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SPHC-2010.Rmd
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SPHC-2010.Rmd
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---
title: "Self-reported sexual identity in Stockholm County in 2010"
author: Guoqiang Zhang, Maya Mathur, Matteo Quartagno
email: guoqiang.zhang@ki.se
output: html_notebook
editor_options:
chunk_output_type: console
---
### 1. Load Packages
```{r}
library(haven)
library(tidyr)
library(tidyverse)
library(dplyr)
library(finalfit)
library(ggplot2)
library(naniar)
library(jomo)
library(mitml)
library(mitools)
library(survey)
library(svyVGAM)
library(vcd)
library(rcompanion)
source("/Users/guoqiang.zhang/Documents/Karolinska Institutet/Research Projects/Trends and Fluidity of Sexual Identity/Sexual_identity_in_Stockholm_County/Helper_functions.R") # helper function
```
### 2. Import and Prepare Data
```{r}
load("/Volumes/LGBT Project data/d_2010.RData")
# sampling strata
n_miss( d_2010$stratum )
d_2010$sampling_strata <- as.factor( d_2010$stratum )
length( unique( d_2010$sampling_strata ) ) # 39 strata
d_2010 <- d_2010 %>%
mutate( sampling_strata_region = recode( sampling_strata,
`2101` = "Upplands-Väsby",
`2102` = "Vallentuna",
`2103` = "Österåker",
`2104` = "Värmdö",
`2105` = "Järfälla",
`2106` = "Ekerö",
`2107` = "Huddinge",
`2108` = "Botkyrka",
`2109` = "Salem",
`2110` = "Haninge",
`2111` = "Tyresö",
`2112` = "Upplands-Bro",
`2113` = "Nykvarn",
`2114` = "Täby",
`2115` = "Danderyd",
`2116` = "Sollentuna",
`2117` = "Södertälje",
`2118` = "Nacka",
`2119` = "Sundbyberg",
`2120` = "Solna",
`2121` = "Lidingö",
`2122` = "Vaxholm",
`2123` = "Norrtälje",
`2124` = "Sigtuna",
`2125` = "Nynäshamn",
`2201` = "Rinkeby-Kista",
`2202` = "Spånga-Tensta",
`2203` = "Hässelby-Vällingby",
`2204` = "Bromma",
`2205` = "Kungsholmen",
`2206` = "Norrmalm",
`2207` = "Östermalm",
`2208` = "Södermalm",
`2209` = "Enskede-Årsta-Vantör",
`2210` = "Skarpnäck",
`2211` = "Farsta",
`2212` = "Älvsjö",
`2213` = "Hägersten-Liljeholmen",
`2214` = "Skärholmen"
) )
# sexual identity in 2010
table( d_2010$F10U87G78, useNA = "always" )
d_2010$F10U87G78[ d_2010$F10U87G78 == 9 ] <- NA
d_2010$sexual_identity_2010 <- factor( ifelse( d_2010$F10U87G78 == 1, "Heterosexual",
ifelse( d_2010$F10U87G78 == 2, "Homosexual",
ifelse( d_2010$F10U87G78 == 3, "Bisexual", "Uncertain" ) ) ),
levels = c( "Heterosexual", "Homosexual", "Bisexual", "Uncertain" ) )
table( d_2010$sexual_identity_2010, useNA = "always" )
# sexual identity in 2014
table( d_2010$F14F103, useNA = "always" )
d_2010$sexual_identity_2014 <- factor( ifelse( d_2010$F14F103 == 1, "Heterosexual",
ifelse( d_2010$F14F103 == 2, "Homosexual",
ifelse( d_2010$F14F103 == 3, "Bisexual", "None of the above" ) ) ),
levels = c( "Heterosexual", "Homosexual", "Bisexual", "None of the above" ) )
table( d_2010$sexual_identity_2014, useNA = "always" )
# sexual identity in 2021
table( d_2010$F21F91, useNA = "always" )
d_2010$sexual_identity_2021 <- factor( ifelse( d_2010$F21F91 == 1, "Heterosexual",
ifelse( d_2010$F21F91 == 2, "Homosexual",
ifelse( d_2010$F21F91 == 3, "Bisexual", "None of the above" ) ) ),
levels = c( "Heterosexual", "Homosexual", "Bisexual", "None of the above" ) )
table( d_2010$sexual_identity_2021, useNA = "always" )
# any change in sexual identity in 2010-2021
d_2010 <- d_2010 %>%
mutate(
sexual_identity_fluidity = ifelse(
replace( as.character( sexual_identity_2010 ), sexual_identity_2010 == "Uncertain", "Other" ) !=
replace( as.character( sexual_identity_2014 ), sexual_identity_2014 == "None of the above", "Other" ) |
replace( as.character( sexual_identity_2010 ), sexual_identity_2010 == "Uncertain", "Other" ) !=
replace( as.character( sexual_identity_2021 ), sexual_identity_2021 == "None of the above", "Other" ) |
replace( as.character( sexual_identity_2014 ), sexual_identity_2014 == "None of the above", "Other" ) !=
replace( as.character( sexual_identity_2021 ), sexual_identity_2021 == "None of the above", "Other" ),
1,
0
)
)
# sex
table( d_2010$kon, useNA = "always" )
d_2010$sex <- factor( ifelse( d_2010$kon == 1, "Male", "Female" ),
levels = c( "Male", "Female" ) )
table( d_2010$sex, useNA = "always" )
# age
summary( d_2010$F10alder )
d_2010$age <- d_2010$F10alder
d_2010$age_cat <- factor( ifelse( d_2010$age <= 29, "18-29",
ifelse( d_2010$age >=30 & d_2010$age <= 44, "30-44",
ifelse( d_2010$age >= 45 & d_2010$age <= 59, "45-59", ">=60" ) ) ),
levels = c( "18-29", "30-44", "45-59", ">=60" ) )
table( d_2010$age_cat, useNA = "always" )
# interaction term between age and sex
d_2010$age_sex <- interaction( d_2010$age_cat, d_2010$sex )
# generation
d_2010$birth_year <- 2010 - d_2010$age
d_2010 <- d_2010 %>%
mutate( generation = case_when(
