NHANES DATA_ DXA WHOLE BODY
Julie Baek
May 13, 2021
Data Preprocessing
Period: 1999 - 2006 (8 yrs)
setwd("C:\\Users\\bjh55\\Desktop\\Rstorage\\BD_NHANES\\WHANES")
'%!in%' <- function(x,y)!('%in%'(x,y))
## Data setting (1999 - 2006)
for (i in 1:4) {
# Demographics data
DEMO <- read_xpt(paste0("DEMO_", LETTERS[i], ".xpt"))
print(length(unique(DEMO$SEQN)))
# Examination data (Whole body DXA)
DXX <- read_xpt(paste0("DXX_", LETTERS[i], ".xpt"))
print(length(unique(DXX$SEQN)))
# Body measures data
BMX <- read_xpt(paste0("BMX_", LETTERS[i], ".xpt"))
print(length(unique(BMX$SEQN)))
# Merge component files
# Keep all records in DEMO, even if that SEQN does not match to DXX or BMX files
one_tmp <- DEMO %>% filter(RIDSTATR == 2 & RIDEXPRG %!in% c(1,3) & RIDAGEYR >= 17) %>% left_join(., select(BMX, "SEQN", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST"), by="SEQN") %>%
left_join(., select(DXX, "SEQN", ends_with("MULT_"), "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE") , by="SEQN")
print(length(unique(one_tmp$SEQN)))
assign(paste0("one_tmp_",LETTERS[i]), one_tmp)
}## [1] 9965
## [1] 6235
## [1] 9282
## [1] 4858
## [1] 11039
## [1] 8242
## [1] 10477
## [1] 5451
## [1] 10122
## [1] 7656
## [1] 9643
## [1] 5224
## [1] 10348
## [1] 6893
## [1] 9950
## [1] 5168# 1999 - 2006 Data Full Join
tmp_all <- select(one_tmp_A, ends_with("_MULT_"), "SEQN", "SDDSRVYR", "WTMEC4YR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE") %>%
full_join(., select(one_tmp_B, ends_with("_MULT_"), "SEQN", "SDDSRVYR", "WTMEC4YR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE")) %>%
full_join(., select(one_tmp_C, ends_with("_MULT_"), "SEQN", "SDDSRVYR", "WTMEC2YR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE")) %>%
full_join(., select(one_tmp_D, ends_with("_MULT_"), "SEQN", "SDDSRVYR", "WTMEC2YR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE")) %>%
mutate(.,
# Generate 8-year MEC weight
WTMEC8YR = ifelse(SDDSRVYR %in% c(1, 2), 1/2 * WTMEC4YR, 1/4 * WTMEC2YR),
# Create factor variables
Gender = factor(RIAGENDR, labels=c("Men", "Women")),
Age.Group = cut(RIDAGEYR, breaks=c(-Inf,19,24,29,34,39,44,49,54,59,64,69,74,79,Inf),labels=c("17-19", "20-24","25-29","30-34","35-39","40-44","45-49","50-54","55-59","60-64","65-69","70-74","75-79","Over 80")),
Age.Group2 = cut(RIDAGEYR, breaks=c(-Inf, 29, 39, 49, 59, 69, 79, Inf), labels=c("17-29", "30-39", "40-49", "50-59", "60-69", "70-79", "Over 80")),
Age.Group3 = cut(RIDAGEYR, breaks=c(-Inf, 17, 18, 19, 20, 21, 29, 39, 49, 59, 69, Inf), labels=c("17", "18", "19", "20", "21", "22-29", "30-39", "40-49", "50-59", "60-69", "Over 70")),
Age.A = ifelse(Age.Group3=="18", 1, 0),
Age.B = ifelse(Age.Group3=="19", 1, 0),
Age.C = ifelse(Age.Group3=="20", 1, 0),
Age.D = ifelse(Age.Group3=="21", 1, 0),
Age.E = ifelse(Age.Group3=="22-29", 1, 0),
Age.F = ifelse(Age.Group3=="30-39", 1, 0),
Age.G = ifelse(Age.Group3=="40-49", 1, 0),
Age.H = ifelse(Age.Group3=="50-59", 1, 0),
Age.I = ifelse(Age.Group3=="60-69", 1, 0),
Age.J = ifelse(Age.Group3=="Over 70", 1, 0),
BMI.Group = cut(BMXBMI, breaks=c(-Inf,18.5,25,30,35,Inf), labels=c("Under 18.5", "18.5-25", "26-30", "31-35", "36 and over")),
TOTLEANH2 = (DXDTOLE/1000)/((BMXHT/100)^2),
WHRATIO = BMXWAIST/BMXHT,
RALEANH2 = DXDRALE/((BMXHT/100)^2),
LALEANH2 = DXDLALE/((BMXHT/100)^2),
RLLEANH2 = DXDRLLE/((BMXHT/100)^2),
LLLEANH2 = DXDLLLE/((BMXHT/100)^2),
TFWHR = DXDTOPF*(BMXWAIST/BMXHT)
)## Joining, by = c("_MULT_", "SEQN", "SDDSRVYR", "WTMEC4YR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE")## Joining, by = c("_MULT_", "SEQN", "SDDSRVYR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE")## Joining, by = c("_MULT_", "SEQN", "SDDSRVYR", "RIAGENDR", "RIDAGEYR", "RIDRETH1", "SDMVSTRA", "SDMVPSU", "BMXWT", "BMXHT", "BMXBMI", "BMXWAIST", "DXAEXSTS", "DXDTOFAT", "DXDTOLE", "DXDTOLI", "DXDTOPF", "DXDLLLE", "DXDLALE", "DXDRALE", "DXDRLLE", "WTMEC2YR")# Select samples -> Whole body scan completed.
length(unique(tmp_all$SEQN)) # Check the number of our samples.## [1] 20701tmp_all.1 <- tmp_all %>% filter(tmp_all[,1] == 1)
tmp_all.2 <- tmp_all %>% filter(tmp_all[,1] == 2)
tmp_all.3 <- tmp_all %>% filter(tmp_all[,1] == 3)
tmp_all.4 <- tmp_all %>% filter(tmp_all[,1] == 4)
tmp_all.5 <- tmp_all %>% filter(tmp_all[,1] == 5)
imput_tmp_all <- list(tmp_all.1, tmp_all.2, tmp_all.3, tmp_all.4, tmp_all.5)
# the "id" is actually the PSU!!!!!
nhc_all <- svydesign(id=~SDMVPSU, weights=~WTMEC8YR, strata=~SDMVSTRA, nest=TRUE, survey.lonely.psu = "adjust", data=imputationList(imput_tmp_all))
nhc_all## Multiple (5) imputations: svydesign(id = ~SDMVPSU, weights = ~WTMEC8YR, strata = ~SDMVSTRA,
## nest = TRUE, survey.lonely.psu = "adjust", data = imputationList(imput_tmp_all))Check the Number of our sample:
By Gender, Age group, Race.
## Multiple imputation results:
## with(nhc_all, svymean(~Gender, na = TRUE))
## MIcombine.default(with(nhc_all, svymean(~Gender, na = TRUE)))
## results se
## GenderMen 0.5044081 0.003324992
## GenderWomen 0.4955919 0.003324992## # A tibble: 2 x 3
## Gender n prop
## * <fct> <dbl> <dbl>
## 1 Men 100617374. 50.4
## 2 Women 98858738. 49.6## # A tibble: 14 x 3
## Age.Group n prop
## * <fct> <dbl> <dbl>
## 1 17-19 10831560. 5.4
## 2 20-24 18336290. 9.2
## 3 25-29 16239598. 8.1
## 4 30-34 18653456. 9.4
## 5 35-39 20165536. 10.1
## 6 40-44 21751620. 10.9
## 7 45-49 21435575. 10.7
## 8 50-54 19186522. 9.6
## 9 55-59 13213386. 6.6
## 10 60-64 11394589. 5.7
## 11 65-69 10176371. 5.1
## 12 70-74 6663387. 3.3
## 13 75-79 5265543. 2.6
## 14 Over 80 6162680. 3.1## # A tibble: 5 x 3
## RIDRETH1 n prop
## * <dbl> <dbl> <dbl>
## 1 1 15159449. 7.6
## 2 2 10575070. 5.3
## 3 3 141644699. 71
## 4 4 22185534. 11.1
## 5 5 9911360. 5## # A tibble: 14 x 3
## Age.Group n prop
## * <fct> <dbl> <dbl>
## 1 17-19 5945047. 5.9
## 2 20-24 9815676. 9.8
## 3 25-29 9264325. 9.2
## 4 30-34 9782666. 9.7
## 5 35-39 10488100. 10.4
## 6 40-44 10934497. 10.9
## 7 45-49 10501236. 10.4
## 8 50-54 9781188. 9.7
## 9 55-59 6639379. 6.6
## 10 60-64 5330206. 5.3
## 11 65-69 4806717. 4.8
## 12 70-74 2891269. 2.9
## 13 75-79 2172320. 2.2
## 14 Over 80 2264748. 2.3## # A tibble: 14 x 3
## Age.Group n prop
## * <fct> <dbl> <dbl>
## 1 17-19 4886513. 4.9
## 2 20-24 8520613. 8.6
## 3 25-29 6975273. 7.1
## 4 30-34 8870790. 9
## 5 35-39 9677436. 9.8
## 6 40-44 10817123. 10.9
## 7 45-49 10934339. 11.1
## 8 50-54 9405334. 9.5
## 9 55-59 6574007. 6.6
## 10 60-64 6064383. 6.1
## 11 65-69 5369653. 5.4
## 12 70-74 3772119. 3.8
## 13 75-79 3093223. 3.1
## 14 Over 80 3897933. 3.9## # A tibble: 7 x 3
## Age.Group2 n prop
## * <fct> <dbl> <dbl>
## 1 17-29 25025048. 24.9
## 2 30-39 20270766. 20.1
## 3 40-49 21435733. 21.3
## 4 50-59 16420567. 16.3
## 5 60-69 10136923. 10.1
## 6 70-79 5063588. 5
## 7 Over 80 2264748. 2.3## # A tibble: 7 x 3
## Age.Group2 n prop
## * <fct> <dbl> <dbl>
## 1 17-29 20382399. 20.6
## 2 30-39 18548226. 18.8
## 3 40-49 21751463. 22
## 4 50-59 15979341. 16.2
## 5 60-69 11434036. 11.6
## 6 70-79 6865342. 6.9
## 7 Over 80 3897933. 3.9## # A tibble: 11 x 3
## Age.Group3 n prop
## * <fct> <dbl> <dbl>
## 1 17 1975032. 2
## 2 18 2065374. 2.1
## 3 19 1904641. 1.9
## 4 20 2033223. 2
## 5 21 2235848. 2.2
## 6 22-29 14810931. 14.7
## 7 30-39 20270766. 20.1
## 8 40-49 21435733. 21.3
## 9 50-59 16420567. 16.3
## 10 60-69 10136923. 10.1
## 11 Over 70 7328336. 7.3## # A tibble: 11 x 3
## Age.Group3 n prop
## * <fct> <dbl> <dbl>
## 1 17 1587463. 1.6
## 2 18 1732048. 1.8
## 3 19 1567002. 1.6
## 4 20 1842263. 1.9
## 5 21 1697745. 1.7
## 6 22-29 11955879. 12.1
## 7 30-39 18548226. 18.8
## 8 40-49 21751463. 22
## 9 50-59 15979341. 16.2
## 10 60-69 11434036. 11.6
## 11 Over 70 10763274. 10.9Distribution of our target values.
By Gender, Age group, BMI group.
## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Gender, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.Men 16.83579 0.3073872
## 18.5-25.Men 22.27656 0.1194408
## 26-30.Men 27.96340 0.0991413
## 31-35.Men 31.97096 0.1184991
## 36 and over.Men 36.75856 0.1580697
## Under 18.5.Women 26.59001 0.3048568
## 18.5-25.Women 34.11953 0.1322851
## 26-30.Women 40.66613 0.1071343
## 31-35.Women 43.99776 0.1017997
## 36 and over.Women 47.90797 0.1426048## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Gender, na = TRUE,
## svyquantile, quantiles = c(0.5), keep.var = TRUE, se = T,
## ci = T))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Gender, na = TRUE, svyquantile, quantiles = c(0.5), keep.var = TRUE,
## se = T, ci = T)))
## results se
## Under 18.5.Men 16.40000 0.4127974
## 18.5-25.Men 22.13421 0.1744641
## 26-30.Men 27.94000 0.1258484
## 31-35.Men 32.18000 0.1462411
## 36 and over.Men 36.58000 0.2287236
## Under 18.5.Women 26.57656 0.4212896
## 18.5-25.Women 34.34000 0.1622571
## 26-30.Women 40.92000 0.1533101
## 31-35.Women 44.00000 0.1354870
## 36 and over.Women 48.14000 0.2118021## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group, na = TRUE, svymean)))
## results se
## Under 18.5.17-19 21.67060 0.6311274
## 18.5-25.17-19 24.79467 0.2811471
## 26-30.17-19 31.27723 0.4219859
## 31-35.17-19 36.95278 0.5340402
## 36 and over.17-19 42.83561 0.7673254
## Under 18.5.20-24 21.78756 1.1224856
## 18.5-25.20-24 26.16099 0.3204712
## 26-30.20-24 31.37494 0.4933393
## 31-35.20-24 36.22976 0.5790418
## 36 and over.20-24 43.51556 0.6672434
## Under 18.5.25-29 22.32186 0.9180107
## 18.5-25.25-29 26.94825 0.3001688
## 26-30.25-29 30.81023 0.3513910
## 31-35.25-29 36.82055 0.5386981
## 36 and over.25-29 42.28226 0.6692687
## Under 18.5.30-34 23.83385 1.2435163
## 18.5-25.30-34 27.36239 0.3515711
## 26-30.30-34 31.42390 0.3401313
## 31-35.30-34 36.69404 0.6344490
## 36 and over.30-34 43.37397 0.4988293
## Under 18.5.35-39 22.73763 2.0788430
## 18.5-25.35-39 28.31206 0.3716338
## 26-30.35-39 31.86403 0.4080180
## 31-35.35-39 35.79044 0.4891566
## 36 and over.35-39 43.36334 0.5703634
## Under 18.5.40-44 23.99492 0.9224410
## 18.5-25.40-44 29.28276 0.3483952
## 26-30.40-44 32.39905 0.3359044
## 31-35.40-44 36.53386 0.4699999
## 36 and over.40-44 42.02589 0.5392224
## Under 18.5.45-49 26.78269 1.0787021
## 18.5-25.45-49 29.24573 0.3857253
## 26-30.45-49 32.58012 0.4122963
## 31-35.45-49 37.63882 0.4759514
## 36 and over.45-49 43.99539 0.5288008
## Under 18.5.50-54 23.61621 1.8413232
## 18.5-25.50-54 30.57210 0.4545269
## 26-30.50-54 33.39985 0.3670701
## 31-35.50-54 37.83450 0.4863833
## 36 and over.50-54 42.64262 0.5192341
## Under 18.5.55-59 21.40761 1.4701127
## 18.5-25.55-59 31.37350 0.5599491
## 26-30.55-59 34.19677 0.4830676
## 31-35.55-59 37.84588 0.5152726
## 36 and over.55-59 45.36226 0.5037674
## Under 18.5.60-64 19.88356 2.2670843
## 18.5-25.60-64 32.12272 0.5282979
## 26-30.60-64 35.40704 0.3984166
## 31-35.60-64 38.94965 0.4137243
## 36 and over.60-64 44.88092 0.5878013
## Under 18.5.65-69 23.65807 1.6947248
## 18.5-25.65-69 32.69215 0.4340091
## 26-30.65-69 35.64502 0.3510023
## 31-35.65-69 40.24220 0.5663818
## 36 and over.65-69 44.60838 0.6314035
## Under 18.5.70-74 27.76751 1.1846519
## 18.5-25.70-74 32.17714 0.4756711
## 26-30.70-74 36.79590 0.4861047
## 31-35.70-74 41.15710 0.5965579
## 36 and over.70-74 46.13014 0.6454266
## Under 18.5.75-79 24.58186 2.3588576
## 18.5-25.75-79 33.62258 0.6443062
## 26-30.75-79 37.48480 0.4192039
## 31-35.75-79 40.86466 0.6921697
## 36 and over.75-79 46.69228 0.7655712
## Under 18.5.Over 80 27.15754 1.4299920
## 18.5-25.Over 80 34.07336 0.3530255
## 26-30.Over 80 37.78556 0.4064728
## 31-35.Over 80 41.99165 0.3997897
## 36 and over.Over 80 46.22688 1.1003633## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group, na = TRUE,
## svyquantile, quantiles = c(0.5), keep.var = TRUE, se = T,
## ci = T))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group, na = TRUE, svyquantile, quantiles = c(0.5), keep.var = TRUE,
## se = T, ci = T)))
## results se
## Under 18.5.17-19 22.83546 1.3410565
## 18.5-25.17-19 23.85382 0.8077948
## 26-30.17-19 30.50681 0.7659629
## 31-35.17-19 36.40342 1.0377280
## 36 and over.17-19 43.60004 1.4827360
## Under 18.5.20-24 21.17720 2.0676281
## 18.5-25.20-24 25.89201 0.8046333
## 26-30.20-24 30.41432 0.8126559
## 31-35.20-24 35.22280 1.0313288
## 36 and over.20-24 44.38440 1.1117552
## Under 18.5.25-29 22.86566 1.9807422
## 18.5-25.25-29 26.33239 0.5885100
## 26-30.25-29 29.40569 0.4175794
## 31-35.25-29 36.52116 1.2085770
## 36 and over.25-29 41.51212 1.2388289
## Under 18.5.30-34 23.83100 1.4660316
## 18.5-25.30-34 27.66506 0.6496740
## 26-30.30-34 29.50288 0.5073021
## 31-35.30-34 37.33200 1.1778606
## 36 and over.30-34 44.43420 0.7297151
## Under 18.5.35-39 21.56987 1.5252654
## 18.5-25.35-39 28.25433 0.6948237
## 26-30.35-39 30.68617 0.7099252
## 31-35.35-39 34.32777 0.9508529
## 36 and over.35-39 44.89093 0.9903095
## Under 18.5.40-44 24.86378 2.6150283
## 18.5-25.40-44 29.16778 0.7482810
## 26-30.40-44 30.63283 0.5462568
## 31-35.40-44 34.59943 1.2043097
## 36 and over.40-44 43.10404 1.0544960
## Under 18.5.45-49 26.51786 2.1280987
## 18.5-25.45-49 29.98663 0.9157126
## 26-30.45-49 30.42779 0.7192710
## 31-35.45-49 37.61561 1.0976843
## 36 and over.45-49 45.37767 0.7768566
## Under 18.5.50-54 22.02513 3.2298673
## 18.5-25.50-54 30.98233 0.6987883
## 26-30.50-54 31.58919 0.3820020
## 31-35.50-54 37.67835 1.3730298
## 36 and over.50-54 43.27415 1.1632604
## Under 18.5.55-59 21.19734 NaN
## 18.5-25.55-59 30.51808 0.9105755
## 26-30.55-59 32.43695 0.9180298
## 31-35.55-59 35.88738 1.4920980
## 36 and over.55-59 47.16209 0.7767502
## Under 18.5.60-64 18.64528 0.6810542
## 18.5-25.60-64 32.52672 1.0989439
## 26-30.60-64 34.88084 1.0919548
## 31-35.60-64 38.60811 1.2496176
## 36 and over.60-64 47.14000 1.0443229
## Under 18.5.65-69 22.25896 NaN
## 18.5-25.65-69 33.40452 0.7744511
## 26-30.65-69 35.07458 0.9934562
## 31-35.65-69 40.38137 1.2860524
## 36 and over.65-69 45.79850 0.8103655
## Under 18.5.70-74 28.47209 NaN
## 18.5-25.70-74 32.71908 0.8543166
## 26-30.70-74 36.64114 1.2086192
## 31-35.70-74 42.36485 1.0854953
## 36 and over.70-74 47.26445 0.9095093
## Under 18.5.75-79 25.03151 NaN
## 18.5-25.75-79 33.89501 1.2669487
## 26-30.75-79 38.48469 1.0803781
## 31-35.75-79 41.95974 1.3955505
## 36 and over.75-79 48.21788 0.9628798
## Under 18.5.Over 80 25.49964 2.5969766
## 18.5-25.Over 80 34.20572 0.5651357
## 26-30.Over 80 38.29865 0.8464856
## 31-35.Over 80 42.77735 0.7765957
## 36 and over.Over 80 47.08226 1.7597005## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + RIDRETH1, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## RIDRETH1, na = TRUE, svymean)))
## results se
## Under 18.5.1 24.59603 0.8725551
## 18.5-25.1 27.97854 0.3118409
## 26-30.1 32.59052 0.2400881
## 31-35.1 37.37082 0.2706221
## 36 and over.1 43.56664 0.3661515
## Under 18.5.2 26.67057 2.3361053
## 18.5-25.2 28.28149 0.5663572
## 26-30.2 33.62376 0.4787048
## 31-35.2 38.63607 0.7341099
## 36 and over.2 43.26368 0.7890223
## Under 18.5.3 23.25130 0.4581193
## 18.5-25.3 29.18759 0.1444031
## 26-30.3 33.32109 0.2032951
## 31-35.3 37.80020 0.2498472
## 36 and over.3 43.66797 0.2523778
## Under 18.5.4 19.05242 0.6748043
## 18.5-25.4 24.62651 0.2817754
## 26-30.4 31.90423 0.2414605
## 31-35.4 37.23163 0.3805348
## 36 and over.4 43.78339 0.3106646
## Under 18.5.5 24.38657 1.4648647
## 18.5-25.5 30.07709 0.4942687
## 26-30.5 33.66335 0.5611041
## 31-35.5 35.64384 0.8277096
## 36 and over.5 43.06668 1.0421191## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 15.73465 0.4507527
## 18.5-25.17-19.Men 18.86657 0.1933846
## 26-30.17-19.Men 26.11311 0.3703496
## 31-35.17-19.Men 32.00163 0.4890500
## 36 and over.17-19.Men 37.08344 0.5471586
## Under 18.5.20-24.Men 16.23929 0.5250669
## 18.5-25.20-24.Men 20.00922 0.2273880
## 26-30.20-24.Men 26.25114 0.3853843
## 31-35.20-24.Men 31.10966 0.5071526
## 36 and over.20-24.Men 36.97270 0.5280719
## Under 18.5.25-29.Men 15.67754 0.5406840
## 18.5-25.25-29.Men 21.68437 0.2702246
## 26-30.25-29.Men 26.87191 0.2782017
## 31-35.25-29.Men 31.11532 0.3699343
## 36 and over.25-29.Men 37.28002 0.4709883
## Under 18.5.30-34.Men 16.98321 1.3031802
## 18.5-25.30-34.Men 21.54765 0.3410600
## 26-30.30-34.Men 27.03160 0.2280380
## 31-35.30-34.Men 30.86226 0.4678854
## 36 and over.30-34.Men 36.05657 0.5958456
## Under 18.5.35-39.Men 17.02993 1.1428383
## 18.5-25.35-39.Men 22.22573 0.3890290
## 26-30.35-39.Men 27.16232 0.3068192
## 31-35.35-39.Men 30.91090 0.3169878
## 36 and over.35-39.Men 35.92398 0.5661108
## Under 18.5.40-44.Men 16.21487 1.3888828
## 18.5-25.40-44.Men 22.89632 0.3415968
## 26-30.40-44.Men 27.39831 0.2194374
## 31-35.40-44.Men 31.13595 0.2942543
## 36 and over.40-44.Men 35.78116 0.4643813
## Under 18.5.45-49.Men 16.21784 0.6516569
## 18.5-25.45-49.Men 22.68177 0.4752111
## 26-30.45-49.Men 27.65718 0.2506235
## 31-35.45-49.Men 31.74313 0.3424752
## 36 and over.45-49.Men 36.44526 0.5573145
## Under 18.5.50-54.Men 18.40121 1.1825122
## 18.5-25.50-54.Men 23.67883 0.4133307
## 26-30.50-54.Men 28.15034 0.2788936
## 31-35.50-54.Men 31.90632 0.3848255
## 36 and over.50-54.Men 36.51689 0.3498893
## Under 18.5.55-59.Men 20.50443 1.6201221
## 18.5-25.55-59.Men 24.99417 0.4290591
## 26-30.55-59.Men 29.05485 0.3071528
## 31-35.55-59.Men 32.37030 0.4108707
## 36 and over.55-59.Men 38.00753 0.6186612
## Under 18.5.60-64.Men 17.08199 1.1846178
## 18.5-25.60-64.Men 25.13270 0.4150290
## 26-30.60-64.Men 29.32819 0.2951990
## 31-35.60-64.Men 33.10378 0.3384544
## 36 and over.60-64.Men 37.33427 0.4115167
## Under 18.5.65-69.Men 17.73870 1.0472858
## 18.5-25.65-69.Men 25.99618 0.4466313
## 26-30.65-69.Men 30.31134 0.1889768
## 31-35.65-69.Men 34.20158 0.4252815
## 36 and over.65-69.Men 37.66484 0.5293741
## Under 18.5.70-74.Men 23.21452 1.7584789
## 18.5-25.70-74.Men 25.54576 0.5018266
## 26-30.70-74.Men 30.87706 0.2545368
## 31-35.70-74.Men 35.38295 0.5228856
## 36 and over.70-74.Men 40.17551 0.8046182
## Under 18.5.75-79.Men 16.49345 1.4447807
## 18.5-25.75-79.Men 27.51410 0.5569083
## 26-30.75-79.Men 31.21802 0.3517536
## 31-35.75-79.Men 35.80896 0.4478400
## 36 and over.75-79.Men 37.15551 0.9902299
## Under 18.5.Over 80.Men 21.83929 2.2782635
## 18.5-25.Over 80.Men 27.79047 0.3170939
## 26-30.Over 80.Men 31.91233 0.2746580
## 31-35.Over 80.Men 35.95829 0.4685920
## 36 and over.Over 80.Men 39.05557 1.0084494
## Under 18.5.17-19.Women 26.65024 0.3983918
## 18.5-25.17-19.Women 32.07084 0.2508937
## 26-30.17-19.Women 38.76162 0.3505124
## 31-35.17-19.Women 43.41947 0.5057549
## 36 and over.17-19.Women 47.89299 0.4073310
## Under 18.5.20-24.Women 26.41293 0.8240736
## 18.5-25.20-24.Women 32.43387 0.2868426
## 26-30.20-24.Women 39.04349 0.3487893
## 31-35.20-24.Women 43.64266 0.3913797
## 36 and over.20-24.Women 47.98942 0.5987831
## Under 18.5.25-29.Women 25.96717 0.6879963
## 18.5-25.25-29.Women 32.77698 0.3445953
## 26-30.25-29.Women 39.04413 0.3408102
## 31-35.25-29.Women 43.08062 0.4023959
## 36 and over.25-29.Women 47.66750 0.5770909
## Under 18.5.30-34.Women 26.79345 1.1851503
## 18.5-25.30-34.Women 32.81824 0.3952607
## 26-30.30-34.Women 39.50874 0.3568595
## 31-35.30-34.Women 43.29207 0.3126553
## 36 and over.30-34.Women 47.06881 0.3716687
## Under 18.5.35-39.Women 25.77416 1.8998274
## 18.5-25.35-39.Women 33.30810 0.2701370
## 26-30.35-39.Women 39.50903 0.3560858
## 31-35.35-39.Women 43.23123 0.4267856
## 36 and over.35-39.Women 47.65263 0.4150786
## Under 18.5.40-44.Women 25.66285 0.5896893
## 18.5-25.40-44.Women 33.79425 0.3580718
## 26-30.40-44.Women 39.76392 0.2938982
## 31-35.40-44.Women 43.11995 0.2934047
## 36 and over.40-44.Women 46.76084 0.4095174
## Under 18.5.45-49.Women 27.36320 