bschneidr

library(dplyr)
library(tidyr)
library(survey)

# Load example data
# 'apipop' has one row per school

  data('api', package = 'survey')

# Draw a sample of school districts

bschneidr

library(dplyr)
library(broom)
library(tidyr)

# Make example data
  my_data <- data.frame(x = sample(1:5, size = 10, replace = TRUE),
                        y = sample(1:5, size = 10, replace = TRUE))

# Generate every pair of variables
  var_list <- colnames(my_data)

bschneidr

describe_design(
  srs_stage(id = "PSU_ID", method = "SRS",
            stratum = "PSU_STRATUM_ID"),
  nonresponse_stage(response_indicator = "PSU_RESPONDENT"),
  pps_stage(id = "SSU_ID", method = "PPS",
            stratum = "SSU_STRATUM_ID"),
  nonresponse_stage(response_indicator = "SSU_RESPONDENT")
)

bschneidr

n = 5

# Establish quadratic form

quad_form_matrix <- matrix(nrow = n, ncol = n)
for (i in seq(n)) {
  for (j in seq(n)) {
    if (i == j) {
      quad_form_matrix[i,j] <- (1/n)
    } else {

bschneidr

#' @title Wilson's confidence interval for complex survey designs
#' @description Calculate Wilson's confidence interval for a proportion,
#' with the effective sample size determined using a design-unbiased
#' estimate of the complex survey design effect.
#'
#' @param x A formula, vector, or matrix.
#' @param design A survey.design or svyrep.design object
#' @param na.rm Should cases with missing values be dropped?
#' @param level The confidence level required
#' @param ... Additional arguments to pass on to \code{svymean()}

bschneidr

#' @title Wilson's confidence interval for complex survey designs
#' @description Calculate Wilson's confidence interval for a proportion,
#' with the effective sample size determined using a design-unbiased
#' estimate of the complex survey design effect.
#'
#' @param x A formula, vector, or matrix.
#' @param design A survey.design or svyrep.design object
#' @param na.rm Should cases with missing values be dropped?
#' @param level The confidence level required
#'

bschneidr

suppressPackageStartupMessages({
  library(survey)
  library(svrep)
  library(polyapost)
})

set.seed(1999)

# Load example survey data ----
  data("lou_vax_survey", package = 'svrep')

bschneidr

library(Rcpp)

# Create an R function for sorting a matrix
# based on an accompanying vectors

cpp_code <- (
  'using namespace Rcpp;

//#include <Rcpp.h>
#include <RcppArmadillo.h>

bschneidr

# https://nces.ed.gov/surveys/piaac/datafiles.asp
# Load the survey data and prepare it for analysis ----
  piaac17_puf <- file.path(
    "C:\\Users\\benja\\Downloads\\spss\\prgusap1_puf.sav"
  ) |> haven::read_sav()
  
  piaac17_rep_svy <- svrepdesign(
    data = piaac17_puf,
    repweights = "SPFWT[1-9]{1}0{0,1}",
    weights = ~ SPFWT0,

bschneidr

# Generate example population and sample ----
  population <- data.frame(
    vax_status = sample(x = c(0,1), prob = c(0.25, 0.75), size = 1000, replace = TRUE),
    response_status = sample(x = c("Respondent", "Nonrespondent"),
                             size = 1000, replace = TRUE, prob = c(0.8, 0.2))
  )
  
  sample_data <- population[sample(x = 1000,size=150),]

# Create a survey design object ----