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 ----

bschneidr

Step 1: Run the following code.

git svn clone svn://r-forge.r-project.org/svnroot/r-survey/pkg/survey/

Step 2: Edit the '.git/config' file with the following:

[svn-remote "r-forge"]
	url = svn://r-forge.r-project.org/svnroot/r-survey/pkg/survey
	fetch = :refs/remotes/r-forge-svn

bschneidr

library(survey)
library(svyVGAM)

# Load some example data
data(nhanes_sxq)

# Save it to a file so it can also be used by Stata
haven::write_dta(data = nhanes_sxq,
                 path = "nhanes_sxq.dta")

bschneidr

library(survey)
library(svyVGAM)
data(api)

# Prepare data with binary, categorical, and ordered outcomes ----

  apiclus2 <- transform(apiclus2, mealcat = cut(meals,c(0,25,50,75,100)))
  apiclus2 <- transform(apiclus2, mealcat_ordered = as.ordered(cut(meals,c(0,25,50,75,100))))
  apiclus2 <- transform(apiclus2, mealcat_high = as.ordered(cut(meals, c(0,50,100))))
  

bschneidr

# Download Franco et al. supplementary materials if you trust this code to safely download materials ----
  trust <- FALSE
  if (trust) {
  download.file("https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6690503/bin/smy019_supplementary_data.zip",
                destfile = "franco-et-al-ci-supp.zip")
  }
  
# Unzip the folder ----
  unzip("franco-et-al-ci-supp.zip", exdir = "franco-et-al-ci-supp")

bschneidr

library(survey)

# Get example data
data(api)
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

# Estimate means by subpopulation
# Use `covmat = TRUE` to also estimate covariances
mns <- svyby(~api99, ~stype,
             dclus1,

bschneidr

library(magrittr)
library(tidyverse)
library(readr)
library(readxl)
library(schneidr)

theme_set(theme_schneidr(base_font_family = 'sans',
                         titles_font_family = 'sans'))

# Generate population

bschneidr

# Define the function

get_hessian <- function(f, as_matrix = FALSE, eval_at = NULL) {
  
  fn_inputs <- all.vars(f); names(fn_inputs) <- fn_inputs
  n_inputs <- length(fn_inputs)
  
  # Obtain the symbolic Hessian as a nested list
  
  result <- lapply(fn_inputs, function(x) lapply(fn_inputs, function(x) NULL))