1.1521280
## 18.5-25.45-49.Women 33.76669 0.4038846
## 26-30.45-49.Women 40.42850 0.2556963
## 31-35.45-49.Women 43.48226 0.3935422
## 36 and over.45-49.Women 47.91837 0.3243785
## Under 18.5.50-54.Women 27.38976 1.7639615
## 18.5-25.50-54.Women 35.23468 0.4360582
## 26-30.50-54.Women 41.52525 0.3041680
## 31-35.50-54.Women 44.07740 0.2966747
## 36 and over.50-54.Women 47.49388 0.3609187
## Under 18.5.55-59.Women 27.09097 1.2294068
## 18.5-25.55-59.Women 36.04799 0.5544781
## 26-30.55-59.Women 41.91659 0.2912774
## 31-35.55-59.Women 44.85528 0.3688391
## 36 and over.55-59.Women 48.99118 0.3528748
## Under 18.5.60-64.Women 24.19384 3.6622989
## 18.5-25.60-64.Women 36.85818 0.4506760
## 26-30.60-64.Women 41.97248 0.3276529
## 31-35.60-64.Women 45.07833 0.2430850
## 36 and over.60-64.Women 48.72931 0.3502399
## Under 18.5.65-69.Women 25.73170 1.8291397
## 18.5-25.65-69.Women 37.36764 0.4053649
## 26-30.65-69.Women 42.12033 0.2831004
## 31-35.65-69.Women 45.61526 0.3825729
## 36 and over.65-69.Women 49.04343 0.3296924
## Under 18.5.70-74.Women 29.72784 1.0517751
## 18.5-25.70-74.Women 37.08766 0.3927909
## 26-30.70-74.Women 42.76425 0.4858565
## 31-35.70-74.Women 45.22728 0.3869709
## 36 and over.70-74.Women 48.89426 0.4781580
## Under 18.5.75-79.Women 26.72136 2.4703933
## 18.5-25.75-79.Women 38.00570 0.5876820
## 26-30.75-79.Women 42.51874 0.3648294
## 31-35.75-79.Women 45.08477 0.5878246
## 36 and over.75-79.Women 49.03266 0.5010993
## Under 18.5.Over 80.Women 28.59596 1.8052059
## 18.5-25.Over 80.Women 37.33760 0.4345367
## 26-30.Over 80.Women 42.12256 0.3071662
## 31-35.Over 80.Women 45.07939 0.4931797
## 36 and over.Over 80.Women 47.83976 0.8749159## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svyquantile, quantiles = c(0.5), keep.var = TRUE,
## se = T, ci = T))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svyquantile, quantiles = c(0.5),
## keep.var = TRUE, se = T, ci = T)))
## results se
## Under 18.5.17-19.Men 15.27473 0.3790354
## 18.5-25.17-19.Men 18.32000 0.1626833
## 26-30.17-19.Men 25.60000 0.3899546
## 31-35.17-19.Men 32.30000 0.7756730
## 36 and over.17-19.Men 36.38741 0.8396903
## Under 18.5.20-24.Men 15.47963 0.3365832
## 18.5-25.20-24.Men 19.42164 0.3020597
## 26-30.20-24.Men 26.14000 0.4283666
## 31-35.20-24.Men 31.52056 0.5793281
## 36 and over.20-24.Men 36.88383 0.8619887
## Under 18.5.25-29.Men 16.09010 1.1427624
## 18.5-25.25-29.Men 21.69627 0.4477935
## 26-30.25-29.Men 26.80249 0.2959180
## 31-35.25-29.Men 31.15344 0.5973106
## 36 and over.25-29.Men 37.40715 0.7963578
## Under 18.5.30-34.Men 15.81309 NaN
## 18.5-25.30-34.Men 21.17389 0.4930955
## 26-30.30-34.Men 27.06334 0.2691796
## 31-35.30-34.Men 30.57812 0.6025477
## 36 and over.30-34.Men 35.90562 0.6886023
## Under 18.5.35-39.Men 16.34329 NaN
## 18.5-25.35-39.Men 22.20899 0.6116618
## 26-30.35-39.Men 27.01241 0.3786641
## 31-35.35-39.Men 30.59090 0.4191788
## 36 and over.35-39.Men 36.11327 0.5794167
## Under 18.5.40-44.Men 14.86810 NaN
## 18.5-25.40-44.Men 23.36498 0.6456941
## 26-30.40-44.Men 27.39074 0.2656893
## 31-35.40-44.Men 31.32481 0.6027262
## 36 and over.40-44.Men 35.64358 0.7018014
## Under 18.5.45-49.Men 15.40000 0.1219876
## 18.5-25.45-49.Men 22.60783 0.5937075
## 26-30.45-49.Men 27.63193 0.3532071
## 31-35.45-49.Men 31.66057 0.5564213
## 36 and over.45-49.Men 36.07881 0.8438405
## Under 18.5.50-54.Men 19.05119 0.3177940
## 18.5-25.50-54.Men 23.99985 0.7116491
## 26-30.50-54.Men 28.32401 0.4546956
## 31-35.50-54.Men 31.69466 0.4014554
## 36 and over.50-54.Men 36.57359 0.5572414
## Under 18.5.55-59.Men 20.21646 NaN
## 18.5-25.55-59.Men 24.34759 0.6936046
## 26-30.55-59.Men 28.79465 0.4593367
## 31-35.55-59.Men 32.52000 0.3661771
## 36 and over.55-59.Men 38.16896 0.7231293
## Under 18.5.60-64.Men 16.89295 NaN
## 18.5-25.60-64.Men 24.91166 0.6557769
## 26-30.60-64.Men 29.03652 0.3281043
## 31-35.60-64.Men 33.17443 0.4440784
## 36 and over.60-64.Men 37.40422 0.4880087
## Under 18.5.65-69.Men 17.56310 NaN
## 18.5-25.65-69.Men 26.07853 0.6796105
## 26-30.65-69.Men 30.23197 0.3195880
## 31-35.65-69.Men 34.12113 0.6890517
## 36 and over.65-69.Men 36.96774 1.0682886
## Under 18.5.70-74.Men 22.45401 NaN
## 18.5-25.70-74.Men 25.68394 0.3890506
## 26-30.70-74.Men 30.62491 0.3943570
## 31-35.70-74.Men 35.08664 0.7277634
## 36 and over.70-74.Men 40.06574 1.0311341
## Under 18.5.75-79.Men 14.78018 NaN
## 18.5-25.75-79.Men 27.47516 0.6903997
## 26-30.75-79.Men 31.21277 0.3137545
## 31-35.75-79.Men 36.17364 0.6870328
## 36 and over.75-79.Men 36.53996 NaN
## Under 18.5.Over 80.Men 21.29472 1.5201615
## 18.5-25.Over 80.Men 27.60836 0.3816349
## 26-30.Over 80.Men 31.68512 0.3588416
## 31-35.Over 80.Men 35.90685 0.6428673
## 36 and over.Over 80.Men 38.88309 1.0136949
## Under 18.5.17-19.Women 26.62875 0.6517191
## 18.5-25.17-19.Women 31.72000 0.2594503
## 26-30.17-19.Women 38.64000 0.4997613
## 31-35.17-19.Women 43.40000 0.6624242
## 36 and over.17-19.Women 48.16669 0.3724808
## Under 18.5.20-24.Women 26.83823 1.2824275
## 18.5-25.20-24.Women 32.25381 0.2920364
## 26-30.20-24.Women 39.48271 0.4159879
## 31-35.20-24.Women 43.51562 0.6626356
## 36 and over.20-24.Women 48.32358 0.8032688
## Under 18.5.25-29.Women 25.75341 1.0409898
## 18.5-25.25-29.Women 33.30173 0.5879575
## 26-30.25-29.Women 39.34000 0.4202378
## 31-35.25-29.Women 43.05649 0.5163049
## 36 and over.25-29.Women 47.92261 0.6753538
## Under 18.5.30-34.Women 25.30480 2.0015765
## 18.5-25.30-34.Women 32.98445 0.6128980
## 26-30.30-34.Women 39.53741 0.3469930
## 31-35.30-34.Women 42.81160 0.6858587
## 36 and over.30-34.Women 47.09091 0.5625682
## Under 18.5.35-39.Women 24.90042 0.8024566
## 18.5-25.35-39.Women 33.47507 0.3528235
## 26-30.35-39.Women 40.00017 0.5142898
## 31-35.35-39.Women 42.19933 0.5727916
## 36 and over.35-39.Women 48.04900 0.5802218
## Under 18.5.40-44.Women 25.03698 NaN
## 18.5-25.40-44.Women 34.39959 0.3671685
## 26-30.40-44.Women 40.24882 0.3711636
## 31-35.40-44.Women 43.26802 0.2774739
## 36 and over.40-44.Women 46.44451 0.4754025
## Under 18.5.45-49.Women 26.56386 NaN
## 18.5-25.45-49.Women 34.05407 0.5459525
## 26-30.45-49.Women 40.54608 0.4160265
## 31-35.45-49.Women 43.22132 0.6899812
## 36 and over.45-49.Women 47.93823 0.4405558
## Under 18.5.50-54.Women 26.14165 NaN
## 18.5-25.50-54.Women 35.41323 0.6664209
## 26-30.50-54.Women 41.73034 0.3897682
## 31-35.50-54.Women 43.88187 0.3984449
## 36 and over.50-54.Women 47.43703 0.5727765
## Under 18.5.55-59.Women 25.46093 NaN
## 18.5-25.55-59.Women 36.24621 0.6152838
## 26-30.55-59.Women 42.26141 0.4046605
## 31-35.55-59.Women 44.91382 0.4252930
## 36 and over.55-59.Women 49.11761 0.4447805
## Under 18.5.60-64.Women 19.73089 NaN
## 18.5-25.60-64.Women 37.21126 0.5986938
## 26-30.60-64.Women 42.44864 0.4181261
## 31-35.60-64.Women 45.46000 0.2832698
## 36 and over.60-64.Women 48.84634 0.3380644
## Under 18.5.65-69.Women 23.93829 NaN
## 18.5-25.65-69.Women 37.62981 0.6733475
## 26-30.65-69.Women 42.21201 0.3020548
## 31-35.65-69.Women 45.69891 0.4688104
## 36 and over.65-69.Women 49.00420 0.5388274
## Under 18.5.70-74.Women 29.89299 NaN
## 18.5-25.70-74.Women 36.97011 0.5185113
## 26-30.70-74.Women 42.68045 0.6232570
## 31-35.70-74.Women 45.32696 0.5605114
## 36 and over.70-74.Women 48.83491 0.8317234
## Under 18.5.75-79.Women 29.14125 NaN
## 18.5-25.75-79.Women 38.10771 0.9690461
## 26-30.75-79.Women 42.69359 0.3901340
## 31-35.75-79.Women 44.49803 0.6220077
## 36 and over.75-79.Women 49.08583 0.6559482
## Under 18.5.Over 80.Women 26.97734 0.6446106
## 18.5-25.Over 80.Women 37.73967 0.5806669
## 26-30.Over 80.Women 42.08793 0.3331382
## 31-35.Over 80.Women 45.00598 0.7416319
## 36 and over.Over 80.Women 47.91260 1.4817768## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOLE, ~BMI.Group + Gender, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOLE, ~BMI.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.Men 43903.07 560.58129
## 18.5-25.Men 52486.92 145.07685
## 26-30.Men 59151.72 138.08272
## 31-35.Men 65820.87 232.20374
## 36 and over.Men 76460.74 484.38290
## Under 18.5.Women 32777.35 282.52328
## 18.5-25.Women 37152.14 89.00896
## 26-30.Women 40942.04 117.86522
## 31-35.Women 45347.00 158.75528
## 36 and over.Women 53464.86 259.88430## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOLE, ~BMI.Group + Age.Group, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOLE, ~BMI.Group +
## Age.Group, na = TRUE, svymean)))
## results se
## Under 18.5.17-19 38178.90 787.3510
## 18.5-25.17-19 45691.09 277.1195
## 26-30.17-19 52975.33 622.4805
## 31-35.17-19 58201.62 807.1816
## 36 and over.17-19 63924.91 1321.3366
## Under 18.5.20-24 38289.50 1322.4834
## 18.5-25.20-24 45869.78 458.4105
## 26-30.20-24 52951.35 538.3669
## 31-35.20-24 58616.88 837.1670
## 36 and over.20-24 65213.36 1476.0380
## Under 18.5.25-29 37543.36 1016.9816
## 18.5-25.25-29 46152.41 407.2311
## 26-30.25-29 54154.25 557.7712
## 31-35.25-29 58496.80 954.6972
## 36 and over.25-29 68598.60 1692.1857
## Under 18.5.30-34 36164.79 909.7532
## 18.5-25.30-34 45551.67 461.6844
## 26-30.30-34 54171.77 442.4452
## 31-35.30-34 57012.11 1094.2718
## 36 and over.30-34 63053.59 1210.0675
## Under 18.5.35-39 37796.73 3232.8022
## 18.5-25.35-39 44731.39 439.9409
## 26-30.35-39 53301.98 500.6189
## 31-35.35-39 58519.37 840.0473
## 36 and over.35-39 63390.25 1174.9813
## Under 18.5.40-44 36016.15 1088.4410
## 18.5-25.40-44 44544.57 527.9932
## 26-30.40-44 53263.89 442.9532
## 31-35.40-44 58434.00 909.9700
## 36 and over.40-44 65024.05 1112.8635
## Under 18.5.45-49 33317.19 702.9244
## 18.5-25.45-49 44052.25 599.7080
## 26-30.45-49 52672.89 566.2781
## 31-35.45-49 56483.49 769.6718
## 36 and over.45-49 62391.62 1147.8492
## Under 18.5.50-54 36358.33 1540.9309
## 18.5-25.50-54 43173.04 558.8621
## 26-30.50-54 52087.81 517.8861
## 31-35.50-54 55439.92 754.1483
## 36 and over.50-54 62660.16 801.0557
## Under 18.5.55-59 38213.21 2073.4651
## 18.5-25.55-59 42418.50 647.6462
## 26-30.55-59 51295.77 596.1424
## 31-35.55-59 56236.10 885.7942
## 36 and over.55-59 59042.72 1166.2937
## Under 18.5.60-64 37869.39 2576.9188
## 18.5-25.60-64 41830.40 625.8299
## 26-30.60-64 49373.71 596.6516
## 31-35.60-64 54250.34 699.0709
## 36 and over.60-64 59429.44 1043.0729
## Under 18.5.65-69 33848.46 1962.5120
## 18.5-25.65-69 41095.75 524.1415
## 26-30.65-69 48735.31 537.5100
## 31-35.65-69 52147.09 826.5839
## 36 and over.65-69 58422.05 1097.1733
## Under 18.5.70-74 31993.16 1510.6934
## 18.5-25.70-74 40649.30 600.0940
## 26-30.70-74 46777.95 698.7798
## 31-35.70-74 49837.18 941.8954
## 36 and over.70-74 54162.50 1399.0275
## Under 18.5.75-79 33816.22 1030.0475
## 18.5-25.75-79 39971.99 714.0382
## 26-30.75-79 44962.78 557.5922
## 31-35.75-79 49985.77 1142.6266
## 36 and over.75-79 51862.34 1686.5102
## Under 18.5.Over 80 32276.32 745.5685
## 18.5-25.Over 80 37186.72 314.7219
## 26-30.Over 80 43178.57 513.4183
## 31-35.Over 80 46759.45 730.8218
## 36 and over.Over 80 48861.72 1230.1797## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOLE, ~BMI.Group + RIDRETH1, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOLE, ~BMI.Group +
## RIDRETH1, na = TRUE, svymean)))
## results se
## Under 18.5.1 33921.72 875.0981
## 18.5-25.1 42840.24 349.3623
## 26-30.1 49203.21 281.0226
## 31-35.1 53018.62 431.5150
## 36 and over.1 58254.16 620.0175
## Under 18.5.2 32570.81 2060.7414
## 18.5-25.2 42718.97 603.6102
## 26-30.2 47762.27 553.7639
## 31-35.2 50458.23 999.1434
## 36 and over.2 57856.30 1626.8139
## Under 18.5.3 36786.80 576.9920
## 18.5-25.3 44307.66 174.8307
## 26-30.3 52467.72 257.7023
## 31-35.3 56749.59 376.7179
## 36 and over.3 62555.75 536.8703
## Under 18.5.4 39890.32 868.5224
## 18.5-25.4 47715.46 345.0197
## 26-30.4 52531.86 313.8698
## 31-35.4 56327.42 635.8933
## 36 and over.4 63139.15 608.4211
## Under 18.5.5 35801.48 1439.7377
## 18.5-25.5 40027.46 509.6244
## 26-30.5 48535.04 886.5618
## 31-35.5 59218.69 1493.0612
## 36 and over.5 65164.46 1834.2816## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOLE, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOLE, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 44474.93 682.6649
## 18.5-25.17-19.Men 52440.70 231.9695
## 26-30.17-19.Men 60155.64 475.7018
## 31-35.17-19.Men 66278.32 594.6478
## 36 and over.17-19.Men 74674.97 1036.0668
## Under 18.5.20-24.Men 45234.07 855.2372
## 18.5-25.20-24.Men 53781.87 345.9127
## 26-30.20-24.Men 60072.69 434.8516
## 31-35.20-24.Men 67583.10 712.9734
## 36 and over.20-24.Men 81272.32 2694.0588
## Under 18.5.25-29.Men 44986.34 1430.2443
## 18.5-25.25-29.Men 53673.34 378.3257
## 26-30.25-29.Men 59557.79 400.1639
## 31-35.25-29.Men 68847.87 644.9320
## 36 and over.25-29.Men 80827.17 1231.4445
## Under 18.5.30-34.Men 41850.24 1405.3215
## 18.5-25.30-34.Men 53206.96 451.4259
## 26-30.30-34.Men 60717.48 401.2712
## 31-35.30-34.Men 66917.58 879.2792
## 36 and over.30-34.Men 79946.69 1835.6051
## Under 18.5.35-39.Men 49706.60 823.9543
## 18.5-25.35-39.Men 53118.98 422.0264
## 26-30.35-39.Men 60184.65 396.0391
## 31-35.35-39.Men 66931.78 648.8452
## 36 and over.35-39.Men 79092.52 1666.6521
## Under 18.5.40-44.Men 43141.33 1117.8209
## 18.5-25.40-44.Men 53651.30 487.6593
## 26-30.40-44.Men 60943.69 380.9446
## 31-35.40-44.Men 68029.13 663.5352
## 36 and over.40-44.Men 76861.89 1207.2382
## Under 18.5.45-49.Men 39619.99 1381.9881
## 18.5-25.45-49.Men 53354.64 615.0817
## 26-30.45-49.Men 59737.63 364.3891
## 31-35.45-49.Men 65965.56 663.8562
## 36 and over.45-49.Men 78121.42 1596.4904
## Under 18.5.50-54.Men 41797.40 1435.2774
## 18.5-25.50-54.Men 51993.63 523.4634
## 26-30.50-54.Men 59448.82 400.8792
## 31-35.50-54.Men 64913.01 514.9183
## 36 and over.50-54.Men 74486.40 969.3192
## Under 18.5.55-59.Men 39531.81 2315.9208
## 18.5-25.55-59.Men 50950.92 496.6718
## 26-30.55-59.Men 58510.88 564.0777
## 31-35.55-59.Men 64848.60 553.3217
## 36 and over.55-59.Men 72579.26 1598.2499
## Under 18.5.60-64.Men 42414.31 1312.5660
## 18.5-25.60-64.Men 50513.19 666.7233
## 26-30.60-64.Men 58048.15 540.1203
## 31-35.60-64.Men 64016.80 612.1977
## 36 and over.60-64.Men 73611.49 1143.9211
## Under 18.5.65-69.Men 41904.21 1286.3558
## 18.5-25.65-69.Men 49566.63 645.8102
## 26-30.65-69.Men 56234.89 517.5598
## 31-35.65-69.Men 62488.45 767.6477
## 36 and over.65-69.Men 70598.97 1016.1204
## Under 18.5.70-74.Men 38659.48 2080.2617
## 18.5-25.70-74.Men 48927.14 634.2577
## 26-30.70-74.Men 55175.85 662.1136
## 31-35.70-74.Men 61028.16 863.2626
## 36 and over.70-74.Men 66948.64 1348.3466
## Under 18.5.75-79.Men 38814.77 3055.7406
## 18.5-25.75-79.Men 47578.64 596.7601
## 26-30.75-79.Men 53609.98 545.9758
## 31-35.75-79.Men 59068.11 688.5210
## 36 and over.75-79.Men 69461.88 2374.7354
## Under 18.5.Over 80.Men 35555.35 1899.6099
## 18.5-25.Over 80.Men 45700.42 449.3520
## 26-30.Over 80.Men 51433.97 318.2552
## 31-35.Over 80.Men 58377.66 708.2854
## 36 and over.Over 80.Men 60032.63 1751.0841
## Under 18.5.17-19.Women 32897.18 435.9829
## 18.5-25.17-19.Women 37406.59 202.7401
## 26-30.17-19.Women 42568.86 295.6322
## 31-35.17-19.Women 47652.66 502.2273
## 36 and over.17-19.Women 54473.33 881.5111
## Under 18.5.20-24.Women 32500.09 796.6768
## 18.5-25.20-24.Women 37801.91 328.1904
## 26-30.20-24.Women 42293.16 390.1034
## 31-35.20-24.Women 45635.53 591.7990
## 36 and over.20-24.Women 54232.60 926.3082
## Under 18.5.25-29.Women 33459.88 680.6984
## 18.5-25.25-29.Women 37824.46 354.8730
## 26-30.25-29.Women 42857.03 408.8420
## 31-35.25-29.Women 47139.10 611.9287
## 36 and over.25-29.Women 55433.76 934.6881
## Under 18.5.30-34.Women 33708.58 698.9478
## 18.5-25.30-34.Women 38368.87 300.4973
## 26-30.30-34.Women 42123.18 506.3764
## 31-35.30-34.Women 45805.14 391.3994
## 36 and over.30-34.Women 54523.59 607.6063
## Under 18.5.35-39.Women 31460.61 601.5614
## 18.5-25.35-39.Women 37846.34 277.6763
## 26-30.35-39.Women 42110.71 416.7546
## 31-35.35-39.Women 45691.30 525.3618
## 36 and over.35-39.Women 54336.83 807.0827
## Under 18.5.40-44.Women 34488.61 994.7402
## 18.5-25.40-44.Women 38111.42 401.8843
## 26-30.40-44.Women 41953.38 345.2910
## 31-35.40-44.Women 46726.80 433.6338
## 36 and over.40-44.Women 56048.24 856.0745
## Under 18.5.45-49.Women 32970.87 755.9950
## 18.5-25.45-49.Women 37645.18 309.9843
## 26-30.45-49.Women 41409.96 351.5672
## 31-35.45-49.Women 47085.46 417.4828
## 36 and over.45-49.Women 54218.56 631.9288
## Under 18.5.50-54.Women 32422.65 1546.2717
## 18.5-25.50-54.Women 37206.83 284.5744
## 26-30.50-54.Women 40694.13 320.8149
## 31-35.50-54.Women 45463.90 406.3798
## 36 and over.50-54.Women 53294.41 684.4468
## Under 18.5.55-59.Women 29915.80 837.6475
## 18.5-25.55-59.Women 36166.31 377.1720
## 26-30.55-59.Women 40463.37 327.0537
## 31-35.55-59.Women 45211.07 448.6632
## 36 and over.55-59.Women 52363.63 623.4970
## Under 18.5.60-64.Women 30876.91 1334.3763
## 18.5-25.60-64.Women 35948.15 419.5814
## 26-30.60-64.Women 40004.90 344.2415
## 31-35.60-64.Women 44011.42 404.2586
## 36 and over.60-64.Women 52197.33 517.4169
## Under 18.5.65-69.Women 31026.43 1817.4680
## 18.5-25.65-69.Women 35180.93 327.0724
## 26-30.65-69.Women 39630.51 346.2951
## 31-35.65-69.Women 42948.58 409.0962
## 36 and over.65-69.Women 50644.28 782.8745
## Under 18.5.70-74.Women 29122.92 1178.8388
## 18.5-25.70-74.Women 34519.58 326.0657
## 26-30.70-74.Women 38309.81 442.6593
## 31-35.70-74.Women 41948.71 516.1655
## 36 and over.70-74.Women 48227.20 937.5872
## Under 18.5.75-79.Women 32494.04 638.0420
## 18.5-25.75-79.Women 34513.87 474.0943
## 26-30.75-79.Women 38016.70 430.4280
## 31-35.75-79.Women 42404.53 670.4101
## 36 and over.75-79.Women 47543.32 708.7216
## Under 18.5.Over 80.Women 31389.45 754.3485
## 18.5-25.Over 80.Women 32763.47 302.4722
## 26-30.Over 80.Women 37082.51 322.2241
## 31-35.Over 80.Women 40813.51 452.2050
## 36 and over.Over 80.Women 46349.32 1013.2641## Multiple imputation results:
## with(nhc_all, svyby(~BMXWT, ~BMI.Group + Gender, na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWT, ~BMI.Group + Gender,
## na = TRUE, svymean)))
## results se
## Under 18.5.Men 54.90673 0.6252308
## 18.5-25.Men 70.29315 0.1909665
## 26-30.Men 85.15600 0.1561022
## 31-35.Men 100.22419 0.3457411
## 36 and over.Men 125.20362 0.7641406
## Under 18.5.Women 46.65580 0.3716245
## 18.5-25.Women 58.98507 0.1347113
## 26-30.Women 71.90514 0.1707630
## 31-35.Women 84.24583 0.2444906
## 36 and over.Women 106.97599 0.5862280## Multiple imputation results:
## with(nhc_all, svyby(~BMXWT, ~BMI.Group + Age.Group, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWT, ~BMI.Group + Age.Group,
## na = TRUE, svymean)))
## results se
## Under 18.5.17-19 50.50189 0.6618751
## 18.5-25.17-19 62.94178 0.2356118
## 26-30.17-19 79.58767 0.6226262
## 31-35.17-19 95.15221 0.8250710
## 36 and over.17-19 114.97741 1.4072340
## Under 18.5.20-24 50.84453 1.1911653
## 18.5-25.20-24 64.32798 0.4306414
## 26-30.20-24 79.62580 0.4450228
## 31-35.20-24 94.33875 0.7988620
## 36 and over.20-24 118.72452 2.3639595
## Under 18.5.25-29 50.15978 1.0221025
## 18.5-25.25-29 65.44826 0.4682722
## 26-30.25-29 80.77662 0.5433172
## 31-35.25-29 95.24566 0.9284537
## 36 and over.25-29 122.13806 1.9907768
## Under 18.5.30-34 49.35456 0.7571193
## 18.5-25.30-34 64.99791 0.5195916
## 26-30.30-34 81.37825 0.4697422
## 31-35.30-34 92.61006 1.0014741
## 36 and over.30-34 114.65426 1.6050469
## Under 18.5.35-39 50.53240 3.1499010
## 18.5-25.35-39 64.66288 0.4675240
## 26-30.35-39 80.62502 0.4684926
## 31-35.35-39 93.77568 0.7850214
## 36 and over.35-39 115.37997 1.7301662
## Under 18.5.40-44 49.68496 1.2935335
## 18.5-25.40-44 65.23071 0.5535069
## 26-30.40-44 81.17863 0.4135232
## 31-35.40-44 94.65122 0.9058726
## 36 and over.40-44 116.59682 1.5503433
## Under 18.5.45-49 47.60717 1.2508839
## 18.5-25.45-49 64.51526 0.6572046
## 26-30.45-49 80.53970 0.4769633
## 31-35.45-49 93.40716 0.7159196
## 36 and over.45-49 114.98903 1.4962573
## Under 18.5.50-54 49.85189 1.2533603
## 18.5-25.50-54 64.43002 0.5784731
## 26-30.50-54 80.66711 0.5011672
## 31-35.50-54 91.85742 0.7969126
## 36 and over.50-54 112.77019 1.0295373
## Under 18.5.55-59 50.72696 2.2794678
## 18.5-25.55-59 64.11109 0.6679424
## 26-30.55-59 80.45053 0.5479658
## 31-35.55-59 93.24176 0.9444702
## 36 and over.55-59 111.53426 1.6850998
## Under 18.5.60-64 48.52631 2.4468190
## 18.5-25.60-64 63.76607 0.6820008
## 26-30.60-64 78.89490 0.5995296
## 31-35.60-64 91.52591 0.7267811
## 36 and over.60-64 111.36447 1.2015292
## Under 18.5.65-69 45.52602 2.3343990
## 18.5-25.65-69 63.25775 0.5265497
## 26-30.65-69 78.27468 0.5796280
## 31-35.65-69 89.96903 0.7653639
## 36 and over.65-69 109.01503 1.3508031
## Under 18.5.70-74 45.92627 1.6320562
## 18.5-25.70-74 62.04972 0.7199007
## 26-30.70-74 76.42229 0.7227544
## 31-35.70-74 87.46367 0.9606154
## 36 and over.70-74 103.65513 1.9193983
## Under 18.5.75-79 46.55296 1.0372799
## 18.5-25.75-79 62.52540 0.6903582
## 26-30.75-79 74.33317 0.5427392
## 31-35.75-79 87.34907 1.2553872
## 36 and over.75-79 100.49963 2.2058015
## Under 18.5.Over 80 46.02001 0.6031801
## 18.5-25.Over 80 58.51702 0.4869543
## 26-30.Over 80 71.79195 0.5333658
## 31-35.Over 80 83.36872 1.0437201
## 36 and over.Over 80 94.14714 1.6520779## Multiple imputation results:
## with(nhc_all, svyby(~BMXWT, ~BMI.Group + RIDRETH1, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWT, ~BMI.Group + RIDRETH1,
## na = TRUE, svymean)))
## results se
## Under 18.5.1 46.32962 0.6920294
## 18.5-25.1 61.63475 0.3261395
## 26-30.1 75.26334 0.2619435
## 31-35.1 87.10461 0.4775630
## 36 and over.1 106.38817 0.7745084
## Under 18.5.2 46.20742 1.3662882
## 18.5-25.2 61.63131 0.5450501
## 26-30.2 74.10313 0.6092567
## 31-35.2 84.49277 0.8938076
## 36 and over.2 105.11424 2.4069038
## Under 18.5.3 49.79798 0.5464989
## 18.5-25.3 64.79335 0.1916380
## 26-30.3 81.10394 0.2030216
## 31-35.3 93.87986 0.3348350
## 36 and over.3 114.56073 0.7089575
## Under 18.5.4 51.23342 0.7862481
## 18.5-25.4 65.71693 0.3405347
## 26-30.4 79.66230 0.2598503
## 31-35.4 92.45719 0.5586196
## 36 and over.4 115.89226 0.7708271
## Under 18.5.5 49.18571 1.2645706
## 18.5-25.5 59.36946 0.5361250
## 26-30.5 75.29032 0.8772654
## 31-35.5 94.70023 1.6687555
## 36 and over.5 117.83472 1.8824014## Multiple imputation results:
## with(nhc_all, svyby(~BMXWT, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWT, ~BMI.Group + Age.