library("dplyr")
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr)
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
library(foreign)
#download datasets - AddHealth
wave1<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0001/da27021p1.dta")
wave3<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0003/da27021p3.dta")
wave4<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0012/da27021p12.dta")
wave4a<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0013/da27021p13.dta")
wave5<-read.xport("/projects/add_health/data/wave_5/wave5.xpt")
wave5age<-read.xport("/projects/add_health/data/wave_5/Constructed Files/Wave V Constructed Age/agew5.xpt")
#wave3section19<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0006/da27021p6.dta")
#download biomarkers Wave 4
w4glu <- read.xport("/projects/add_health/data/wave_4/biomarkers/glu_a1c.xpt")
w4crp <- read.xport("/projects/add_health/data/wave_4/biomarkers/crp_ebv.xpt")
w4lipids <- read.xport("/projects/add_health/data/wave_4/biomarkers/lipids.xpt")
#weightW4 <- read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0028/da27021p28.dta")
#weightW4<-read.xport("/projects/add_health/data/wave_4/Wave IV Grand Sample Weights/weights4/weights4.xpt")
#download biomarkers wave 5
w5brenal<-read.xport("/projects/add_health/data/wave_5/brenal5.xpt")
w5blipids<-read.xport("/projects/add_health/data/wave_5/blipids5.xpt")
w5bcardio<-read.xport("/projects/add_health/data/wave_5/bcardio5.xpt")
w5bglua1c<-read.xport("/projects/add_health/data/wave_5/bglua1c5.xpt")
w5banthro<-read.xport("/projects/add_health/data/wave_5/banthro5.xpt")
w5bcrp<-read.xport("/projects/add_health/data/wave_5/bcrp5.xpt")
w5bdemo<-read.xport("/projects/add_health/data/wave_5/bdemo5.xpt")
w5bweight<-read.xport("/projects/add_health/data/wave_5/bweight5.xpt")
#w5bweight=read.dta("/projects/add_health/data/wave_5/wave5weight.dta")
friendship <- read.dta("/projects/add_health/data/11613344/ICPSR_27033/DS0001/schoolnetworkvariables.dta")
w1school<-read.dta("/projects/add_health/data/11613344/ICPSR_27021/DS0019/da27021p19.dta")
w1school$s11<-w1school$S11
w1school$s17<-w1school$S17
w1school$s25<-w1school$S25
w1school$s26<-w1school$S26
w1school$s44<-w1school$S44
w1school$s46a<-w1school$S46A
w1school$s46d<-w1school$S46D
w1school2<-w1school%>%
dplyr::select(aid,s11,s17,s25,s26,s44,s46a,s46d)
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
names(wave5)[1] <- "aid"
names(wave5age)[1] <- "aid"
names(w4glu)[1] <- "aid"
names(w4crp)[1] <- "aid"
names(w4lipids)[1] <- "aid"
#names(weightW4)[1] <- "aid"
names(w5brenal)[1] <- "aid"
names(w5blipids)[1] <- "aid"
names(w5bcardio)[1] <- "aid"
names(w5bglua1c)[1] <- "aid"
names(w5banthro)[1] <- "aid"
names(w5bcrp)[1] <- "aid"
names(w5bdemo)[1] <- "aid"
names(w5bweight)[1] <- "aid"
#change uppercase to lower case
#wave5$aid<-wave5$AID
#wave5age$aid<-wave5age$AID
#w4glu$aid<-w4glu$AID
#w4crp$aid<-w4crp$AID
#w4lipids$aid<-w4lipids$AID
#remove variables
#wave5 = subset(wave5, select = -c(AID) )
#w4glu = subset(w4glu, select = -c(AID) )
#w4crp = subset(w4crp, select = -c(AID) )
#w4lipids = subset(w4lipids, select = -c(AID) )
#merge datasets
