data-raw/brfss_2017_mrp_phys_ncr.R
In uva-bi-sdad/dc.sdad.health.outcomes: Social Data Commons: Health, Behavior, Diet: Health Outcomes
library(tidycensus)
library(dplyr)
library(purrr)
library(data.table)
library(cmdstanr)
library(posterior)
library(survey)
source("~/git/mrp_utils.R")
### get data to fit MRP model ###
ncr_brfss <- readRDS("~/projects_data/ncr_2011_clean.rds")
# get ACS county data #
acs_var_2011 <- c("B16009_001", "B16009_002",
"B23025_001", "B23025_002",
"B06009_001", "B06009_002")
acs_cnty_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "county",
state = s,
variables = acs_var_2011,
year = 2011,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_cnty_var <- acs_cnty_var %>%
transmute(fips = GEOID,
frac_poverty = B16009_002E / B16009_001E,
frac_in_labor_force = B23025_002E / B23025_001E,
frac_less_than_HS = B06009_002E / B06009_001E)
acs_cnty_var$frac_poverty <- (acs_cnty_var$frac_poverty - mean(acs_cnty_var$frac_poverty))/sd(acs_cnty_var$frac_poverty)
acs_cnty_var$frac_in_labor_force <- (acs_cnty_var$frac_in_labor_force - mean(acs_cnty_var$frac_in_labor_force))/sd(acs_cnty_var$frac_in_labor_force)
acs_cnty_var$frac_less_than_HS <- (acs_cnty_var$frac_less_than_HS - mean(acs_cnty_var$frac_less_than_HS))/sd(acs_cnty_var$frac_less_than_HS)
acs_cnty_var <- acs_cnty_var %>%
filter(fips %in% unique(ncr_brfss$fips)) %>%
arrange(fips) %>%
as.data.frame()
### get prediction data at tract level ###
# get ACS tract data for model #
acs_var_tract <- c("B17020_001", "B17020_002",
"B23025_001", "B23025_002",
"B06009_001", "B06009_002")
acs_tract_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "tract",
state = s,
variables = acs_var_tract,
year = 2017,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_tract_var <- acs_tract_var %>%
transmute(tract_fips = GEOID,
fips = substr(tract_fips, 1, 5),
st_fips = substr(tract_fips, 1, 2),
frac_poverty = B17020_002E / B17020_001E,
frac_in_labor_force = B23025_002E / B23025_001E,
frac_less_than_HS = B06009_002E / B06009_001E)
acs_tract_var <- acs_tract_var[complete.cases(acs_tract_var),]
acs_tract_var$frac_poverty <- (acs_tract_var$frac_poverty - mean(acs_tract_var$frac_poverty))/sd(acs_tract_var$frac_poverty)
acs_tract_var$frac_in_labor_force <- (acs_tract_var$frac_in_labor_force - mean(acs_tract_var$frac_in_labor_force))/sd(acs_tract_var$frac_in_labor_force)
acs_tract_var$frac_less_than_HS <- (acs_tract_var$frac_less_than_HS - mean(acs_tract_var$frac_less_than_HS))/sd(acs_tract_var$frac_less_than_HS)
acs_tract_var <- acs_tract_var %>%
arrange(fips, tract_fips) %>%
as.data.frame()
sort_fips <- sort(unique(acs_tract_var$fips))
acs_tract_var$fips_cat <- match(acs_tract_var$fips, sort_fips)
sort_st_fips <- sort(unique(acs_tract_var$st_fips))
acs_tract_var$st_fips_cat <- match(acs_tract_var$st_fips, sort_st_fips)
# get poststratification data #
acs_ps_vars <- c(
# male, 18-19, white
"B01001A_007",
# male, 20-24, white
"B01001A_008",
# male, 25-29, white
"B01001A_009",
# male, 30-34, white
"B01001A_010",
# male, 35-44, white
"B01001A_011",
# male, 45-54, white
"B01001A_012",
# male, 55-64, white
"B01001A_013",
# male, 65-74, white
"B01001A_014",
# male, 75-84, white
"B01001A_015",
# male, 85+, white
"B01001A_016",
# female, 18-19, white
"B01001A_022",
# female, 20-24, white
"B01001A_023",
# female, 25-29, white
"B01001A_024",
# female, 30-34, white
"B01001A_025",
# female, 35-44, white
"B01001A_026",
# female, 45-54, white
"B01001A_027",
# female, 55-64, white
"B01001A_028",
# female, 65-74, white
"B01001A_029",
# female, 75-84, white
"B01001A_030",
# female, 85+, white
"B01001A_031",
# male, 18-19, black
"B01001B_007",
# male, 20-24, black
"B01001B_008",
# male, 25-29, black
"B01001B_009",
# male, 30-34, black
"B01001B_010",
# male, 35-44, black
"B01001B_011",
# male, 45-54, black
"B01001B_012",
# male, 55-64, black
"B01001B_013",
# male, 65-74, black
"B01001B_014",
# male, 75-84, black
"B01001B_015",
# male, 85+, black
"B01001B_016",
# female, 18-19, black
"B01001B_022",
# female, 20-24, black
"B01001B_023",
