AuxSurvey (version 1.0)

postStr_wt: Weighted or Unweighted Post-Stratification Estimator

Description

This function performs post-stratification adjustment for survey data, which adjusts the sample weights to match the marginal distributions of auxiliary variables in the population. It supports both weighted and unweighted estimations for various outcome variables, including Gaussian (continuous) and Binomial (binary) outcomes. The function computes estimates and confidence intervals (CIs) for the outcome variable using post-stratification based on the specified auxiliary variables.

Usage

postStr_wt(
  svysmpl,
  svypopu,
  auxVars,
  svyVar,
  subset = NULL,
  family = gaussian(),
  invlvls,
  weights = NULL
)

Value

A list where each element contains the post-stratification estimate and confidence intervals (CIs) for a subset or the entire dataset. The list includes: - est: The post-stratification estimate for the outcome variable. - se: The standard error of the estimate. - tCI: The confidence intervals for the estimate. - sample_size: The sample size for the subset or entire dataset. - population_size: The population size, if provided, including the finite population correction (FPC).

Arguments

svysmpl

A dataframe or tibble representing the sample data (samples). This should contain the outcome variable and any auxiliary variables.

svypopu

A dataframe or tibble representing the population data (population). This is used to compute the finite population correction (FPC) for post-stratification.

auxVars

A character vector containing the names of auxiliary variables to be used for post-stratification. These variables will be used to adjust the weights.

svyVar

The outcome variable for which the post-stratification estimate is calculated.

subset

A character vector representing filtering conditions to select subsets of the sample and population. Default is NULL, in which case the analysis is performed on the entire dataset. If subsets are specified, estimates for both the whole data and the subsets will also be calculated.

family

The distribution family of the outcome variable. Supported options are: gaussian for continuous outcomes and binomial for binary outcomes.

invlvls

A numeric vector specifying the confidence levels for the post-stratification estimators. If more than one value is provided, multiple CIs will be calculated.

weights

A numeric vector of case weights. The length should match the number of cases in svysmpl. These weights are used in the weighted post-stratification adjustment.

Examples

## Simulate data with nonlinear association (setting 3).
data = simulate(N = 3000, discretize = 3, setting = 3, seed = 123)
population = data$population  # Population data (3000 cases)
samples = data$samples        # Sample data (600 cases)
ipw = 1 / samples$true_pi    # Compute inverse probability weights

## Perform weighted post-stratification with auxiliary variables
auxVars = c("Z1", "Z2", "Z3")
Weighted_postStratify = postStr_wt(svysmpl = samples, svypopu = population, auxVars = auxVars,
                                   svyVar = "Y1", subset = NULL, family = gaussian(),
                                   invlvls = c(0.95), weights = ipw)
Weighted_postStratify

## Perform unweighted post-stratification
Unweighted_postStratify = postStr_wt(svysmpl = samples, svypopu = population, auxVars = auxVars,
                                     svyVar = "Y1", subset = NULL, family = gaussian(),
                                     invlvls = c(0.95), weights = NULL)
Unweighted_postStratify