postStrat: Compute a post-stratified estimator

Description

Calculates a generalized regression estimator for a finite population mean/proportion or total based on sample data collected from a complex sampling design and auxiliary population data.

Usage

postStrat(y, x_sample, x_pop, pi = NULL, N = NULL, var_est = FALSE,
  var_method = "lin_HB", pi2 = NULL, data_type = "raw", B = 1000,
  strata = NULL)

Arguments

A numeric vector of the sampled response variable.

x_sample

A vector containing the post-stratum for each sampled unit.

x_pop

A vector or data frame, depending on data_type. If data_type = "raw", then a vector containing the post-stratum for each population unit. If data_type = "totals" or "means", then a data frame, where the first column lists the possible post-strata and the second column contains the population total or proportion in each post-stratum.

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

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

var_est

Default to FALSE, logical for whether or not to compute estimate of variance

var_method

The method to use when computing the variance estimator. Options are a Taylor linearized technique: "lin_HB"= Hajek-Berger estimator, "lin_HH" = Hansen-Hurwitz estimator, "lin_HTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "lin_HT" = Horvitz-Thompson estimator or a resampling technique: "bootstrap_SRS" = bootstrap variance estimator under simple random sampling without replacement, "unconditional_SRS" = simple random sampling variance estimator which accounts for random strata.

pi2

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

data_type

Default to "raw", takes values "raw", "totals" or "means" for whether the user is providing the raw population stratum memberships, the population totals of each stratum, or the population proportions of each stratum.

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

strata

A factor vector of the stratum membership. If NULL, all units are put into the same stratum. Must have same length as y.

Value

A list of output containing:

pop_total: Estimate of population total
pop_mean: Estimate of the population mean
pop_total_var: Estimated variance of population total estimate
pop_mean_var: Estimated variance of population mean estimate
strat_ests: Table of total and mean estimates for each strata
weights: Survey weights produced by post stratification

References

coc77mase

sar92mase

Examples

Run this code

# NOT RUN {
library(survey)
data(api) 
postStrat(y = apisrs$api00, x_sample = apisrs$awards, 
x_pop = data.frame(table(apipop$awards)), data_type = "totals", 
pi = apisrs$pw^(-1))

# }