a<-merge(wave1,wave3,by="aid")
bb<-merge(a,wave4,by="aid")
b<-merge(bb,wave4a,by="aid")
c<-merge(b,wave5,by="aid")
d<-merge(c,w4glu,by="aid")
e<-merge(d,w4crp,by="aid")
f<-merge(e,w4lipids,by="aid")
addhealtha<-merge(f,w5bweight,by="aid")
addhealthb<-merge(addhealtha,wave5age,by="aid")
addhealthc<-merge(addhealthb,w5brenal,by="aid")
addhealthd<-merge(addhealthc,w5blipids,by="aid")
addhealthe<-merge(addhealthd,w5bcardio,by="aid")
addhealthf<-merge(addhealthe,w5bglua1c,by="aid")
addhealthg<-merge(addhealthf,w5banthro,by="aid")
addhealthh<-merge(addhealthg,w5bcrp,by="aid")
addhealthi<-merge(addhealthh,w5bdemo,by="aid")
addhealthj<-merge(addhealthi,friendship,by="aid")
addhealth<-merge(addhealthj,w1school2,by="aid")
addhealth$livesMother<-Recode(addhealth$s11,recodes="0=0;1=1;else=NA")
addhealth$livesFather<-Recode(addhealth$s17,recodes="0=0;1=1;else=NA")
addhealth$livesParent<-ifelse(addhealth$livesMother==1&addhealth$livesFather==1,"lives_2_parents",
ifelse(addhealth$livesMother==1&addhealth$livesFather==0,"lives_mom",
ifelse(addhealth$livesMother==0&addhealth$livesFather==1,"lives_dad",
ifelse(addhealth$livesMother==0&addhealth$livesFather==0,"lives_other",NA))))
addhealth$livesParent2<-ifelse(addhealth$livesMother==1&addhealth$livesFather==1,"lives_2_parents",
ifelse(addhealth$livesMother==1&addhealth$livesFather==0,"lives_1_parent",
ifelse(addhealth$livesMother==0&addhealth$livesFather==1,"lives_1_parent",
ifelse(addhealth$livesMother==0&addhealth$livesFather==0,"lives_other",NA))))
addhealth$adopt<-ifelse(addhealth$s25==0,"no",
ifelse(addhealth$s25==1&addhealth$s26==0,"adoptNONbioparent",
ifelse(addhealth$s25==1&addhealth$s26==1,"adoptbioparent",NA)))
addhealth$schoolClubJoin<-addhealth$s44
addhealth$teacherTrouble<-Recode(addhealth$s46a,recodes="0:1=0;2:4=1;else=NA")
addhealth$studentTrouble<-Recode(addhealth$s46d,recodes="0:1=0;2:4=1;else=NA")
table(addhealth$schoolClubJoin)
##
## 0 1
## 4980 907
library(zoo)
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(car)
#age wave 4
#birth month wave 4
#addhealth$birthmonthw4<-Recode(addhealth$H4OD1M,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
#birth year wave 4
#addhealth$birthyearw4<-addhealth$H4OD1Y
#addhealth$birthyearw4 <- Recode(addhealth$H4OD1Y,recodes="1974=1974;1975=1975;1976=1976;1977=1977;1978=1978;1979=1979;1980=1980;1981=1981;1982=1982;1983=1983")
#combine year and month for birth date wave 4
#addhealth$birthdatew4 <- as.yearmon(paste(addhealth$birthyearw4, addhealth$birthmonthw4), "%Y %m")
#interview date w4
#addhealth$interviewmonthw4 <- Recode(addhealth$IMONTH4,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
#addhealth$interviewyearw4 <- Recode(addhealth$IYEAR4,recodes="2007=2007;2008=2008;2009=2009")
#addhealth$interviewdatew4<-as.yearmon(paste(addhealth$interviewyearw4,addhealth$interviewmonthw4),"%Y %m")
#age - derived from date of birth subtracted from wave interview date
#addhealth$ageW4<-(as.numeric(round(addhealth$interviewdatew4 - addhealth$birthdatew4)))
#table(addhealth$ageW4)
#Date of birth variables; Wave 1: H1GI1M (month), H1GI1Y (year); Wave 3: H3OD1M (month), H3OD1Y (year); Wave 4: H4OD1M (month), H4OD1Y (year)
