mirror of
https://github.com/Rucknium/misc-research.git
synced 2025-01-03 09:09:25 +00:00
First draft Financial Marginalization and Cryptocurrency Payments regressions
This commit is contained in:
Rucknium
parent
e1110188df
commit
27d419e16e
2 changed files with 2212 additions and 36 deletions
|
|
@ -5,29 +5,686 @@ date: '2022-05-26'
|
|||
output: html_document
|
||||
---
|
||||
|
||||
```{css, echo=FALSE}
|
||||
body .main-container {
|
||||
max-width: 1480px !important;
|
||||
width: 1480px !important;
|
||||
}
|
||||
body {
|
||||
max-width: 1480px !important;
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
```{r setup, include=FALSE}
|
||||
knitr::opts_chunk$set(echo = TRUE)
|
||||
# SURVEY OF HOUSEHOLD ECONOMICS AND DECISIONMAKING
|
||||
knitr::opts_chunk$set(echo = TRUE, size = "small", tidy = FALSE)
|
||||
options(width = 170)
|
||||
|
||||
|
||||
|
||||
# or_glm function adapted from "oddsratio" package so it can
|
||||
# handle svyglm functions:
|
||||
|
||||
or_svyglm <- function (data, model, incr, ci = 0.95)
|
||||
{
|
||||
if (any(class(model) %in% "glm")) {
|
||||
preds <- names(coefficients(model))[2:length(coefficients(model))]
|
||||
coef <- coefficients(model)[2:length(coefficients(model))]
|
||||
}
|
||||
increments <- list()
|
||||
odds_ratios <- list()
|
||||
ci_low <- list()
|
||||
ci_high <- list()
|
||||
for (i in preds) {
|
||||
if (any(class(model) %in% "glm")) {
|
||||
ci_list <- data.frame(suppressMessages(confint(model,
|
||||
level = ci)))[-1, ]
|
||||
}
|
||||
if (is.numeric(data[[i]]) | is.integer(data[[i]])) {
|
||||
odds_ratios[[i]] <- round(exp(as.numeric(coef[[i]]) *
|
||||
as.numeric(incr[[i]])), 3)
|
||||
if (!class(model)[1] == "glmmPQL") {
|
||||
ci_low[[i]] <- round(exp(ci_list[i, 1] * as.numeric(incr[[i]])),
|
||||
3)
|
||||
ci_high[[i]] <- round(exp(ci_list[i, 2] * as.numeric(incr[[i]])),
|
||||
3)
|
||||
}
|
||||
increments[[i]] <- as.numeric(incr[[i]])
|
||||
or <- odds_ratios[[i]]
|
||||
}
|
||||
else {
|
||||
odds_ratios[[i]] <- round(exp(as.numeric(coef[[i]])),
|
||||
3)
|
||||
if (!class(model)[1] == "glmmPQL") {
|
||||
ci_low[[i]] <- round(exp(ci_list[i, 1]), 3)
|
||||
ci_high[[i]] <- round(exp(ci_list[i, 2]), 3)
|
||||
}
|
||||
increments[[i]] <- "Indicator variable"
|
||||
or <- odds_ratios[[i]]
|
||||
}
|
||||
}
|
||||
result <- data.frame(predictor = names(odds_ratios), oddsratio = unlist(odds_ratios,
|
||||
use.names = FALSE), ci_low = unlist(ci_low, use.names = FALSE),
|
||||
ci_high = unlist(ci_high, use.names = FALSE), increment = unlist(increments,
|
||||
use.names = FALSE))
|
||||
if (any(class(model) %in% "glm")) {
|
||||
col_names <- gsub("\\.\\.", replacement = "", names(ci_list))
|
||||
col_names <- gsub("X", replacement = "", col_names)
|
||||
colnames(result)[3] <- paste0("ci_low (", col_names[1],
|
||||
")")
|
||||
colnames(result)[4] <- paste0("ci_high (", col_names[2],
|
||||
")")
|
||||
}
|
||||
return(result)
|
||||
}
|
||||
|
||||
```
|
||||
|
||||
## Load packages
|
||||
|
||||
```{r load-packages}
|
||||
# install.packages("readstata13")
|
||||
# install.packages("survey")
|
||||
# install.packages("corrplot")
|
||||
|
||||
library(readstata13)
|
||||
library(survey)
|
||||
library(corrplot)
|
||||
|
||||
```
|
||||
|
||||
|
||||
## Download and import data
|
||||
|
||||
```{r get-data}
|
||||
|
||||
# download.file("https://www.federalreserve.gov/consumerscommunities/files/SHED_public_use_data_2021_(Stata).zip",
|
||||
# "data/SHED_public_use_data_2021_(Stata).zip")
|
||||
# unzip("data/SHED_public_use_data_2021_(Stata).zip", exdir = "data")
|
||||
|
||||
|
||||
SHED <- readstata13::read.dta13("data/public2021.dta", generate.factors = TRUE, nonint.factors = TRUE)