# female, 25-29, black
"B01001B_024",
# female, 30-34, black
"B01001B_025",
# female, 35-44, black
"B01001B_026",
# female, 45-54, black
"B01001B_027",
# female, 55-64, black
"B01001B_028",
# female, 65-74, black
"B01001B_029",
# female, 75-84, black
"B01001B_030",
# female, 85+, black
"B01001B_031",
# male, 18-19, aian
"B01001C_007",
# male, 20-24, aian
"B01001C_008",
# male, 25-29, aian
"B01001C_009",
# male, 30-34, aian
"B01001C_010",
# male, 35-44, aian
"B01001C_011",
# male, 45-54, aian
"B01001C_012",
# male, 55-64, aian
"B01001C_013",
# male, 65-74, aian
"B01001C_014",
# male, 75-84, aian
"B01001C_015",
# male, 85+, aian
"B01001C_016",
# female, 18-19, aian
"B01001C_022",
# female, 20-24, aian
"B01001C_023",
# female, 25-29, aian
"B01001C_024",
# female, 30-34, aian
"B01001C_025",
# female, 35-44, aian
"B01001C_026",
# female, 45-54, aian
"B01001C_027",
# female, 55-64, aian
"B01001C_028",
# female, 65-74, aian
"B01001C_029",
# female, 75-84, aian
"B01001C_030",
# female, 85+, aian
"B01001C_031",
# male, 18-19, asian
"B01001D_007",
# male, 20-24, asian
"B01001D_008",
# male, 25-29, asian
"B01001D_009",
# male, 30-34, asian
"B01001D_010",
# male, 35-44, asian
"B01001D_011",
# male, 45-54, asian
"B01001D_012",
# male, 55-64, asian
"B01001D_013",
# male, 65-74, asian
"B01001D_014",
# male, 75-84, asian
"B01001D_015",
# male, 85+, asian
"B01001D_016",
# female, 18-19, asian
"B01001D_022",
# female, 20-24, asian
"B01001D_023",
# female, 25-29, asian
"B01001D_024",
# female, 30-34, asian
"B01001D_025",
# female, 35-44, asian
"B01001D_026",
# female, 45-54, asian
"B01001D_027",
# female, 55-64, asian
"B01001D_028",
# female, 65-74, asian
"B01001D_029",
# female, 75-84, asian
"B01001D_030",
# female, 85+, asian
"B01001D_031",
# male, 18-19, nhpi
"B01001E_007",
# male, 20-24, nhpi
"B01001E_008",
# male, 25-29, nhpi
"B01001E_009",
# male, 30-34, nhpi
"B01001E_010",
# male, 35-44, nhpi
"B01001E_011",
# male, 45-54, nhpi
"B01001E_012",
# male, 55-64, nhpi
"B01001E_013",
# male, 65-74, nhpi
"B01001E_014",
# male, 75-84, nhpi
"B01001E_015",
# male, 85+, nhpi
"B01001E_016",
# female, 18-19, nhpi
"B01001E_022",
# female, 20-24, nhpi
"B01001E_023",
# female, 25-29, nhpi
"B01001E_024",
# female, 30-34, nhpi
"B01001E_025",
# female, 35-44, nhpi
"B01001E_026",
# female, 45-54, nhpi
"B01001E_027",
# female, 55-64, nhpi
"B01001E_028",
# female, 65-74, nhpi
"B01001E_029",
# female, 75-84, nhpi
"B01001E_030",
# female, 85+, nhpi
"B01001E_031",
# male, 18-19, other
"B01001F_007",
# male, 20-24, other
"B01001F_008",
# male, 25-29, other
"B01001F_009",
# male, 30-34, other
"B01001F_010",
# male, 35-44, other
"B01001F_011",
# male, 45-54, other
"B01001F_012",
# male, 55-64, other
"B01001F_013",
# male, 65-74, other
"B01001F_014",
# male, 75-84, other
"B01001F_015",
# male, 85+, other
"B01001F_016",
# female, 18-19, other
"B01001F_022",
# female, 20-24, other
"B01001F_023",
# female, 25-29, other
"B01001F_024",
# female, 30-34, other
"B01001F_025",
# female, 35-44, other
"B01001F_026",
# female, 45-54, other
"B01001F_027",
# female, 55-64, other
"B01001F_028",
# female, 65-74, other
"B01001F_029",
# female, 75-84, other
"B01001F_030",
# female, 85+, other
"B01001F_031",
# male, 18-19, multi
"B01001G_007",
# male, 20-24, multi
"B01001G_008",
# male, 25-29, multi
"B01001G_009",
# male, 30-34, multi
"B01001G_010",
# male, 35-44, multi
"B01001G_011",
# male, 45-54, multi
"B01001G_012",
# male, 55-64, multi
"B01001G_013",
# male, 65-74, multi
"B01001G_014",
# male, 75-84, multi
"B01001G_015",
# male, 85+, multi
"B01001G_016",
# female, 18-19, multi
"B01001G_022",
# female, 20-24, multi
"B01001G_023",
# female, 25-29, multi
"B01001G_024",
# female, 30-34, multi
"B01001G_025",
# female, 35-44, multi
"B01001G_026",
# female, 45-54, multi
"B01001G_027",
# female, 55-64, multi
"B01001G_028",
# female, 65-74, multi
"B01001G_029",
# female, 75-84, multi
"B01001G_030",
# female, 85+, multi
"B01001G_031",
# male, 18-19, hispanic
"B01001I_007",
# male, 20-24, hispanic
"B01001I_008",
# male, 25-29, hispanic
"B01001I_009",