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 54.81874 0.7334647
## 18.5-25.17-19.Men 67.24485 0.3219268
## 26-30.17-19.Men 84.49524 0.6890563
## 31-35.17-19.Men 100.85225 0.8840257
## 36 and over.17-19.Men 122.16118 1.6428263
## Under 18.5.20-24.Men 56.33118 1.0816893
## 18.5-25.20-24.Men 70.01534 0.4841712
## 26-30.20-24.Men 84.45828 0.5307682
## 31-35.20-24.Men 101.39045 1.0111411
## 36 and over.20-24.Men 133.00243 4.8296455
## Under 18.5.25-29.Men 55.55574 1.8531918
## 18.5-25.25-29.Men 71.31922 0.5363885
## 26-30.25-29.Men 84.35871 0.4524967
## 31-35.25-29.Men 103.58021 1.1204228
## 36 and over.25-29.Men 132.86086 2.3069365
## Under 18.5.30-34.Men 52.55675 1.3465910
## 18.5-25.30-34.Men 70.55740 0.6449462
## 26-30.30-34.Men 86.17459 0.5308240
## 31-35.30-34.Men 100.14672 1.1730404
## 36 and over.30-34.Men 129.10345 3.1468662
## Under 18.5.35-39.Men 62.17699 0.8147737
## 18.5-25.35-39.Men 71.04451 0.6709378
## 26-30.35-39.Men 85.57748 0.4791976
## 31-35.35-39.Men 100.24243 0.7773604
## 36 and over.35-39.Men 127.73066 3.1248473
## Under 18.5.40-44.Men 53.75682 1.1253356
## 18.5-25.40-44.Men 72.31551 0.6253311
## 26-30.40-44.Men 86.97669 0.4839799
## 31-35.40-44.Men 102.14058 0.9378036
## 36 and over.40-44.Men 125.54243 2.3884439
## Under 18.5.45-49.Men 49.17622 2.1026541
## 18.5-25.45-49.Men 71.77577 0.8015652
## 26-30.45-49.Men 85.54056 0.4788676
## 31-35.45-49.Men 100.13743 0.8360854
## 36 and over.45-49.Men 127.39702 3.0837334
## Under 18.5.50-54.Men 53.23790 1.2904192
## 18.5-25.50-54.Men 70.84037 0.7365835
## 26-30.50-54.Men 85.89730 0.5026645
## 31-35.50-54.Men 98.79304 0.9406373
## 36 and over.50-54.Men 121.44686 1.4569546
## Under 18.5.55-59.Men 51.91236 2.5914603
## 18.5-25.55-59.Men 70.77906 0.7432729
## 26-30.55-59.Men 85.61119 0.8291290
## 31-35.55-59.Men 99.59040 0.8054420
## 36 and over.55-59.Men 121.13811 2.5709455
## Under 18.5.60-64.Men 52.71650 1.7443790
## 18.5-25.60-64.Men 70.13545 0.8546599
## 26-30.60-64.Men 85.35225 0.6534910
## 31-35.60-64.Men 99.36144 0.9498065
## 36 and over.60-64.Men 121.64518 1.9445112
## Under 18.5.65-69.Men 53.02164 1.6344848
## 18.5-25.65-69.Men 69.86070 0.8547780
## 26-30.65-69.Men 83.97163 0.7416946
## 31-35.65-69.Men 98.63556 0.9451825
## 36 and over.65-69.Men 117.09933 1.5582301
## Under 18.5.70-74.Men 52.65605 1.7587355
## 18.5-25.70-74.Men 68.51498 0.9219874
## 26-30.70-74.Men 83.07525 0.8440807
## 31-35.70-74.Men 98.25048 1.0044310
## 36 and over.70-74.Men 115.84741 2.3094753
## Under 18.5.75-79.Men 48.56300 3.3263551
## 18.5-25.75-79.Men 68.66913 0.6883618
## 26-30.75-79.Men 81.25995 0.6577043
## 31-35.75-79.Men 95.72816 0.9735935
## 36 and over.75-79.Men 114.29154 4.1529013
## Under 18.5.Over 80.Men 47.43729 2.0243778
## 18.5-25.Over 80.Men 66.27424 0.6026705
## 26-30.Over 80.Men 78.86564 0.4245780
## 31-35.Over 80.Men 95.13212 1.0351081
## 36 and over.Over 80.Men 102.20011 3.1597276
## Under 18.5.17-19.Women 46.88050 0.5204947
## 18.5-25.17-19.Women 57.64921 0.3068772
## 26-30.17-19.Women 72.46839 0.4884022
## 31-35.17-19.Women 87.68691 0.8052025
## 36 and over.17-19.Women 108.66136 2.0516853
## Under 18.5.20-24.Women 46.27053 1.0805136
## 18.5-25.20-24.Women 58.52865 0.4768100
## 26-30.20-24.Women 72.39323 0.6504676
## 31-35.20-24.Women 84.27940 0.9746432
## 36 and over.20-24.Women 108.96161 1.9625929
## Under 18.5.25-29.Women 47.19937 0.8798057
## 18.5-25.25-29.Women 58.94733 0.5990405
## 26-30.25-29.Women 73.28752 0.6858530
## 31-35.25-29.Women 86.10057 0.8165809
## 36 and over.25-29.Women 110.59429 2.2984877
## Under 18.5.30-34.Women 47.97117 0.8947181
## 18.5-25.30-34.Women 59.76666 0.5032527
## 26-30.30-34.Women 72.56704 0.8003854
## 31-35.30-34.Women 84.08313 0.6313164
## 36 and over.30-34.Women 107.35830 1.2679110
## Under 18.5.35-39.Women 44.33741 1.0289237
## 18.5-25.35-39.Women 59.42444 0.4190593
## 26-30.35-39.Women 72.57229 0.6157891
## 31-35.35-39.Women 83.91454 0.8526309
## 36 and over.35-39.Women 108.29838 1.8185396
## Under 18.5.40-44.Women 48.81201 1.4784875
## 18.5-25.40-44.Women 60.25918 0.5432529
## 26-30.40-44.Women 72.59720 0.5144024
## 31-35.40-44.Women 85.51332 0.6984366
## 36 and over.40-44.Women 109.76432 1.9211609
## Under 18.5.45-49.Women 47.52096 1.3271480
## 18.5-25.45-49.Women 59.43718 0.4757879
## 26-30.45-49.Women 72.55881 0.5722107
## 31-35.45-49.Women 86.73654 0.7036729
## 36 and over.45-49.Women 108.54196 1.3734149
## Under 18.5.50-54.Women 46.78141 1.8751639
## 18.5-25.50-54.Women 60.08108 0.4891645
## 26-30.50-54.Women 72.57161 0.5817508
## 31-35.50-54.Women 84.55359 0.6907965
## 36 and over.50-54.Women 105.89872 1.6405367
## Under 18.5.55-59.Women 43.26778 0.2810073
## 18.5-25.55-59.Women 59.22510 0.6477394
## 26-30.55-59.Women 72.70258 0.5552188
## 31-35.55-59.Women 85.11473 0.9322304
## 36 and over.55-59.Women 106.79561 1.4530725
## Under 18.5.60-64.Women 42.07959 0.6668080
## 18.5-25.60-64.Women 59.44642 0.7568985
## 26-30.60-64.Women 71.88494 0.5818675
## 31-35.60-64.Women 83.28902 0.5454597
## 36 and over.60-64.Women 105.99567 1.1708451
## Under 18.5.65-69.Women 42.90020 2.5358259
## 18.5-25.65-69.Women 58.62974 0.5087032
## 26-30.65-69.Women 71.32947 0.5456960
## 31-35.65-69.Women 82.26026 0.6657208
## 36 and over.65-69.Women 103.78465 1.7039151
## Under 18.5.70-74.Women 43.02870 1.5274608
## 18.5-25.70-74.Women 57.25708 0.5930984
## 26-30.70-74.Women 69.65318 0.6661791
## 31-35.70-74.Women 79.86009 1.0756634
## 36 and over.70-74.Women 97.99550 1.9360171
## Under 18.5.75-79.Women 46.02128 0.9986844
## 18.5-25.75-79.Women 58.11698 0.7915674
## 26-30.75-79.Women 68.76155 0.5517711
## 31-35.75-79.Women 80.37402 1.2120925
## 36 and over.75-79.Women 97.11503 1.6992774
## Under 18.5.Over 80.Women 45.63668 0.5448062
## 18.5-25.Over 80.Women 54.50315 0.5740034
## 26-30.Over 80.Women 66.56850 0.5117243
## 31-35.Over 80.Women 77.44437 0.8381997
## 36 and over.Over 80.Women 92.33598 1.8906629## Multiple imputation results:
## with(nhc_all, svyby(~BMXHT, ~BMI.Group + Gender, na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXHT, ~BMI.Group + Gender,
## na = TRUE, svymean)))
## results se
## Under 18.5.Men 176.2592 0.8583244
## 18.5-25.Men 176.1551 0.1861533
## 26-30.Men 176.0291 0.1469389
## 31-35.Men 176.4612 0.2560169
## 36 and over.Men 177.0943 0.3069614
## Under 18.5.Women 163.1603 0.4455935
## 18.5-25.Women 162.8030 0.1529019
## 26-30.Women 162.0122 0.1703638
## 31-35.Women 161.3582 0.1839686
## 36 and over.Women 162.2061 0.2039648## Multiple imputation results:
## with(nhc_all, svyby(~BMXHT, ~BMI.Group + Age.Group, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXHT, ~BMI.Group + Age.Group,
## na = TRUE, svymean)))
## results se
## Under 18.5.17-19 169.0384 1.1356900
## 18.5-25.17-19 170.2684 0.3027598
## 26-30.17-19 171.1974 0.6260768
## 31-35.17-19 171.8072 0.7676168
## 36 and over.17-19 169.1831 1.0665525
## Under 18.5.20-24 170.4622 1.8164241
## 18.5-25.20-24 170.2181 0.4987106
## 26-30.20-24 170.6607 0.4670854
## 31-35.20-24 171.0005 0.6552419
## 36 and over.20-24 169.7360 0.7841693
## Under 18.5.25-29 169.6728 1.4607719
## 18.5-25.25-29 170.5118 0.4763144
## 26-30.25-29 172.0411 0.5296657
## 31-35.25-29 171.1675 0.9325536
## 36 and over.25-29 171.6867 1.2838144
## Under 18.5.30-34 167.1101 1.1809771
## 18.5-25.30-34 169.7107 0.5466193
## 26-30.30-34 171.7782 0.4831240
## 31-35.30-34 169.2577 0.8620477
## 36 and over.30-34 168.3875 0.7748696
## Under 18.5.35-39 167.9952 4.2647486
## 18.5-25.35-39 169.3843 0.4368738
## 26-30.35-39 171.3750 0.4564576
## 31-35.35-39 170.2365 0.6868417
## 36 and over.35-39 168.1871 0.6973963
## Under 18.5.40-44 167.0193 1.4242659
## 18.5-25.40-44 169.5071 0.6108022
## 26-30.40-44 171.8009 0.4061932
## 31-35.40-44 171.0522 0.7592887
## 36 and over.40-44 169.3486 0.8689666
## Under 18.5.45-49 164.5314 1.4797697
## 18.5-25.45-49 168.9659 0.7000010
## 26-30.45-49 171.0832 0.4270895
## 31-35.45-49 170.1919 0.6378669
## 36 and over.45-49 167.6084 0.7407630
## Under 18.5.50-54 168.4764 1.9474808
## 18.5-25.50-54 168.7282 0.6403125
## 26-30.50-54 170.8210 0.5108038
## 31-35.50-54 168.6026 0.7315214
## 36 and over.50-54 168.7451 0.6557772
## Under 18.5.55-59 168.6078 3.3289520
## 18.5-25.55-59 168.2371 0.7445279
## 26-30.55-59 170.8695 0.4756719
## 31-35.55-59 169.7517 0.7706497
## 36 and over.55-59 165.6751 0.9495503
## Under 18.5.60-64 167.0783 3.1247417
## 18.5-25.60-64 167.5916 0.6373411
## 26-30.60-64 168.9650 0.5557425
## 31-35.60-64 168.1613 0.6023048
## 36 and over.60-64 167.0635 0.7647645
## Under 18.5.65-69 163.2827 4.0299933
## 18.5-25.65-69 166.1514 0.6113284
## 26-30.65-69 168.5634 0.5397599
## 31-35.65-69 167.0425 0.7752439
## 36 and over.65-69 166.2034 0.8801682
## Under 18.5.70-74 161.1501 2.4052521
## 18.5-25.70-74 165.2555 0.6842983
## 26-30.70-74 166.4329 0.6560164
## 31-35.70-74 164.5361 0.9078005
## 36 and over.70-74 163.0163 1.0130051
## Under 18.5.75-79 162.2076 2.0108712
## 18.5-25.75-79 165.5169 0.8594352
## 26-30.75-79 164.6335 0.5738487
## 31-35.75-79 164.8830 1.1107256
## 36 and over.75-79 161.5080 1.3678877
## Under 18.5.Over 80 161.4120 1.1946782
## 18.5-25.Over 80 160.7570 0.3767045
## 26-30.Over 80 162.1164 0.6000183
## 31-35.Over 80 161.2014 0.8670539
## 36 and over.Over 80 157.3201 1.2065380## Multiple imputation results:
## with(nhc_all, svyby(~BMXHT, ~BMI.Group + RIDRETH1, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXHT, ~BMI.Group + RIDRETH1,
## na = TRUE, svymean)))
## results se
## Under 18.5.1 162.3237 0.9877763
## 18.5-25.1 164.9001 0.3647413
## 26-30.1 165.4287 0.2195375
## 31-35.1 164.3767 0.4315468
## 36 and over.1 162.9262 0.4840485
## Under 18.5.2 160.5450 2.1527415
## 18.5-25.2 165.3545 0.7180547
## 26-30.2 164.4088 0.6018594
## 31-35.2 161.9660 0.7913389
## 36 and over.2 163.2332 1.0236992
## Under 18.5.3 168.3014 0.7433127
## 18.5-25.3 169.7009 0.1886443
## 26-30.3 171.7463 0.2245088
## 31-35.3 170.4546 0.2976466
## 36 and over.3 168.7292 0.3659332
## Under 18.5.4 170.6140 1.1973509
## 18.5-25.4 171.4309 0.3423076
## 26-30.4 170.2477 0.2664270
## 31-35.4 168.9307 0.5114754
## 36 and over.4 167.2672 0.3799195
## Under 18.5.5 166.4284 1.8282109
## 18.5-25.5 163.3265 0.6118404
## 26-30.5 165.6341 0.8414239
## 31-35.5 170.7443 1.3338592
## 36 and over.5 170.5182 1.2600415## Multiple imputation results:
## with(nhc_all, svyby(~BMXHT, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXHT, ~BMI.Group + Age.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 175.8932 1.2660669
## 18.5-25.17-19.Men 176.1145 0.3163088
## 26-30.17-19.Men 176.6252 0.5764665
## 31-35.17-19.Men 177.6701 0.7531912
## 36 and over.17-19.Men 176.6803 0.7664262
## Under 18.5.20-24.Men 178.8667 1.5875223
## 18.5-25.20-24.Men 177.1794 0.5023020
## 26-30.20-24.Men 175.9604 0.4782032
## 31-35.20-24.Men 177.4686 0.7777987
## 36 and over.20-24.Men 179.4868 1.1831216
## Under 18.5.25-29.Men 178.3566 2.7121520
## 18.5-25.25-29.Men 177.0181 0.5484676
## 26-30.25-29.Men 176.0213 0.4398390
## 31-35.25-29.Men 179.1231 0.9255350
## 36 and over.25-29.Men 179.7011 1.0537107
## Under 18.5.30-34.Men 171.7232 1.6874824
## 18.5-25.30-34.Men 175.7433 0.6691378
## 26-30.30-34.Men 176.7112 0.4970652
## 31-35.30-34.Men 176.2413 0.8215399
## 36 and over.30-34.Men 178.0957 1.0139350
## Under 18.5.35-39.Men 183.9577 1.5452238
## 18.5-25.35-39.Men 176.1345 0.4869333
## 26-30.35-39.Men 176.3616 0.4548626
## 31-35.35-39.Men 176.2322 0.6384297
## 36 and over.35-39.Men 178.1247 0.8094977
## Under 18.5.40-44.Men 174.3791 1.6029540
## 18.5-25.40-44.Men 177.3550 0.7047603
## 26-30.40-44.Men 177.8606 0.4778497
## 31-35.40-44.Men 177.7553 0.7226600
## 36 and over.40-44.Men 177.1238 0.9346340
## Under 18.5.45-49.Men 172.5287 3.0770116
## 18.5-25.45-49.Men 176.9518 0.7376897
## 26-30.45-49.Men 176.1200 0.4515139
## 31-35.45-49.Men 177.0522 0.6580450
## 36 and over.45-49.Men 177.4617 0.9873840
## Under 18.5.50-54.Men 173.9822 1.6701667
## 18.5-25.50-54.Men 176.6956 0.7074550
## 26-30.50-54.Men 176.7115 0.4771799
## 31-35.50-54.Men 175.6946 0.7713635
## 36 and over.50-54.Men 176.6149 0.7022304
## Under 18.5.55-59.Men 170.8301 3.6475235
## 18.5-25.55-59.Men 175.9352 0.7266743
## 26-30.55-59.Men 176.2750 0.6182671
## 31-35.55-59.Men 175.9842 0.6220297
## 36 and over.55-59.Men 174.5739 1.7430752
## Under 18.5.60-64.Men 172.7226 1.4589672
## 18.5-25.60-64.Men 174.8769 0.7806985
## 26-30.60-64.Men 175.4931 0.5219396
## 31-35.60-64.Men 175.2135 0.6918605
## 36 and over.60-64.Men 176.5005 0.7222963
## Under 18.5.65-69.Men 178.8562 2.3195821
## 18.5-25.65-69.Men 174.4718 0.8480507
## 26-30.65-69.Men 174.5807 0.6755814
## 31-35.65-69.Men 175.2970 0.8426865
## 36 and over.65-69.Men 174.7408 0.7142899
## Under 18.5.70-74.Men 171.0293 2.7453784
## 18.5-25.70-74.Men 172.8110 0.9109835
## 26-30.70-74.Men 173.5265 0.7002004
## 31-35.70-74.Men 174.2288 0.8358327
## 36 and over.70-74.Men 172.8881 1.2112743
## Under 18.5.75-79.Men 167.0257 6.3384151
## 18.5-25.75-79.Men 172.9894 0.9047067
## 26-30.75-79.Men 171.8922 0.7111233
## 31-35.75-79.Men 172.4988 0.6029991
## 36 and over.75-79.Men 174.9084 2.0530623
## Under 18.5.Over 80.Men 167.0671 3.3308399
## 18.5-25.Over 80.Men 170.6025 0.6528539
## 26-30.Over 80.Men 170.1821 0.4243244
## 31-35.Over 80.Men 172.2686 0.8383610
## 36 and over.Over 80.Men 165.3267 2.3752267
## Under 18.5.17-19.Women 163.2879 0.9926265
## 18.5-25.17-19.Women 163.0779 0.2770245
## 26-30.17-19.Women 163.3234 0.4055256
## 31-35.17-19.Women 164.1286 0.6455816
## 36 and over.17-19.Women 162.5915 0.7982828
## Under 18.5.20-24.Women 163.4557 1.4216524
## 18.5-25.20-24.Women 163.1198 0.4274694
## 26-30.20-24.Women 162.7288 0.5776726
## 31-35.20-24.Women 161.7736 0.8414597
## 36 and over.20-24.Women 163.0686 0.7544294
## Under 18.5.25-29.Women 164.9086 1.1371855
## 18.5-25.25-29.Women 163.3074 0.5201204
## 26-30.25-29.Women 163.7195 0.5667931
## 31-35.25-29.Women 162.4384 0.8542868
## 36 and over.25-29.Women 163.0587 0.7049830
## Under 18.5.30-34.Women 165.1172 1.4072741
## 18.5-25.30-34.Women 164.0343 0.5666585
## 26-30.30-34.Women 162.7159 0.7355039
## 31-35.30-34.Women 161.3565 0.4963367
## 36 and over.30-34.Women 163.4855 0.5857677
## Under 18.5.35-39.Women 159.5030 1.3740925
## 18.5-25.35-39.Women 163.8433 0.4285056
## 26-30.35-39.Women 163.2666 0.5566781
## 31-35.35-39.Women 161.0936 0.7020556
## 36 and over.35-39.Women 162.4891 0.6262562
## Under 18.5.40-44.Women 165.4415 1.4423711
## 18.5-25.40-44.Women 164.0001 0.5717674
## 26-30.40-44.Women 162.8322 0.5044266
## 31-35.40-44.Women 162.8736 0.5327268
## 36 and over.40-44.Women 163.4100 0.6934729
## Under 18.5.45-49.Women 164.0920 1.6064282
## 18.5-25.45-49.Women 163.3805 0.5233335
## 26-30.45-49.Women 163.0451 0.4689757
## 31-35.45-49.Women 163.3925 0.5417146
## 36 and over.45-49.Women 162.4887 0.4860206
## Under 18.5.50-54.Women 163.4836 2.4697788
## 18.5-25.50-54.Women 163.3229 0.5341810
## 26-30.50-54.Women 161.7035 0.5318172
## 31-35.50-54.Women 161.1341 0.5626705
## 36 and over.50-54.Women 162.5127 0.7846284
## Under 18.5.55-59.Women 154.6242 0.2107554
## 18.5-25.55-59.Women 162.5962 0.6773220
## 26-30.55-59.Women 162.7539 0.6253975
## 31-35.55-59.Women 161.7733 0.6432178
## 36 and over.55-59.Women 161.2844 0.6932576
## Under 18.5.60-64.Women 158.3944 1.6032126
## 18.5-25.60-64.Women 162.6507 0.6543383
## 26-30.60-64.Women 161.8782 0.5799213
## 31-35.60-64.Women 160.7479 0.5218918
## 36 and over.60-64.Women 162.1354 0.5667446
## Under 18.5.65-69.Women 157.8271 3.6703066
## 18.5-25.65-69.Women 160.3196 0.4837210
## 26-30.65-69.Women 161.2275 0.5250552
## 31-35.65-69.Women 159.7001 0.6675134
## 36 and over.65-69.Women 160.6798 0.8926716
## Under 18.5.70-74.Women 156.8965 2.3941707
## 18.5-25.70-74.Women 159.6546 0.6249448
## 26-30.70-74.Women 159.2153 0.6523993
## 31-35.70-74.Women 157.7037 0.9243240
## 36 and over.70-74.Women 158.4338 0.9374887
## Under 18.5.75-79.Women 160.9332 1.9201647
## 18.5-25.75-79.Women 160.1549 0.8590570
## 26-30.75-79.Women 158.7950 0.6060750
## 31-35.75-79.Women 158.5434 0.9377297
## 36 and over.75-79.Women 158.2195 0.8491985
## Under 18.5.Over 80.Women 159.8824 0.9584372
## 18.5-25.Over 80.Women 155.6626 0.4121350
## 26-30.Over 80.Women 156.1605 0.5011078
## 31-35.Over 80.Women 155.6277 0.5915286
## 36 and over.Over 80.Women 155.5194 1.0218087## Multiple imputation results:
## with(nhc_all, svyby(~BMXWAIST, ~BMI.Group + Gender, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWAIST, ~BMI.