mase (version 0.1.3)

horvitzThompson: Compute the Horvitz-Thompson Estimator

Description

Calculate the Horvitz-Thompson Estimator for a finite population mean/proportion or total based on sample data collected from a complex sampling design.

Usage

horvitzThompson(
  y,
  pi = NULL,
  N = NULL,
  pi2 = NULL,
  var_est = FALSE,
  var_method = "LinHB",
  B = 1000
)

Value

List of output containing:

• pop_total:Estimate of population total

• pop_mean:Estimate of population mean

• pop_total_var: Estimated variance of population total estimate

• pop_mean_var: Estimated variance of population mean estimate

Arguments

y

A numeric vector of the sampled response variable.

pi

A numeric vector of inclusion probabilities for each sampled unit in y. If NULL, then simple random sampling without replacement is assumed.

N

A numeric value of the population size. If NULL, it is estimated with the sum of the inverse of the pis.

pi2

A square matrix of the joint inclusion probabilities. Needed for the "LinHT" variance estimator.

var_est

A logical indicating whether or not to compute a variance estimator. Default is FALSE.

var_method

The method to use when computing the variance estimator. Options are a Taylor linearized technique: "LinHB"= Hajek-Berger estimator, "LinHH" = Hansen-Hurwitz estimator, "LinHTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "LinHT" = Horvitz-Thompson estimator or a resampling technique: "bootstrapSRS" = bootstrap variance estimator under simple random sampling without replacement. The default is "LinHB".

B

The number of bootstrap samples if computing the bootstrap variance estimator. Default is 1000.

References

hor52mase

Examples

library(survey)
data(api)
horvitzThompson(y = apisrs$api00, pi = apisrs$pw^(-1))
horvitzThompson(y = apisrs$api00, pi = apisrs$pw^(-1), var_est = TRUE, var_method = "LinHTSRS")