# male, 30-34, hispanic
"B01001I_010",
# male, 35-44, hispanic
"B01001I_011",
# male, 45-54, hispanic
"B01001I_012",
# male, 55-64, hispanic
"B01001I_013",
# male, 65-74, hispanic
"B01001I_014",
# male, 75-84, hispanic
"B01001I_015",
# male, 85+, hispanic
"B01001I_016",
# female, 18-19, hispanic
"B01001I_022",
# female, 20-24, hispanic
"B01001I_023",
# female, 25-29, hispanic
"B01001I_024",
# female, 30-34, hispanic
"B01001I_025",
# female, 35-44, hispanic
"B01001I_026",
# female, 45-54, hispanic
"B01001I_027",
# female, 55-64, hispanic
"B01001I_028",
# female, 65-74, hispanic
"B01001I_029",
# female, 75-84, hispanic
"B01001I_030",
# female, 85+, hispanic
"B01001I_031"
)
acs_ps_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "tract",
state = s,
variables = acs_ps_vars,
year = 2017,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_ps_var <- acs_ps_var %>%
transmute(fips = substr(GEOID, 1, 5),
tract_fips = GEOID,
male_18_24_white = B01001A_007E + B01001A_008E,
male_25_34_white = B01001A_009E + B01001A_010E,
male_35_44_white = B01001A_011E,
male_45_54_white = B01001A_012E,
male_55_64_white = B01001A_013E,
male_65_74_white = B01001A_014E,
male_75_plus_white = B01001A_015E + B01001A_016E,
female_18_24_white = B01001A_022E + B01001A_023E,
female_25_34_white = B01001A_024E + B01001A_025E,
female_35_44_white = B01001A_026E,
female_45_54_white = B01001A_027E,
female_55_64_white = B01001A_028E,
female_65_74_white = B01001A_029E,
female_75_plus_white = B01001A_030E + B01001A_031E,
male_18_24_black = B01001B_007E + B01001B_008E,
male_25_34_black = B01001B_009E + B01001B_010E,
male_35_44_black = B01001B_011E,
male_45_54_black = B01001B_012E,
male_55_64_black = B01001B_013E,
male_65_74_black = B01001B_014E,
male_75_plus_black = B01001B_015E + B01001B_016E,
female_18_24_black = B01001B_022E + B01001B_023E,
female_25_34_black = B01001B_024E + B01001B_025E,
female_35_44_black = B01001B_026E,
female_45_54_black = B01001B_027E,
female_55_64_black = B01001B_028E,
female_65_74_black = B01001B_029E,
female_75_plus_black = B01001B_030E + B01001B_031E,
male_18_24_aian = B01001C_007E + B01001C_008E,
male_25_34_aian = B01001C_009E + B01001C_010E,
male_35_44_aian = B01001C_011E,
male_45_54_aian = B01001C_012E,
male_55_64_aian = B01001C_013E,
male_65_74_aian = B01001C_014E,
male_75_plus_aian = B01001C_015E + B01001C_016E,
female_18_24_aian = B01001C_022E + B01001C_023E,
female_25_34_aian = B01001C_024E + B01001C_025E,
female_35_44_aian = B01001C_026E,
female_45_54_aian = B01001C_027E,
female_55_64_aian = B01001C_028E,
female_65_74_aian = B01001C_029E,
female_75_plus_aian = B01001C_030E + B01001C_031E,
male_18_24_asian = B01001D_007E + B01001D_008E,
male_25_34_asian = B01001D_009E + B01001D_010E,
male_35_44_asian = B01001D_011E,
male_45_54_asian = B01001D_012E,
male_55_64_asian = B01001D_013E,
male_65_74_asian = B01001D_014E,
male_75_plus_asian = B01001D_015E + B01001D_016E,
female_18_24_asian = B01001D_022E + B01001D_023E,
female_25_34_asian = B01001D_024E + B01001D_025E,
female_35_44_asian = B01001D_026E,
female_45_54_asian = B01001D_027E,
female_55_64_asian = B01001D_028E,
female_65_74_asian = B01001D_029E,
female_75_plus_asian = B01001D_030E + B01001D_031E,
male_18_24_nhpi = B01001E_007E + B01001E_008E,
male_25_34_nhpi = B01001E_009E + B01001E_010E,
male_35_44_nhpi = B01001E_011E,
male_45_54_nhpi = B01001E_012E,
male_55_64_nhpi = B01001E_013E,
male_65_74_nhpi = B01001E_014E,
male_75_plus_nhpi = B01001E_015E + B01001E_016E,
female_18_24_nhpi = B01001E_022E + B01001E_023E,
female_25_34_nhpi = B01001E_024E + B01001E_025E,
female_35_44_nhpi = B01001E_026E,
female_45_54_nhpi = B01001E_027E,
female_55_64_nhpi = B01001E_028E,
female_65_74_nhpi = B01001E_029E,
female_75_plus_nhpi = B01001E_030E + B01001E_031E,
male_18_24_other = B01001F_007E + B01001F_008E,
male_25_34_other = B01001F_009E + B01001F_010E,
male_35_44_other = B01001F_011E,
male_45_54_other = B01001F_012E,
male_55_64_other = B01001F_013E,
male_65_74_other = B01001F_014E,
male_75_plus_other = B01001F_015E + B01001F_016E,