birth_year >= 1981 & birth_year <= 1996 ~ "Millennials (1981–1996)",
birth_year >= 1965 & birth_year <= 1980 ~ "Generation X (1965–1980)",
birth_year >= 1946 & birth_year <= 1964 ~ "Baby Boomers (1946–1964)",
birth_year >= 1928 & birth_year <= 1945 ~ "Silent Generation (1928–1945)",
birth_year >= 1901 & birth_year <= 1927 ~ "Greatest Generation (1901–1927)"
) )
d_2010$generation <- factor(
d_2010$generation,
levels = c( "Millennials (1981–1996)", "Generation X (1965–1980)", "Baby Boomers (1946–1964)", "Silent Generation (1928–1945)", "Greatest Generation (1901–1927)" ) )
summary( d_2010$generation )
# country of birth
table( d_2010$fodelseland, useNA = "always" )
d_2010$country_of_birth <- factor( ifelse( d_2010$fodelseland == "Afrika", "Africa",
ifelse( d_2010$fodelseland == "Asien", "Asia",
ifelse( d_2010$fodelseland == "Europa", "Europe",
ifelse( d_2010$fodelseland == "Sverige", "Sweden", "Americas" ) ) ) ),
levels = c( "Africa", "Americas", "Asia", "Europe", "Sweden" ) )
table( d_2010$country_of_birth, useNA = "always" )
# education
table( d_2010$utbniva2010, useNA = "always" )
d_2010$education <- factor( ifelse( d_2010$utbniva2010 <= 2, "<=9 years",
ifelse( d_2010$utbniva2010 <= 4, "10-12 years", ">=13 years" ) ),
levels = c( "<=9 years", "10-12 years", ">=13 years" ) )
table( d_2010$education, useNA = "always" )
# occupation
table( d_2010$SSYK_kl, useNA = "always" )
d_2010$occupation <- factor(
ifelse(
d_2010$SSYK_kl == "Yrken inom byggverksamhet och tillverkning" |
d_2010$SSYK_kl == "Yrken inom lantbruk, trädgård, skogsbruk och fiske" |
d_2010$SSYK_kl == "Yrken inom maskinell tillverkning och transport m.m.",
"Manual and field trades",
ifelse(
d_2010$SSYK_kl == "Service-, omsorgs- och försäljningsyrken" |
d_2010$SSYK_kl == "Yrken inom administration och kundtjänst" |
d_2010$SSYK_kl == "Yrken med krav på kortare utbildning eller introduktion",
"Service and support",
ifelse(
d_2010$SSYK_kl == "Yrken med krav på fördjupad högskolekompetens" |
d_2010$SSYK_kl == "Yrken med krav på högskolekompetens eller motsvarande" |
d_2010$SSYK_kl == "Chefsyrken" |
d_2010$SSYK_kl == "Militära yrken",
"Expertise and leadership",
NA
)
)
),
levels = c(
"Manual and field trades",
"Service and support",
"Expertise and leadership"
)
)
table( d_2010$occupation, useNA = "always" )
# disposable income
summary( d_2010$dispink2010, useNA = "always" )
d_2010$dispink2010 <- d_2010$dispink2010*( 1960/1733 ) # Consumer Price Index (CPI)-adjusted income
d_2010$income <- factor( ifelse( d_2010$dispink2010 <= 2500, "<=2,500",
ifelse( d_2010$dispink2010 > 2500 & d_2010$dispink2010 <= 3500, "(2,500, 3,500]",
ifelse( d_2010$dispink2010 > 3500 & d_2010$dispink2010 <= 4500, "(3,500, 4,500]", ">4,500" ) ) ),
levels = c( "<=2,500", "(2,500, 3,500]", "(3,500, 4,500]", ">4,500" ) )
table( d_2010$income, useNA = "always" )
# marital status
table( d_2010$civil2010, useNA = "always" )
d_2010$marital_status <- factor( ifelse( d_2010$civil2010 == "G" | d_2010$civil2010 == "RP", "Currently married",
ifelse( d_2010$civil2010 == "OG", "Never married", "Other" ) ),
levels = c( "Never married", "Currently married", "Other" ) )
table( d_2010$marital_status, useNA = "always" )
# living alone
table( d_2010$F10U53aG57a, useNA = "always" )
d_2010$F10U53aG57a[ d_2010$F10U53aG57a == 9 ] <- NA
d_2010$living_alone <- factor( ifelse( d_2010$F10U53aG57a == 1, "no", "yes" ),
levels = c( "yes", "no" ) )
table( d_2010$living_alone, useNA = "always" )
# personal support
table( d_2010$F10U57G62, useNA = "always" )
d_2010$F10U57G62[ d_2010$F10U57G62 == 9 ] <- NA
d_2010$personal_support <- factor( ifelse( d_2010$F10U57G62 <= 2, "yes", "no" ),
levels = c( "yes", "no" ) )
table( d_2010$personal_support, useNA = "always" )
##### demographic characteristics #####
# among respondents (n = 30,767)
# make characteristics table by sexual identity
explanatory = c( "sex", "age_cat", "country_of_birth", "education", "occupation", "income", "marital_status", "living_alone", "personal_support" )
dependent = "sexual_identity_2010"
d_2010_table_1 <- d_2010 %>%
summary_factorlist( dependent,
explanatory,
na_include = TRUE,
na_include_dependent = TRUE,
total_col = TRUE,
add_col_totals = TRUE,
column = FALSE )
# Fisher's test
x1 <- table( d_2010$sex, d_2010$sexual_identity_2010 )
x1
format( round( fisher.test( x1[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x1[, c( 1, 3 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x1[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
x2 <- table( d_2010$age_cat, d_2010$sexual_identity_2010 )
x2
format( round( fisher.test( x2[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x2[, c( 1, 3 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x2[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