|
||||
|
||||
# S16_b
|
||||
# In the past year, have you done the following
|
||||
# with cryptocurrency, such as Bitcoin or Ethereum?
|
||||
# - Used to buy something or make a payment
|
||||
SHED$crypto.used.as.payment <- SHED$S16_b == "Yes"
|
||||
|
||||
# BK1
|
||||
# Do you and/or your spouse or partner
|
||||
# currently have a checking, savings or money
|
||||
# market account?
|
||||
SHED$does.not.have.bank.account <- SHED$BK1 == "No"
|
||||
|
||||
# C2A
|
||||
# Do you currently have at least one credit card?
|
||||
SHED$does.not.have.credit.card <- SHED$C2A == "No"
|
||||
|
||||
# ppgender
|
||||
# Gender [Ipsos source]
|
||||
SHED$is.male <- SHED$ppgender == "Male"
|
||||
|
||||
# race_5cat Race 5 categories
|
||||
SHED$race <- SHED$race_5cat
|
||||
|
||||
# ED0
|
||||
# What is the highest level of school you have
|
||||
# completed or the highest degree you have
|
||||
# received?
|
||||
SHED$education.level <- relevel(SHED$ED0, "High school degree or GED")
|
||||
|
||||
# ED1
|
||||
# Which one of the following broad categories
|
||||
# best describes your (current/most recent)
|
||||
# educational program?
|
||||
SHED$education.subject <- relevel(SHED$ED1, "Business/management")
|
||||
|
||||
# pppa_lgb [Ipsos source]
|
||||
# Q230: Which of the following best describes how you think of yourself?
|
||||
SHED$lgbtq <- relevel(SHED$pppa_lgb, "Straight, that is, not gay")
|
||||
|
||||
# ppp20197 [Ipsos source]
|
||||
# QEG22: Are you a citizen of the United States?
|
||||
SHED$is.noncitizen <- SHED$ppp20197 == "No"