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.Men 72.15605 0.3968813
## 18.5-25.Men 84.94854 0.1897704
## 26-30.Men 98.45902 0.1640902
## 31-35.Men 110.29027 0.2128452
## 36 and over.Men 127.92047 0.5056716
## Under 18.5.Women 69.05233 0.3881204
## 18.5-25.Women 79.64892 0.1784911
## 26-30.Women 92.57103 0.2068690
## 31-35.Women 102.90805 0.2108432
## 36 and over.Women 117.54420 0.4048685## Multiple imputation results:
## with(nhc_all, svyby(~BMXWAIST, ~BMI.Group + Age.Group, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWAIST, ~BMI.Group +
## Age.Group, na = TRUE, svymean)))
## results se
## Under 18.5.17-19 68.94125 0.3501543
## 18.5-25.17-19 77.09348 0.2119662
## 26-30.17-19 91.31922 0.4004158
## 31-35.17-19 103.66153 0.6312842
## 36 and over.17-19 118.11872 1.0442436
## Under 18.5.20-24 69.34743 0.6383943
## 18.5-25.20-24 79.17518 0.2177071
## 26-30.20-24 91.42009 0.4366200
## 31-35.20-24 104.61524 0.6729107
## 36 and over.20-24 121.93906 1.2040631
## Under 18.5.25-29 70.33770 0.9765037
## 18.5-25.25-29 80.81877 0.3070604
## 26-30.25-29 93.37654 0.3974984
## 31-35.25-29 104.62942 0.4869166
## 36 and over.25-29 123.87989 1.5157728
## Under 18.5.30-34 68.73648 0.6419168
## 18.5-25.30-34 81.15197 0.4177458
## 26-30.30-34 94.46770 0.3647077
## 31-35.30-34 102.96416 0.7408492
## 36 and over.30-34 118.74491 1.0399662
## Under 18.5.35-39 70.75034 1.8837614
## 18.5-25.35-39 81.60928 0.3824945
## 26-30.35-39 94.45296 0.3677461
## 31-35.35-39 105.33738 0.4722640
## 36 and over.35-39 120.91441 1.2833576
## Under 18.5.40-44 71.56422 0.6026799
## 18.5-25.40-44 82.69878 0.3759271
## 26-30.40-44 94.99597 0.3914226
## 31-35.40-44 106.57544 0.5006400
## 36 and over.40-44 121.16954 1.0172437
## Under 18.5.45-49 70.21658 0.9695397
## 18.5-25.45-49 82.64131 0.4921307
## 26-30.45-49 96.55280 0.3600507
## 31-35.45-49 107.17331 0.5511517
## 36 and over.45-49 121.87449 1.1332777
## Under 18.5.50-54 71.51747 1.2637254
## 18.5-25.50-54 83.85424 0.4250476
## 26-30.50-54 97.49402 0.3072645
## 31-35.50-54 107.05273 0.5298894
## 36 and over.50-54 121.70507 0.7540192
## Under 18.5.55-59 73.63346 1.9721247
## 18.5-25.55-59 84.53121 0.5982478
## 26-30.55-59 98.18314 0.4462992
## 31-35.55-59 109.51093 0.6335008
## 36 and over.55-59 122.04554 1.2247347
## Under 18.5.60-64 71.93484 1.8313102
## 18.5-25.60-64 85.58968 0.5409121
## 26-30.60-64 98.68412 0.4115270
## 31-35.60-64 109.64932 0.4830366
## 36 and over.60-64 122.96828 0.8069942
## Under 18.5.65-69 69.64954 2.8948098
## 18.5-25.65-69 86.87281 0.5427661
## 26-30.65-69 99.67352 0.4828573
## 31-35.65-69 109.74477 0.5956257
## 36 and over.65-69 122.58771 0.6198953
## Under 18.5.70-74 74.49565 1.8261347
## 18.5-25.70-74 86.55489 0.5490721
## 26-30.70-74 100.26255 0.5781270
## 31-35.70-74 109.49968 0.5444947
## 36 and over.70-74 122.72362 1.5197180
## Under 18.5.75-79 72.11578 1.1653399
## 18.5-25.75-79 88.22898 0.5445629
## 26-30.75-79 99.94705 0.4401341
## 31-35.75-79 109.06652 0.8111297
## 36 and over.75-79 118.88017 1.6334721
## Under 18.5.Over 80 69.94087 1.3916811
## 18.5-25.Over 80 87.14352 0.4866726
## 26-30.Over 80 99.63475 0.3679053
## 31-35.Over 80 108.92835 0.6135781
## 36 and over.Over 80 117.22970 2.3818375## Multiple imputation results:
## with(nhc_all, svyby(~BMXWAIST, ~BMI.Group + RIDRETH1, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWAIST, ~BMI.Group +
## RIDRETH1, na = TRUE, svymean)))
## results se
## Under 18.5.1 69.67164 0.7561178
## 18.5-25.1 81.80304 0.2580076
## 26-30.1 94.39986 0.1881814
## 31-35.1 104.41165 0.3230856
## 36 and over.1 118.87695 0.7388237
## Under 18.5.2 69.59577 0.7934493
## 18.5-25.2 81.08037 0.5386171
## 26-30.2 93.13989 0.6121671
## 31-35.2 102.44320 0.7152432
## 36 and over.2 116.18959 1.5492751
## Under 18.5.3 70.46005 0.3792266
## 18.5-25.3 82.59333 0.1685291
## 26-30.3 97.05493 0.1720553
## 31-35.3 107.84031 0.1984285
## 36 and over.3 122.23433 0.4702746
## Under 18.5.4 68.91762 0.5430895
## 18.5-25.4 79.69081 0.2854952
## 26-30.4 93.11172 0.2171518
## 31-35.4 104.00415 0.3485865
## 36 and over.4 120.24530 0.4216701
## Under 18.5.5 69.19286 0.9050044
## 18.5-25.5 80.35272 0.5258761
## 26-30.5 93.94349 0.4749090
## 31-35.5 107.78231 0.9852230
## 36 and over.5 124.24075 1.5168458## Multiple imputation results:
## with(nhc_all, svyby(~BMXWAIST, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~BMXWAIST, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 70.02591 0.4826075
## 18.5-25.17-19.Men 77.63177 0.2633561
## 26-30.17-19.Men 92.25791 0.4646573
## 31-35.17-19.Men 105.58240 0.7019616
## 36 and over.17-19.Men 120.79572 1.1644250
## Under 18.5.20-24.Men 71.00736 0.6992997
## 18.5-25.20-24.Men 80.91886 0.3189718
## 26-30.20-24.Men 93.10946 0.5221453
## 31-35.20-24.Men 106.86609 0.8325545
## 36 and over.20-24.Men 127.86545 2.2764751
## Under 18.5.25-29.Men 72.50587 0.9049533
## 18.5-25.25-29.Men 83.44175 0.3642222
## 26-30.25-29.Men 94.87967 0.3812432
## 31-35.25-29.Men 107.77663 0.6321703
## 36 and over.25-29.Men 130.73440 1.8173419
## Under 18.5.30-34.Men 68.90438 0.8151482
## 18.5-25.30-34.Men 84.23906 0.5440469
## 26-30.30-34.Men 96.78172 0.4018892
## 31-35.30-34.Men 106.95829 0.7513739
## 36 and over.30-34.Men 126.78156 2.1532333
## Under 18.5.35-39.Men 77.40627 1.7923829
## 18.5-25.35-39.Men 85.35098 0.5276771
## 26-30.35-39.Men 96.84881 0.4364788
## 31-35.35-39.Men 108.56523 0.4040165
## 36 and over.35-39.Men 127.62379 2.1311941
## Under 18.5.40-44.Men 71.82623 0.9893189
## 18.5-25.40-44.Men 86.68977 0.3716773
## 26-30.40-44.Men 98.02911 0.3720032
## 31-35.40-44.Men 110.23399 0.5193613
## 36 and over.40-44.Men 126.05391 1.2329985
## Under 18.5.45-49.Men 69.73650 2.0915679
## 18.5-25.45-49.Men 86.97930 0.6235085
## 26-30.45-49.Men 99.30897 0.3691892
## 31-35.45-49.Men 110.68043 0.5622611
## 36 and over.45-49.Men 130.11509 2.0407731
## Under 18.5.50-54.Men 74.62800 1.3760424
## 18.5-25.50-54.Men 88.39182 0.5275941
## 26-30.50-54.Men 99.88031 0.3955084
## 31-35.50-54.Men 110.54914 0.6844954
## 36 and over.50-54.Men 126.30894 0.8924208
## Under 18.5.55-59.Men 74.46568 2.2076920
## 18.5-25.55-59.Men 89.81819 0.7753703
## 26-30.55-59.Men 101.13179 0.5562246
## 31-35.55-59.Men 112.62097 0.5101592
## 36 and over.55-59.Men 130.00145 1.5013805
## Under 18.5.60-64.Men 74.66652 1.1871093
## 18.5-25.60-64.Men 89.79391 0.5934596
## 26-30.60-64.Men 102.21887 0.4012237
## 31-35.60-64.Men 113.96264 0.5262082
## 36 and over.60-64.Men 129.81320 1.3614027
## Under 18.5.65-69.Men 75.92301 2.3429219
## 18.5-25.65-69.Men 91.30375 0.5945588
## 26-30.65-69.Men 103.40507 0.4742581
## 31-35.65-69.Men 114.68689 0.6506386
## 36 and over.65-69.Men 127.84890 0.8669222
## Under 18.5.70-74.Men 80.26516 1.9236971
## 18.5-25.70-74.Men 90.29878 0.6788257
## 26-30.70-74.Men 103.63781 0.4965415
## 31-35.70-74.Men 114.91333 0.7357081
## 36 and over.70-74.Men 130.97614 2.0148924
## Under 18.5.75-79.Men 71.55467 1.1452586
## 18.5-25.75-79.Men 92.75838 0.5869434
## 26-30.75-79.Men 103.48040 0.5782424
## 31-35.75-79.Men 114.27146 0.7160722
## 36 and over.75-79.Men 128.32564 3.3984865
## Under 18.5.Over 80.Men 73.56706 1.5153775
## 18.5-25.Over 80.Men 92.03219 0.4209035
## 26-30.Over 80.Men 103.15649 0.3275026
## 31-35.Over 80.Men 116.13475 0.9369925
## 36 and over.Over 80.Men 126.20485 2.1019046
## Under 18.5.17-19.Women 68.03134 0.4606805
## 18.5-25.17-19.Women 76.43935 0.3416612
## 26-30.17-19.Women 89.96674 0.7154371
## 31-35.17-19.Women 101.14757 0.7803429
## 36 and over.17-19.Women 115.68395 1.7609220
## Under 18.5.20-24.Women 67.96362 0.9040656
## 18.5-25.20-24.Women 77.40681 0.4014266
## 26-30.20-24.Women 88.92693 0.6738174
## 31-35.20-24.Women 101.43041 1.0987250
## 36 and over.20-24.Women 117.98753 1.5368872
## Under 18.5.25-29.Women 69.14817 1.3898224
## 18.5-25.25-29.Women 77.94837 0.4713024
## 26-30.25-29.Women 90.19573 0.5652330
## 31-35.25-29.Women 101.27858 0.8602678
## 36 and over.25-29.Women 116.82811 1.9587027
## Under 18.5.30-34.Women 68.66394 0.7356035
## 18.5-25.30-34.Women 78.27155 0.5336746
## 26-30.30-34.Women 90.26478 0.5740605
## 31-35.30-34.Women 98.55396 0.9099082
## 36 and over.30-34.Women 114.87290 1.0394771
## Under 18.5.35-39.Women 67.20934 1.3825274
## 18.5-25.35-39.Women 78.60426 0.4509455
## 26-30.35-39.Women 90.63450 0.5272125
## 31-35.35-39.Women 100.34224 0.8654658
## 36 and over.35-39.Women 116.99296 1.4227893
## Under 18.5.40-44.Women 71.51631 0.6426218
## 18.5-25.40-44.Women 79.94131 0.5235032
## 26-30.40-44.Women 90.56405 0.6068256
## 31-35.40-44.Women 102.09615 0.6759923
## 36 and over.40-44.Women 117.52126 1.4057873
## Under 18.5.45-49.Women 70.24296 0.9855581
## 18.5-25.45-49.Women 79.63979 0.4548778
## 26-30.45-49.Women 92.18352 0.5759314
## 31-35.45-49.Women 103.65914 0.8191898
## 36 and over.45-49.Women 117.60031 1.2803680
## Under 18.5.50-54.Women 69.24601 1.4975487
## 18.5-25.50-54.Women 80.86597 0.4370633
## 26-30.50-54.Women 93.80180 0.4781783
## 31-35.50-54.Women 103.46758 0.6932605
## 36 and over.50-54.Women 118.27807 1.1074124
## Under 18.5.55-59.Women 68.39667 0.8430218
## 18.5-25.55-59.Women 80.82420 0.7796012
## 26-30.55-59.Women 93.78274 0.7557158
## 31-35.55-59.Women 105.62161 0.8747162
## 36 and over.55-59.Women 118.40536 1.2375098
## Under 18.5.60-64.Women 67.73209 1.4946871
## 18.5-25.60-64.Women 82.70879 0.6791011
## 26-30.60-64.Women 94.91434 0.5694742
## 31-35.60-64.Women 105.00715 0.7035539
## 36 and over.60-64.Women 119.33508 1.0586120
## Under 18.5.65-69.Women 66.83091 3.3774333
## 18.5-25.65-69.Women 83.76852 0.6625182
## 26-30.65-69.Women 95.12858 0.6013239
## 31-35.65-69.Women 105.27202 0.7355761
## 36 and over.65-69.Women 119.25423 0.7716269
## Under 18.5.70-74.Women 72.01154 2.1057105
## 18.5-25.70-74.Women 83.72455 0.7717502
## 26-30.70-74.Women 96.85109 0.9271659
## 31-35.70-74.Women 105.73201 0.6821730
## 36 and over.70-74.Women 118.42304 1.8494010
## Under 18.5.75-79.Women 72.27666 1.4543953
## 18.5-25.75-79.Women 85.08828 0.8401243
## 26-30.75-79.Women 97.14083 0.6872978
## 31-35.75-79.Women 104.69923 1.0146992
## 36 and over.75-79.Women 116.46310 1.4981259
## Under 18.5.Over 80.Women 69.07586 1.4419145
## 18.5-25.Over 80.Women 84.52933 0.6471206
## 26-30.Over 80.Women 96.93708 0.4930077
## 31-35.Over 80.Women 105.40666 0.6441234
## 36 and over.Over 80.Women 115.37191 2.2968202## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~Age.Group + Gender, na = TRUE,
## svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~Age.Group +
## Gender, na = TRUE, svymean)))
## results se
## 17-19.Men 22.63426 0.2660333
## 20-24.Men 24.64485 0.3115893
## 25-29.Men 26.28288 0.2640241
## 30-34.Men 26.36185 0.2364455
## 35-39.Men 27.17665 0.2858616
## 40-44.Men 28.11467 0.2238751
## 45-49.Men 28.24186 0.2633631
## 50-54.Men 29.01408 0.2746816
## 55-59.Men 29.82390 0.2988272
## 60-64.Men 30.24194 0.3236189
## 65-69.Men 31.16652 0.2451168
## 70-74.Men 30.96424 0.3424775
## 75-79.Men 31.24850 0.3055828
## Over 80.Men 30.76948 0.2319936
## 17-19.Women 35.16876 0.2957482
## 20-24.Women 36.70029 0.3768444
## 25-29.Women 37.28420 0.3408838
## 30-34.Women 38.28933 0.3667912
## 35-39.Women 38.74577 0.3840080
## 40-44.Women 39.11658 0.3191313
## 45-49.Women 40.08398 0.3113287
## 50-54.Women 40.92301 0.2819920
## 55-59.Women 42.23428 0.3891043
## 60-64.Women 42.46001 0.2422301
## 65-69.Women 42.73227 0.2956462
## 70-74.Women 42.21890 0.3610763
## 75-79.Women 42.11111 0.3333930
## Over 80.Women 40.46854 0.2995536## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~Age.Group + Gender, na = TRUE,
## svyvar))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~Age.Group +
## Gender, na = TRUE, svyvar)))
## results se
## 17-19.Men 50.14313 2.866316
## 20-24.Men 49.02520 2.223799
## 25-29.Men 40.27795 2.324850
## 30-34.Men 35.96430 1.894620
## 35-39.Men 35.57748 1.785797
## 40-44.Men 29.57675 1.741965
## 45-49.Men 31.42844 2.009821
## 50-54.Men 31.31750 1.619104
## 55-59.Men 29.67944 2.442800
## 60-64.Men 27.98811 1.979850
## 65-69.Men 28.40481 1.603103
## 70-74.Men 32.27416 2.983954
## 75-79.Men 24.55182 2.299261
## Over 80.Men 25.48551 1.766761
## 17-19.Women 48.34412 3.215043
## 20-24.Women 51.87077 2.958178
## 25-29.Women 49.75413 3.599063
## 30-34.Women 51.05500 3.034180
## 35-39.Women 49.18443 2.753334
## 40-44.Women 42.18589 2.898097
## 45-49.Women 47.10460 2.972216
## 50-54.Women 37.88170 2.871356
## 55-59.Women 38.47908 2.502006
## 60-64.Women 32.22830 2.343342
## 65-69.Women 33.13407 2.655132
## 70-74.Women 34.11740 2.963002
## 75-79.Women 31.43752 4.169206
## Over 80.Women 33.75126 2.600861## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~Age.Group + Gender, na = TRUE,
## svyquantile, quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
## keep.var = TRUE, se = T, ci = T))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~Age.Group +
## Gender, na = TRUE, svyquantile, quantiles = c(0.05, 0.25,
## 0.5, 0.75, 0.95), keep.var = TRUE, se = T, ci = T)))
## results se
## 17-19.Men:0.05 14.02068 0.1631904
## 20-24.Men:0.05 14.52284 0.2666032
## 25-29.Men:0.05 16.18000 0.4244534
## 30-34.Men:0.05 16.28064 0.4661662
## 35-39.Men:0.05 17.49358 0.5532458
## 40-44.Men:0.05 18.66053 0.4879690
## 45-49.Men:0.05 18.97482 0.4849083
## 50-54.Men:0.05 19.24941 0.4786821
## 55-59.Men:0.05 20.60647 0.7687073
## 60-64.Men:0.05 21.52000 0.5243413
## 65-69.Men:0.05 22.49348 0.5635891
## 70-74.Men:0.05 21.58013 0.8489295
## 75-79.Men:0.05 23.39979 0.9863876
## Over 80.Men:0.05 21.91987 0.6216889
## 17-19.Women:0.05 25.01783 0.4692852
## 20-24.Women:0.05 25.37170 0.7871277
## 25-29.Women:0.05 24.91373 0.6180139
## 30-34.Women:0.05 25.62293 1.1592820
## 35-39.Women:0.05 26.87648 0.4203742
## 40-44.Women:0.05 26.70000 0.8260136
## 45-49.Women:0.05 27.32400 0.6252655
## 50-54.Women:0.05 29.83379 0.8108042
## 55-59.Women:0.05 29.98866 0.7962906
## 60-64.Women:0.05 32.08179 0.9105858
## 65-69.Women:0.05 32.67765 0.5981664
## 70-74.Women:0.05 31.23694 0.9801550
## 75-79.Women:0.05 30.96692 1.5909615
## Over 80.Women:0.05 29.94864 1.1090123
## 17-19.Men:0.25 16.90000 0.2333490
## 20-24.Men:0.25 19.10000 0.2467252
## 25-29.Men:0.25 21.88230 0.3621212
## 30-34.Men:0.25 22.01621 0.3665720
## 35-39.Men:0.25 23.22091 0.4922161
## 40-44.Men:0.25 24.77276 0.3258529
## 45-49.Men:0.25 24.67365 0.4574354
## 50-54.Men:0.25 25.35491 0.4052020
## 55-59.Men:0.25 26.60249 0.4492341
## 60-64.Men:0.25 26.64000 0.3555123
## 65-69.Men:0.25 27.56000 0.1937276
## 70-74.Men:0.25 27.31158 0.4340555
## 75-79.Men:0.25 27.78144 0.4910776
## Over 80.Men:0.25 27.38000 0.2741030
## 17-19.Women:0.25 30.14000 0.3443604
## 20-24.Women:0.25 31.56646 0.4430049
## 25-29.Women:0.25 32.48000 0.7071285
## 30-34.Women:0.25 33.20000 0.6560114
## 35-39.Women:0.25 33.83198 0.4205113
## 40-44.Women:0.25 34.90217 0.4586845
## 45-49.Women:0.25 35.40501 0.5824665
## 50-54.Women:0.25 37.16000 0.5231864
## 55-59.Women:0.25 38.31482 0.6199665
## 60-64.Women:0.25 38.64245 0.3738779
## 65-69.Women:0.25 39.16000 0.4045800
## 70-74.Women:0.25 38.74153 0.7373495
## 75-79.Women:0.25 39.13679 0.5682373
## Over 80.Women:0.25 37.04294 0.4961399
## 17-19.Men:0.5 20.66000 0.3747849
## 20-24.Men:0.5 23.74867 0.4164495
## 25-29.Men:0.5 26.04566 0.2606456
## 30-34.Men:0.5 26.68356 0.2288235
## 35-39.Men:0.5 26.93355 0.2911092
## 40-44.Men:0.5 28.00000 0.2815797
## 45-49.Men:0.5 28.14000 0.3222499
## 50-54.Men:0.5 29.28000 0.3922663
## 55-59.Men:0.5 29.97028 0.3055326
## 60-64.Men:0.5 30.08000 0.3791861
## 65-69.Men:0.5 31.02336 0.2675779
## 70-74.Men:0.5 30.88058 0.4352439
## 75-79.Men:0.5 31.15180 0.3060767
## Over 80.Men:0.5 30.88000 0.2650145
## 17-19.Women:0.5 34.28658 0.4280256
## 20-24.Women:0.5 36.32228 0.6649902
## 25-29.Women:0.5 37.76711 0.3238385
## 30-34.Women:0.5 38.83627 0.4968163
## 35-39.Women:0.5 39.12000 0.5910776
## 40-44.Women:0.5 39.81559 0.4587310
## 45-49.Women:0.5 40.58000 0.3808043
## 50-54.Women:0.5 41.61654 0.3447494
## 55-59.Women:0.5 42.92032 0.3376618
## 60-64.Women:0.5 43.02219 0.2633932
## 65-69.Women:0.5 43.08000 0.4207205
## 70-74.Women:0.5 42.86000 0.4446604
## 75-79.Women:0.5 42.76508 0.3111720
## Over 80.Women:0.5 41.04611 0.3309705
## 17-19.Men:0.75 27.11031 0.4670280
## 20-24.Men:0.75 29.44000 0.5622650
## 25-29.Men:0.75 30.10954 0.3624387
## 30-34.Men:0.75 30.12607 0.3791387
## 35-39.Men:0.75 31.06288 0.3137112
## 40-44.Men:0.75 31.90545 0.3733267
## 45-49.Men:0.75 31.90715 0.3721638
## 50-54.Men:0.75 32.36515 0.3768058
## 55-59.Men:0.75 33.38000 0.3554947
## 60-64.Men:0.75 34.02543 0.4606745
## 65-69.Men:0.75 34.82308 0.3905283
## 70-74.Men:0.75 34.53623 0.5455058
## 75-79.Men:0.75 34.65991 0.3936375
## Over 80.Men:0.75 34.01811 0.3858129
## 17-19.Women:0.75 39.79441 0.6218171
## 20-24.Women:0.75 41.76226 0.5171455
## 25-29.Women:0.75 42.24547 0.4657743
## 30-34.Women:0.75 43.58899 0.5128872
## 35-39.Women:0.75 43.70753 0.5759279
## 40-44.Women:0.75 43.52673 0.3025080
## 45-49.Women:0.75 45.06135 0.3600967
## 50-54.Women:0.75 45.30000 0.2905118
## 55-59.Women:0.75 46.20027 0.5204788
## 60-64.Women:0.75 46.67141 0.3383925
## 65-69.Women:0.75 46.57323 0.3231248
## 70-74.Women:0.75 46.44000 0.3376649
## 75-79.Women:0.75 45.57324 0.5307812
## Over 80.Women:0.75 44.42283 0.4236540
## 17-19.Men:0.95 36.16000 0.9224050
## 20-24.Men:0.95 37.00073 0.5600533
## 25-29.Men:0.95 37.57984 0.8251035
## 30-34.Men:0.95 36.55506 0.4581204
## 35-39.Men:0.95 37.38391 0.7066229
## 40-44.Men:0.95 37.02826 0.6133111
## 45-49.Men:0.95 38.04241 0.5907135
## 50-54.Men:0.95 38.14085 0.4815592
## 55-59.Men:0.95 38.93772 0.8044845
## 60-64.Men:0.95 38.92416 0.5304992
## 65-69.Men:0.95 39.85824 0.4765754
## 70-74.Men:0.95 40.71322 0.7261693
## 75-79.Men:0.95 39.00116 0.8279273
## Over 80.Men:0.95 38.88896 0.6396777
## 17-19.Women:0.95 48.20000 0.5024277
## 20-24.Women:0.95 49.22080 0.6938227
## 25-29.Women:0.95 48.57026 0.7841573
## 30-34.Women:0.95 49.52688 0.6660882
## 35-39.Women:0.95 50.29977 0.4424290
## 40-44.Women:0.95 49.62157 0.6256631
## 45-49.Women:0.95 50.69530 0.4402624
## 50-54.Women:0.95 50.07378 0.6210419
## 55-59.Women:0.95 51.48036 0.5181849
## 60-64.Women:0.95 50.75792 0.4917691
## 65-69.Women:0.95 51.72508 0.5457791
## 70-74.Women:0.95 50.87307 0.4146517
## 75-79.Women:0.95 50.09103 0.5813399
## Over 80.Women:0.95 48.99255 0.6150032## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 15.73465 0.4507527
## 18.5-25.17-19.Men 18.86657 0.1933846
## 26-30.17-19.Men 26.11311 0.3703496
## 31-35.17-19.Men 32.00163 0.4890500
## 36 and over.17-19.Men 37.08344 0.5471586
## Under 18.5.20-24.Men 16.23929 0.5250669
## 18.5-25.20-24.Men 20.00922 0.2273880
## 26-30.20-24.Men 26.25114 0.3853843
## 31-35.20-24.Men 31.10966 0.5071526
## 36 and over.20-24.Men 36.97270 0.5280719
## Under 18.5.25-29.Men 15.67754 0.5406840
## 18.5-25.25-29.Men 21.68437 0.2702246
## 26-30.25-29.Men 26.87191 0.2782017
## 31-35.25-29.Men 31.11532 0.3699343
## 36 and over.25-29.Men 37.28002 0.4709883
## Under 18.5.30-34.Men 16.98321 1.3031802
## 18.5-25.30-34.Men 21.54765 0.3410600
## 26-30.30-34.Men 27.03160 0.2280380
## 31-35.30-34.Men 30.86226 0.4678854
## 36 and over.30-34.Men 36.05657 0.5958456
## Under 18.5.35-39.Men 17.02993 1.1428383
## 18.5-25.35-39.Men 22.22573 0.3890290
## 26-30.35-39.Men 27.16232 0.3068192
## 31-35.35-39.Men 30.91090 0.3169878
## 36 and over.35-39.Men 35.92398 0.5661108
## Under 18.5.40-44.Men 16.21487 1.3888828
## 18.5-25.40-44.Men 22.89632 0.3415968
## 26-30.40-44.Men 27.39831 0.2194374
## 31-35.40-44.Men 31.13595 0.2942543
## 36 and over.40-44.Men 35.78116 0.4643813
## Under 18.5.45-49.Men 16.21784 0.6516569
## 18.5-25.45-49.Men 22.68177 0.4752111
## 26-30.45-49.Men 27.65718 0.2506235
## 31-35.45-49.Men 31.74313 0.3424752
## 36 and over.45-49.Men 36.44526 0.5573145
## Under 18.5.50-54.Men 18.40121 1.1825122
## 18.5-25.50-54.Men 23.67883 0.4133307
## 26-30.50-54.Men 28.15034 0.2788936
## 31-35.50-54.Men 