x3 <- table( d_2010$country_of_birth, d_2010$sexual_identity_2010 )
x3
format( round( fisher.test( x3[, c( 1, 2 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x3[, c( 1, 3 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x3[, c( 1, 4 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x4 <- table( d_2010$education, d_2010$sexual_identity_2010 )
x4
format( round( fisher.test( x4[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x4[, c( 1, 3 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x4[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
x5 <- table( d_2010$income, d_2010$sexual_identity_2010 )
x5
format( round( fisher.test( x5[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x5[, c( 1, 3 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x5[, c( 1, 4 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x6 <- table( d_2010$marital_status, d_2010$sexual_identity_2010 )
x6
format( round( fisher.test( x6[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x6[, c( 1, 3 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x6[, c( 1, 4 ) ], simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x7 <- table( d_2010$living_alone, d_2010$sexual_identity_2010 )
x7
format( round( fisher.test( x7[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x7[, c( 1, 3 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x7[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
x8 <- table( d_2010$personal_support, d_2010$sexual_identity_2010 )
x8
format( round( fisher.test( x8[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x8[, c( 1, 3 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x8[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
x9 <- table( d_2010$occupation, d_2010$sexual_identity_2010 )
x9
format( round( fisher.test( x9[, c( 1, 2 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x9[, c( 1, 3 ) ] )$p.value, 3 ), nsmall = 3 )
format( round( fisher.test( x9[, c( 1, 4 ) ] )$p.value, 3 ), nsmall = 3 )
# make characteristics table by change of sexual identity
explanatory = c( "sex", "age_cat", "country_of_birth", "education", "occupation", "income", "marital_status", "living_alone", "personal_support", "sexual_identity_2010" )
dependent = "sexual_identity_fluidity"
d_2010_table_2 <- d_2010 %>%
mutate( sexual_identity_fluidity = factor( sexual_identity_fluidity, levels = c( 0, 1 ), labels = c( "no change", "change" ) ) ) %>%
summary_factorlist( dependent,
explanatory,
na_include = TRUE,
na_include_dependent = TRUE,
total_col = TRUE,
add_col_totals = TRUE,
column = FALSE )
# Fisher's test
x1 <- table( d_2010$sex, d_2010$sexual_identity_fluidity )
x1
format( round( fisher.test( x1 )$p.value, 3 ), nsmall = 3 )
x2 <- table( d_2010$age_cat, d_2010$sexual_identity_fluidity )
x2
format( round( fisher.test( x2, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x3 <- table( d_2010$country_of_birth, d_2010$sexual_identity_fluidity )
x3
format( round( fisher.test( x3, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x4 <- table( d_2010$education, d_2010$sexual_identity_fluidity )
x4
format( round( fisher.test( x4, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x5 <- table( d_2010$occupation, d_2010$sexual_identity_fluidity )
x5
format( round( fisher.test( x5, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x6 <- table( d_2010$income, d_2010$sexual_identity_fluidity )
x6
format( round( fisher.test( x6, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x7 <- table( d_2010$marital_status, d_2010$sexual_identity_fluidity )
x7
format( round( fisher.test( x7, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x8 <- table( d_2010$living_alone, d_2010$sexual_identity_fluidity )
x8
format( round( fisher.test( x8, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x9 <- table( d_2010$personal_support, d_2010$sexual_identity_fluidity )
x9
format( round( fisher.test( x9, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
x10 <- table( d_2010$sexual_identity_2010, d_2010$sexual_identity_fluidity )
x10
format( round( fisher.test( x10, simulate.p.value = TRUE )$p.value, 3 ), nsmall = 3 )
##### design weights #####
summary( d_2010$F10dvikt )
d_2010$design_weight <- d_2010$F10dvikt
##### calibrated weights #####
d_2010$calibrated_weight <- d_2010$F10kalvikt
##### non-response #####
# unit non-response
d_2010$design_weight_unit_nonresponse <- d_2010$F10dbvikt # weights calculated assuming Missing Completely At Random (MCAR) within each stratum
summary( d_2010$design_weight_unit_nonresponse )
sum( d_2010$design_weight_unit_nonresponse ) # No. of source population = 1,602,868
unitresponse_prob <- d_2010 %>%
group_by( sampling_strata_region ) %>%
summarise( unitresponse_prob = unique( design_weight ) / unique( design_weight_unit_nonresponse ),
