estim_pi_ij: Calculate Joint Inclusion Probabilities Using Simulations
In ksauby/ACS: Adaptive Cluster Sampling

estim_pi_ij

R Documentation

Calculate Joint Inclusion Probabilities Using Simulations

Calculate inclusion probabilities for each unit in a population using simulations.

estim_pi_ij(
  popdata,
  sims,
  n1_vec,
  SamplingDesign = "ACS",
  y_variable,
  f_max = NULL,
  popvar = NULL
)

`popdata`	A data frame providing information about the population to be sampled. Required columns include geographic coordinate columns `x` and `y`, as well as a column containing the variable of interest, whose name is provided to the function via the `yvar` argument.
`sims`	Number of simulations per population.
`n1_vec`	Vector of initial sample size(s) for the initial simple random sample(s) without replacement; can be a single value or vector of values.
`SamplingDesign`	A character string supplying the sampling design to use; accepted options include ACS" (unrestricted adaptive cluster sampling) and "RACS" (restricted ACS). The default is "ACS".
`f_max`	The maximum number of expanding sets of adjacent units surveyed per primary unit in the adaptive phase of ACS. For more information, see Sauby and Christman (in prep).
`popvar`	A character string (OR VECTOR??) used to identify different populations. The default value is NULL.
`yvar`	A string giving the name of the variable of interest, y, in the supplied dataframe popdata. This variable determines the condition under which adaptive cluster sampling takes place. In the dataframe $popdata$ this variable y must be numeric.