female_18_24_other = B01001F_022E + B01001F_023E,
female_25_34_other = B01001F_024E + B01001F_025E,
female_35_44_other = B01001F_026E,
female_45_54_other = B01001F_027E,
female_55_64_other = B01001F_028E,
female_65_74_other = B01001F_029E,
female_75_plus_other = B01001F_030E + B01001F_031E,
male_18_24_multi = B01001G_007E + B01001G_008E,
male_25_34_multi = B01001G_009E + B01001G_010E,
male_35_44_multi = B01001G_011E,
male_45_54_multi = B01001G_012E,
male_55_64_multi = B01001G_013E,
male_65_74_multi = B01001G_014E,
male_75_plus_multi = B01001G_015E + B01001G_016E,
female_18_24_multi = B01001G_022E + B01001G_023E,
female_25_34_multi = B01001G_024E + B01001G_025E,
female_35_44_multi = B01001G_026E,
female_45_54_multi = B01001G_027E,
female_55_64_multi = B01001G_028E,
female_65_74_multi = B01001G_029E,
female_75_plus_multi = B01001G_030E + B01001G_031E,
male_18_24_hispanic = B01001I_007E + B01001I_008E,
male_25_34_hispanic = B01001I_009E + B01001I_010E,
male_35_44_hispanic = B01001I_011E,
male_45_54_hispanic = B01001I_012E,
male_55_64_hispanic = B01001I_013E,
male_65_74_hispanic = B01001I_014E,
male_75_plus_hispanic = B01001I_015E + B01001I_016E,
female_18_24_hispanic = B01001I_022E + B01001I_023E,
female_25_34_hispanic = B01001I_024E + B01001I_025E,
female_35_44_hispanic = B01001I_026E,
female_45_54_hispanic = B01001I_027E,
female_55_64_hispanic = B01001I_028E,
female_65_74_hispanic = B01001I_029E,
female_75_plus_hispanic = B01001I_030E + B01001I_031E
)
# construct poststratification matrix
ps_mat <- acs_ps_var %>%
filter(tract_fips %in% unique(acs_tract_var$tract_fips)) %>%
arrange(fips, tract_fips)
tract_name <- ps_mat$tract_fips
ps_mat <- ps_mat %>%
select(-c(1, 2)) %>%
as.data.frame()
rownames(ps_mat) <- tract_name
# indicator for which counties are missing from BRFSS data
county_missing_ind <- as.numeric(!(unique(acs_tract_var$fips) %in% acs_cnty_var$fips))
#### get state prev est from brfss ####
ncr_brfss_2017 <- readRDS("~/projects_data/ncr_brfss_2017.rds")
options(survey.lonely.psu = "adjust")
ncr_brfss_2017_design <- svydesign(ids = ~ xpsu ,
strata = ~ xststr ,
data = ncr_brfss_2017 ,
weights = ~ xllcpwt ,
nest = TRUE)
st_prev_logit <- qlogis(unname(svymean(~physhlth_binary, ncr_brfss_2017_design)[1]))
### MRP ###
brfss_mrp <- cmdstan_model("~/git/brfss_mrp_ncr.stan")
# physical health #
data_list_phys <- list(N = nrow(ncr_brfss),
y = ncr_brfss$physhlth_binary,
J = ncol(ps_mat),
num_obs_cnty = length(unique(ncr_brfss$fips_cat)),
num_cnty = length(unique(acs_tract_var$fips)),
num_tract = length(unique(acs_tract_var$tract_fips)),
num_age = length(unique(ncr_brfss$age_cat)),
num_race = length(unique(ncr_brfss$race2)),
state_prev = st_prev_logit,
gender = ncr_brfss$sex,
cnty = ncr_brfss$fips_cat,
age = ncr_brfss$age_cat,
race = ncr_brfss$race2,
poverty = acs_cnty_var$frac_poverty,
labor_force = acs_cnty_var$frac_in_labor_force,
less_than_HS = acs_cnty_var$frac_less_than_HS,
poverty_tract = acs_tract_var$frac_poverty,
labor_force_tract = acs_tract_var$frac_in_labor_force,
less_than_HS_tract = acs_tract_var$frac_less_than_HS,
N_pop = as.vector(t(ps_mat)),
cnty_miss_ind = county_missing_ind,
tract_cnty_map = acs_tract_var$fips_cat)
brfss_mrp_fit_phys <- brfss_mrp$sample(data = data_list_phys,
seed = 50,
refresh = 0,
chains = 4,
parallel_chains = 4,
iter_warmup = 2500,
iter_sampling = 2500,
step_size = 0.1,
adapt_delta = 0.985)
mrp_phys_draws <- brfss_mrp_fit_phys$draws("y_tract")
res_phys <- as.data.frame(summarise_draws(mrp_phys_draws,
mean, median, sd, mad))
phys_ci <- matrix(NA, nrow = nrow(ps_mat), ncol = 2)
for (i in 1:nrow(ps_mat)) {
phys_ci[i,] <- spin(mrp_phys_draws[,,i],
lower = 0,
upper = 1,
conf = 0.95)
}
phys_tract_res <- data.frame(tract_code = rownames(ps_mat),
mean_phys = res_phys$mean,
median_phys = res_phys$median,
sd_phys = res_phys$sd,
mad_phys = res_phys$mad,
lower_pi_phys = phys_ci[,1],
upper_pi_phys = phys_ci[,2])
saveRDS(phys_tract_res, "~/git/vdh_brfss_res/phys_2017_pred_ncr.rds")
uva-bi-sdad/dc.sdad.health.outcomes documentation built on June 17, 2022, 9:37 p.m.