no.of.population = sum( design_weight_unit_nonresponse ),
sample_size = unique( no.of.population )/unique( design_weight ) ) # calculate overall unit response rate, and no. of population and sample size within each stratum
d_2010 <- d_2010 %>%
left_join( unitresponse_prob, by = "sampling_strata_region" )
# item non-response
itemresponse_prob <- d_2010 %>%
group_by( sampling_strata_region ) %>%
summarise( itemresponse_prob = sum( !is.na( sexual_identity_2010 ) ) / n() ) # calculate item response rate
d_2010 <- d_2010 %>%
left_join( itemresponse_prob, by = "sampling_strata_region" ) %>%
mutate( itemresponse_prob = ifelse( is.na( sexual_identity_2010 ), 0, itemresponse_prob ) )
##### summary of stratified sampling #####
sampling_frame_2010 <- as.data.frame( d_2010 %>%
group_by( sampling_strata_region ) %>%
reframe( no.of.population = unique( no.of.population ),
sample_size = unique( no.of.population/design_weight ),
unitresponse = n(),
itemresponse = sum( itemresponse_prob != 0 ) ) )
sampling_frame_2010$unitresponse_rate <- sampling_frame_2010$unitresponse/sampling_frame_2010$sample_size
sampling_frame_2010$itemresponse_rate <- sampling_frame_2010$itemresponse/sampling_frame_2010$unitresponse
sampling_frame_2010$overallresponse_rate <- sampling_frame_2010$itemresponse/sampling_frame_2010$sample_size
sampling_frame_2010$unitresponse_label <- paste0( sampling_frame_2010$unitresponse, " (",
sprintf( "%.1f", sampling_frame_2010$unitresponse_rate*100 ), "%)" )
sampling_frame_2010$overallresponse_label <- paste0( sampling_frame_2010$itemresponse, " (",
sprintf( "%.1f", sampling_frame_2010$overallresponse_rate*100 ), "%)" )
round( sum( sampling_frame_2010$unitresponse )/( sum( sampling_frame_2010$sample_size ) ), 3 ) # overall unit response rate
round( sum( sampling_frame_2010$itemresponse )/( sum( sampling_frame_2010$unitresponse ) ), 3 ) # overall item response rate
round( sum( sampling_frame_2010$itemresponse )/( sum( sampling_frame_2010$sample_size ) ), 3 ) # overall response rate
writexl::write_xlsx( sampling_frame_2010, "sampling_frame_2010.xlsx" )
##### replicating non-respondent rows for sampling design #####
# because d_2010 includes data only for respondents (unit response), we need to duplicate rows for non-respondents
# so that the final dataset represents the sampling process
rows_to_add <- d_2010 %>%
group_by( sampling_strata_region ) %>%
summarise( to_add = unique( sample_size ) - n(),
no.of.population = unique( no.of.population ),
sample_size = unique( sample_size ),
design_weight = unique( design_weight ) )
rows_to_add$to_add <- round( rows_to_add$to_add )
sum( rows_to_add$to_add ) # 24,573 non-respondents
replicated_rows <- rows_to_add[ rep( row.names( rows_to_add ), rows_to_add$to_add ), c( 1, 3:5 ) ]
d_2010_complete <- bind_rows( d_2010, replicated_rows ) # 55,340 individuals in the original sample
```
### 3. Check Calibrated Weights and Proxy Auxiliary Variables
```{r}
survey_design_calibrated <- svydesign( ids = ~ 1,
strata = ~ sampling_strata_region,
weights = ~ calibrated_weight,
fpc = ~ no.of.population,
data = d_2010 )
# we were unable to re-calibrate the weights as recommended (https://cran.r-project.org/web/packages/survey/vignettes/precalibrated.pdf), because the original auxiliary variables used for calibration were not available anymore
svytable( ~ sampling_strata_region, survey_design_calibrated ) # calculate population totals for each sampling stratum
round( summary( d_2010$calibrated_weight ), 2 ) # check the distribution of calibrated weights
round( summary( d_2010$calibrated_weight / d_2010$design_weight ), 2 ) # check the ratios of weights
ggplot( d_2010, aes( x = calibrated_weight ) ) +
geom_histogram( binwidth = 1, fill = "steelblue", color = "steelblue" ) +
theme_classic() +
scale_x_continuous( breaks = c( 0, 100, 200, 300 ),
expand = c( 0.005, 0 ) ) +
scale_y_continuous( limits = c( 0, 620 ),
breaks = c( 0, 100, 200, 300, 400, 500, 600 ),
expand = c( 0, 0 ) ) +
labs( x = "Calibrated Weights", y = "Frequency" ) +
theme( axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.title.x = element_text( family = "Arial", size = 11 )
)
# calculate strength of correlation between sexual identity and proxy auxiliary variables
d_2010_correlation <- d_2010 %>%
mutate(
age_auxiliary = factor(
case_when(
age <= 29 ~ "<=29",
age >=30 & age <= 44 ~ "30-44",
age >=45 & age <= 64 ~ "45-64",
age >= 65 ~ ">=65" ),
levels = c( "<=29", "30-44", "45-64", ">=65" ) ),
sex_auxiliary = sex,
country_of_birth_auxiliary = as.factor(
case_when(
country_of_birth == "Africa" | country_of_birth == "Americas" | country_of_birth == "Asia" ~ "Outside Europe",
country_of_birth == "Sweden" ~ "Sweden",