31.90632 0.3848255
## 36 and over.50-54.Men 36.51689 0.3498893
## Under 18.5.55-59.Men 20.50443 1.6201221
## 18.5-25.55-59.Men 24.99417 0.4290591
## 26-30.55-59.Men 29.05485 0.3071528
## 31-35.55-59.Men 32.37030 0.4108707
## 36 and over.55-59.Men 38.00753 0.6186612
## Under 18.5.60-64.Men 17.08199 1.1846178
## 18.5-25.60-64.Men 25.13270 0.4150290
## 26-30.60-64.Men 29.32819 0.2951990
## 31-35.60-64.Men 33.10378 0.3384544
## 36 and over.60-64.Men 37.33427 0.4115167
## Under 18.5.65-69.Men 17.73870 1.0472858
## 18.5-25.65-69.Men 25.99618 0.4466313
## 26-30.65-69.Men 30.31134 0.1889768
## 31-35.65-69.Men 34.20158 0.4252815
## 36 and over.65-69.Men 37.66484 0.5293741
## Under 18.5.70-74.Men 23.21452 1.7584789
## 18.5-25.70-74.Men 25.54576 0.5018266
## 26-30.70-74.Men 30.87706 0.2545368
## 31-35.70-74.Men 35.38295 0.5228856
## 36 and over.70-74.Men 40.17551 0.8046182
## Under 18.5.75-79.Men 16.49345 1.4447807
## 18.5-25.75-79.Men 27.51410 0.5569083
## 26-30.75-79.Men 31.21802 0.3517536
## 31-35.75-79.Men 35.80896 0.4478400
## 36 and over.75-79.Men 37.15551 0.9902299
## Under 18.5.Over 80.Men 21.83929 2.2782635
## 18.5-25.Over 80.Men 27.79047 0.3170939
## 26-30.Over 80.Men 31.91233 0.2746580
## 31-35.Over 80.Men 35.95829 0.4685920
## 36 and over.Over 80.Men 39.05557 1.0084494
## Under 18.5.17-19.Women 26.65024 0.3983918
## 18.5-25.17-19.Women 32.07084 0.2508937
## 26-30.17-19.Women 38.76162 0.3505124
## 31-35.17-19.Women 43.41947 0.5057549
## 36 and over.17-19.Women 47.89299 0.4073310
## Under 18.5.20-24.Women 26.41293 0.8240736
## 18.5-25.20-24.Women 32.43387 0.2868426
## 26-30.20-24.Women 39.04349 0.3487893
## 31-35.20-24.Women 43.64266 0.3913797
## 36 and over.20-24.Women 47.98942 0.5987831
## Under 18.5.25-29.Women 25.96717 0.6879963
## 18.5-25.25-29.Women 32.77698 0.3445953
## 26-30.25-29.Women 39.04413 0.3408102
## 31-35.25-29.Women 43.08062 0.4023959
## 36 and over.25-29.Women 47.66750 0.5770909
## Under 18.5.30-34.Women 26.79345 1.1851503
## 18.5-25.30-34.Women 32.81824 0.3952607
## 26-30.30-34.Women 39.50874 0.3568595
## 31-35.30-34.Women 43.29207 0.3126553
## 36 and over.30-34.Women 47.06881 0.3716687
## Under 18.5.35-39.Women 25.77416 1.8998274
## 18.5-25.35-39.Women 33.30810 0.2701370
## 26-30.35-39.Women 39.50903 0.3560858
## 31-35.35-39.Women 43.23123 0.4267856
## 36 and over.35-39.Women 47.65263 0.4150786
## Under 18.5.40-44.Women 25.66285 0.5896893
## 18.5-25.40-44.Women 33.79425 0.3580718
## 26-30.40-44.Women 39.76392 0.2938982
## 31-35.40-44.Women 43.11995 0.2934047
## 36 and over.40-44.Women 46.76084 0.4095174
## Under 18.5.45-49.Women 27.36320 1.1521280
## 18.5-25.45-49.Women 33.76669 0.4038846
## 26-30.45-49.Women 40.42850 0.2556963
## 31-35.45-49.Women 43.48226 0.3935422
## 36 and over.45-49.Women 47.91837 0.3243785
## Under 18.5.50-54.Women 27.38976 1.7639615
## 18.5-25.50-54.Women 35.23468 0.4360582
## 26-30.50-54.Women 41.52525 0.3041680
## 31-35.50-54.Women 44.07740 0.2966747
## 36 and over.50-54.Women 47.49388 0.3609187
## Under 18.5.55-59.Women 27.09097 1.2294068
## 18.5-25.55-59.Women 36.04799 0.5544781
## 26-30.55-59.Women 41.91659 0.2912774
## 31-35.55-59.Women 44.85528 0.3688391
## 36 and over.55-59.Women 48.99118 0.3528748
## Under 18.5.60-64.Women 24.19384 3.6622989
## 18.5-25.60-64.Women 36.85818 0.4506760
## 26-30.60-64.Women 41.97248 0.3276529
## 31-35.60-64.Women 45.07833 0.2430850
## 36 and over.60-64.Women 48.72931 0.3502399
## Under 18.5.65-69.Women 25.73170 1.8291397
## 18.5-25.65-69.Women 37.36764 0.4053649
## 26-30.65-69.Women 42.12033 0.2831004
## 31-35.65-69.Women 45.61526 0.3825729
## 36 and over.65-69.Women 49.04343 0.3296924
## Under 18.5.70-74.Women 29.72784 1.0517751
## 18.5-25.70-74.Women 37.08766 0.3927909
## 26-30.70-74.Women 42.76425 0.4858565
## 31-35.70-74.Women 45.22728 0.3869709
## 36 and over.70-74.Women 48.89426 0.4781580
## Under 18.5.75-79.Women 26.72136 2.4703933
## 18.5-25.75-79.Women 38.00570 0.5876820
## 26-30.75-79.Women 42.51874 0.3648294
## 31-35.75-79.Women 45.08477 0.5878246
## 36 and over.75-79.Women 49.03266 0.5010993
## Under 18.5.Over 80.Women 28.59596 1.8052059
## 18.5-25.Over 80.Women 37.33760 0.4345367
## 26-30.Over 80.Women 42.12256 0.3071662
## 31-35.Over 80.Women 45.07939 0.4931797
## 36 and over.Over 80.Women 47.83976 0.8749159## Multiple imputation results:
## with(nhc_all, svyby(~TOTLEANH2, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~TOTLEANH2, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 14.34831 0.09324549
## 18.5-25.17-19.Men 16.87768 0.05702826
## 26-30.17-19.Men 19.23751 0.08848227
## 31-35.17-19.Men 20.95953 0.16565792
## 36 and over.17-19.Men 23.88052 0.29779440
## Under 18.5.20-24.Men 14.09554 0.11833470
## 18.5-25.20-24.Men 17.09811 0.06205393
## 26-30.20-24.Men 19.34792 0.09735518
## 31-35.20-24.Men 21.43289 0.15907198
## 36 and over.20-24.Men 25.09021 0.52677895
## Under 18.5.25-29.Men 14.11747 0.18034377
## 18.5-25.25-29.Men 17.09735 0.08762647
## 26-30.25-29.Men 19.18938 0.08003100
## 31-35.25-29.Men 21.44119 0.12522078
## 36 and over.25-29.Men 24.97067 0.28148920
## Under 18.5.30-34.Men 14.16020 0.34761824
## 18.5-25.30-34.Men 17.18292 0.06910879
## 26-30.30-34.Men 19.40112 0.07612244
## 31-35.30-34.Men 21.50430 0.15296492
## 36 and over.30-34.Men 25.11650 0.46697734
## Under 18.5.35-39.Men 14.68329 0.23760718
## 18.5-25.35-39.Men 17.09043 0.09489124
## 26-30.35-39.Men 19.31791 0.08295714
## 31-35.35-39.Men 21.48799 0.11499063
## 36 and over.35-39.Men 24.88621 0.43905034
## Under 18.5.40-44.Men 14.19047 0.31388599
## 18.5-25.40-44.Men 17.02975 0.09059111
## 26-30.40-44.Men 19.23172 0.06493167
## 31-35.40-44.Men 21.49045 0.12744536
## 36 and over.40-44.Men 24.44705 0.28463578
## Under 18.5.45-49.Men 13.30332 0.28674527
## 18.5-25.45-49.Men 17.00412 0.12600767
## 26-30.45-49.Men 19.23206 0.07664280
## 31-35.45-49.Men 21.01557 0.09766395
## 36 and over.45-49.Men 24.82509 0.51921465
## Under 18.5.50-54.Men 13.72862 0.45704930
## 18.5-25.50-54.Men 16.62829 0.13093685
## 26-30.50-54.Men 19.00158 0.09052705
## 31-35.50-54.Men 21.01752 0.12903009
## 36 and over.50-54.Men 23.82277 0.18958115
## Under 18.5.55-59.Men 13.49914 0.39898644
## 18.5-25.55-59.Men 16.43365 0.12009401
## 26-30.55-59.Men 18.78961 0.11741819
## 31-35.55-59.Men 20.92247 0.12528121
## 36 and over.55-59.Men 23.74481 0.33611554
## Under 18.5.60-64.Men 14.20094 0.30651425
## 18.5-25.60-64.Men 16.46554 0.13645687
## 26-30.60-64.Men 18.80660 0.11108040
## 31-35.60-64.Men 20.82609 0.11564187
## 36 and over.60-64.Men 23.64447 0.29234879
## Under 18.5.65-69.Men 13.09273 0.40871340
## 18.5-25.65-69.Men 16.22897 0.16097870
## 26-30.65-69.Men 18.42299 0.08678838
## 31-35.65-69.Men 20.29639 0.12604339
## 36 and over.65-69.Men 23.09945 0.23830858
## Under 18.5.70-74.Men 13.16817 0.38624905
## 18.5-25.70-74.Men 16.33893 0.09930974
## 26-30.70-74.Men 18.28186 0.10462560
## 31-35.70-74.Men 20.06886 0.15948283
## 36 and over.70-74.Men 22.37880 0.30718308
## Under 18.5.75-79.Men 13.80566 0.32678687
## 18.5-25.75-79.Men 15.89434 0.14517249
## 26-30.75-79.Men 18.11431 0.10730487
## 31-35.75-79.Men 19.82563 0.16145091
## 36 and over.75-79.Men 22.64721 0.41276797
## Under 18.5.Over 80.Men 12.71320 0.47875401
## 18.5-25.Over 80.Men 15.67095 0.06516348
## 26-30.Over 80.Men 17.73169 0.08918354
## 31-35.Over 80.Men 19.64951 0.19561727
## 36 and over.Over 80.Men 21.99061 0.58926889
## Under 18.5.17-19.Women 12.32138 0.09300042
## 18.5-25.17-19.Women 14.03642 0.05508032
## 26-30.17-19.Women 15.93308 0.07579449
## 31-35.17-19.Women 17.65640 0.11075435
## 36 and over.17-19.Women 20.57453 0.27424830
## Under 18.5.20-24.Women 12.13889 0.17840290
## 18.5-25.20-24.Women 14.18667 0.08349890
## 26-30.20-24.Women 15.93872 0.09598678
## 31-35.20-24.Women 17.41582 0.16966707
## 36 and over.20-24.Women 20.35120 0.26058654
## Under 18.5.25-29.Women 12.27965 0.14518895
## 18.5-25.25-29.Women 14.15782 0.07770767
## 26-30.25-29.Women 15.96551 0.11358784
## 31-35.25-29.Women 17.81343 0.12346631
## 36 and over.25-29.Women 20.77467 0.25533963
## Under 18.5.30-34.Women 12.36661 0.20863891
## 18.5-25.30-34.Women 14.23666 0.07749112
## 26-30.30-34.Women 15.86433 0.10743800
## 31-35.30-34.Women 17.57569 0.11920396
## 36 and over.30-34.Women 20.35942 0.19753401
## Under 18.5.35-39.Women 12.37324 0.23305038
## 18.5-25.35-39.Women 14.07537 0.08030149
## 26-30.35-39.Women 15.75014 0.10684442
## 31-35.35-39.Women 17.56857 0.13979662
## 36 and over.35-39.Women 20.52469 0.23634499
## Under 18.5.40-44.Women 12.56792 0.23775899
## 18.5-25.40-44.Women 14.13841 0.07901834
## 26-30.40-44.Women 15.79188 0.09157498
## 31-35.40-44.Women 17.58837 0.09935920
## 36 and over.40-44.Women 20.93772 0.24523957
## Under 18.5.45-49.Women 12.23745 0.21334239
## 18.5-25.45-49.Women 14.07299 0.07445876
## 26-30.45-49.Women 15.55799 0.09018330
## 31-35.45-49.Women 17.61859 0.13875025
## 36 and over.45-49.Women 20.51341 0.20974050
## Under 18.5.50-54.Women 12.10242 0.33825983
## 18.5-25.50-54.Women 13.93079 0.09136187
## 26-30.50-54.Women 15.54730 0.08178125
## 31-35.50-54.Women 17.48187 0.09579101
## 36 and over.50-54.Women 20.15299 0.20461691
## Under 18.5.55-59.Women 12.51462 0.38470257
## 18.5-25.55-59.Women 13.67045 0.11195736
## 26-30.55-59.Women 15.26200 0.09608589
## 31-35.55-59.Women 17.25049 0.11435626
## 36 and over.55-59.Women 20.11133 0.22312640
## Under 18.5.60-64.Women 12.29194 0.37008241
## 18.5-25.60-64.Women 13.56121 0.10179112
## 26-30.60-64.Women 15.23968 0.08503346
## 31-35.60-64.Women 17.00099 0.11132263
## 36 and over.60-64.Women 19.84320 0.18184679
## Under 18.5.65-69.Women 12.40077 0.30152517
## 18.5-25.65-69.Women 13.67702 0.09930044
## 26-30.65-69.Women 15.23043 0.10232976
## 31-35.65-69.Women 16.81589 0.11638984
## 36 and over.65-69.Women 19.56158 0.20114550
## Under 18.5.70-74.Women 11.77749 0.14442573
## 18.5-25.70-74.Women 13.53587 0.09977806
## 26-30.70-74.Women 15.10170 0.10961982
## 31-35.70-74.Women 16.84941 0.10503382
## 36 and over.70-74.Women 19.19368 0.25859533
## Under 18.5.75-79.Women 12.56585 0.35285639
## 18.5-25.75-79.Women 13.43510 0.09441970
## 26-30.75-79.Women 15.05436 0.11266903
## 31-35.75-79.Women 16.82659 0.17441761
## 36 and over.75-79.Women 18.99413 0.23661123
## Under 18.5.Over 80.Women 12.26802 0.23795322
## 18.5-25.Over 80.Women 13.49292 0.09396660
## 26-30.Over 80.Women 15.18013 0.07429699
## 31-35.Over 80.Women 16.84888 0.13907604
## 36 and over.Over 80.Women 19.14163 0.31361488## Multiple imputation results:
## with(nhc_all, svyby(~WHRATIO, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~WHRATIO, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 0.3984966 0.002881321
## 18.5-25.17-19.Men 0.4409816 0.001398023
## 26-30.17-19.Men 0.5226767 0.002139614
## 31-35.17-19.Men 0.5945703 0.003800357
## 36 and over.17-19.Men 0.6834812 0.006278701
## Under 18.5.20-24.Men 0.3973772 0.003758393
## 18.5-25.20-24.Men 0.4571188 0.001711868
## 26-30.20-24.Men 0.5296209 0.003103266
## 31-35.20-24.Men 0.6018835 0.003664787
## 36 and over.20-24.Men 0.7132557 0.010221386
## Under 18.5.25-29.Men 0.4070831 0.005817502
## 18.5-25.25-29.Men 0.4714251 0.001847519
## 26-30.25-29.Men 0.5393976 0.002301248
## 31-35.25-29.Men 0.6028365 0.003056028
## 36 and over.25-29.Men 0.7279531 0.009525250
## Under 18.5.30-34.Men 0.4015096 0.005651780
## 18.5-25.30-34.Men 0.4797099 0.002974578
## 26-30.30-34.Men 0.5480114 0.002137190
## 31-35.30-34.Men 0.6074185 0.004428405
## 36 and over.30-34.Men 0.7117793 0.011645674
## Under 18.5.35-39.Men 0.4207906 0.006046308
## 18.5-25.35-39.Men 0.4843660 0.002448650
## 26-30.35-39.Men 0.5497835 0.002322795
## 31-35.35-39.Men 0.6165792 0.002678166
## 36 and over.35-39.Men 0.7167150 0.011372770
## Under 18.5.40-44.Men 0.4134332 0.007219700
## 18.5-25.40-44.Men 0.4889726 0.002159322
## 26-30.40-44.Men 0.5517405 0.002352331
## 31-35.40-44.Men 0.6207323 0.003173391
## 36 and over.40-44.Men 0.7130867 0.007783878
## Under 18.5.45-49.Men 0.4055214 0.015910521
## 18.5-25.45-49.Men 0.4915637 0.003090772
## 26-30.45-49.Men 0.5640988 0.002027485
## 31-35.45-49.Men 0.6255442 0.002809765
## 36 and over.45-49.Men 0.7342015 0.012290808
## Under 18.5.50-54.Men 0.4255695 0.008127732
## 18.5-25.50-54.Men 0.5003934 0.002984262
## 26-30.50-54.Men 0.5654432 0.001959200
## 31-35.50-54.Men 0.6293325 0.003264874
## 36 and over.50-54.Men 0.7143722 0.005286939
## Under 18.5.55-59.Men 0.4358298 0.007868687
## 18.5-25.55-59.Men 0.5100409 0.004011422
## 26-30.55-59.Men 0.5739614 0.002575252
## 31-35.55-59.Men 0.6406391 0.002937792
## 36 and over.55-59.Men 0.7496827 0.011149809
## Under 18.5.60-64.Men 0.4322467 0.004000897
## 18.5-25.60-64.Men 0.5136721 0.002373754
## 26-30.60-64.Men 0.5828547 0.002306355
## 31-35.60-64.Men 0.6508029 0.002752332
## 36 and over.60-64.Men 0.7359649 0.007626167
## Under 18.5.65-69.Men 0.4240322 0.008047478
## 18.5-25.65-69.Men 0.5235763 0.003095647
## 26-30.65-69.Men 0.5922231 0.002572474
## 31-35.65-69.Men 0.6543595 0.004302752
## 36 and over.65-69.Men 0.7319389 0.005325657
## Under 18.5.70-74.Men 0.4692159 0.008672045
## 18.5-25.70-74.Men 0.5221578 0.003741226
## 26-30.70-74.Men 0.5972973 0.002469399
## 31-35.70-74.Men 0.6604858 0.004637149
## 36 and over.70-74.Men 0.7579582 0.012031851
## Under 18.5.75-79.Men 0.4319101 0.022362008
## 18.5-25.75-79.Men 0.5372985 0.003675895
## 26-30.75-79.Men 0.6015823 0.003257729
## 31-35.75-79.Men 0.6642824 0.004662526
## 36 and over.75-79.Men 0.7307279 0.012031568
## Under 18.5.Over 80.Men 0.4376635 0.013256925
## 18.5-25.Over 80.Men 0.5400620 0.002354912
## 26-30.Over 80.Men 0.6070902 0.002310217
## 31-35.Over 80.Men 0.6724158 0.006080407
## 36 and over.Over 80.Men 0.7531562 0.014856153
## Under 18.5.17-19.Women 0.4169379 0.002807607
## 18.5-25.17-19.Women 0.4691344 0.001988966
## 26-30.17-19.Women 0.5509142 0.004127687
## 31-35.17-19.Women 0.6164465 0.004350320
## 36 and over.17-19.Women 0.7109713 0.009674851
## Under 18.5.20-24.Women 0.4160489 0.005767242
## 18.5-25.20-24.Women 0.4748574 0.002369217
## 26-30.20-24.Women 0.5463726 0.003897538
## 31-35.20-24.Women 0.6273439 0.007171943
## 36 and over.20-24.Women 0.7229744 0.008306988
## Under 18.5.25-29.Women 0.4190490 0.006837600
## 18.5-25.25-29.Women 0.4775536 0.002618236
## 26-30.25-29.Women 0.5508013 0.003322843
## 31-35.25-29.Women 0.6242511 0.005430168
## 36 and over.25-29.Women 0.7162364 0.011107962
## Under 18.5.30-34.Women 0.4163913 0.005961016
## 18.5-25.30-34.Women 0.4775656 0.003035502
## 26-30.30-34.Women 0.5551574 0.002400382
## 31-35.30-34.Women 0.6112117 0.005526435
## 36 and over.30-34.Women 0.7027236 0.006672518
## Under 18.5.35-39.Women 0.4213300 0.007789811
## 18.5-25.35-39.Women 0.4801206 0.002800694
## 26-30.35-39.Women 0.5556045 0.003039456
## 31-35.35-39.Women 0.6229062 0.004483346
## 36 and over.35-39.Women 0.7195230 0.008250861
## Under 18.5.40-44.Women 0.4324449 0.003516943
## 18.5-25.40-44.Women 0.4880790 0.003557898
## 26-30.40-44.Women 0.5564074 0.003334124
## 31-35.40-44.Women 0.6270651 0.003566713
## 36 and over.40-44.Women 0.7195701 0.008170893
## Under 18.5.45-49.Women 0.4283870 0.006107028
## 18.5-25.45-49.Women 0.4879708 0.002544230
## 26-30.45-49.Women 0.5655598 0.003360282
## 31-35.45-49.Women 0.6345233 0.004469850
## 36 and over.45-49.Women 0.7243113 0.007272505
## Under 18.5.50-54.Women 0.4239530 0.011236114
## 18.5-25.50-54.Women 0.4955793 0.002716820
## 26-30.50-54.Women 0.5804340 0.002862683
## 31-35.50-54.Women 0.6426012 0.004288085
## 36 and over.50-54.Women 0.7282654 0.006283043
## Under 18.5.55-59.Women 0.4423281 0.004850685
## 18.5-25.55-59.Women 0.4978236 0.004871125
## 26-30.55-59.Women 0.5766227 0.004796855
## 31-35.55-59.Women 0.6530112 0.004960039
## 36 and over.55-59.Women 0.7347946 0.008448156
## Under 18.5.60-64.Women 0.4273329 0.005837530
## 18.5-25.60-64.Women 0.5084911 0.003707411
## 26-30.60-64.Women 0.5868222 0.003060316
## 31-35.60-64.Women 0.6529737 0.004198167
## 36 and over.60-64.Women 0.7363392 0.006170931
## Under 18.5.65-69.Women 0.4206361 0.010809230
## 18.5-25.65-69.Women 0.5234284 0.004290669
## 26-30.65-69.Women 0.5900586 0.003642881
## 31-35.65-69.Women 0.6595351 0.004843789
## 36 and over.65-69.Women 0.7423080 0.005606810
## Under 18.5.70-74.Women 0.4589118 0.011274421
## 18.5-25.70-74.Women 0.5252136 0.005059840
## 26-30.70-74.Women 0.6083435 0.005386057
## 31-35.70-74.Women 0.6707441 0.003600362
## 36 and over.70-74.Women 0.7494196 0.009743614
## Under 18.5.75-79.Women 0.4463343 0.006097082
## 18.5-25.75-79.Women 0.5307452 0.005546982
## 26-30.75-79.Women 0.6115258 0.004805592
## 31-35.75-79.Women 0.6616960 0.006609453
## 36 and over.75-79.Women 0.7362129 0.009832806
## Under 18.5.Over 80.Women 0.4330611 0.009631482
## 18.5-25.Over 80.Women 0.5433682 0.003987085
## 26-30.Over 80.Women 0.6211068 0.003019416
## 31-35.Over 80.Women 0.6771605 0.004390929
## 36 and over.Over 80.Women 0.7384849 0.012928901## Multiple imputation results:
## with(nhc_all, svyby(~RALEANH2, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~RALEANH2, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 902.2532 10.155340
## 18.5-25.17-19.Men 1095.5254 6.904152
## 26-30.17-19.Men 1245.4267 10.807655
## 31-35.17-19.Men 1332.6523 17.957507
## 36 and over.17-19.Men 1505.0202 27.127496
## Under 18.5.20-24.Men 868.4373 16.598880
## 18.5-25.20-24.Men 1126.5614 7.620089
## 26-30.20-24.Men 1276.3202 11.142923
## 31-35.20-24.Men 1406.8535 21.011702
## 36 and over.20-24.Men 1598.3835 39.087121
## Under 18.5.25-29.Men 890.3169 25.142590
## 18.5-25.25-29.Men 1129.7929 9.495058
## 26-30.25-29.Men 1287.6748 9.487874
## 31-35.25-29.Men 1418.1510 15.920919
## 36 and over.25-29.Men 1578.2865 35.347802
## Under 18.5.30-34.Men 939.5958 47.317213
## 18.5-25.30-34.Men 1147.4989 9.464346
## 26-30.30-34.Men 1292.4729 10.185316
## 31-35.30-34.Men 1443.5842 16.556867
## 36 and over.30-34.Men 1629.8223 38.146583
## Under 18.5.35-39.Men 901.1337 24.596243
## 18.5-25.35-39.Men 1135.5641 10.688120
## 26-30.35-39.Men 1294.5145 12.787157
## 31-35.35-39.Men 1429.6124 14.020460
## 36 and over.35-39.Men 1614.5679 29.961143
## Under 18.5.40-44.Men 939.1302 41.373516
## 18.5-25.40-44.Men 1110.2926 10.781285
## 26-30.40-44.Men 1286.7200 8.519431
## 31-35.40-44.Men 1415.8214 13.514168
## 36 and over.40-44.Men 1588.8369 26.921509
## Under 18.5.45-49.Men 842.2843 21.313844
## 18.5-25.45-49.Men 1118.7540 13.462362
## 26-30.45-49.Men 1280.7213 10.034835
## 31-35.45-49.Men 1380.0575 15.739622
## 36 and over.45-49.Men 1589.6714 34.621494
## Under 18.5.50-54.Men 876.0000 44.343963
## 18.5-25.50-54.Men 1063.6438 12.868929
## 26-30.50-54.Men 1242.6062 10.377306
## 31-35.50-54.Men 1363.4847 17.540685
## 36 and over.50-54.Men 1512.0210 20.504041
## Under 18.5.55-59.Men 819.3492 38.479458
## 18.5-25.55-59.Men 1050.3198 14.960478
## 26-30.55-59.Men 1217.2021 11.141190
## 31-35.55-59.Men 1342.9767 12.827216
## 36 and over.55-59.Men 1482.8175 26.738665
## Under 18.5.60-64.Men 904.2693 36.856698
## 18.5-25.60-64.Men 1030.2480 12.827307
## 26-30.60-64.Men 1198.2883 10.694208
## 31-35.60-64.Men 1325.5213 13.284031
## 36 and over.60-64.Men 1489.2299 25.672006
## Under 18.5.65-69.Men 896.1190 49.025422
## 18.5-25.65-69.Men 1014.9848 15.129870
## 26-30.65-69.Men 1149.1111 10.161869
## 31-35.65-69.Men 1250.2623 13.885645
## 36 and over.65-69.Men 1405.0577 22.662118
## Under 18.5.70-74.Men 811.6879 26.675750
## 18.5-25.70-74.Men 1004.5753 9.394083
## 26-30.70-74.Men 1134.3799 12.902645
## 31-35.70-74.Men 1223.9200 18.826786
## 36 and over.70-74.Men 1331.3798 30.492910
## Under 18.5.75-79.Men 928.5114 45.889582
## 