|
||||
|
||||
# I40
|
||||
# Which of the following categories best
|
||||
# describes the total income that you and/or
|
||||
# your spouse or partner received from all
|
||||
# sources, before taxes and deductions, in the
|
||||
# past 12 months?
|
||||
SHED$income.category <- SHED$I40
|
||||
|
||||
# B2
|
||||
# Overall, which one of the following best
|
||||
# describes how well you are managing
|
||||
# financially these days?
|
||||
SHED$overall.financial.wellbeing <- SHED$B2
|
||||
|
||||
# I41_b
|
||||
# Supplemental Nutrition Assistance Program
|
||||
# (SNAP or food stamps) - In the past 12
|
||||
# months, have you received any of the
|
||||
# following?
|
||||
SHED$received.food.stamps <- SHED$I41_b
|
||||
|
||||
# FL0
|
||||
# On a scale from zero to ten, where zero is
|
||||
# not at all willing to take risks and ten is
|
||||
# very willing to take risks, what number would
|
||||
# you be on the scale?
|
||||
|
||||
SHED$risk.tolerance <- as.numeric(SHED$FL0) - 1
|
||||
|
||||
# B10
|
||||
# Overall, on a scale from zero to ten, where
|
||||
# zero is not at all satisfied and ten is
|
||||
# completely satisfied, how satisfied are you
|
||||
# with life as a whole these days?
|
||||
SHED$life.satisfaction <- as.numeric(SHED$B10) - 1
|
||||
|
||||
# ppage
|
||||
# Age [Ipsos source]
|
||||
# ppcmdate
|
||||
# Date member completed Core survey
|
||||
# Must correct age variable for time of initial Ipsos survey
|
||||
SHED$age <- SHED$ppage + (2021 - as.numeric(substr(SHED$ppcmdate, 1, 4)))
|
||||
|
||||
# ind1
|
||||
# IND1: Industry (tight scale) in current or main job
|
||||
SHED$job.industry <- relevel(SHED$ind1, "Retail/Stores/Shopping (including Online Retail)")
|
||||
|
||||
# ppcm0160 [Ipsos source]
|
||||
# Q26: Occupation (detailed) in current or main job
|
||||
SHED$job.occupation <- relevel(SHED$ppcm0160, "Retail Sales")
|
||||
|
||||
# ppcm1301 [Ipsos source]
|
||||
# GOVEMP1: Employer type
|
||||
SHED$employer.type <- relevel(SHED$ppcm1301, "Private-for-profit company")
|
||||
|
||||
# ppmsacat
|
||||
# MSA Status [Ipsos source]
|
||||
SHED$resides.in.metro.statistical.area <- SHED$ppmsacat == "Metro"
|
||||
|
||||
# ppfs0596 [Ipsos source]
|
||||
# Q22: What is the approximate total amount of
|
||||
# your household's savings and investments?
|
||||
SHED$total.household.savings <- relevel(SHED$ppfs0596, "$100,000 - $249,999")
|
||||
|
||||
# A1_a
|
||||
# In the past 12 months, has each of the following happened to you:
|
||||
# - Turned down for credit
|
||||
SHED$rejected.for.credit <- SHED$A1_a == "Yes"
|
||||
|
||||
# BK2_a
|
||||
# In the past 12 months, did you and/or spouse or partner:
|
||||
# - Purchase a money order from a place other than a bank
|
||||
SHED$purchase.non.bank.money.order <- SHED$BK2_a == "Yes"
|
||||
|
||||
# BK2_b
|
||||
# In the past 12 months, did you and/or spouse or partner:
|
||||
# - Cash a check at a place other than a bank
|
||||
SHED$cash.check.non.bank <- SHED$BK2_b == "Yes"
|
||||
|
||||
# BK2_c
|
||||
# In the past 12 months, did you and/or spouse or partner:
|
||||
# - Take out a payday loan or payday advance
|
||||
SHED$take.payday.loan <- SHED$BK2_c == "Yes"
|
||||
|
||||
# BK2_d
|
||||
# In the past 12 months, did you and/or spouse or partner:
|
||||
# - Take out a pawn shop loan or an auto title loan
|
||||
SHED$take.auto.or.pawn.shop.loan <- SHED$BK2_d == "Yes"
|
||||
|
||||
# BK2_e
|
||||
# In the past 12 months, did you and/or spouse or partner:
|
||||
# - Obtain a tax refund advance to receive your refund faster
|
||||
SHED$take.tax.refund.advance <- SHED$BK2_e == "Yes"
|
||||
|
||||
# BNPL1
|
||||
# In the past year, have you used a “Buy Now
|
||||
# Pay Later” service to buy something?
|
||||
SHED$used.buy.now.pay.later <- SHED$BNPL1 == "Yes"
|
||||
|
||||
# ppfs1482 [Ipsos source]
|
||||
# Q108: Where do you think your credit score falls
|
||||
SHED$perceived.credit.score <- relevel(SHED$ppfs1482, "Fair")
|
||||
|
||||
# GE2A
|
||||
# Some people earn money by selling items at
|
||||
# places like flea markets and garage sales or
|
||||
# through online marketplaces like eBay or
|
||||
# Etsy. In the past month, have you made money
|
||||
# by selling items in any of these ways?
|
||||
|
||||
SHED$informal.selling.of.goods <- SHED$GE2A == "Yes"
