names(NHANES)
[1] "ID" "SurveyYr" "Gender" "Age" "AgeDecade"
[6] "AgeMonths" "Race1" "Race3" "Education" "MaritalStatus"
[11] "HHIncome" "HHIncomeMid" "Poverty" "HomeRooms" "HomeOwn"
[16] "Work" "Weight" "Length" "HeadCirc" "Height"
[21] "BMI" "BMICatUnder20yrs" "BMI_WHO" "Pulse" "BPSysAve"
[26] "BPDiaAve" "BPSys1" "BPDia1" "BPSys2" "BPDia2"
[31] "BPSys3" "BPDia3" "Testosterone" "DirectChol" "TotChol"
[36] "UrineVol1" "UrineFlow1" "UrineVol2" "UrineFlow2" "Diabetes"
[41] "DiabetesAge" "HealthGen" "DaysPhysHlthBad" "DaysMentHlthBad" "LittleInterest"
[46] "Depressed" "nPregnancies" "nBabies" "Age1stBaby" "SleepHrsNight"
[51] "SleepTrouble" "PhysActive" "PhysActiveDays" "TVHrsDay" "CompHrsDay"
[56] "TVHrsDayChild" "CompHrsDayChild" "Alcohol12PlusYr" "AlcoholDay" "AlcoholYear"
[61] "SmokeNow" "Smoke100" "Smoke100n" "SmokeAge" "Marijuana"
[66] "AgeFirstMarij" "RegularMarij" "AgeRegMarij" "HardDrugs" "SexEver"
[71] "SexAge" "SexNumPartnLife" "SexNumPartYear" "SameSex" "SexOrientation"
[76] "PregnantNow" NHANES %>%
select(Age, Gender, Race1, Education, Work, Weight, Length, SmokeAge, SmokeNow) %>%
tbl_summary(by = Gender) %>%
add_n() %>%
add_p()head_earn <- glm(HHIncome ~ Gender + Education + Race1, data = NHANES )
‘-’ not meaningful for factors‘-’ not meaningful for factors‘-’ not meaningful for factorsError in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, :
NA/NaN/Inf in 'y'| Characteristic | OR1 | 95% CI1 | p-value |
|---|---|---|---|
| Gender | |||
| female | — | — | |
| male | 2.13 | 1.46, 3.15 | <0.001 |
| Education | |||
| 8th Grade | — | — | |
| 9 - 11th Grade | 1.28 | 0.68, 2.34 | 0.4 |
| High School | 2.52 | 1.32, 4.76 | 0.005 |
| Some College | 3.30 | 1.73, 6.18 | <0.001 |
| College Grad | 4.75 | 2.33, 9.75 | <0.001 |
| Race1 | |||
| Black | — | — | |
| Hispanic | 1.59 | 0.77, 3.54 | 0.2 |
| Mexican | 1.11 | 0.60, 2.06 | 0.7 |
| White | 2.60 | 1.60, 4.13 | <0.001 |
| Other | 3.30 | 1.35, 9.90 | 0.017 |
|
1
OR = Odds Ratio, CI = Confidence Interval
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