18.5-25.75-79.Men 960.5220 19.025305
## 26-30.75-79.Men 1113.4330 12.783845
## 31-35.75-79.Men 1183.1601 21.085871
## 36 and over.75-79.Men 1373.5688 52.177886
## Under 18.5.Over 80.Men 741.6107 43.277415
## 18.5-25.Over 80.Men 920.7766 8.408617
## 26-30.Over 80.Men 1048.8228 9.109095
## 31-35.Over 80.Men 1141.3515 20.073109
## 36 and over.Over 80.Men 1265.9032 30.743480
## Under 18.5.17-19.Women 646.7637 9.787347
## 18.5-25.17-19.Women 733.1666 4.964662
## 26-30.17-19.Women 838.8701 7.888221
## 31-35.17-19.Women 944.0528 10.823533
## 36 and over.17-19.Women 1107.6974 17.653132
## Under 18.5.20-24.Women 625.5727 13.560613
## 18.5-25.20-24.Women 736.9966 6.607966
## 26-30.20-24.Women 853.1052 9.787407
## 31-35.20-24.Women 920.7139 18.337416
## 36 and over.20-24.Women 1081.1484 21.366008
## Under 18.5.25-29.Women 664.8396 13.307387
## 18.5-25.25-29.Women 730.3943 8.116419
## 26-30.25-29.Women 840.1309 9.549017
## 31-35.25-29.Women 954.8131 13.156083
## 36 and over.25-29.Women 1109.8564 18.905827
## Under 18.5.30-34.Women 640.4755 21.059258
## 18.5-25.30-34.Women 742.9819 7.566305
## 26-30.30-34.Women 837.1052 10.562220
## 31-35.30-34.Women 927.9410 10.654580
## 36 and over.30-34.Women 1059.2424 17.510658
## Under 18.5.35-39.Women 680.9595 15.229170
## 18.5-25.35-39.Women 737.4533 9.826455
## 26-30.35-39.Women 831.4220 10.038350
## 31-35.35-39.Women 947.1440 15.198848
## 36 and over.35-39.Women 1086.8679 17.078039
## Under 18.5.40-44.Women 654.3274 15.309425
## 18.5-25.40-44.Women 732.5673 5.370477
## 26-30.40-44.Women 841.3664 9.917418
## 31-35.40-44.Women 938.5779 12.742241
## 36 and over.40-44.Women 1113.9814 21.369330
## Under 18.5.45-49.Women 646.3731 26.391673
## 18.5-25.45-49.Women 734.6684 7.647533
## 26-30.45-49.Women 822.4282 8.766956
## 31-35.45-49.Women 940.0723 12.493770
## 36 and over.45-49.Women 1077.4962 17.518477
## Under 18.5.50-54.Women 635.0416 34.226557
## 18.5-25.50-54.Women 716.5947 7.155887
## 26-30.50-54.Women 809.5852 8.008362
## 31-35.50-54.Women 913.5487 10.427031
## 36 and over.50-54.Women 1057.3658 14.262610
## Under 18.5.55-59.Women 706.4626 9.644690
## 18.5-25.55-59.Women 703.7217 10.917836
## 26-30.55-59.Women 799.7886 10.744181
## 31-35.55-59.Women 913.6362 12.215059
## 36 and over.55-59.Women 1032.5673 17.587326
## Under 18.5.60-64.Women 617.9123 12.537367
## 18.5-25.60-64.Women 697.5902 8.625430
## 26-30.60-64.Women 782.8276 9.849148
## 31-35.60-64.Women 896.9846 14.203630
## 36 and over.60-64.Women 1034.7592 16.292280
## Under 18.5.65-69.Women 653.3302 28.136640
## 18.5-25.65-69.Women 706.7095 9.053040
## 26-30.65-69.Women 778.8077 8.856030
## 31-35.65-69.Women 857.9585 13.484518
## 36 and over.65-69.Women 1010.4967 18.557533
## Under 18.5.70-74.Women 628.7195 12.043957
## 18.5-25.70-74.Women 697.3904 8.967405
## 26-30.70-74.Women 778.0764 8.908727
## 31-35.70-74.Women 880.7671 13.032561
## 36 and over.70-74.Women 993.1256 22.846223
## Under 18.5.75-79.Women 639.3546 36.859944
## 18.5-25.75-79.Women 691.5005 10.607516
## 26-30.75-79.Women 750.7653 13.450404
## 31-35.75-79.Women 869.4390 19.331542
## 36 and over.75-79.Women 966.8471 26.173804
## Under 18.5.Over 80.Women 631.7664 25.616035
## 18.5-25.Over 80.Women 686.3762 9.625247
## 26-30.Over 80.Women 756.0610 9.848658
## 31-35.Over 80.Women 827.9587 16.110001
## 36 and over.Over 80.Women 941.9910 23.695581## Multiple imputation results:
## with(nhc_all, svyby(~LALEANH2, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~LALEANH2, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 857.8388 9.545570
## 18.5-25.17-19.Men 1039.0126 6.435894
## 26-30.17-19.Men 1183.4158 10.066723
## 31-35.17-19.Men 1263.1244 16.615217
## 36 and over.17-19.Men 1450.9682 25.615055
## Under 18.5.20-24.Men 837.9300 18.399191
## 18.5-25.20-24.Men 1064.2976 8.127188
## 26-30.20-24.Men 1213.6030 11.048761
## 31-35.20-24.Men 1354.9156 18.457525
## 36 and over.20-24.Men 1550.2598 39.778459
## Under 18.5.25-29.Men 844.2543 30.160138
## 18.5-25.25-29.Men 1076.5608 9.788636
## 26-30.25-29.Men 1230.0122 10.866694
## 31-35.25-29.Men 1369.6921 16.183033
## 36 and over.25-29.Men 1533.5368 37.397479
## Under 18.5.30-34.Men 867.2907 50.375339
## 18.5-25.30-34.Men 1096.7990 9.446311
## 26-30.30-34.Men 1246.1687 9.106420
## 31-35.30-34.Men 1375.1653 16.062095
## 36 and over.30-34.Men 1570.9767 41.997726
## Under 18.5.35-39.Men 833.5299 19.578682
## 18.5-25.35-39.Men 1084.1245 10.056851
## 26-30.35-39.Men 1239.8217 12.152269
## 31-35.35-39.Men 1375.0565 14.131811
## 36 and over.35-39.Men 1571.2001 32.190005
## Under 18.5.40-44.Men 905.1814 45.400862
## 18.5-25.40-44.Men 1071.4254 10.187030
## 26-30.40-44.Men 1234.7843 8.717696
## 31-35.40-44.Men 1374.9869 15.452277
## 36 and over.40-44.Men 1541.4340 26.010268
## Under 18.5.45-49.Men 796.0043 12.821349
## 18.5-25.45-49.Men 1061.7516 13.223148
## 26-30.45-49.Men 1226.6705 10.036702
## 31-35.45-49.Men 1322.2905 15.625627
## 36 and over.45-49.Men 1544.7150 40.741536
## Under 18.5.50-54.Men 847.5363 34.004277
## 18.5-25.50-54.Men 1016.7381 12.306266
## 26-30.50-54.Men 1195.4493 9.727915
## 31-35.50-54.Men 1317.9241 15.029084
## 36 and over.50-54.Men 1480.5479 17.746317
## Under 18.5.55-59.Men 799.6927 43.975541
## 18.5-25.55-59.Men 1003.7995 13.864161
## 26-30.55-59.Men 1158.3446 11.816033
## 31-35.55-59.Men 1296.1320 13.680992
## 36 and over.55-59.Men 1425.6818 28.814820
## Under 18.5.60-64.Men 836.5880 36.948725
## 18.5-25.60-64.Men 987.7020 13.612347
## 26-30.60-64.Men 1145.3879 11.069011
## 31-35.60-64.Men 1277.7144 15.374156
## 36 and over.60-64.Men 1450.8616 26.580610
## Under 18.5.65-69.Men 798.3684 75.775930
## 18.5-25.65-69.Men 974.2526 15.522624
## 26-30.65-69.Men 1104.2871 9.497744
## 31-35.65-69.Men 1206.6002 15.742094
## 36 and over.65-69.Men 1379.2788 22.058375
## Under 18.5.70-74.Men 795.0167 30.440070
## 18.5-25.70-74.Men 948.7070 11.150500
## 26-30.70-74.Men 1083.4015 12.568709
## 31-35.70-74.Men 1176.2801 20.082037
## 36 and over.70-74.Men 1261.9665 32.020201
## Under 18.5.75-79.Men 841.1736 31.743314
## 18.5-25.75-79.Men 926.8365 18.394477
## 26-30.75-79.Men 1073.3092 15.617142
## 31-35.75-79.Men 1155.5534 20.603001
## 36 and over.75-79.Men 1347.9034 32.280232
## Under 18.5.Over 80.Men 705.7823 26.674893
## 18.5-25.Over 80.Men 874.1556 8.895511
## 26-30.Over 80.Men 998.0591 9.603968
## 31-35.Over 80.Men 1084.8964 18.494990
## 36 and over.Over 80.Men 1212.4821 46.416325
## Under 18.5.17-19.Women 608.0165 9.565186
## 18.5-25.17-19.Women 692.8709 5.020494
## 26-30.17-19.Women 794.0354 7.767042
## 31-35.17-19.Women 905.5960 10.272602
## 36 and over.17-19.Women 1068.5928 21.223259
## Under 18.5.20-24.Women 580.5616 17.564307
## 18.5-25.20-24.Women 694.8403 6.268368
## 26-30.20-24.Women 804.1114 8.944232
## 31-35.20-24.Women 886.8582 17.125449
## 36 and over.20-24.Women 1043.1105 21.237083
## Under 18.5.25-29.Women 629.8199 15.253437
## 18.5-25.25-29.Women 690.7734 7.822685
## 26-30.25-29.Women 805.5393 12.055319
## 31-35.25-29.Women 910.9169 13.471531
## 36 and over.25-29.Women 1069.3444 21.630751
## Under 18.5.30-34.Women 605.3011 23.894263
## 18.5-25.30-34.Women 698.7239 6.531829
## 26-30.30-34.Women 793.1287 9.947349
## 31-35.30-34.Women 890.3975 10.819315
## 36 and over.30-34.Women 1024.5975 17.780820
## Under 18.5.35-39.Women 611.8746 20.137067
## 18.5-25.35-39.Women 692.6310 9.608194
## 26-30.35-39.Women 787.0892 9.666963
## 31-35.35-39.Women 897.5912 11.496782
## 36 and over.35-39.Women 1054.8180 17.433423
## Under 18.5.40-44.Women 607.3923 16.440147
## 18.5-25.40-44.Women 689.0865 5.773303
## 26-30.40-44.Women 796.4435 8.924319
## 31-35.40-44.Women 889.3975 11.882550
## 36 and over.40-44.Women 1070.1780 21.038775
## Under 18.5.45-49.Women 619.9381 24.688212
## 18.5-25.45-49.Women 687.6427 7.615782
## 26-30.45-49.Women 777.7711 9.328571
## 31-35.45-49.Women 898.6734 12.715643
## 36 and over.45-49.Women 1048.1408 18.261450
## Under 18.5.50-54.Women 588.0167 29.016787
## 18.5-25.50-54.Women 682.6559 7.113089
## 26-30.50-54.Women 765.0834 7.945199
## 31-35.50-54.Women 879.6747 9.975399
## 36 and over.50-54.Women 1017.2950 15.051955
## Under 18.5.55-59.Women 652.0214 11.260315
## 18.5-25.55-59.Women 667.5630 10.352466
## 26-30.55-59.Women 760.4413 8.195179
## 31-35.55-59.Women 883.4914 12.084468
## 36 and over.55-59.Women 1016.4109 18.144417
## Under 18.5.60-64.Women 586.4738 51.772383
## 18.5-25.60-64.Women 658.3102 8.110082
## 26-30.60-64.Women 750.9185 9.369375
## 31-35.60-64.Women 852.8347 13.694292
## 36 and over.60-64.Women 1009.4724 17.013526
## Under 18.5.65-69.Women 604.2587 27.541214
## 18.5-25.65-69.Women 663.2220 9.038433
## 26-30.65-69.Women 739.3348 10.413286
## 31-35.65-69.Women 832.9343 10.866340
## 36 and over.65-69.Women 969.1454 15.605309
## Under 18.5.70-74.Women 572.9292 11.956339
## 18.5-25.70-74.Women 661.7472 9.000418
## 26-30.70-74.Women 743.0604 8.382014
## 31-35.70-74.Women 840.9566 11.584468
## 36 and over.70-74.Women 971.9392 25.669321
## Under 18.5.75-79.Women 628.1206 33.468907
## 18.5-25.75-79.Women 643.1583 11.495311
## 26-30.75-79.Women 708.9922 11.632746
## 31-35.75-79.Women 835.9256 18.200009
## 36 and over.75-79.Women 957.6617 24.176587
## Under 18.5.Over 80.Women 605.1687 16.914298
## 18.5-25.Over 80.Women 645.6543 7.876179
## 26-30.Over 80.Women 732.8951 8.848141
## 31-35.Over 80.Women 796.3521 17.467471
## 36 and over.Over 80.Women 914.9342 26.820425## Multiple imputation results:
## with(nhc_all, svyby(~RLLEANH2, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~RLLEANH2, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 2373.808 33.72851
## 18.5-25.17-19.Men 2849.344 13.95307
## 26-30.17-19.Men 3258.923 17.11929
## 31-35.17-19.Men 3553.758 38.09140
## 36 and over.17-19.Men 4110.644 68.95553
## Under 18.5.20-24.Men 2340.495 27.88612
## 18.5-25.20-24.Men 2822.866 15.68086
## 26-30.20-24.Men 3199.334 22.61614
## 31-35.20-24.Men 3549.423 29.92854
## 36 and over.20-24.Men 4276.138 114.34493
## Under 18.5.25-29.Men 2272.454 37.24749
## 18.5-25.25-29.Men 2785.394 20.23055
## 26-30.25-29.Men 3143.554 18.44335
## 31-35.25-29.Men 3560.851 27.70248
## 36 and over.25-29.Men 4219.319 65.24518
## Under 18.5.30-34.Men 2259.587 76.65042
## 18.5-25.30-34.Men 2784.291 20.18442
## 26-30.30-34.Men 3163.928 15.66689
## 31-35.30-34.Men 3498.632 36.97783
## 36 and over.30-34.Men 4218.942 103.52356
## Under 18.5.35-39.Men 2455.128 104.72551
## 18.5-25.35-39.Men 2754.193 25.40653
## 26-30.35-39.Men 3136.084 17.58674
## 31-35.35-39.Men 3456.578 25.02840
## 36 and over.35-39.Men 4078.319 95.18313
## Under 18.5.40-44.Men 2321.391 69.36526
## 18.5-25.40-44.Men 2759.032 24.16924
## 26-30.40-44.Men 3085.922 18.04292
## 31-35.40-44.Men 3443.133 31.32527
## 36 and over.40-44.Men 3938.890 60.39687
## Under 18.5.45-49.Men 1984.340 69.60387
## 18.5-25.45-49.Men 2735.324 25.13480
## 26-30.45-49.Men 3060.681 19.40611
## 31-35.45-49.Men 3343.285 26.71286
## 36 and over.45-49.Men 3967.600 94.50230
## Under 18.5.50-54.Men 2112.655 112.54609
## 18.5-25.50-54.Men 2636.744 26.74351
## 26-30.50-54.Men 3016.210 20.57867
## 31-35.50-54.Men 3320.160 29.38880
## 36 and over.50-54.Men 3780.609 45.54410
## Under 18.5.55-59.Men 2200.292 75.06128
## 18.5-25.55-59.Men 2597.205 29.28507
## 26-30.55-59.Men 2963.438 23.18570
## 31-35.55-59.Men 3270.844 37.66624
## 36 and over.55-59.Men 3743.503 66.49442
## Under 18.5.60-64.Men 2286.113 92.16188
## 18.5-25.60-64.Men 2570.592 27.94006
## 26-30.60-64.Men 2952.693 24.96621
## 31-35.60-64.Men 3227.743 27.32951
## 36 and over.60-64.Men 3708.648 63.18615
## Under 18.5.65-69.Men 2011.231 125.48726
## 18.5-25.65-69.Men 2551.922 36.03587
## 26-30.65-69.Men 2870.760 18.26817
## 31-35.65-69.Men 3154.127 30.32723
## 36 and over.65-69.Men 3550.560 44.14833
## Under 18.5.70-74.Men 1939.770 110.96541
## 18.5-25.70-74.Men 2542.556 20.77112
## 26-30.70-74.Men 2818.002 26.30640
## 31-35.70-74.Men 3119.615 36.43456
## 36 and over.70-74.Men 3405.440 86.55695
## Under 18.5.75-79.Men 2082.185 74.78803
## 18.5-25.75-79.Men 2455.496 34.13812
## 26-30.75-79.Men 2824.368 23.51762
## 31-35.75-79.Men 3071.548 36.63080
## 36 and over.75-79.Men 3621.716 87.14210
## Under 18.5.Over 80.Men 1988.187 121.87491
## 18.5-25.Over 80.Men 2405.564 18.47854
## 26-30.Over 80.Men 2731.838 18.38589
## 31-35.Over 80.Men 3055.952 44.70655
## 36 and over.Over 80.Men 3445.446 149.06949
## Under 18.5.17-19.Women 1965.731 21.47435
## 18.5-25.17-19.Women 2296.621 11.58288
## 26-30.17-19.Women 2695.303 16.14573
## 31-35.17-19.Women 3054.272 30.16767
## 36 and over.17-19.Women 3655.644 64.60808
## Under 18.5.20-24.Women 1900.159 45.71528
## 18.5-25.20-24.Women 2312.884 19.05754
## 26-30.20-24.Women 2638.778 24.82515
## 31-35.20-24.Women 2923.765 41.90520
## 36 and over.20-24.Women 3519.345 59.96402
## Under 18.5.25-29.Women 1934.039 40.06873
## 18.5-25.25-29.Women 2295.305 20.38695
## 26-30.25-29.Women 2626.602 24.29286
## 31-35.25-29.Women 2991.550 30.06202
## 36 and over.25-29.Women 3586.524 56.31343
## Under 18.5.30-34.Women 1942.390 50.99399
## 18.5-25.30-34.Women 2297.828 16.27500
## 26-30.30-34.Women 2587.081 24.63127
## 31-35.30-34.Women 2927.707 25.82063
## 36 and over.30-34.Women 3498.960 42.38781
## Under 18.5.35-39.Women 1853.246 68.47853
## 18.5-25.35-39.Women 2259.504 20.26075
## 26-30.35-39.Women 2554.473 24.95747
## 31-35.35-39.Women 2857.373 33.43786
## 36 and over.35-39.Women 3494.054 50.63304
## Under 18.5.40-44.Women 1911.736 57.84822
## 18.5-25.40-44.Women 2228.463 22.21882
## 26-30.40-44.Women 2540.695 16.87876
## 31-35.40-44.Women 2874.968 24.65661
## 36 and over.40-44.Women 3511.389 45.66203
## Under 18.5.45-49.Women 1895.230 53.54643
## 18.5-25.45-49.Women 2202.728 21.00647
## 26-30.45-49.Women 2483.793 21.39026
## 31-35.45-49.Women 2852.021 31.34034
## 36 and over.45-49.Women 3449.389 43.71758
## Under 18.5.50-54.Women 1820.387 88.02606
## 18.5-25.50-54.Women 2156.883 25.09116
## 26-30.50-54.Women 2453.156 14.73415
## 31-35.50-54.Women 2771.471 26.39734
## 36 and over.50-54.Women 3288.544 46.74194
## Under 18.5.55-59.Women 1944.189 146.03373
## 18.5-25.55-59.Women 2104.715 24.32932
## 26-30.55-59.Women 2391.030 24.44870
## 31-35.55-59.Women 2706.163 37.76286
## 36 and over.55-59.Women 3319.586 50.23812
## Under 18.5.60-64.Women 2018.677 140.31110
## 18.5-25.60-64.Women 2074.853 22.44787
## 26-30.60-64.Women 2386.899 21.57738
## 31-35.60-64.Women 2665.722 24.77366
## 36 and over.60-64.Women 3288.685 38.54314
## Under 18.5.65-69.Women 1923.024 119.73982
## 18.5-25.65-69.Women 2081.859 24.67998
## 26-30.65-69.Women 2365.667 23.27448
## 31-35.65-69.Women 2641.739 26.60555
## 36 and over.65-69.Women 3215.263 51.27665
## Under 18.5.70-74.Women 1696.228 59.80173
## 18.5-25.70-74.Women 2043.601 21.34135
## 26-30.70-74.Women 2335.493 25.45292
## 31-35.70-74.Women 2651.441 26.60358
## 36 and over.70-74.Women 3106.325 46.02973
## Under 18.5.75-79.Women 1919.448 85.03953
## 18.5-25.75-79.Women 2059.162 22.31726
## 26-30.75-79.Women 2306.238 24.58262
## 31-35.75-79.Women 2683.740 40.15781
## 36 and over.75-79.Women 3089.920 72.99048
## Under 18.5.Over 80.Women 1911.589 59.91553
## 18.5-25.Over 80.Women 2056.282 22.43259
## 26-30.Over 80.Women 2344.790 16.94276
## 31-35.Over 80.Women 2629.904 34.14187
## 36 and over.Over 80.Women 3296.385 119.63646## Multiple imputation results:
## with(nhc_all, svyby(~LLLEANH2, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~LLLEANH2, ~BMI.Group +
## Age.Group + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 2350.153 36.84369
## 18.5-25.17-19.Men 2800.092 13.27288
## 26-30.17-19.Men 3207.957 16.41918
## 31-35.17-19.Men 3489.043 37.79154
## 36 and over.17-19.Men 4080.846 66.69529
## Under 18.5.20-24.Men 2322.429 29.10689
## 18.5-25.20-24.Men 2781.142 16.02371
## 26-30.20-24.Men 3151.751 23.61237
## 31-35.20-24.Men 3490.546 29.35418
## 36 and over.20-24.Men 4205.091 105.68622
## Under 18.5.25-29.Men 2248.853 29.83673
## 18.5-25.25-29.Men 2738.054 20.87086
## 26-30.25-29.Men 3085.982 18.03234
## 31-35.25-29.Men 3521.355 27.46530
## 36 and over.25-29.Men 4135.974 58.12628
## Under 18.5.30-34.Men 2223.584 74.66109
## 18.5-25.30-34.Men 2728.597 21.51052
## 26-30.30-34.Men 3116.399 14.62207
## 31-35.30-34.Men 3459.203 32.65692
## 36 and over.30-34.Men 4136.242 90.93103
## Under 18.5.35-39.Men 2442.656 92.27046
## 18.5-25.35-39.Men 2708.433 24.15974
## 26-30.35-39.Men 3087.920 18.42897
## 31-35.35-39.Men 3424.370 23.36573
## 36 and over.35-39.Men 4009.834 91.24625
## Under 18.5.40-44.Men 2276.116 76.24649
## 18.5-25.40-44.Men 2712.404 24.11557
## 26-30.40-44.Men 3048.598 18.52104
## 31-35.40-44.Men 3413.363 28.10035
## 36 and over.40-44.Men 3879.448 56.02048
## Under 18.5.45-49.Men 2039.019 16.07502
## 18.5-25.45-49.Men 2678.422 25.20111
## 26-30.45-49.Men 3014.877 20.17379
## 31-35.45-49.Men 3291.086 28.15585
## 36 and over.45-49.Men 3936.817 89.90867
## Under 18.5.50-54.Men 2070.966 131.00110
## 18.5-25.50-54.Men 2596.839 25.45322
## 26-30.50-54.Men 2977.299 18.53021
## 31-35.50-54.Men 3281.051 27.53716
## 36 and over.50-54.Men 3706.982 42.72074
## Under 18.5.55-59.Men 2141.960 90.50557
## 18.5-25.55-59.Men 2563.881 28.45431
## 26-30.55-59.Men 2924.623 23.23770
## 31-35.55-59.Men 3230.490 32.21203
## 36 and over.55-59.Men 3687.144 61.98748
## Under 18.5.60-64.Men 2223.670 53.64325
## 18.5-25.60-64.Men 2533.656 29.76645
## 26-30.60-64.Men 2900.147 22.72307
## 31-35.60-64.Men 3187.651 23.58508
## 36 and over.60-64.Men 3639.671 60.20263
## Under 18.5.65-69.Men 1978.278 119.55043
## 18.5-25.65-69.Men 2500.472 37.25460
## 26-30.65-69.Men 2836.753 18.87281
## 31-35.65-69.Men 3094.730 27.20908
## 36 and over.65-69.Men 3572.380 51.26383
## Under 18.5.70-74.Men 1906.301 181.45531
## 18.5-25.70-74.Men 2483.895 21.32891
## 26-30.70-74.Men 2773.681 26.67089
## 31-35.70-74.Men 3082.618 37.80219
## 36 and over.70-74.Men 3420.457 96.18830
## Under 18.5.75-79.Men 2113.429 84.94090
## 18.5-25.75-79.Men 2386.520 29.75111
## 26-30.75-79.Men 2777.542 21.24319
## 31-35.75-79.Men 3062.566 36.24769
## 36 and over.75-79.Men 3617.141 91.46448
## Under 18.5.Over 80.Men 1961.661 113.74472
## 18.5-25.Over 80.Men 2362.186 18.58361
## 26-30.Over 80.Men 2717.604 19.48712
## 31-35.Over 80.Men 3048.600 48.75948
## 36 and over.Over 80.Men 3344.289 130.70094
## Under 18.5.17-19.Women 1943.891 21.83018
## 18.5-25.17-19.Women 2263.349 11.82126
## 26-30.17-19.Women 2647.036 16.62343
## 31-35.17-19.Women 3008.596 28.71378
## 36 and over.17-19.Women 3596.911 62.08829
## Under 18.5.20-24.Women 1866.785 47.99875
## 18.5-25.20-24.Women 2274.380 19.40400
## 26-30.20-24.Women 2594.893 23.99174
## 31-35.20-24.Women 2899.645 38.64578
## 36 and over.20-24.Women 3465.767 57.27795
## Under 18.5.25-29.Women 1866.486 38.17255
## 18.5-25.25-29.Women 2263.358 19.98602
## 26-30.25-29.Women 2597.019 27.84305
## 31-35.25-29.Women 2948.661 30.85253
## 36 and over.25-29.Women 3545.233 49.44479
## Under 18.5.30-34.Women 1901.294 45.09585
## 18.5-25.30-34.Women 2247.210 16.16098
## 26-30.30-34.Women 2544.613 24.83687
## 31-35.30-34.Women 2890.133 27.23412
## 36 and over.30-34.Women 3475.975 42.40403
## Under 18.5.35-39.Women 1842.993 69.44202
## 18.5-25.35-39.Women 2223.555 18.36666
## 26-30.35-39.Women 2521.440 25.21538
## 31-35.35-39.Women 2814.445 30.89789
## 36 and over.35-39.Women 3446.184 52.46191
## Under 18.5.40-44.Women 1919.745 62.63826
## 18.5-25.40-44.Women 2198.624 21.16001
## 26-30.40-44.Women 2505.470 17.26148
## 31-35.40-44.Women 2843.074 24.73268
## 36 and over.40-44.Women 3463.541 48.04165
## Under 18.5.45-49.Women 1882.546 61.03496
## 18.5-25.45-49.Women 