|
||||
|
||||
# GE1A
|
||||
# In the past month, have you done any
|
||||
# freelance or gig-work, either to supplement
|
||||
# your income or as your main job?
|
||||
|
||||
SHED$freelance.or.gig.work <- SHED$GE1A == "Yes"
|
||||
|
||||
# E7
|
||||
# During the past 12 months, have you
|
||||
# personally experienced discrimination or
|
||||
# unfair treatment because of your race,
|
||||
# ethnicity, age, religion, disability status,
|
||||
# sexual orientation, gender, or gender
|
||||
# identity?
|
||||
SHED$experienced.discrimination <- SHED$E7 == "Yes"
|
||||
|
||||
# E8_b
|
||||
# In the past 12 months, did you personally experience
|
||||
# discrimination or unfair treatment while
|
||||
# doing any of the following?
|
||||
# - Banking or applying for a loan
|
||||
SHED$experienced.discrimination.in.banking <- SHED$E8_b %in% "Yes"
|
||||
|
||||
# xlaptop
|
||||
# Is R a KP laptop user?
|
||||
SHED$is.kp.laptop.user <- SHED$xlaptop %in% "Yes"
|
||||
|
||||
# devicetype2
|
||||
# DOV: Device Type - at the end of survey
|
||||
SHED$respondent.device.type <- relevel(SHED$devicetype2, "WinPC")
|
||||
|
||||
|
||||
SHED <- svydesign(ids = ~0, data = SHED, weights = SHED$weight_pop)
|
||||
|
||||
# weight_pop used as survey weights, in accordance with suggestion by:
|
||||
# https://www.federalreserve.gov/consumerscommunities/files/SHED_2021codebook.pdf
|
||||
|
||||
|
||||
```
|
||||
|
||||
## R Markdown
|
||||
## Checking correlations between main variables
|
||||
|
||||
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
|
||||
|
||||
When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
|
||||
```{r initial-correlations}
|
||||
|
||||
|
||||
svyvar.covariance <- svyvar(~ age + is.male + crypto.used.as.payment +
|
||||
does.not.have.bank.account + does.not.have.credit.card, SHED, na.rm = TRUE)
|
||||
attr(svyvar.covariance, "var") <- NULL
|
||||
svyvar.correlation <- cov2cor(as.matrix(svyvar.covariance))
|
||||
|
||||
print(round(svyvar.correlation, 3))
|
||||
|
||||
corrplot(svyvar.correlation, tl.col = "darkred", tl.srt = 35,
|
||||
method = "shade", number.digits = 2, addshade = "all", diag = FALSE,
|
||||
title = "\n\n Correlation Plot",
|
||||
addCoef.col = "black", type = "lower")
|
||||
|
||||
```{r cars}
|
||||
summary(cars)
|
||||
```
|
||||
|
||||
## Including Plots
|
||||
# Main results
|
||||
|
||||
## does.not.have.bank.account
|
||||
|
||||
```{r main-results-does-not-have-bank-account}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.bank.account,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## does.not.have.credit.card
|
||||
|
||||
```{r main-results-does-not-have-credit-card}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + does.not.have.credit.card,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
|
||||
```
|
||||
|
||||
# Additional results on financial marginalization
|
||||
|
||||
## rejected.for.credit
|
||||
|
||||
```{r other-financial-marginalization-rejected-for-credit}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + rejected.for.credit,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## purchase.non.bank.money.order
|
||||
|
||||
```{r other-financial-marginalization-purchase-non-bank-money-order}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + purchase.non.bank.money.order,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## cash.check.non.bank
|
||||
|
||||
```{r other-financial-marginalization-cash-check-non-bank}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + cash.check.non.bank,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## take.payday.loan
|
||||
|
||||
```{r other-financial-marginalization-take-payday-loan}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.payday.loan,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## take.auto.or.pawn.shop.loan
|
||||
|
||||
```{r other-financial-marginalization-take-auto-or-pawn-shop-loan}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.auto.or.pawn.shop.loan,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## take.tax.refund.advance
|
||||
|
||||
```{r other-financial-marginalization-take-tax-refund-advance}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + take.tax.refund.advance,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## used.buy.now.pay.later
|
||||
|
||||
```{r other-financial-marginalization-used-buy-now-pay-later}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + used.buy.now.pay.later,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## experienced.discrimination.in.banking
|
||||
|
||||
```{r other-financial-marginalization-experienced-discrimination-in-banking}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + experienced.discrimination.in.banking,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## perceived.credit.score
|
||||
|
||||
```{r other-financial-marginalization-perceived-credit-score}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + perceived.credit.score,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
# General societal marginalization
|
||||
|
||||
## experienced.discrimination
|
||||
|
||||