R Package Documentation
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library(tidycensus)
library(dplyr)
library(purrr)
library(data.table)
library(cmdstanr)
library(posterior)
library(survey)
source("~/git/mrp_utils.R")
### get data to fit MRP model ###
ncr_brfss <- readRDS("~/projects_data/ncr_2011_clean.rds")
# get ACS county data #
acs_var_2011 <- c("B16009_001", "B16009_002",
"B23025_001", "B23025_002",
"B06009_001", "B06009_002")
acs_cnty_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "county",
state = s,
variables = acs_var_2011,
year = 2011,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_cnty_var <- acs_cnty_var %>%
transmute(fips = GEOID,
frac_poverty = B16009_002E / B16009_001E,
frac_in_labor_force = B23025_002E / B23025_001E,
frac_less_than_HS = B06009_002E / B06009_001E)
acs_cnty_var$frac_poverty <- (acs_cnty_var$frac_poverty - mean(acs_cnty_var$frac_poverty))/sd(acs_cnty_var$frac_poverty)
acs_cnty_var$frac_in_labor_force <- (acs_cnty_var$frac_in_labor_force - mean(acs_cnty_var$frac_in_labor_force))/sd(acs_cnty_var$frac_in_labor_force)
acs_cnty_var$frac_less_than_HS <- (acs_cnty_var$frac_less_than_HS - mean(acs_cnty_var$frac_less_than_HS))/sd(acs_cnty_var$frac_less_than_HS)
acs_cnty_var <- acs_cnty_var %>%
filter(fips %in% unique(ncr_brfss$fips)) %>%
arrange(fips) %>%
as.data.frame()
### get prediction data at tract level ###
# get ACS tract data for model #
acs_var_tract <- c("B17020_001", "B17020_002",
"B23025_001", "B23025_002",
"B06009_001", "B06009_002")
acs_tract_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "tract",
state = s,
variables = acs_var_tract,
year = 2017,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_tract_var <- acs_tract_var %>%
transmute(tract_fips = GEOID,
fips = substr(tract_fips, 1, 5),
st_fips = substr(tract_fips, 1, 2),
frac_poverty = B17020_002E / B17020_001E,
frac_in_labor_force = B23025_002E / B23025_001E,
frac_less_than_HS = B06009_002E / B06009_001E)
acs_tract_var <- acs_tract_var[complete.cases(acs_tract_var),]
acs_tract_var$frac_poverty <- (acs_tract_var$frac_poverty - mean(acs_tract_var$frac_poverty))/sd(acs_tract_var$frac_poverty)
acs_tract_var$frac_in_labor_force <- (acs_tract_var$frac_in_labor_force - mean(acs_tract_var$frac_in_labor_force))/sd(acs_tract_var$frac_in_labor_force)
acs_tract_var$frac_less_than_HS <- (acs_tract_var$frac_less_than_HS - mean(acs_tract_var$frac_less_than_HS))/sd(acs_tract_var$frac_less_than_HS)
acs_tract_var <- acs_tract_var %>%
arrange(fips, tract_fips) %>%
as.data.frame()
sort_fips <- sort(unique(acs_tract_var$fips))
acs_tract_var$fips_cat <- match(acs_tract_var$fips, sort_fips)
sort_st_fips <- sort(unique(acs_tract_var$st_fips))
acs_tract_var$st_fips_cat <- match(acs_tract_var$st_fips, sort_st_fips)
# get poststratification data #
acs_ps_vars <- c(
# male, 18-19, white
"B01001A_007",
# male, 20-24, white
"B01001A_008",
# male, 25-29, white
"B01001A_009",
# male, 30-34, white
"B01001A_010",
# male, 35-44, white
"B01001A_011",
# male, 45-54, white
"B01001A_012",
# male, 55-64, white
"B01001A_013",
# male, 65-74, white
"B01001A_014",
# male, 75-84, white
"B01001A_015",
# male, 85+, white
"B01001A_016",
# female, 18-19, white
"B01001A_022",
# female, 20-24, white
"B01001A_023",
# female, 25-29, white
"B01001A_024",
# female, 30-34, white
"B01001A_025",
# female, 35-44, white
"B01001A_026",
# female, 45-54, white
"B01001A_027",
# female, 55-64, white
"B01001A_028",
# female, 65-74, white
"B01001A_029",
# female, 75-84, white
"B01001A_030",
# female, 85+, white
"B01001A_031",
# male, 18-19, black
"B01001B_007",
# male, 20-24, black
"B01001B_008",
# male, 25-29, black
"B01001B_009",
# male, 30-34, black
"B01001B_010",
# male, 35-44, black
"B01001B_011",
# male, 45-54, black
"B01001B_012",
# male, 55-64, black
"B01001B_013",
# male, 65-74, black
"B01001B_014",
# male, 75-84, black
"B01001B_015",
# male, 85+, black
"B01001B_016",
# female, 18-19, black
"B01001B_022",
# female, 20-24, black
"B01001B_023",
# female, 25-29, black
"B01001B_024",
# female, 30-34, black
"B01001B_025",