2171.752 19.95786
## 26-30.45-49.Women 2450.856 23.62189
## 31-35.45-49.Women 2811.585 29.50678
## 36 and over.45-49.Women 3406.140 43.64962
## Under 18.5.50-54.Women 1830.361 86.42001
## 18.5-25.50-54.Women 2121.602 21.68461
## 26-30.50-54.Women 2420.848 16.82577
## 31-35.50-54.Women 2711.866 26.92178
## 36 and over.50-54.Women 3241.857 49.12806
## Under 18.5.55-59.Women 1847.961 160.97197
## 18.5-25.55-59.Women 2054.453 25.37685
## 26-30.55-59.Women 2344.303 25.03780
## 31-35.55-59.Women 2682.837 33.47038
## 36 and over.55-59.Women 3277.124 50.94305
## Under 18.5.60-64.Women 1947.885 193.27271
## 18.5-25.60-64.Women 2048.284 21.22905
## 26-30.60-64.Women 2345.709 20.32156
## 31-35.60-64.Women 2633.073 25.37714
## 36 and over.60-64.Women 3262.765 36.55910
## Under 18.5.65-69.Women 1768.672 116.96275
## 18.5-25.65-69.Women 2042.971 24.66015
## 26-30.65-69.Women 2328.852 21.66931
## 31-35.65-69.Women 2616.956 25.71532
## 36 and over.65-69.Women 3212.298 49.68498
## Under 18.5.70-74.Women 1659.845 59.91614
## 18.5-25.70-74.Women 1992.000 22.37788
## 26-30.70-74.Women 2298.950 27.72682
## 31-35.70-74.Women 2597.844 29.09336
## 36 and over.70-74.Women 3069.865 53.89291
## Under 18.5.75-79.Women 1850.166 101.58011
## 18.5-25.75-79.Women 2007.757 27.23694
## 26-30.75-79.Women 2285.319 22.17181
## 31-35.75-79.Women 2667.641 49.16285
## 36 and over.75-79.Women 3089.058 71.35488
## Under 18.5.Over 80.Women 1806.090 47.23532
## 18.5-25.Over 80.Women 2019.814 21.73086
## 26-30.Over 80.Women 2323.601 17.15005
## 31-35.Over 80.Women 2602.393 31.14577
## 36 and over.Over 80.Women 3266.305 149.74626## Multiple imputation results:
## with(nhc_all, svyby(~DXDTOPF, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~DXDTOPF, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 15.92597 0.2880911
## 18.5-25.17-29.Men 20.16221 0.1350881
## 26-30.17-29.Men 26.51774 0.1857842
## 31-35.17-29.Men 31.26481 0.2937423
## 36 and over.17-29.Men 37.13195 0.2825926
## Under 18.5.30-39.Men 17.00404 0.8905113
## 18.5-25.30-39.Men 21.87514 0.2549725
## 26-30.30-39.Men 27.09983 0.1898124
## 31-35.30-39.Men 30.88955 0.2654829
## 36 and over.30-39.Men 35.98202 0.4166836
## Under 18.5.40-49.Men 16.21545 1.1365183
## 18.5-25.40-49.Men 22.79274 0.2742798
## 26-30.40-49.Men 27.53098 0.1704249
## 31-35.40-49.Men 31.40377 0.2342873
## 36 and over.40-49.Men 36.10276 0.3745531
## Under 18.5.50-59.Men 19.39257 1.0632240
## 18.5-25.50-59.Men 24.23040 0.3262502
## 26-30.50-59.Men 28.50327 0.2270554
## 31-35.50-59.Men 32.11418 0.2621986
## 36 and over.50-59.Men 37.04016 0.3443565
## Under 18.5.60-69.Men 17.31624 0.8761308
## 18.5-25.60-69.Men 25.53662 0.3195867
## 26-30.60-69.Men 29.82102 0.1818538
## 31-35.60-69.Men 33.56595 0.2626835
## 36 and over.60-69.Men 37.50991 0.3274938
## Under 18.5.70-79.Men 21.37736 1.6579852
## 18.5-25.70-79.Men 26.38905 0.3508113
## 26-30.70-79.Men 31.02863 0.2330131
## 31-35.70-79.Men 35.56106 0.4205657
## 36 and over.70-79.Men 39.29516 0.7288182
## Under 18.5.Over 80.Men 21.83929 2.2782635
## 18.5-25.Over 80.Men 27.79047 0.3170939
## 26-30.Over 80.Men 31.91233 0.2746580
## 31-35.Over 80.Men 35.95829 0.4685920
## 36 and over.Over 80.Men 39.05557 1.0084494
## Under 18.5.17-29.Women 26.34154 0.3964190
## 18.5-25.17-29.Women 32.43661 0.1826140
## 26-30.17-29.Women 38.98581 0.2054122
## 31-35.17-29.Women 43.35662 0.2644618
## 36 and over.17-29.Women 47.85575 0.3880336
## Under 18.5.30-39.Women 26.39055 1.0181271
## 18.5-25.30-39.Women 33.07120 0.2415657
## 26-30.30-39.Women 39.50890 0.2554525
## 31-35.30-39.Women 43.26245 0.2883490
## 36 and over.30-39.Women 47.37787 0.2522193
## Under 18.5.40-49.Women 26.48777 0.7438327
## 18.5-25.40-49.Women 33.78077 0.2751313
## 26-30.40-49.Women 40.09134 0.1938172
## 31-35.40-49.Women 43.29849 0.2553757
## 36 and over.40-49.Women 47.43002 0.2820883
## Under 18.5.50-59.Women 27.36197 1.6053391
## 18.5-25.50-59.Women 35.55999 0.3620473
## 26-30.50-59.Women 41.68079 0.2354801
## 31-35.50-59.Women 44.38882 0.2173313
## 36 and over.50-59.Women 48.18971 0.2787760
## Under 18.5.60-69.Women 25.28404 1.7057731
## 18.5-25.60-69.Women 37.09267 0.3101707
## 26-30.60-69.Women 42.04228 0.2141829
## 31-35.60-69.Women 45.32612 0.2311031
## 36 and over.60-69.Women 48.87854 0.2272309
## Under 18.5.70-79.Women 28.58608 1.0990563
## 18.5-25.70-79.Women 37.48806 0.3770101
## 26-30.70-79.Women 42.64121 0.3165314
## 31-35.70-79.Women 45.17346 0.3583882
## 36 and over.70-79.Women 48.95483 0.3398502
## Under 18.5.Over 80.Women 28.59596 1.8052059
## 18.5-25.Over 80.Women 37.33760 0.4345367
## 26-30.Over 80.Women 42.12256 0.3071662
## 31-35.Over 80.Women 45.07939 0.4931797
## 36 and over.Over 80.Women 47.83976 0.8749159## Multiple imputation results:
## with(nhc_all, svyby(~TOTLEANH2, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~TOTLEANH2, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 14.18993 0.06620679
## 18.5-25.17-29.Men 17.03047 0.04004614
## 26-30.17-29.Men 19.25454 0.05431048
## 31-35.17-29.Men 21.35487 0.09969056
## 36 and over.17-29.Men 24.81654 0.17940967
## Under 18.5.30-39.Men 14.39347 0.25142194
## 18.5-25.30-39.Men 17.13825 0.06255668
## 26-30.30-39.Men 19.35769 0.05924171
## 31-35.30-39.Men 21.49515 0.08314862
## 36 and over.30-39.Men 24.98702 0.33232299
## Under 18.5.40-49.Men 14.01790 0.27411439
## 18.5-25.40-49.Men 17.01738 0.08077877
## 26-30.40-49.Men 19.23190 0.05672116
## 31-35.40-49.Men 21.28099 0.08887941
## 36 and over.40-49.Men 24.63012 0.31377043
## Under 18.5.50-59.Men 13.62046 0.29834080
## 18.5-25.50-59.Men 16.54667 0.09440543
## 26-30.50-59.Men 18.91887 0.07759519
## 31-35.50-59.Men 20.97494 0.07400680
## 36 and over.50-59.Men 23.79540 0.17325527
## Under 18.5.60-69.Men 13.80563 0.36498424
## 18.5-25.60-69.Men 16.35488 0.11339339
## 26-30.60-69.Men 18.61431 0.06962916
## 31-35.60-69.Men 20.60309 0.09012827
## 36 and over.60-69.Men 23.35489 0.20169354
## Under 18.5.70-79.Men 13.34242 0.30260729
## 18.5-25.70-79.Men 16.14846 0.08016229
## 26-30.70-79.Men 18.20738 0.08313514
## 31-35.70-79.Men 19.96716 0.13211376
## 36 and over.70-79.Men 22.45705 0.28037480
## Under 18.5.Over 80.Men 12.71320 0.47875401
## 18.5-25.Over 80.Men 15.67095 0.06516348
## 26-30.Over 80.Men 17.73169 0.08918354
## 31-35.Over 80.Men 19.64951 0.19561727
## 36 and over.Over 80.Men 21.99061 0.58926889
## Under 18.5.17-29.Women 12.24173 0.09128891
## 18.5-25.17-29.Women 14.13676 0.04349584
## 26-30.17-29.Women 15.94785 0.06096156
## 31-35.17-29.Women 17.63195 0.09260492
## 36 and over.17-29.Women 20.54418 0.17237104
## Under 18.5.30-39.Women 12.36923 0.15672174
## 18.5-25.30-39.Women 14.15337 0.04841495
## 26-30.30-39.Women 15.80121 0.08162463
## 31-35.30-39.Women 17.57222 0.09218202
## 36 and over.30-39.Women 20.44691 0.14721283
## Under 18.5.40-49.Women 12.40759 0.18149430
## 18.5-25.40-49.Women 14.10641 0.05547401
## 26-30.40-49.Women 15.67664 0.06054191
## 31-35.40-49.Women 17.60326 0.08285978
## 36 and over.40-49.Women 20.69242 0.16301033
## Under 18.5.50-59.Women 12.14076 0.30749334
## 18.5-25.50-59.Women 13.82666 0.07071320
## 26-30.50-59.Women 15.43390 0.06508160
## 31-35.50-59.Women 17.38923 0.06875895
## 36 and over.50-59.Women 20.13363 0.15118564
## Under 18.5.60-69.Women 12.36909 0.24049902
## 18.5-25.60-69.Women 13.61451 0.07230279
## 26-30.60-69.Women 15.23531 0.06857433
## 31-35.60-69.Women 16.91557 0.08611172
## 36 and over.60-69.Women 19.70941 0.13307205
## Under 18.5.70-79.Women 12.07689 0.19461417
## 18.5-25.70-79.Women 13.49192 0.06758383
## 26-30.70-79.Women 15.07798 0.07823139
## 31-35.70-79.Women 16.84079 0.10249077
## 36 and over.70-79.Women 19.10634 0.17362320
## Under 18.5.Over 80.Women 12.26802 0.23795322
## 18.5-25.Over 80.Women 13.49292 0.09396660
## 26-30.Over 80.Women 15.18013 0.07429699
## 31-35.Over 80.Women 16.84888 0.13907604
## 36 and over.Over 80.Women 19.14163 0.31361488## Multiple imputation results:
## with(nhc_all, svyby(~WHRATIO, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~WHRATIO, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 0.4001118 0.002485802
## 18.5-25.17-29.Men 0.4564718 0.001067242
## 26-30.17-29.Men 0.5330169 0.001606299
## 31-35.17-29.Men 0.6009577 0.002233341
## 36 and over.17-29.Men 0.7143128 0.005330513
## Under 18.5.30-39.Men 0.4101081 0.005311050
## 18.5-25.30-39.Men 0.4819382 0.002131547
## 26-30.30-39.Men 0.5489352 0.001684260
## 31-35.30-39.Men 0.6125958 0.002928598
## 36 and over.30-39.Men 0.7146035 0.008696827
## Under 18.5.40-49.Men 0.4116873 0.006706126
## 18.5-25.40-49.Men 0.4902322 0.001866726
## 26-30.40-49.Men 0.5580544 0.001629438
## 31-35.40-49.Men 0.6228596 0.002098653
## 36 and over.40-49.Men 0.7232868 0.007500328
## Under 18.5.50-59.Men 0.4303823 0.006720898
## 18.5-25.50-59.Men 0.5043982 0.002250269
## 26-30.50-59.Men 0.5687621 0.001852720
## 31-35.50-59.Men 0.6344142 0.002224896
## 36 and over.50-59.Men 0.7267989 0.005615645
## Under 18.5.60-69.Men 0.4293165 0.005162250
## 18.5-25.60-69.Men 0.5182492 0.001918554
## 26-30.60-69.Men 0.5875297 0.001770305
## 31-35.60-69.Men 0.6522941 0.002164307
## 36 and over.60-69.Men 0.7339121 0.004736224
## Under 18.5.70-79.Men 0.4590186 0.009121708
## 18.5-25.70-79.Men 0.5284346 0.003100723
## 26-30.70-79.Men 0.5991768 0.001771581
## 31-35.70-79.Men 0.6620595 0.003473638
## 36 and over.70-79.Men 0.7504025 0.009528780
## Under 18.5.Over 80.Men 0.4376635 0.013256925
## 18.5-25.Over 80.Men 0.5400620 0.002354912
## 26-30.Over 80.Men 0.6070902 0.002310217
## 31-35.Over 80.Men 0.6724158 0.006080407
## 36 and over.Over 80.Men 0.7531562 0.014856153
## Under 18.5.17-29.Women 0.4173048 0.003195112
## 18.5-25.17-29.Women 0.4740916 0.001442081
## 26-30.17-29.Women 0.5490019 0.002241852
## 31-35.17-29.Women 0.6241723 0.004198810
## 36 and over.17-29.Women 0.7183992 0.005788710
## Under 18.5.30-39.Women 0.4183434 0.003531828
## 18.5-25.30-39.Women 0.4788840 0.002384013
## 26-30.30-39.Women 0.5554050 0.002076413
## 31-35.30-39.Women 0.6168386 0.003738097
## 36 and over.30-39.Women 0.7115330 0.004342369
## Under 18.5.40-49.Women 0.4304762 0.003399723
## 18.5-25.40-49.Women 0.4880264 0.002172367
## 26-30.40-49.Women 0.5608969 0.002426273
## 31-35.40-49.Women 0.6307340 0.002416063
## 36 and over.40-49.Women 0.7222901 0.005566249
## Under 18.5.50-59.Women 0.4256618 0.010224711
## 18.5-25.50-59.Women 0.4964770 0.002622391
## 26-30.50-59.Women 0.5789162 0.002547487
## 31-35.50-59.Women 0.6467752 0.003187396
## 36 and over.50-59.Women 0.7313280 0.005578684
## Under 18.5.60-69.Women 0.4229463 0.007332110
## 18.5-25.60-69.Women 0.5153120 0.002798640
## 26-30.60-69.Women 0.5883299 0.002623972
## 31-35.60-69.Women 0.6560048 0.002804911
## 36 and over.60-69.Women 0.7391187 0.004337805
## Under 18.5.70-79.Women 0.4543716 0.007868627
## 18.5-25.70-79.Women 0.5276236 0.004132627
## 26-30.70-79.Women 0.6099302 0.003414640
## 31-35.70-79.Women 0.6673968 0.003501497
## 36 and over.70-79.Women 0.7436238 0.006910621
## Under 18.5.Over 80.Women 0.4330611 0.009631482
## 18.5-25.Over 80.Women 0.5433682 0.003987085
## 26-30.Over 80.Women 0.6211068 0.003019416
## 31-35.Over 80.Women 0.6771605 0.004390929
## 36 and over.Over 80.Women 0.7384849 0.012928901## Multiple imputation results:
## with(nhc_all, svyby(~RALEANH2, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~RALEANH2, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 885.6327 10.227848
## 18.5-25.17-29.Men 1118.0398 5.200218
## 26-30.17-29.Men 1276.2703 6.508599
## 31-35.17-29.Men 1398.4516 12.601970
## 36 and over.17-29.Men 1572.3397 22.131431
## Under 18.5.30-39.Men 922.4434 27.567676
## 18.5-25.30-39.Men 1141.7347 7.301046
## 26-30.30-39.Men 1293.5385 9.175084
## 31-35.30-39.Men 1435.7446 9.722775
## 36 and over.30-39.Men 1621.2453 25.159311
## Under 18.5.40-49.Men 920.2909 32.648256
## 18.5-25.40-49.Men 1114.3774 8.535421
## 26-30.40-49.Men 1283.6457 7.183284
## 31-35.40-49.Men 1400.0464 10.873004
## 36 and over.40-49.Men 1589.2410 21.716504
## Under 18.5.50-59.Men 849.2973 34.125586
## 18.5-25.50-59.Men 1058.0566 9.602666
## 26-30.50-59.Men 1232.6937 8.673818
## 31-35.50-59.Men 1354.2974 9.591336
## 36 and over.50-59.Men 1501.7695 15.871882
## Under 18.5.60-69.Men 901.3620 29.948394
## 18.5-25.60-69.Men 1023.1081 10.061362
## 26-30.60-69.Men 1173.6370 7.641276
## 31-35.60-69.Men 1293.8377 10.230686
## 36 and over.60-69.Men 1444.5072 17.022443
## Under 18.5.70-79.Men 843.6208 23.591163
## 18.5-25.70-79.Men 985.7018 9.570538
## 26-30.70-79.Men 1125.0683 11.064245
## 31-35.70-79.Men 1206.8781 16.598105
## 36 and over.70-79.Men 1343.6781 28.006193
## Under 18.5.Over 80.Men 741.6107 43.277415
## 18.5-25.Over 80.Men 920.7766 8.408617
## 26-30.Over 80.Men 1048.8228 9.109095
## 31-35.Over 80.Men 1141.3515 20.073109
## 36 and over.Over 80.Men 1265.9032 30.743480
## Under 18.5.17-29.Women 644.9918 8.283550
## 18.5-25.17-29.Women 733.9717 4.241143
## 26-30.17-29.Women 845.1983 6.591546
## 31-35.17-29.Women 939.7006 10.811436
## 36 and over.17-29.Women 1096.2578 14.016791
## Under 18.5.30-39.Women 656.4776 14.662633
## 18.5-25.30-39.Women 740.1270 5.411527
## 26-30.30-39.Women 833.9638 7.699247
## 31-35.30-39.Women 937.2889 9.654177
## 36 and over.30-39.Women 1073.8670 12.312053
## Under 18.5.40-49.Women 650.4684 16.101166
## 18.5-25.40-49.Women 733.5949 4.721499
## 26-30.40-49.Women 832.0361 6.750187
## 31-35.40-49.Women 939.3143 8.383070
## 36 and over.40-49.Women 1092.8890 14.217025
## Under 18.5.50-59.Women 641.6834 31.307799
## 18.5-25.50-59.Women 711.4458 6.515243
## 26-30.50-59.Women 805.6913 6.667889
## 31-35.50-59.Women 913.5837 7.683077
## 36 and over.50-59.Women 1045.8413 10.977401
## Under 18.5.60-69.Women 643.0203 21.275993
## 18.5-25.60-69.Women 701.7874 6.110838
## 26-30.60-69.Women 780.9300 6.704362
## 31-35.60-69.Women 878.9744 10.406727
## 36 and over.60-69.Women 1023.2322 11.111769
## Under 18.5.70-79.Women 632.7584 17.408160
## 18.5-25.70-79.Women 694.8215 6.947488
## 26-30.70-79.Women 764.3894 7.500010
## 31-35.70-79.Women 876.4893 12.138198
## 36 and over.70-79.Women 981.6247 19.315464
## Under 18.5.Over 80.Women 631.7664 25.616035
## 18.5-25.Over 80.Women 686.3762 9.625247
## 26-30.Over 80.Women 756.0610 9.848658
## 31-35.Over 80.Women 827.9587 16.110001
## 36 and over.Over 80.Women 941.9910 23.695581## Multiple imputation results:
## with(nhc_all, svyby(~LALEANH2, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~LALEANH2, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 846.4727 10.600125
## 18.5-25.17-29.Men 1060.2434 5.255490
## 26-30.17-29.Men 1216.0418 7.247509
## 31-35.17-29.Men 1344.8285 11.789274
## 36 and over.17-29.Men 1524.6680 23.669031
## Under 18.5.30-39.Men 852.2348 28.905840
## 18.5-25.30-39.Men 1090.6775 7.140817
## 26-30.30-39.Men 1242.8559 8.185002
## 31-35.30-39.Men 1375.1042 9.504765
## 36 and over.30-39.Men 1571.1023 28.378643
## Under 18.5.40-49.Men 883.9433 36.873037
## 18.5-25.40-49.Men 1066.7554 8.210000
## 26-30.40-49.Men 1230.6261 7.129204
## 31-35.40-49.Men 1351.7432 12.186734
## 36 and over.40-49.Men 1543.0229 25.027527
## Under 18.5.50-59.Men 824.9850 33.084765
## 18.5-25.50-59.Men 1011.3125 10.485637
## 26-30.50-59.Men 1180.9713 8.685264
## 31-35.50-59.Men 1308.1615 9.762535
## 36 and over.50-59.Men 1461.2878 15.911663
## Under 18.5.60-69.Men 822.9547 38.519778
## 18.5-25.60-69.Men 981.4106 10.884622
## 26-30.60-69.Men 1124.7851 7.487850
## 31-35.60-69.Men 1247.7757 12.179008
## 36 and over.60-69.Men 1412.8279 18.269957
## Under 18.5.70-79.Men 807.6334 21.831629
## 18.5-25.70-79.Men 939.3371 8.481218
## 26-30.70-79.Men 1078.9151 11.210931
## 31-35.70-79.Men 1167.6142 15.908403
## 36 and over.70-79.Men 1287.0176 25.782089
## Under 18.5.Over 80.Men 705.7823 26.674893
## 18.5-25.Over 80.Men 874.1556 8.895511
## 26-30.Over 80.Men 998.0591 9.603968
## 31-35.Over 80.Men 1084.8964 18.494990
## 36 and over.Over 80.Men 1212.4821 46.416325
## Under 18.5.17-29.Women 605.1938 10.320805
## 18.5-25.17-29.Women 693.0842 3.874504
## 26-30.17-29.Women 802.5892 7.345901
## 31-35.17-29.Women 900.6328 10.373577
## 36 and over.17-29.Women 1057.1347 15.010909
## Under 18.5.30-39.Women 607.8994 16.580707
## 18.5-25.30-39.Women 695.5776 5.579801
## 26-30.30-39.Women 789.7903 7.628863
## 31-35.30-39.Women 893.8993 8.409365
## 36 and over.30-39.Women 1040.5959 12.870589
## Under 18.5.40-49.Women 613.4788 14.408996
## 18.5-25.40-49.Women 688.3803 5.090669
## 26-30.40-49.Women 787.2441 6.692919
## 31-35.40-49.Women 893.9684 8.767274
## 36 and over.40-49.Women 1057.4381 13.898908
## Under 18.5.50-59.Women 593.9688 26.461808
## 18.5-25.50-59.Women 676.6190 6.519955
## 26-30.50-59.Women 763.2383 5.670057
## 31-35.50-59.Women 881.2027 7.742978
## 36 and over.50-59.Women 1016.8841 11.063423
## Under 18.5.60-69.Women 599.0816 24.941836
## 18.5-25.60-69.Women 660.5709 5.783964
## 26-30.60-69.Women 745.4502 7.652560
## 31-35.60-69.Women 843.6508 9.153797
## 36 and over.60-69.Women 990.3133 12.142029
## Under 18.5.70-79.Women 593.8891 17.946228
## 18.5-25.70-79.Women 653.6396 7.692197
## 26-30.70-79.Women 725.9870 7.222889
## 31-35.70-79.Women 839.0567 10.709690
## 36 and over.70-79.Women 965.6906 19.074486
## Under 18.5.Over 80.Women 605.1687 16.914298
## 18.5-25.Over 80.Women 645.6543 7.876179
## 26-30.Over 80.Women 732.8951 8.848141
## 31-35.Over 80.Women 796.3521 17.467471
## 36 and over.Over 80.Women 914.9342 26.820425## Multiple imputation results:
## with(nhc_all, svyby(~RLLEANH2, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~RLLEANH2, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 2335.834 20.42722
## 18.5-25.17-29.Men 2819.724 11.14454
## 26-30.17-29.Men 3183.572 12.07874
## 31-35.17-29.Men 3554.542 20.80708
## 36 and over.17-29.Men 4220.376 41.94835
## Under 18.5.30-39.Men 2346.790 77.33961
## 18.5-25.30-39.Men 2769.754 17.99815
## 26-30.30-39.Men 3149.395 11.05933
## 31-35.30-39.Men 3475.035 19.25853
## 36 and over.30-39.Men 4139.875 72.79899
## Under 18.5.40-49.Men 2255.825 65.78163
## 18.5-25.40-49.Men 2747.587 19.14942
## 26-30.40-49.Men 3072.986 14.87116
## 31-35.40-49.Men 3399.092 22.97360
## 36 and over.40-49.Men 3952.793 61.01054
## Under 18.5.50-59.Men 2153.963 71.93199
## 18.5-25.50-59.Men 2620.164 18.52305
## 26-30.50-59.Men 2995.619 16.25350
## 31-35.50-59.Men 3298.067 22.77860
## 36 and over.50-59.Men 3767.584 38.28870
## Under 18.5.60-69.Men 2188.060 96.02586
## 18.5-25.60-69.Men 2561.858 24.39463
## 26-30.60-69.Men 2911.622 17.21898
## 31-35.60-69.Men 3196.751 21.01457
## 36 and over.60-69.Men 3624.652 42.34127
## Under 18.5.70-79.Men 1978.699 84.61489
## 18.5-25.70-79.Men 2505.257 18.05443
## 26-30.70-79.Men 2820.832 17.66921
## 31-35.70-79.Men 3099.518 27.12514
## 36 and over.70-79.Men 3468.486 78.27011
## Under 18.5.Over 80.Men 1988.187 121.87491
## 18.5-25.Over 80.Men 2405.564 18.47854
## 26-30.Over 80.Men 2731.838 18.38589
## 31-35.Over 80.Men 3055.952 44.70655
## 36 and over.Over 80.Men 3445.446 149.06949
## Under 18.5.17-29.Women 1931.664 22.60624
## 18.5-25.17-29.Women 2303.163 10.89775
## 26-30.17-29.Women 2645.718 15.09154
## 31-35.17-29.Women 2975.525 24.57843
## 36 and over.17-29.Women 3567.879 40.83689
## Under 18.5.30-39.Women 1907.154 43.77640
## 18.5-25.30-39.Women 2278.038 12.62774
## 26-30.30-39.Women 2569.056 19.83106
## 31-35.30-39.Women 2893.469 20.32531
## 36 and over.30-39.Women 3496.363 33.30873
## Under 18.5.40-49.Women 1903.728 39.74289
## 18.5-25.40-49.Women 2215.876 16.72910
## 26-30.40-49.Women 2512.661 13.43560
## 31-35.40-49.Women 2863.661 18.23603
## 36 and over.40-49.Women 3475.546 33.95892
## Under 