addhealth$birthmonthw4 <- Recode(addhealth$H4OD1M,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
addhealth$birthyearw1 <- factor(ifelse(addhealth$H1GI1Y=="74",1974,
ifelse(addhealth$H1GI1Y=="75",1975,
ifelse(addhealth$H1GI1Y=="76",1976,
ifelse(addhealth$H1GI1Y=="77",1977,
ifelse(addhealth$H1GI1Y=="78",1978,
ifelse(addhealth$H1GI1Y=="79",1979,
ifelse(addhealth$H1GI1Y=="80",1980,
ifelse(addhealth$H1GI1Y=="81",1981,
ifelse(addhealth$H1GI1Y=="82",1982,
ifelse(addhealth$H1GI1Y=="83",1983,1979)))))))))))
addhealth$birthmonthw1 <- Recode(addhealth$H1GI1M,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12;else=NA")
addhealth$birthyearw4<-addhealth$H4OD1Y
addhealth$birthyearw4 <- Recode(addhealth$H4OD1Y,recodes="1974=1974;1975=1975;1976=1976;1977=1977;1978=1978;1979=1979;1980=1980;1981=1981;1982=1982;1983=1983")
addhealth$interviewmonthw4 <- Recode(addhealth$IMONTH4,recodes="1=1;2=2;3=3;4=4;5=5;6=6;7=7;8=8;9=9;10=10;11=11;12=12")
table(addhealth$interviewmonthw4)
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 277 1056 1141 1147 870 450 332 241 147 98 81 47
addhealth$interviewyearw4 <- Recode(addhealth$IYEAR4,recodes="2007=2007;2008=2008;2009=2009")
addhealth$iyearfix <- Recode(addhealth$iyear,recodes="'94'='1994';'95'='1995'")
addhealth$birthdatew4 <- as.yearmon(paste(addhealth$birthyearw4, addhealth$birthmonthw4), "%Y %m")
addhealth$interviewdatew4<-as.yearmon(paste(addhealth$interviewyearw4,addhealth$interviewmonthw4),"%Y %m")
addhealth$birthdatew3<-as.yearmon(paste(addhealth$H3OD1Y,addhealth$H3OD1M),"%Y %m")
addhealth$interviewdatew3<-as.yearmon(paste(addhealth$IYEAR3,addhealth$IMONTH3 ),"%Y %m")
addhealth$birthdatew1 <- as.yearmon(paste(addhealth$birthyearw1, addhealth$birthmonthw1), "%Y %m")
addhealth$interviewdatew1 <- as.yearmon(paste(addhealth$iyearfix, addhealth$imonth), "%Y %m")
addhealth$ageW4<-(as.numeric(round(addhealth$interviewdatew4 - addhealth$birthdatew4)))
addhealth$ageW3<-(as.numeric(round(addhealth$interviewdatew3 - addhealth$birthdatew3)))
addhealth$ageW1<-(as.numeric(round(addhealth$interviewdatew1 - addhealth$birthdatew1)))
addhealth$ageW5<-addhealth$AGE
#sex assigned at birth
addhealth$sex<-Recode(addhealth$H5OD2A,recodes="1='Male';2='Female';else=NA")
addhealth$sexW5<-factor(addhealth$sex,levels=c("Male","Female"))
#Sexual Orientation
# Wave 3
addhealth$sxorW3<-Recode(addhealth$H3SE13, recodes="1:2='Straight'; 3='Bisexual';4:5='Gay';else=NA")
addhealth$sexualOrientationW3 <- factor(addhealth$sxorW3, levels = c("Straight", "Gay", "Bisexual"))
# Wave 4
addhealth$sxorW4<-Recode(addhealth$H4SE31, recodes="1:2='Straight'; 3='Bisexual';4:5='Gay';else=NA")
addhealth$sexualOrientationW4 <- factor(addhealth$sxorW4, levels = c("Straight", "Gay", "Bisexual"))
# Wave 5
addhealth$sxorW5<-Recode(addhealth$H5SE15,recodes="1:2='Straight';3='Bisexual';4:5='Gay';else=NA")
addhealth$sexualOrientationW5 <- factor(addhealth$sxorW5, levels = c("Straight", "Gay", "Bisexual"))
#education wave 5
addhealth$educW5<-Recode(addhealth$H5OD11,recodes="1:2=0;3:9=1;10:16=2;else=NA")
addhealth$educW5<-as.factor(addhealth$educW5)
#employment status wave 1
#addhealth$H1EE3[addhealth$H1EE3 == 6] <- NA