# female, 35-44, black
"B01001B_026",
# female, 45-54, black
"B01001B_027",
# female, 55-64, black
"B01001B_028",
# female, 65-74, black
"B01001B_029",
# female, 75-84, black
"B01001B_030",
# female, 85+, black
"B01001B_031",
# male, 18-19, aian
"B01001C_007",
# male, 20-24, aian
"B01001C_008",
# male, 25-29, aian
"B01001C_009",
# male, 30-34, aian
"B01001C_010",
# male, 35-44, aian
"B01001C_011",
# male, 45-54, aian
"B01001C_012",
# male, 55-64, aian
"B01001C_013",
# male, 65-74, aian
"B01001C_014",
# male, 75-84, aian
"B01001C_015",
# male, 85+, aian
"B01001C_016",
# female, 18-19, aian
"B01001C_022",
# female, 20-24, aian
"B01001C_023",
# female, 25-29, aian
"B01001C_024",
# female, 30-34, aian
"B01001C_025",
# female, 35-44, aian
"B01001C_026",
# female, 45-54, aian
"B01001C_027",
# female, 55-64, aian
"B01001C_028",
# female, 65-74, aian
"B01001C_029",
# female, 75-84, aian
"B01001C_030",
# female, 85+, aian
"B01001C_031",
# male, 18-19, asian
"B01001D_007",
# male, 20-24, asian
"B01001D_008",
# male, 25-29, asian
"B01001D_009",
# male, 30-34, asian
"B01001D_010",
# male, 35-44, asian
"B01001D_011",
# male, 45-54, asian
"B01001D_012",
# male, 55-64, asian
"B01001D_013",
# male, 65-74, asian
"B01001D_014",
# male, 75-84, asian
"B01001D_015",
# male, 85+, asian
"B01001D_016",
# female, 18-19, asian
"B01001D_022",
# female, 20-24, asian
"B01001D_023",
# female, 25-29, asian
"B01001D_024",
# female, 30-34, asian
"B01001D_025",
# female, 35-44, asian
"B01001D_026",
# female, 45-54, asian
"B01001D_027",
# female, 55-64, asian
"B01001D_028",
# female, 65-74, asian
"B01001D_029",
# female, 75-84, asian
"B01001D_030",
# female, 85+, asian
"B01001D_031",
# male, 18-19, nhpi
"B01001E_007",
# male, 20-24, nhpi
"B01001E_008",
# male, 25-29, nhpi
"B01001E_009",
# male, 30-34, nhpi
"B01001E_010",
# male, 35-44, nhpi
"B01001E_011",
# male, 45-54, nhpi
"B01001E_012",
# male, 55-64, nhpi
"B01001E_013",
# male, 65-74, nhpi
"B01001E_014",
# male, 75-84, nhpi
"B01001E_015",
# male, 85+, nhpi
"B01001E_016",
# female, 18-19, nhpi
"B01001E_022",
# female, 20-24, nhpi
"B01001E_023",
# female, 25-29, nhpi
"B01001E_024",
# female, 30-34, nhpi
"B01001E_025",
# female, 35-44, nhpi
"B01001E_026",
# female, 45-54, nhpi
"B01001E_027",
# female, 55-64, nhpi
"B01001E_028",
# female, 65-74, nhpi
"B01001E_029",
# female, 75-84, nhpi
"B01001E_030",
# female, 85+, nhpi
"B01001E_031",
# male, 18-19, other
"B01001F_007",
# male, 20-24, other
"B01001F_008",
# male, 25-29, other
"B01001F_009",
# male, 30-34, other
"B01001F_010",
# male, 35-44, other
"B01001F_011",
# male, 45-54, other
"B01001F_012",
# male, 55-64, other
"B01001F_013",
# male, 65-74, other
"B01001F_014",
# male, 75-84, other
"B01001F_015",
# male, 85+, other
"B01001F_016",
# female, 18-19, other
"B01001F_022",
# female, 20-24, other
"B01001F_023",
# female, 25-29, other
"B01001F_024",
# female, 30-34, other
"B01001F_025",
# female, 35-44, other
"B01001F_026",
# female, 45-54, other
"B01001F_027",
# female, 55-64, other
"B01001F_028",
# female, 65-74, other
"B01001F_029",
# female, 75-84, other
"B01001F_030",
# female, 85+, other
"B01001F_031",
# male, 18-19, multi
"B01001G_007",
# male, 20-24, multi
"B01001G_008",
# male, 25-29, multi
"B01001G_009",
# male, 30-34, multi
"B01001G_010",
# male, 35-44, multi
"B01001G_011",
# male, 45-54, multi
"B01001G_012",
# male, 55-64, multi
"B01001G_013",
# male, 65-74, multi
"B01001G_014",
# male, 75-84, multi
"B01001G_015",
# male, 85+, multi
"B01001G_016",
# female, 18-19, multi
"B01001G_022",
# female, 20-24, multi
"B01001G_023",
# female, 25-29, multi
"B01001G_024",
# female, 30-34, multi
"B01001G_025",
# female, 35-44, multi
"B01001G_026",
# female, 45-54, multi
"B01001G_027",
# female, 55-64, multi
"B01001G_028",
# female, 65-74, multi
"B01001G_029",
# female, 75-84, multi
"B01001G_030",
# female, 85+, multi
"B01001G_031",
# male, 18-19, hispanic
"B01001I_007",
# male, 20-24, hispanic
"B01001I_008",
# male, 25-29, hispanic
"B01001I_009",
# male, 30-34, hispanic
"B01001I_010",