```{r general-societal-marginalization-experienced-discrimination}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + experienced.discrimination,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## race
|
||||
|
||||
```{r general-societal-marginalization-race}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + race,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## is.noncitizen
|
||||
|
||||
```{r general-societal-marginalization-is.noncitizen}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + is.noncitizen,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## lgbtq
|
||||
|
||||
```{r general-societal-marginalization-lgbtq}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + lgbtq,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
# Data quality sanity checks
|
||||
|
||||
## education.subject
|
||||
|
||||
```{r data-quality-checks-education-subject}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + education.subject,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## job.industry
|
||||
|
||||
```{r data-quality-checks-job-industry}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + job.industry,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## job.occupation
|
||||
|
||||
```{r data-quality-checks-job-occupation}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + job.occupation,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## risk.tolerance
|
||||
|
||||
```{r data-quality-checks-risk-tolerance}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + risk.tolerance,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1, risk.tolerance = 1))
|
||||
|
||||
|
||||
```
|
||||
|
||||
|
||||
# Miscellaneous
|
||||
|
||||
## education.level
|
||||
|
||||
```{r miscellaneous-education-level}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + education.level,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## income.category
|
||||
|
||||
```{r miscellaneous-income-category}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + income.category,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## total.household.savings
|
||||
|
||||
```{r miscellaneous-total-household-savings}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + total.household.savings,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## overall.financial.wellbeing
|
||||
|
||||
```{r miscellaneous-overall-financial-wellbeing}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + overall.financial.wellbeing,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## received.food.stamps
|
||||
|
||||
```{r miscellaneous-received-food-stamps}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + received.food.stamps,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## life.satisfaction
|
||||
|
||||
```{r miscellaneous-life-satisfaction}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + life.satisfaction,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1, life.satisfaction = 1))
|
||||
|
||||
```
|
||||
|
||||
## employer.type
|
||||
|
||||
```{r miscellaneous-employer-type}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + employer.type,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## employer.type = Self-employed & job.occupation = Computer and Mathematical
|
||||
|
||||
```{r miscellaneous-self-employed-computer-industry}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male +
|
||||
I(employer.type == "Self-employed") * I(job.occupation == "Computer and Mathematical"),
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
print(or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1)))
|
||||
|
||||
```
|
||||
|
||||
## informal.selling.of.goods
|
||||
|
||||
```{r miscellaneous-informal-selling-of-goods}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + informal.selling.of.goods,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## freelance.or.gig.work
|
||||
|
||||
```{r miscellaneous-freelance-or-gig-work}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + freelance.or.gig.work,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## resides.in.metro.statistical.area
|
||||
|
||||
```{r miscellaneous-resides-in-metro-statistical-area}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + resides.in.metro.statistical.area,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## respondent.device.type
|
||||
|
||||
```{r miscellaneous-respondent-device-type}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + respondent.device.type,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
```
|
||||
|
||||
## is.kp.laptop.user
|
||||
|
||||
```{r miscellaneous-is-kp-laptop-user}
|
||||
|
||||
svyglm.fit <- svyglm(crypto.used.as.payment ~ age + is.male + is.kp.laptop.user,
|
||||
SHED, family = stats::quasibinomial(link = "logit"))
|
||||
summary(svyglm.fit)
|
||||
or_svyglm(SHED$variables, svyglm.fit, incr = list(age = 1))
|
||||
|
||||
You can also embed plots, for example:
|
||||
|
||||
```{r pressure, echo=FALSE}
|
||||
plot(pressure)
|
||||
```
|
||||
|
||||
Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
Loading…
Reference in a new issue