country_of_birth == "Europe" ~ "Europe" ) ),
marital_status_auxiliary = as.factor(
ifelse( marital_status == "Currently married",
"Currently married",
"Other" ) ),
income_auxiliary = factor(
case_when(
dispink2010 < 1500 ~ "<=149,999",
dispink2010 >= 1500 & dispink2010 < 3000 ~ "150,000-299,999",
dispink2010 >= 3000 ~ ">=300,000" ),
levels = c( "<=149,999", "150,000-299,999", ">=300,000" ) ),
education_auxiliary = education,
sampling_strata_region_auxiliary = sampling_strata_region
)
d_2010_correlation$sexual_identity_fluidity <- as.factor( d_2010_correlation$sexual_identity_fluidity )
summary( d_2010_correlation )
# sexual identity in 2010
assocstats( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$sex_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$country_of_birth_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$marital_status_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$sampling_strata_region_auxiliary ) )
freemanTheta( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation[ d_2010_correlation$sex == "Male", ]$sexual_identity_2010, d_2010_correlation[ d_2010_correlation$sex == "Male", ]$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation[ d_2010_correlation$sex == "Female", ]$sexual_identity_2010, d_2010_correlation[ d_2010_correlation$sex == "Female", ]$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$income_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$sexual_identity_2010, d_2010_correlation$education_auxiliary ), group = "row", digits = 2 )
# change in sexual identity during 2010-2021
assocstats( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$sex_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$country_of_birth_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$marital_status_auxiliary ) )
assocstats( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$sampling_strata_region_auxiliary ) )
freemanTheta( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation[ d_2010_correlation$sex == "Male", ]$sexual_identity_fluidity, d_2010_correlation[ d_2010_correlation$sex == "Male", ]$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation[ d_2010_correlation$sex == "Female", ]$sexual_identity_fluidity, d_2010_correlation[ d_2010_correlation$sex == "Female", ]$age_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$income_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$sexual_identity_fluidity, d_2010_correlation$education_auxiliary ), group = "row", digits = 2 )
# references
freemanTheta( table( d_2010_correlation$age_auxiliary, d_2010_correlation$education_auxiliary ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$age_auxiliary, d_2010_correlation$income ), group = "row", digits = 2 )
freemanTheta( table( d_2010_correlation$education_auxiliary, d_2010_correlation$income ), group = "row", digits = 2 )
```
### 4. Multiple Imputation
#### 4.1. Obtain incomplete dataset
```{r}
summary( d_2010 )
selected_variables <- c( "sampling_strata_region", "calibrated_weight", "no.of.population", "sexual_identity_2010", "sexual_identity_fluidity", "age", "sex", "country_of_birth", "education", "dispink2010", "marital_status", "living_alone", "personal_support" )
d_2010_incomplete <- d_2010 %>%
select( all_of( selected_variables ) ) %>%
rename( income = dispink2010 ) %>%
mutate(
sexual_identity_fluidity = as.factor( sexual_identity_fluidity ),
country_of_birth = factor(
case_when(
country_of_birth == "Africa" | country_of_birth == "Americas" | country_of_birth == "Asia" ~ "Outside Europe",
country_of_birth == "Sweden" ~ "Sweden",
country_of_birth == "Europe" ~ "Europe" ),
levels = c( "Sweden", "Europe", "Outside Europe" ) )
)
# create weight strata (n = 25) for two-level multivariate normal imputation
d_2010_incomplete$weight_strata <- as.factor( cut( d_2010_incomplete$calibrated_weight,
breaks = quantile( d_2010_incomplete$calibrated_weight,
probs = seq( 0, 1, length.out = 26 ) ),
labels = 1:25,
include.lowest = TRUE,
right = TRUE ) )
ggplot( d_2010_incomplete %>%
group_by( weight_strata ) %>%
summarise( mean_weight = mean( calibrated_weight ), sd_weight = sd( calibrated_weight ) ),
aes( y = reorder( weight_strata, -mean_weight ), x = mean_weight ) ) +
geom_line( aes( group = 1 ) ) +
geom_point() +
geom_errorbarh( aes( xmin = mean_weight - sd_weight, xmax = mean_weight + sd_weight ), height = 0.4, alpha = 0.5 ) +
scale_x_continuous( limits = c( 0, 205 ),
breaks = c( 0, 50, 100, 150, 200 ),
expand = c( 0, 0 ) ) +
labs( y = "Calibrated Weight Strata", x = "Mean and Standard Deviation of Calibrated Weights" ) +
theme_classic() +
theme( axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.title.x = element_text( family = "Arial", size = 11 )
)
summary( d_2010_incomplete )