#addhealth$H1EE3[addhealth$H1EE3 == 8] <- NA
#addhealth$employmentW1<-addhealth$H1EE3
#employment status wave 3
#addhealth$H3LM1[addhealth$H3LM1 == 6] <- NA
#addhealth$H3LM1[addhealth$H3LM1 == 8] <- NA
#addhealth$H3LM2[addhealth$H3LM2 == 6] <- NA
#addhealth$H3LM2[addhealth$H3LM2 == 7] <- NA
#addhealth$H3LM2[addhealth$H3LM2 == 8] <- NA
#addhealth$H3LM2[addhealth$H3LM2 == 9] <- NA
#addhealth$H3LM7[addhealth$H3LM7 == 7] <- NA
#addhealth$employmentW3<-ifelse(addhealth$H3LM1<2&addhealth$H3LM7>0,1,
# ifelse(addhealth$H3LM1<2&addhealth$H3LM7<1,0,
# ifelse(addhealth$H3LM2<2&addhealth$H3LM7>0,1,
# ifelse(addhealth$H3LM2<2&addhealth$H3LM7<1,0,NA))))
#employment status wave 4
#addhealth$H4LM1[addhealth$H4LM1 == 6] <- NA
#addhealth$H4LM2[addhealth$H4LM2 == 6] <- NA
#addhealth$H4LM2[addhealth$H4LM2 == 7] <- NA
#addhealth$employmentW4<-ifelse(addhealth$H4LM1<2&addhealth$H4LM14<11,0,
# ifelse(addhealth$H4LM2<2&addhealth$H4LM14<11,0,1))
#employment status Wave 5
#addhealth$employmentW5<-ifelse(addhealth$H5LM5==1,1,0)
#addhealth$employmentW1<-as.factor(addhealth$employmentW1)
#addhealth$employmentW3<-as.factor(addhealth$employmentW3)
#addhealth$employmentW4<-as.factor(addhealth$employmentW4)
#addhealth$employmentW5<-as.factor(addhealth$employmentW5)
#marital status Wave 1
addhealth$H1GI15[addhealth$H1GI15 == 6] <- NA
addhealth$H1GI15[addhealth$H1GI15 == 7] <- NA
addhealth$H1GI15[addhealth$H1GI15 == 8] <- NA
addhealth$maritalW1<-ifelse(addhealth$ageW1<15,0,
ifelse(addhealth$ageW1>14&addhealth$H1GI15<1,0,
ifelse(addhealth$ageW1>14&addhealth$H1GI15>0,1,NA)))
#marital status Wave 3 NOTE 0=never married, 1=married, 2=widow, 3=divorce
addhealth$H3MR1[addhealth$H3MR1 == 6] <- NA
addhealth$H3MR1[addhealth$H3MR1 == 8] <- NA
addhealth$H3MR1[addhealth$H3MR1 == 9] <- NA
addhealth$H3MR3_A[addhealth$H3MR3_A == 7] <- NA
addhealth$H3MR3_A[addhealth$H3MR3_A == 8] <- NA
addhealth$maritalW3<-ifelse(addhealth$H3MR1==0,0,
ifelse(addhealth$H3MR1>0&addhealth$H3MR3_A==1,1,2))
#marital status wave 4
addhealth$H4TR1[addhealth$H4TR1 == 6] <- NA
addhealth$H4TR1[addhealth$H4TR1 == 8] <- NA
addhealth$maritalW4<-ifelse(addhealth$H4TR1==0,0,
ifelse(addhealth$H4TR1>0&addhealth$H4TR13==1,1,
ifelse(addhealth$H4TR1>0&addhealth$H4TR13!=1,2,NA)))
#marital status wave 5
addhealth$maritalW5<-Recode(addhealth$H5HR1,recodes="1=1;2:4=2;5=0;else=NA")
#addhealth$maritalW1<-as.factor(addhealth$maritalW1)
#addhealth$maritalW3<-as.factor(addhealth$maritalW3)
#addhealth$maritalW4<-as.factor(addhealth$maritalW4)
#addhealth$maritalW5<-as.factor(addhealth$maritalW5)
#table(addhealth$maritalW5)
#combine race to one variable. This is for white, black, hispanic, asian, native american, pacific islander, and other.
addhealth$raceW5all <- factor(ifelse(addhealth$H5OD4A==1,"a_white",
ifelse(addhealth$H5OD4B==1, "b_black",
ifelse(addhealth$H5OD4C==1, "c_hispanic",
ifelse(addhealth$H5OD4D==1,"d_asian",
ifelse(addhealth$H5OD4F==1,"e_nativeamer",
ifelse(addhealth$H5OD4E==1,"f_pacificisl",
ifelse(addhealth$H5OD4G==1,"g_other",NA))))))))
#combine race to one variable. This is for white, black, hispanic, asian, native american, pacific islander, and other.