# male, 35-44, hispanic
"B01001I_011",
# male, 45-54, hispanic
"B01001I_012",
# male, 55-64, hispanic
"B01001I_013",
# male, 65-74, hispanic
"B01001I_014",
# male, 75-84, hispanic
"B01001I_015",
# male, 85+, hispanic
"B01001I_016",
# female, 18-19, hispanic
"B01001I_022",
# female, 20-24, hispanic
"B01001I_023",
# female, 25-29, hispanic
"B01001I_024",
# female, 30-34, hispanic
"B01001I_025",
# female, 35-44, hispanic
"B01001I_026",
# female, 45-54, hispanic
"B01001I_027",
# female, 55-64, hispanic
"B01001I_028",
# female, 65-74, hispanic
"B01001I_029",
# female, 75-84, hispanic
"B01001I_030",
# female, 85+, hispanic
"B01001I_031"
)
acs_ps_var <- map_df(c(51, 11, 24),
function(s) {
get_acs(geography = "tract",
state = s,
variables = acs_ps_vars,
year = 2017,
survey = "acs5",
cache_table = TRUE,
output = "wide",
geometry = FALSE,
keep_geo_vars = FALSE)
}
)
acs_ps_var <- acs_ps_var %>%
transmute(fips = substr(GEOID, 1, 5),
tract_fips = GEOID,
male_18_24_white = B01001A_007E + B01001A_008E,
male_25_34_white = B01001A_009E + B01001A_010E,
male_35_44_white = B01001A_011E,
male_45_54_white = B01001A_012E,
male_55_64_white = B01001A_013E,
male_65_74_white = B01001A_014E,
male_75_plus_white = B01001A_015E + B01001A_016E,
female_18_24_white = B01001A_022E + B01001A_023E,
female_25_34_white = B01001A_024E + B01001A_025E,
female_35_44_white = B01001A_026E,
female_45_54_white = B01001A_027E,
female_55_64_white = B01001A_028E,
female_65_74_white = B01001A_029E,
female_75_plus_white = B01001A_030E + B01001A_031E,
male_18_24_black = B01001B_007E + B01001B_008E,
male_25_34_black = B01001B_009E + B01001B_010E,
male_35_44_black = B01001B_011E,
male_45_54_black = B01001B_012E,
male_55_64_black = B01001B_013E,
male_65_74_black = B01001B_014E,
male_75_plus_black = B01001B_015E + B01001B_016E,
female_18_24_black = B01001B_022E + B01001B_023E,
female_25_34_black = B01001B_024E + B01001B_025E,
female_35_44_black = B01001B_026E,
female_45_54_black = B01001B_027E,
female_55_64_black = B01001B_028E,
female_65_74_black = B01001B_029E,
female_75_plus_black = B01001B_030E + B01001B_031E,
male_18_24_aian = B01001C_007E + B01001C_008E,
male_25_34_aian = B01001C_009E + B01001C_010E,
male_35_44_aian = B01001C_011E,
male_45_54_aian = B01001C_012E,
male_55_64_aian = B01001C_013E,
male_65_74_aian = B01001C_014E,
male_75_plus_aian = B01001C_015E + B01001C_016E,
female_18_24_aian = B01001C_022E + B01001C_023E,
female_25_34_aian = B01001C_024E + B01001C_025E,
female_35_44_aian = B01001C_026E,
female_45_54_aian = B01001C_027E,
female_55_64_aian = B01001C_028E,
female_65_74_aian = B01001C_029E,
female_75_plus_aian = B01001C_030E + B01001C_031E,
male_18_24_asian = B01001D_007E + B01001D_008E,
male_25_34_asian = B01001D_009E + B01001D_010E,
male_35_44_asian = B01001D_011E,
male_45_54_asian = B01001D_012E,
male_55_64_asian = B01001D_013E,
male_65_74_asian = B01001D_014E,
male_75_plus_asian = B01001D_015E + B01001D_016E,
female_18_24_asian = B01001D_022E + B01001D_023E,
female_25_34_asian = B01001D_024E + B01001D_025E,
female_35_44_asian = B01001D_026E,
female_45_54_asian = B01001D_027E,
female_55_64_asian = B01001D_028E,
female_65_74_asian = B01001D_029E,
female_75_plus_asian = B01001D_030E + B01001D_031E,
male_18_24_nhpi = B01001E_007E + B01001E_008E,
male_25_34_nhpi = B01001E_009E + B01001E_010E,
male_35_44_nhpi = B01001E_011E,
male_45_54_nhpi = B01001E_012E,
male_55_64_nhpi = B01001E_013E,
male_65_74_nhpi = B01001E_014E,
male_75_plus_nhpi = B01001E_015E + B01001E_016E,
female_18_24_nhpi = B01001E_022E + B01001E_023E,
female_25_34_nhpi = B01001E_024E + B01001E_025E,
female_35_44_nhpi = B01001E_026E,
female_45_54_nhpi = B01001E_027E,
female_55_64_nhpi = B01001E_028E,
female_65_74_nhpi = B01001E_029E,
female_75_plus_nhpi = B01001E_030E + B01001E_031E,
male_18_24_other = B01001F_007E + B01001F_008E,
male_25_34_other = B01001F_009E + B01001F_010E,
male_35_44_other = B01001F_011E,
male_45_54_other = B01001F_012E,
male_55_64_other = B01001F_013E,
male_65_74_other = B01001F_014E,
male_75_plus_other = B01001F_015E + B01001F_016E,