sapply( d_2010_incomplete, class ) # all continuous variables are numeric, and all categorical variables are factor
save( d_2010_incomplete, file = "d_2010_incomplete.RData" )
```
#### 4.2. Visualize missing pattern
```{r}
# identify missing variables
vis_miss( d_2010_incomplete )
n_miss( d_2010_incomplete )
miss_var_summary( d_2010_incomplete ) # 57.1% missing in sexual_identity_fluidity, 3.8% in sexual_identity_2010, 1.3% in personal_support, 1.1% in living_alone, and 0.7% in education
```
#### 4.3. Two-level multivariate normal imputation
```{r}
# use multiple imputation to account for item non-response
# assume Missing At Random (MAR)
# specify imputation model
fml_imp_2010 <- sexual_identity_fluidity + sexual_identity_2010 + personal_support + living_alone + education ~ 1 + age*sex + country_of_birth + income + marital_status + ( 1 | weight_strata )
# perform a "dry run"
imp_ini_2010 <- jomoImpute( data = d_2010_incomplete, # incomplete dataset
formula = fml_imp_2010, # imputation model
random.L1 = "full", # random covariance matrix of residuals
n.burn = 2, # number of burn-in iterations
n.iter = 10, # number of between-imputation iterations
m = 2, # number of imputation
seed = 12345 # set the seed so that results can be reproduced
)
summary( imp_ini_2010 ) # check output
# increase the number of burn-in and between-imputation iterations
# to determine the number of iterations for the final imputation
imp_preliminary_2010 <- jomoImpute( data = d_2010_incomplete,
formula = fml_imp_2010,
random.L1 = "full",
n.burn = 5000,
n.iter = 2000,
m = 2,
seed = 12345
) # took around two hours
save( imp_preliminary_2010, file = "imp_preliminary_2010.RData" )
summary( imp_preliminary_2010 ) # summarize model and display convergence statistics
plot( imp_preliminary_2010, trace = "all", print = "beta" ) # check trace and autocorrelation plots
# final imputation with the chosen number of iterations
imp_final_2010 <- jomoImpute( data = d_2010_incomplete,
formula = fml_imp_2010,
random.L1 = "full",
n.burn = 2000,
n.iter = 1000,
m = 80,
seed = 12345
) # took around 20 hours
save( imp_final_2010, file = "imp_final_2010.RData" )
summary( imp_final_2010 )
plot( imp_final_2010, trace = "all", print = "beta" )
```
#### 4.4. Validate imputed data
```{r}
# extract imputed datasets
original_data_2010 <- mitmlComplete( imp_final_2010, print = 0 ) # extract original incomplete dataset
implist_2010 <- mitmlComplete( imp_final_2010, print = "all" ) # extract all imputed datasets
original_data_2010$imputation <- "0"
all_data_2010 <- bind_rows( original_data_2010,
bind_rows( implist_2010, .id = "imputation" ) ) # merge datasets
all_data_2010$imputation <- as.numeric( all_data_2010$imputation )
summary( all_data_2010 )
# sexual identity in 2010
ggplot( all_data_2010[ !is.na( all_data_2010$sexual_identity_2010 ), ],
aes( fill = sexual_identity_2010, x = imputation ) ) +
geom_bar( position = "fill" ) +
scale_y_continuous( labels = scales::percent ) +
scale_fill_discrete( name = "Sexual identity in 2010" ) +
labs(
x = "Imputation number",
y = "Proportion",
caption = "Notes: Imputation number 0 represents the original incomplete dataset." ) +
theme_classic() +
theme( axis.title.x = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
legend.text = element_text( family = "Arial", size = 10 ),
legend.title = element_text( family = "Arial", size = 10 ),
legend.position = "bottom",
plot.caption = element_text( family = "Arial", size = 10, hjust = 0 )
)
# change in sexual identity during 2010-2021
ggplot( all_data_2010[ !is.na( all_data_2010$sexual_identity_fluidity ), ],
aes( fill = sexual_identity_fluidity, x = imputation ) ) +
geom_bar( position = "fill" ) +
scale_y_continuous( labels = scales::percent ) +
scale_fill_discrete( name = "Change in sexual identity during 2010-2021", labels = c( "No", "Yes" ) ) +
labs(
x = "Imputation number",
y = "Proportion",
caption = "Notes: Imputation number 0 represents the original incomplete dataset." ) +
theme_classic() +
theme( axis.title.x = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
legend.text = element_text( family = "Arial", size = 10 ),
legend.title = element_text( family = "Arial", size = 10 ),
legend.position = "bottom",
plot.caption = element_text( family = "Arial", size = 10, hjust = 0 )
)
# education
ggplot( all_data_2010[ !is.na( all_data_2010$education ), ],
aes( fill = education, x = imputation ) ) +
geom_bar( position = "fill" ) +
scale_y_continuous( labels = scales::percent ) +
scale_fill_discrete( name = "Level of education" ) +
labs(
x = "Imputation number",
y = "Proportion",
caption = "Notes: Imputation number 0 represents the original incomplete dataset." ) +
theme_classic() +
theme( axis.title.x = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
