estim_pi_i: Estimate Inclusion Probabilities Using Simulations

In ksauby/ACS: Adaptive Cluster Sampling

Estimate Inclusion Probabilities Using Simulations

Description

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

Usage

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

Arguments

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).
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.

Value

Returns a dataframe with the following columns: realization n.networks N.SRSWOR.plots SamplingDesign simulations coords mean_m max_m min_m median_m

References

\insertRef

saubyadaptiveACSampling

ksauby/ACS documentation built on Aug. 18, 2022, 3:33 a.m.