist approach on the N_map component of this object. As a result a data.frame with "observed" abundances is returned.

Usage

get_observations(
  sim_data,
  sim_results,
  type = c("random_one_layer", "random_all_layers", "from_data", "monitoring_based"),
  obs_error = c("rlnorm", "rbinom"),
  obs_error_param = NULL,
  ...
)

Arguments

sim_data

sim_data object from initialise containing simulation parameters

sim_results

sim_results object; returned by sim function

type

character vector of length 1; describes the sampling type (case-sensitive):

• "random_one_layer" - cells from which the population numbers will be sampled are selected randomly; selected cells will be the same for all time steps

• "random_all_layers" - cells from which the abundance will be sampled are selected randomly; a new set of cells will be selected for each time step

• "from_data" - cells for which abundance will be sampled are provided by the user in a data.frame with three columns: "x", "y" and "time_step"

• "monitoring_based" - cells from which abundance will be sampled are provided by the user in a matrix object with two columns: "x" and "y"; abundance from given cell is then sampled by different virtual observers in different time steps; whether a survey will be made by the same observer for several years and whether it will not be made at all is defined using a geometric distribution (rgeom)

obs_error

character vector of length 1; name of the distribution defining the observation process: "rlnorm" (the log normal distribution) or "rbinom" (the binomial distribution)

obs_error_param

numeric vector of length 1; standard deviation (on a log scale) of the random noise in observation process generated from the log-normal distribution (rlnorm) or probability of detection (success) when the binomial distribution ("rbinom") is used

...

other necessary internal parameters:

• prop

  numeric vector of length 1; proportion of cells to be sampled (default prop = 0.1); used when type = "random_one_layer" or "random_all_layers",

• points

  data.frame or matrix with 3 named numeric columns ("x", "y" and "time_step") containing coordinates and time steps from which observations should be obtained; used when type = "from_data",

• cells_coords

  data.frame or matrix with 2 named columns: "x" and "y"; survey plots coordinates; used when type = "monitoring_based"

• prob

  numeric vector of length 1; a parameter defining the shape of - rgeom distribution - it defines whether an observation will be made by the same observer for several years and whether it will not be made at all (default prob = 0.3); used when type = "monitoring_based"

• progress_bar

  logical vector of length 1; determines if progress bar for observational process should be displayed (default progress_bar = FALSE); used when type = "monitoring_based"

Value

data.frame object with geographic coordinates, time steps, estimated abundances, including observation error (if obs_error_param is provided) and observer identifiers (if type = "monitoring_based")

Examples

if (FALSE) {

library(terra)
n1_small <- rast(system.file("input_maps/n1_small.tif", package = "rangr"))
K_small <- rast(system.file("input_maps/K_small.tif", package = "rangr"))

# prepare data
sim_data <- initialise(
  n1_map = n1_small,
  K_map = K_small,
  r = log(2),
  rate = 1 / 1e3
)

sim_1 <- sim(obj = sim_data, time = 110, burn = 10)

# 1. random_one_layer
sample1 <- get_observations(
  sim_data,
  sim_1,
  type = "random_one_layer",
  prop = 0.1
)

# 2. random_all_layers
sample2 <- get_observations(
  sim_data,
  sim_1,
  type = "random_all_layers",
  prop = 0.15
)

# 3. from_data
sample3 <- get_observations(
  sim_data,
  sim_1,
  type = "from_data",
  points = observations_points
)

# 4. monitoring_based
# define observations sites
all_points <- xyFromCell(sim_data$id, cells(sim_data$K_map))
sample_idx <- sample(1:nrow(all_points), size = 20)
sample_points <- all_points[sample_idx, ]

sample4 <- get_observations(
  sim_data,
  sim_1,
  type = "monitoring_based",
  cells_coords = sample_points,
  prob = 0.3,
  progress_bar = TRUE
)

# 5. noise "rlnorm"
sample5 <- get_observations(sim_data,
  sim_1,
  type = "random_one_layer",
  obs_error = "rlnorm",
  obs_error_param = log(1.2)
)

# 6. noise "rbinom"
sample6 <- get_observations(sim_data,
  sim_1,
  type = "random_one_layer",
  obs_error = "rbinom",
  obs_error_param = 0.8
)

}