legend.text = element_text( family = "Arial", size = 10 ),
legend.title = element_text( family = "Arial", size = 10 ),
legend.position = "bottom",
plot.caption = element_text( family = "Arial", size = 10, hjust = 0 )
)
# living status
ggplot( all_data_2010[ !is.na( all_data_2010$living_alone ), ],
aes( fill = living_alone, x = imputation ) ) +
geom_bar( position = "fill" ) +
scale_y_continuous( labels = scales::percent ) +
scale_fill_discrete( name = "Living alone", labels = c( "Yes", "No" ) ) +
labs(
x = "Imputation number",
y = "Proportion",
caption = "Notes: Imputation number 0 represents the original incomplete dataset." ) +
theme_classic() +
theme( axis.title.x = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
legend.text = element_text( family = "Arial", size = 10 ),
legend.title = element_text( family = "Arial", size = 10 ),
legend.position = "bottom",
plot.caption = element_text( family = "Arial", size = 10, hjust = 0 )
)
# personal support
ggplot( all_data_2010[ !is.na( all_data_2010$personal_support ), ],
aes( fill = personal_support, x = imputation ) ) +
geom_bar( position = "fill" ) +
scale_y_continuous( labels = scales::percent ) +
scale_fill_discrete( name = "Personal support", labels = c( "Yes", "No" ) ) +
labs(
x = "Imputation number",
y = "Proportion",
caption = "Notes: Imputation number 0 represents the original incomplete dataset." ) +
theme_classic() +
theme( axis.title.x = element_text( family = "Arial", size = 11 ),
axis.text.x = element_text( family = "Arial", size = 11 ),
axis.text.y = element_text( family = "Arial", size = 11 ),
axis.title.y = element_text( family = "Arial", size = 11 ),
legend.text = element_text( family = "Arial", size = 10 ),
legend.title = element_text( family = "Arial", size = 10 ),
legend.position = "bottom",
plot.caption = element_text( family = "Arial", size = 10, hjust = 0 )
)
```
#### 4.5. Prepare for survey analyses
```{r}
# transform certain variables in imputed datasets
implist_2010 <- mitmlComplete( imp_final_2010, print = "all" ) # extract all imputed datasets
summary( implist_2010[[1]] )
implist_2010_transformed <- lapply( implist_2010, function( df ) {
within( df, {
# age
age_exp <- factor(
case_when( age <= 29 ~ "18-29",
age >= 30 & age <= 44 ~ "30-44",
age >= 45 & age <= 59 ~ "45-59",
age >= 60 ~ ">=60" ),
levels = c( "45-59", "18-29", "30-44", ">=60" ) )
age_conf <- age
# age group
age_group <- cut( age,
breaks = seq( 17, 79, by = 2 ),
include.lowest = TRUE,
right = TRUE )
# generation
birth_year <- 2010 - age
generation <- factor(
case_when(
birth_year >= 1981 & birth_year <= 1996 ~ "Millennials (1981–1996)",
birth_year >= 1965 & birth_year <= 1980 ~ "Generation X (1965–1980)",
birth_year >= 1946 & birth_year <= 1964 ~ "Baby Boomers (1946–1964)",
birth_year >= 1928 & birth_year <= 1945 ~ "Silent Generation (1928–1945)",
birth_year >= 1901 & birth_year <= 1927 ~ "Greatest Generation (1901–1927)" ),
levels = c( "Millennials (1981–1996)", "Generation X (1965–1980)", "Baby Boomers (1946–1964)", "Silent Generation (1928–1945)", "Greatest Generation (1901–1927)" )
)
# sex
sex_exp <- sex
sex_conf <- sex
# interaction term between age and sex
age_sex <- interaction( age_exp, sex_exp )
# country of birth
country_of_birth_exp <- country_of_birth
country_of_birth_conf <- country_of_birth
# education
education_exp <- relevel( education, ref = ">=13 years" )
education_conf <- education
# disposable income
income_exp <- factor(
case_when( income <= 2500 ~ "<=2,500",
income > 2500 & income <= 3500 ~ "(2,500, 3,500]",
income > 3500 & income <= 4500 ~ "(3,500, 4,500]",
income > 4500 ~ ">4,500" ),
levels = c( ">4,500", "<=2,500", "(2,500, 3,500]", "(3,500, 4,500]" ) )
income_conf <- income
# marital status
marital_status_exp <- relevel( marital_status, ref = "Currently married" )
marital_status_conf <- marital_status
# living status
living_alone <- as.factor( ifelse( living_alone == "yes", "living_alone_yes", "living_alone_no" ) )
living_status_exp <- relevel( living_alone, ref = "living_alone_no" )
living_status_conf <- living_alone
# personal support
personal_support <- as.factor( ifelse( personal_support == "yes", "personal_support_yes", "personal_support_no" ) )
personal_support_exp <- relevel( personal_support, ref = "personal_support_yes" )
personal_support_conf <- personal_support
# sexual identity in 2010
levels( sexual_identity_2010 )[ levels( sexual_identity_2010 ) == "Uncertain" ] <- "Other"
sexual_identity_2010_exp <- sexual_identity_2010
sexual_identity_2010_conf <- sexual_identity_2010
sexual_identity_lgb <- factor(
case_when( sexual_identity_2010 == "Homosexual" | sexual_identity_2010 == "Bisexual" ~ "LGB",
sexual_identity_2010 == "Heterosexual" ~ "Heterosexual",