female_18_24_other = B01001F_022E + B01001F_023E,
female_25_34_other = B01001F_024E + B01001F_025E,
female_35_44_other = B01001F_026E,
female_45_54_other = B01001F_027E,
female_55_64_other = B01001F_028E,
female_65_74_other = B01001F_029E,
female_75_plus_other = B01001F_030E + B01001F_031E,
male_18_24_multi = B01001G_007E + B01001G_008E,
male_25_34_multi = B01001G_009E + B01001G_010E,
male_35_44_multi = B01001G_011E,
male_45_54_multi = B01001G_012E,
male_55_64_multi = B01001G_013E,
male_65_74_multi = B01001G_014E,
male_75_plus_multi = B01001G_015E + B01001G_016E,
female_18_24_multi = B01001G_022E + B01001G_023E,
female_25_34_multi = B01001G_024E + B01001G_025E,
female_35_44_multi = B01001G_026E,
female_45_54_multi = B01001G_027E,
female_55_64_multi = B01001G_028E,
female_65_74_multi = B01001G_029E,
female_75_plus_multi = B01001G_030E + B01001G_031E,
male_18_24_hispanic = B01001I_007E + B01001I_008E,
male_25_34_hispanic = B01001I_009E + B01001I_010E,
male_35_44_hispanic = B01001I_011E,
male_45_54_hispanic = B01001I_012E,
male_55_64_hispanic = B01001I_013E,
male_65_74_hispanic = B01001I_014E,
male_75_plus_hispanic = B01001I_015E + B01001I_016E,
female_18_24_hispanic = B01001I_022E + B01001I_023E,
female_25_34_hispanic = B01001I_024E + B01001I_025E,
female_35_44_hispanic = B01001I_026E,
female_45_54_hispanic = B01001I_027E,
female_55_64_hispanic = B01001I_028E,
female_65_74_hispanic = B01001I_029E,
female_75_plus_hispanic = B01001I_030E + B01001I_031E
)
# construct poststratification matrix
ps_mat <- acs_ps_var %>%
filter(tract_fips %in% unique(acs_tract_var$tract_fips)) %>%
arrange(fips, tract_fips)
tract_name <- ps_mat$tract_fips
ps_mat <- ps_mat %>%
select(-c(1, 2)) %>%
as.data.frame()
rownames(ps_mat) <- tract_name
# indicator for which counties are missing from BRFSS data
county_missing_ind <- as.numeric(!(unique(acs_tract_var$fips) %in% acs_cnty_var$fips))
#### get state prev est from brfss ####
ncr_brfss_2017 <- readRDS("~/projects_data/ncr_brfss_2017.rds")
options(survey.lonely.psu = "adjust")
ncr_brfss_2017_design <- svydesign(ids = ~ xpsu ,
strata = ~ xststr ,
data = ncr_brfss_2017 ,
weights = ~ xllcpwt ,
nest = TRUE)
st_prev_logit <- qlogis(unname(svymean(~physhlth_binary, ncr_brfss_2017_design)[1]))
### MRP ###
brfss_mrp <- cmdstan_model("~/git/brfss_mrp_ncr.stan")
# physical health #
data_list_phys <- list(N = nrow(ncr_brfss),
y = ncr_brfss$physhlth_binary,
J = ncol(ps_mat),
num_obs_cnty = length(unique(ncr_brfss$fips_cat)),
num_cnty = length(unique(acs_tract_var$fips)),
num_tract = length(unique(acs_tract_var$tract_fips)),
num_age = length(unique(ncr_brfss$age_cat)),
num_race = length(unique(ncr_brfss$race2)),
state_prev = st_prev_logit,
gender = ncr_brfss$sex,
cnty = ncr_brfss$fips_cat,
age = ncr_brfss$age_cat,
race = ncr_brfss$race2,
poverty = acs_cnty_var$frac_poverty,
labor_force = acs_cnty_var$frac_in_labor_force,
less_than_HS = acs_cnty_var$frac_less_than_HS,
poverty_tract = acs_tract_var$frac_poverty,
labor_force_tract = acs_tract_var$frac_in_labor_force,
less_than_HS_tract = acs_tract_var$frac_less_than_HS,
N_pop = as.vector(t(ps_mat)),
cnty_miss_ind = county_missing_ind,
tract_cnty_map = acs_tract_var$fips_cat)
brfss_mrp_fit_phys <- brfss_mrp$sample(data = data_list_phys,
seed = 50,
refresh = 0,
chains = 4,
parallel_chains = 4,
iter_warmup = 2500,
iter_sampling = 2500,
step_size = 0.1,
adapt_delta = 0.985)
mrp_phys_draws <- brfss_mrp_fit_phys$draws("y_tract")
res_phys <- as.data.frame(summarise_draws(mrp_phys_draws,
mean, median, sd, mad))
phys_ci <- matrix(NA, nrow = nrow(ps_mat), ncol = 2)
for (i in 1:nrow(ps_mat)) {
phys_ci[i,] <- spin(mrp_phys_draws[,,i],
lower = 0,
upper = 1,
conf = 0.95)
}
phys_tract_res <- data.frame(tract_code = rownames(ps_mat),
mean_phys = res_phys$mean,
median_phys = res_phys$median,
sd_phys = res_phys$sd,
mad_phys = res_phys$mad,
lower_pi_phys = phys_ci[,1],
upper_pi_phys = phys_ci[,2])
saveRDS(phys_tract_res, "~/git/vdh_brfss_res/phys_2017_pred_ncr.rds")
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