18.5.50-59.Women 1831.900 80.93618
## 18.5-25.50-59.Women 2136.017 17.36623
## 26-30.50-59.Women 2428.462 14.93672
## 31-35.50-59.Women 2745.325 16.64156
## 36 and over.50-59.Women 3302.970 33.53873
## Under 18.5.60-69.Women 1950.868 95.49333
## 18.5-25.60-69.Women 2078.078 17.85842
## 26-30.60-69.Women 2376.876 16.09178
## 31-35.60-69.Women 2654.654 18.33216
## 36 and over.60-69.Women 3253.803 30.55780
## Under 18.5.70-79.Women 1780.999 56.94016
## 18.5-25.70-79.Women 2050.388 14.53014
## 26-30.70-79.Women 2320.832 18.67559
## 31-35.70-79.Women 2663.639 23.62410
## 36 and over.70-79.Women 3099.145 43.35479
## Under 18.5.Over 80.Women 1911.589 59.91553
## 18.5-25.Over 80.Women 2056.282 22.43259
## 26-30.Over 80.Women 2344.790 16.94276
## 31-35.Over 80.Women 2629.904 34.14187
## 36 and over.Over 80.Women 3296.385 119.63646## Multiple imputation results:
## with(nhc_all, svyby(~LLLEANH2, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~LLLEANH2, ~BMI.Group +
## Age.Group2 + Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 2314.463 19.86436
## 18.5-25.17-29.Men 2774.014 10.97647
## 26-30.17-29.Men 3130.726 12.84702
## 31-35.17-29.Men 3502.083 21.14375
## 36 and over.17-29.Men 4151.246 38.01832
## Under 18.5.30-39.Men 2321.280 77.54486
## 18.5-25.30-39.Men 2718.858 18.07851
## 26-30.30-39.Men 3101.534 10.65871
## 31-35.30-39.Men 3439.658 16.65222
## 36 and over.30-39.Men 4065.167 65.13375
## Under 18.5.40-49.Men 2229.994 65.11193
## 18.5-25.40-49.Men 2695.999 19.60159
## 26-30.40-49.Men 3031.317 15.65954
## 31-35.40-49.Men 3359.428 21.79641
## 36 and over.40-49.Men 3907.230 55.72242
## Under 18.5.50-59.Men 2104.429 83.51880
## 18.5-25.50-59.Men 2583.019 17.24879
## 26-30.50-59.Men 2956.745 15.54138
## 31-35.50-59.Men 3258.400 20.41177
## 36 and over.50-59.Men 3700.018 33.98649
## Under 18.5.60-69.Men 2136.137 76.53602
## 18.5-25.60-69.Men 2518.133 26.22153
## 26-30.60-69.Men 2868.369 15.31146
## 31-35.60-69.Men 3148.532 18.62130
## 36 and over.60-69.Men 3603.918 40.56162
## Under 18.5.70-79.Men 1962.918 135.19164
## 18.5-25.70-79.Men 2442.177 18.98200
## 26-30.70-79.Men 2775.398 18.18788
## 31-35.70-79.Men 3074.234 25.29088
## 36 and over.70-79.Men 3477.791 79.45404
## Under 18.5.Over 80.Men 1961.661 113.74472
## 18.5-25.Over 80.Men 2362.186 18.58361
## 26-30.Over 80.Men 2717.604 19.48712
## 31-35.Over 80.Men 3048.600 48.75948
## 36 and over.Over 80.Men 3344.289 130.70094
## Under 18.5.17-29.Women 1890.739 22.16829
## 18.5-25.17-29.Women 2268.056 11.45051
## 26-30.17-29.Women 2606.424 15.96341
## 31-35.17-29.Women 2939.503 24.13765
## 36 and over.17-29.Women 3517.834 38.38690
## Under 18.5.30-39.Women 1878.249 40.19878
## 18.5-25.30-39.Women 2234.995 12.19723
## 26-30.30-39.Women 2531.804 19.17689
## 31-35.30-39.Women 2853.289 19.74405
## 36 and over.30-39.Women 3460.204 35.76589
## Under 18.5.40-49.Women 1901.698 46.36110
## 18.5-25.40-49.Women 2185.482 15.82004
## 26-30.40-49.Women 2478.563 14.12715
## 31-35.40-49.Women 2827.557 18.65926
## 36 and over.40-49.Women 3430.357 33.83258
## Under 18.5.50-59.Women 1831.998 79.73252
## 18.5-25.50-59.Women 2094.743 16.24552
## 26-30.50-59.Women 2390.423 16.54347
## 31-35.50-59.Women 2700.244 17.93813
## 36 and over.50-59.Women 3258.246 36.26288
## Under 18.5.60-69.Women 1820.840 105.69881
## 18.5-25.60-69.Women 2045.839 16.83964
## 26-30.60-69.Women 2337.751 16.30473
## 31-35.60-69.Women 2625.635 18.25757
## 36 and over.60-69.Women 3238.789 29.01073
## Under 18.5.70-79.Women 1732.123 58.08545
## 18.5-25.70-79.Women 1998.872 18.35952
## 26-30.70-79.Women 2292.119 18.53672
## 31-35.70-79.Women 2624.201 27.23053
## 36 and over.70-79.Women 3078.265 43.79899
## Under 18.5.Over 80.Women 1806.090 47.23532
## 18.5-25.Over 80.Women 2019.814 21.73086
## 26-30.Over 80.Women 2323.601 17.15005
## 31-35.Over 80.Women 2602.393 31.14577
## 36 and over.Over 80.Women 3266.305 149.74626## Multiple imputation results:
## with(nhc_all, svyby(~TFWHR, ~BMI.Group + Age.Group2 + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~TFWHR, ~BMI.Group + Age.Group2 +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-29.Men 6.388291 0.13435656
## 18.5-25.17-29.Men 9.310294 0.07663535
## 26-30.17-29.Men 14.245330 0.13435782
## 31-35.17-29.Men 18.870458 0.22770466
## 36 and over.17-29.Men 26.677659 0.38257185
## Under 18.5.30-39.Men 6.994564 0.40128484
## 18.5-25.30-39.Men 10.628804 0.16633856
## 26-30.30-39.Men 14.974726 0.13883737
## 31-35.30-39.Men 19.013796 0.23383659
## 36 and over.30-39.Men 25.842629 0.58625045
## Under 18.5.40-49.Men 6.616501 0.36337722
## 18.5-25.40-49.Men 11.255864 0.16283667
## 26-30.40-49.Men 15.429811 0.12539469
## 31-35.40-49.Men 19.616080 0.18737778
## 36 and over.40-49.Men 26.293003 0.51277641
## Under 18.5.50-59.Men 8.352011 0.53080099
## 18.5-25.50-59.Men 12.356991 0.19111771
## 26-30.50-59.Men 16.279255 0.16885493
## 31-35.50-59.Men 20.398580 0.20620149
## 36 and over.50-59.Men 27.045456 0.44804380
## Under 18.5.60-69.Men 7.447658 0.42318035
## 18.5-25.60-69.Men 13.284094 0.17914351
## 26-30.60-69.Men 17.541924 0.14964726
## 31-35.60-69.Men 21.941616 0.20779292
## 36 and over.60-69.Men 27.569427 0.35632204
## Under 18.5.70-79.Men 9.921460 0.87301088
## 18.5-25.70-79.Men 14.019488 0.23959018
## 26-30.70-79.Men 18.608426 0.17900795
## 31-35.70-79.Men 23.606629 0.37075682
## 36 and over.70-79.Men 29.659673 0.86123622
## Under 18.5.Over 80.Men 9.488902 0.96258337
## 18.5-25.Over 80.Men 15.205244 0.22185409
## 26-30.Over 80.Men 19.381320 0.19401541
## 31-35.Over 80.Men 24.158741 0.42823141
## 36 and over.Over 80.Men 29.629884 0.94278968
## Under 18.5.17-29.Women 11.010949 0.19062928
## 18.5-25.17-29.Women 15.454860 0.11626138
## 26-30.17-29.Women 21.453555 0.16302535
## 31-35.17-29.Women 27.088219 0.24780765
## 36 and over.17-29.Women 34.451218 0.45963921
## Under 18.5.30-39.Women 11.092128 0.48207944
## 18.5-25.30-39.Women 15.928518 0.17185592
## 26-30.30-39.Women 21.996724 0.18136675
## 31-35.30-39.Women 26.684041 0.25693001
## 36 and over.30-39.Women 33.809665 0.32248196
## Under 18.5.40-49.Women 11.405746 0.35100661
## 18.5-25.40-49.Women 16.580480 0.18409732
## 26-30.40-49.Women 22.505556 0.15086246
## 31-35.40-49.Women 27.328706 0.20541672
## 36 and over.40-49.Women 34.356642 0.41403676
## Under 18.5.50-59.Women 11.737353 0.93890895
## 18.5-25.50-59.Women 17.756781 0.23940009
## 26-30.50-59.Women 24.149509 0.18058008
## 31-35.50-59.Women 28.739903 0.21875841
## 36 and over.50-59.Women 35.357441 0.41360029
## Under 18.5.60-69.Women 10.722574 0.81279377
## 18.5-25.60-69.Women 19.176941 0.21798153
## 26-30.60-69.Women 24.764147 0.18584396
## 31-35.60-69.Women 29.722262 0.18405742
## 36 and over.60-69.Women 36.169031 0.30002596
## Under 18.5.70-79.Women 13.109921 0.59906386
## 18.5-25.70-79.Women 19.876320 0.30899955
## 26-30.70-79.Women 26.004751 0.27419221
## 31-35.70-79.Women 30.158693 0.27282171
## 36 and over.70-79.Women 36.600730 0.47428240
## Under 18.5.Over 80.Women 12.315987 0.96700388
## 18.5-25.Over 80.Women 20.432699 0.30942813
## 26-30.Over 80.Women 26.191965 0.27453159
## 31-35.Over 80.Women 30.449831 0.41565617
## 36 and over.Over 80.Women 35.315622 0.69093570## Multiple imputation results:
## with(nhc_all, svyby(~TFWHR, ~BMI.Group + Age.Group + Gender,
## na = TRUE, svymean))
## MIcombine.default(with(nhc_all, svyby(~TFWHR, ~BMI.Group + Age.Group +
## Gender, na = TRUE, svymean)))
## results se
## Under 18.5.17-19.Men 6.290394 0.1991118
## 18.5-25.17-19.Men 8.393436 0.1077515
## 26-30.17-19.Men 13.792279 0.2429305
## 31-35.17-19.Men 19.117010 0.3620370
## 36 and over.17-19.Men 25.491361 0.5650039
## Under 18.5.20-24.Men 6.468360 0.2382197
## 18.5-25.20-24.Men 9.248025 0.1322321
## 26-30.20-24.Men 14.040169 0.2709265
## 31-35.20-24.Men 18.831431 0.3909231
## 36 and over.20-24.Men 26.450241 0.6905264
## Under 18.5.25-29.Men 6.396459 0.2564011
## 18.5-25.25-29.Men 10.326538 0.1483086
## 26-30.25-29.Men 14.567170 0.1977399
## 31-35.25-29.Men 18.802204 0.2842508
## 36 and over.25-29.Men 27.351935 0.6644394
## Under 18.5.30-34.Men 6.842782 0.5627924
## 18.5-25.30-34.Men 10.425405 0.2207374
## 26-30.30-34.Men 14.892858 0.1640446
## 31-35.30-34.Men 18.836640 0.3938631
## 36 and over.30-34.Men 25.722806 0.7490048
## Under 18.5.35-39.Men 7.183135 0.5495073
## 18.5-25.35-39.Men 10.849781 0.2361071
## 26-30.35-39.Men 15.049902 0.2205843
## 31-35.35-39.Men 19.150101 0.2620828
## 36 and over.35-39.Men 25.932215 0.7980755
## Under 18.5.40-44.Men 6.618212 0.4411371
## 18.5-25.40-44.Men 11.258628 0.1911661
## 26-30.40-44.Men 15.182022 0.1699245
## 31-35.40-44.Men 19.379379 0.2397024
## 36 and over.40-44.Men 25.639576 0.5677073
## Under 18.5.45-49.Men 6.610462 0.5334394
## 18.5-25.45-49.Men 11.252896 0.2918438
## 26-30.45-49.Men 15.666083 0.1831366
## 31-35.45-49.Men 19.914791 0.2719901
## 36 and over.45-49.Men 26.992209 0.8224687
## Under 18.5.50-54.Men 7.576531 0.3213047
## 18.5-25.50-54.Men 11.984222 0.2376461
## 26-30.50-54.Men 15.975088 0.2010069
## 31-35.50-54.Men 20.106727 0.3098679
## 36 and over.50-54.Men 26.156929 0.4032086
## Under 18.5.55-59.Men 9.009515 0.8203072
## 18.5-25.55-59.Men 12.880598 0.2956413
## 26-30.55-59.Men 16.755769 0.2240819
## 31-35.55-59.Men 20.756091 0.3200585
## 36 and over.55-59.Men 28.681675 0.8705163
## Under 18.5.60-64.Men 7.390905 0.5432113
## 18.5-25.60-64.Men 12.963152 0.2327751
## 26-30.60-64.Men 17.099436 0.2212367
## 31-35.60-64.Men 21.603531 0.2732954
## 36 and over.60-64.Men 27.548883 0.5207979
## Under 18.5.65-69.Men 7.550005 0.5608600
## 18.5-25.65-69.Men 13.658676 0.2796402
## 26-30.65-69.Men 17.984507 0.1688966
## 31-35.65-69.Men 22.408582 0.3731485
## 36 and over.65-69.Men 27.588925 0.5424149
## Under 18.5.70-74.Men 10.942425 0.9516132
## 18.5-25.70-74.Men 13.387185 0.3074688
## 26-30.70-74.Men 18.472595 0.1943640
## 31-35.70-74.Men 23.462086 0.4667707
## 36 and over.70-74.Men 30.572775 1.0325364
## Under 18.5.75-79.Men 7.207322 0.8985033
## 18.5-25.75-79.Men 14.911420 0.3494067
## 26-30.75-79.Men 18.781345 0.2913825
## 31-35.75-79.Men 23.810811 0.4241588
## 36 and over.75-79.Men 27.282021 1.0137710
## Under 18.5.Over 80.Men 9.488902 0.9625834
## 18.5-25.Over 80.Men 15.205244 0.2218541
## 26-30.Over 80.Men 19.381320 0.1940154
## 31-35.Over 80.Men 24.158741 0.4282314
## 36 and over.Over 80.Men 29.629884 0.9427897
## Under 18.5.17-19.Women 11.120775 0.1954720
## 18.5-25.17-19.Women 15.134376 0.1645600
## 26-30.17-19.Women 21.416315 0.2957463
## 31-35.17-19.Women 26.822855 0.4143886
## 36 and over.17-19.Women 33.921801 0.5414388
## Under 18.5.20-24.Women 11.029145 0.4422787
## 18.5-25.20-24.Women 15.467719 0.1864215
## 26-30.20-24.Women 21.383794 0.3006557
## 31-35.20-24.Women 27.411816 0.3648559
## 36 and over.20-24.Women 34.809365 0.6654390
## Under 18.5.25-29.Women 10.885744 0.3336697
## 18.5-25.25-29.Women 15.731043 0.2177028
## 26-30.25-29.Women 21.549084 0.2416285
## 31-35.25-29.Women 26.901503 0.3503651
## 36 and over.25-29.Women 34.257490 0.8072567
## Under 18.5.30-34.Women 11.209969 0.6233487
## 18.5-25.30-34.Women 15.772101 0.2646874
## 26-30.30-34.Women 21.985733 0.2175936
## 31-35.30-34.Women 26.484785 0.3675574
## 36 and over.30-34.Women 33.183032 0.4797974
## Under 18.5.35-39.Women 10.911842 0.8872610
## 18.5-25.35-39.Women 16.075208 0.1784851
## 26-30.35-39.Women 22.005563 0.2635493
## 31-35.35-39.Women 26.898906 0.2937534
## 36 and over.35-39.Women 34.381282 0.6094161
## Under 18.5.40-44.Women 11.082402 0.2114263
## 18.5-25.40-44.Women 16.591968 0.2673059
## 26-30.40-44.Women 22.139528 0.2145493
## 31-35.40-44.Women 27.056765 0.2822369
## 36 and over.40-44.Women 33.741831 0.5437118
## Under 18.5.45-49.Women 11.748892 0.5787662
## 18.5-25.45-49.Women 16.568400 0.2511540
## 26-30.45-49.Women 22.885717 0.1816807
## 31-35.45-49.Women 27.609572 0.3367803
## 36 and over.45-49.Women 34.813483 0.5320685
## Under 18.5.50-54.Women 11.710926 1.0337915
## 18.5-25.50-54.Women 17.547996 0.2767236
## 26-30.50-54.Women 24.127956 0.2229869
## 31-35.50-54.Women 28.354823 0.3120417
## 36 and over.50-54.Women 34.726319 0.4514285
## Under 18.5.55-59.Women 11.995103 0.6738433
## 18.5-25.55-59.Women 18.069985 0.4013998
## 26-30.55-59.Women 24.182076 0.2795948
## 31-35.55-59.Women 29.315209 0.3647531
## 36 and over.55-59.Women 36.071815 0.5739714
## Under 18.5.60-64.Women 10.341506 1.5932658
## 18.5-25.60-64.Women 18.821429 0.3140641
## 26-30.60-64.Women 24.664521 0.2567257
## 31-35.60-64.Women 29.404780 0.2353497
## 36 and over.60-64.Women 35.917777 0.4729573
## Under 18.5.65-69.Women 10.923262 0.8895533
## 18.5-25.65-69.Women 19.599981 0.2751432
## 26-30.65-69.Women 24.878061 0.2385518
## 31-35.65-69.Women 30.092030 0.3469482
## 36 and over.65-69.Women 36.456545 0.4242479
## Under 18.5.70-74.Women 13.676586 0.6804521
## 18.5-25.70-74.Women 19.622590 0.3284647
## 26-30.70-74.Women 26.022453 0.4402517
## 31-35.70-74.Women 30.328200 0.2991722
## 36 and over.70-74.Women 36.836056 0.6672673
## Under 18.5.75-79.Women 12.106775 1.1940583
## 18.5-25.75-79.Women 20.204964 0.4577414
## 26-30.75-79.Women 25.986821 0.3141950
## 31-35.75-79.Women 29.870012 0.5172775
## 36 and over.75-79.Women 36.299830 0.6396065
## Under 18.5.Over 80.Women 12.315987 0.9670039
## 18.5-25.Over 80.Women 20.432699 0.3094281
## 26-30.Over 80.Women 26.191965 0.2745316
## 31-35.Over 80.Women 30.449831 0.4156562
## 36 and over.Over 80.Women 35.315622 0.6909357## Multiple imputation results:
## with(nhc_all, svyby(~TFWHR, ~Age.Group + Gender, na = TRUE, svyquantile,
## quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95), keep.var = TRUE,
## se = T, ci = T))
## MIcombine.default(with(nhc_all, svyby(~TFWHR, ~Age.Group + Gender,
## na = TRUE, svyquantile, quantiles = c(0.05, 0.25, 0.5, 0.75,
## 0.95), keep.var = TRUE, se = T, ci = T)))
## results se
## 17-19.Men:0.05 5.734969 0.08101517
## 20-24.Men:0.05 6.124015 0.12489355
## 25-29.Men:0.05 6.903535 0.17903423
## 30-34.Men:0.05 7.235851 0.26125798
## 35-39.Men:0.05 7.823469 0.34921550
## 40-44.Men:0.05 8.871628 0.26038494
## 45-49.Men:0.05 8.888644 0.36138168
## 50-54.Men:0.05 8.988146 0.32825310
## 55-59.Men:0.05 9.992873 0.45961950
## 60-64.Men:0.05 10.859383 0.31036898
## 65-69.Men:0.05 11.299557 0.35697507
## 70-74.Men:0.05 10.738273 0.64148986
## 75-79.Men:0.05 12.370986 0.68483168
## Over 80.Men:0.05 11.078987 0.54365587
## 17-19.Women:0.05 10.670698 0.21065926
## 20-24.Women:0.05 10.746113 0.45146158
## 25-29.Women:0.05 11.125744 0.39061600
## 30-34.Women:0.05 10.866560 0.55949668
## 35-39.Women:0.05 12.163095 0.24985556
## 40-44.Women:0.05 11.689667 0.38476667
## 45-49.Women:0.05 12.384641 0.58989380
## 50-54.Women:0.05 13.685663 0.54464618
## 55-59.Women:0.05 13.981877 0.70164956
## 60-64.Women:0.05 15.977364 0.69889374
## 65-69.Women:0.05 16.058803 0.52052185
## 70-74.Women:0.05 15.301843 0.55710116
## 75-79.Women:0.05 16.225390 0.92288460
## Over 80.Women:0.05 14.603166 0.89464669
## 17-19.Men:0.25 7.255493 0.11497469
## 20-24.Men:0.25 8.548371 0.16882444
## 25-29.Men:0.25 10.564616 0.24703918
## 30-34.Men:0.25 10.754656 0.32519864
## 35-39.Men:0.25 11.846017 0.38473742
## 40-44.Men:0.25 12.740972 0.25794988
## 45-49.Men:0.25 13.096568 0.27623185
## 50-54.Men:0.25 13.752865 0.24505458
## 55-59.Men:0.25 14.415794 0.26930507
## 60-64.Men:0.25 14.642964 0.31072577
## 65-69.Men:0.25 15.791851 0.19112286
## 70-74.Men:0.25 14.966622 0.40100777
## 75-79.Men:0.25 15.619552 0.37592432
## Over 80.Men:0.25 15.286724 0.25088601
## 17-19.Women:0.25 13.546770 0.21241346
## 20-24.Women:0.25 14.609870 0.35098453
## 25-29.Women:0.25 15.205192 0.42949578
## 30-34.Women:0.25 15.959200 0.45065895
## 35-39.Women:0.25 16.272398 0.31226936
## 40-44.Women:0.25 17.735616 0.38388620
## 45-49.Women:0.25 17.908011 0.41283322
## 50-54.Women:0.25 19.044162 0.45848838
## 55-59.Women:0.25 20.359957 0.64196906
## 60-64.Women:0.25 21.110636 0.37956094
## 65-69.Women:0.25 21.815451 0.34176496
## 70-74.Women:0.25 21.349299 0.48679439
## 75-79.Women:0.25 22.007185 0.46287828
## Over 80.Women:0.25 20.581831 0.32829624
## 17-19.Men:0.5 9.583542 0.21799544
## 20-24.Men:0.5 11.950923 0.25952385
## 25-29.Men:0.5 13.799951 0.26790070
## 30-34.Men:0.5 14.341343 0.27039823
## 35-39.Men:0.5 14.880650 0.28289666
## 40-44.Men:0.5 15.708676 0.27189549
## 45-49.Men:0.5 16.128311 0.23159799
## 50-54.Men:0.5 16.885012 0.25552883
## 55-59.Men:0.5 17.441119 0.37608769
## 60-64.Men:0.5 17.865732 0.39633638
## 65-69.Men:0.5 18.445573 0.30557151
## 70-74.Men:0.5 18.395020 0.39906167
## 75-79.Men:0.5 18.678577 0.33549320
## Over 80.Men:0.5 17.881888 0.23194700
## 17-19.Women:0.5 16.687739 0.28510610
## 20-24.Women:0.5 18.046655 0.41788002
## 25-29.Women:0.5 19.531074 0.31217963
## 30-34.Women:0.5 20.707798 0.38044914
## 35-39.Women:0.5 20.899540 0.53387542
## 40-44.Women:0.5 22.055480 0.45188614
## 45-49.Women:0.5 22.945118 0.38197593
## 50-54.Women:0.5 24.438388 0.25970728
## 55-59.Women:0.5 25.093720 0.52684384
## 60-64.Women:0.5 25.819956 0.47382627
## 65-69.Women:0.5 25.998973 0.50525232
## 70-74.Women:0.5 26.167286 0.63508158
## 75-79.Women:0.5 25.998455 0.48411462
## Over 80.Women:0.5 24.499194 0.36457918
## 17-19.Men:0.75 14.106501 0.38497646
## 20-24.Men:0.75 16.405505 0.49507674
## 25-29.Men:0.75 16.951018 0.33832002
## 30-34.Men:0.75 17.297976 0.30856615
## 35-39.Men:0.75 18.475969 0.33732017
## 40-44.Men:0.75 19.210375 0.48219488
## 45-49.Men:0.75 19.117017 0.47179613
## 50-54.Men:0.75 20.147110 0.40004038
## 55-59.Men:0.75 20.817753 0.38337530
## 60-64.Men:0.75 21.910347 0.52506511
## 65-69.Men:0.75 22.703313 0.53891906
## 70-74.Men:0.75 22.047518 0.39620918
## 75-79.Men:0.75 21.833892 0.31573327
## Over 80.Men:0.75 21.193217 0.36815743
## 17-19.Women:0.75 21.825458 0.48533551
## 20-24.Women:0.75 24.432498 0.63814837
## 25-29.Women:0.75 25.041058 0.55836510
## 30-34.Women:0.75 26.870630 0.52792646
## 35-39.Women:0.75 26.574933 0.66389440
## 40-44.Women:0.75 27.254440 0.42001333
## 45-49.Women:0.75 28.626488 0.70295980
## 50-54.Women:0.75 29.083363 0.37934230
## 55-59.Women:0.75 30.764577 0.51425443
## 60-64.Women:0.75 30.416902 0.39308148
## 65-69.Women:0.75 31.418438 0.53370082
## 70-74.Women:0.75 30.547977 0.54735180
## 75-79.Women:0.75 29.160097 0.42121610
## Over 80.Women:0.75 28.137750 0.43536641
## 17-19.Men:0.95 23.055580 0.70709295
## 20-24.Men:0.95 24.639546 0.99955722
## 25-29.Men:0.95 26.405770 1.42535894
## 30-34.Men:0.95 24.524735 0.82149734
## 35-39.Men:0.95 25.768494 0.79889694
## 40-44.Men:0.95 24.973087 0.60445978
## 45-49.Men:0.95 26.840724 1.23107379
## 50-54.Men:0.95 26.854681 0.60757708
## 55-59.Men:0.95 28.467254 1.34514406
## 60-64.Men:0.95 28.545825 0.83612472
## 65-69.Men:0.95 28.794322 0.90028400
## 70-74.Men:0.95 28.584502 1.02923148
## 75-79.Men:0.95 26.511700 1.06585277
## Over 80.Men:0.95 26.294956 0.49980688
## 17-19.Women:0.95 31.868211 0.99657248
## 20-24.Women:0.95 35.027986 0.95532827
## 25-29.Women:0.95 34.242639 1.10364777
## 30-34.Women:0.95 34.782894 1.09566548
## 35-39.Women:0.95 37.777858 1.06133976
## 40-44.Women:0.95 34.918625 0.85803965
## 45-49.Women:0.95 37.694044 1.11089162
## 50-54.Women:0.95 36.793398 0.70671318
## 55-59.Women:0.95 39.298453 0.92636829
## 60-64.Women:0.95 38.906935 0.85323851
## 65-69.Women:0.95 39.432514 0.96286440
## 70-74.Women:0.95 36.669874 0.72321768
## 75-79.Women:0.95 36.314894 0.99758560
## Over 80.Women:0.95 33.937865 1.05853917Plot Total Fat % * WHtR
By Gender.
## Warning: Removed 3183 rows containing non-finite values (stat_density).
## Warning: Removed 2462 rows containing non-finite values (stat_density).