sexual_identity_2010 == "Other" ~ "Other" ),
levels = c( "Heterosexual", "LGB", "Other" ) )
# sexual minority
sexual_minority <- ifelse( sexual_identity_2010 == "Heterosexual", 0, 1 )
# homosexual
homosexual <- ifelse( sexual_identity_2010 == "Homosexual", 1, 0 )
# bisexual
bisexual <- ifelse( sexual_identity_2010 == "Bisexual", 1, 0 )
# other
other <- ifelse( sexual_identity_2010 == "Other", 1, 0 )
# sexual identity fluidity
sexual_identity_fluidity_cat <- factor( ifelse( sexual_identity_fluidity == "1", "changed", "unchanged" ),
levels = c( "changed", "unchanged" ) )
sexual_identity_fluidity <- ifelse( sexual_identity_fluidity == "0", 0, 1 )
} )
} )
imputed_data_2010_transformed <- bind_rows( implist_2010_transformed, .id = "imputation" ) # merge datasets
imputed_data_2010_transformed$imputation <- as.numeric( imputed_data_2010_transformed$imputation )
summary( imputed_data_2010_transformed )
n_miss( imputed_data_2010_transformed ) # 0 missing
# use calibrated weights to account for unit non-response
# create survey design
survey_design_imp <- svydesign( ids = ~ 1,
strata = ~ sampling_strata_region,
weights = ~ calibrated_weight,
fpc = ~ no.of.population,
data = imputationList( implist_2010_transformed )
) # without pre-calibration
```
### 5. Estimation of Proportions of Sexual Identities
#### 5.1. Complete-case analysis
##### 5.1.1. Define survey design
```{r}
# prepare dataset
d_2010_complete_cc <- d_2010_complete
d_2010_complete_cc$country_of_birth <- factor(
ifelse( d_2010_complete_cc$country_of_birth == "Sweden", "Sweden",
ifelse( d_2010_complete_cc$country_of_birth == "Europe", "Europe", "Outside Europe" ) ),
levels = c( "Sweden", "Europe", "Outside Europe" )
)
d_2010_complete_cc$living_alone <- as.factor(
ifelse( d_2010_complete_cc$living_alone == "yes",
"living_alone_yes",
"living_alone_no" )
)
d_2010_complete_cc$personal_support <- as.factor(
ifelse( d_2010_complete_cc$personal_support == "yes",
"personal_support_yes",
"personal_support_no" )
)
# create survey design
survey_design_cc <- svydesign( ids = ~ 1,
strata = ~ sampling_strata_region,
weights = ~ design_weight,
fpc = ~ no.of.population,
data = d_2010_complete_cc )
categories <- c( "Heterosexual", "Homosexual", "Bisexual", "Uncertain" )
# the following analyses assume MCAR
```
##### 5.1.2. By sex
```{r}
list_of_df <- list()
for ( cat in categories ) {
prop_cc_sex <- svyby( formula = as.formula( paste0( "~ I( sexual_identity_2010 == '", cat, "' )" ) ),
by = ~ sex,
design = subset( survey_design_cc, !is.na( sexual_identity_2010 ) & !is.na( sex ) ),
FUN = svyciprop,
vartype = "ci",
method = "beta" )
colnames( prop_cc_sex ) <- c( "subgroup", paste0( cat, "_point_estimate_2010" ), paste0( cat, "_lower_ci_2010" ), paste0( cat, "_upper_ci_2010" ) )
list_of_df[[cat]] <- prop_cc_sex
}
prop_cc_sex <- Reduce( function( df1, df2 ) {
merge( df1, df2, by = "subgroup" )
},
list_of_df )
prop_cc_sex <- left_join( prop_cc_sex,
d_2010_complete_cc[ !is.na( d_2010_complete_cc$sexual_identity_2010 ) & !is.na( d_2010_complete_cc$sex ), ] %>%
group_by( subgroup = sex ) %>%
summarise( sample_size_2010 = n() ),
by = "subgroup" ) %>%
mutate( sample_size_2010 = prettyNum( sample_size_2010, big.mark = ",", preserve.width = "none" ) )
```
##### 5.1.3. By age group
###### 5.1.3.1. Calculate proportion by pre-specified age cut-off
```{r}
list_of_df <- list()
for ( cat in categories ) {
prop_cc_age <- svyby( formula = as.formula( paste0( "~ I( sexual_identity_2010 == '", cat, "' )" ) ),
by = ~ age_cat,
design = subset( survey_design_cc, !is.na( sexual_identity_2010 ) & !is.na( age_cat ) ),
FUN = svyciprop,
vartype = "ci",
method = "beta" )
colnames( prop_cc_age ) <- c( "subgroup", paste0( cat, "_point_estimate_2010" ), paste0( cat, "_lower_ci_2010" ), paste0( cat, "_upper_ci_2010" ) )
list_of_df[[cat]] <- prop_cc_age
}
prop_cc_age <- Reduce( function( df1, df2 ) {
merge( df1, df2, by = "subgroup" )
},
list_of_df )
prop_cc_age <- left_join( prop_cc_age,
d_2010_complete_cc[ !is.na( d_2010_complete_cc$sexual_identity_2010 ) & !is.na( d_2010_complete_cc$age_cat ), ] %>%
group_by( subgroup = age_cat ) %>%
summarise( sample_size_2010 = n() ),
by = "subgroup" ) %>%
mutate( sample_size_2010 = prettyNum( sample_size_2010, big.mark = ",", preserve.width = "none" ) )
```
###### 5.1.3.2. Calculate proportion and proportion ratio by varying age cut-offs
```{r}
d_2010_iteration_age <- d_2010_complete
min_age <- min( d_2010_iteration_age$age, na.rm = TRUE )
results_df <- data.frame()
for ( cut_off in ( min_age ):100 ) {
d_2010_iteration_age$age_iteration <- ifelse( d_2010_iteration_age$age <= cut_off, "young", "old" )
survey_design_iteration_age <- svydesign( ids = ~ 1,
strata = ~ sampling_strata_region,
weights = ~ design_weight,