addhealth$raceW5min <- factor(ifelse(addhealth$H5OD4A==1,"a_white",
ifelse(addhealth$H5OD4B==1, "b_black",
ifelse(addhealth$H5OD4C==1, "c_hispanic",NA))))
library(StatMeasures)
#biomarkers wave 5
#1 systolic bp
addhealth$H5SBP<-Recode(addhealth$H5SBP,recodes="9999=NA;9997=NA;9996=NA")
addhealth$systolicbpW5<-ifelse(addhealth$H5SBP>=140,1,0)
#2 diastolic bp
addhealth$H5DBP<-Recode(addhealth$H5DBP,recodes="9999=NA;9997=NA;9996=NA")
addhealth$diastolicbpW5<-ifelse(addhealth$H5DBP>=90,1,0)
#3 pulse rate
addhealth$H5PR<-Recode(addhealth$H5PR,recodes="9999=NA;9997=NA;9996=NA")
addhealth$pulserateW5<-ifelse(addhealth$H5PR>=85,1,0)
#4 pulse pressure
addhealth$H5PP<-Recode(addhealth$H5PP,recodes="9999=NA;9997=NA;9996=NA")
addhealth$pulsepressureW5<-ifelse(addhealth$H5PP>=60,1,0)
#5 TOTAL CHOLESTEROL W5
addhealth$tcW5<-decile(addhealth$H5TC)
addhealth$totalcholW5<-ifelse(addhealth$tcW5>=8,1,0)
table(addhealth$totalcholW5)
##
## 0 1
## 3741 1519
#6 HDL, HIGH-DENSITY LIPOPROTEIN CHOLESTEROL
addhealth$H5HDL<-Recode(addhealth$H5HDL,recodes="9999=NA")
addhealth$hdlW5<-decile(addhealth$H5HDL)
addhealth$hidensity_lipW5<-ifelse(addhealth$hdlW5<=2,1,0)
#7 TRIGLYCERIDES
addhealth$TG<-decile(addhealth$H5TG)
addhealth$triglycerideW5<-ifelse(addhealth$TG>=8,1,0)
#8 HEMOGLOBIN
addhealth$hemoglobinW5<-ifelse(addhealth$H5HBA1C>=6.4,1,0)
table(addhealth$hemoglobinW5)
##
## 0 1
## 4901 246
#9 glucose
addhealth$GLUCOSE<-Recode(addhealth$H5GLUCOS,recodes="9999=NA")
addhealth$glucoseW5<-ifelse(addhealth$GLUCOSE>=200,1,0)
table(addhealth$glucoseW5)
##
## 0 1
## 5123 132
#10 BMI
addhealth$H5BMI<-Recode(addhealth$H5BMI,recodes="9999=NA;9996=NA;9997=NA;9994=NA")
addhealth$bmiW5<-ifelse(addhealth$H5BMI>=30,1,0)
table(addhealth$bmiW5)
##
## 0 1
## 2982 2780
#11 waist circumference
addhealth$waistW5<-Recode(addhealth$H5WSTCLS,recodes="1=0;2=1")
#12 high-sensitivity C-reactive protein test.
addhealth$hsCRPW5<-ifelse(addhealth$H5CRP>=3,1,0)
table(addhealth$hsCRPW5)
##
## 0 1
## 3124 1938
# COMBINE BIOMARKERS
addhealth$scoreW5<-addhealth$systolicbpW5+addhealth$diastolicbpW5+addhealth$pulserateW5+addhealth$pulsepressureW5+addhealth$totalcholW5+addhealth$hidensity_lipW5+addhealth$triglycerideW5+addhealth$hemoglobinW5+addhealth$glucoseW5+addhealth$bmiW5+addhealth$waistW5+addhealth$hsCRPW5
table(addhealth$scoreW5)
##
## 0 1 2 3 4 5 6 7 8 9 10
## 784 687 761 788 608 418 337 169 76 32 20
#biomarkers Wave 4
#1 systolic bp
addhealth$H4SBP<-Recode(addhealth$H4SBP,recodes="999=NA;997=NA;996=NA")
addhealth$systolicbp<-ifelse(addhealth$H4SBP>=140,1,0)
#2 diastolic bp
addhealth$H4DBP<-Recode(addhealth$H4DBP,recodes="999=NA;997=NA;996=NA")
addhealth$diastolicbp<-ifelse(addhealth$H4DBP>=90,1,0)
#3 pulse rate
addhealth$H4PR<-Recode(addhealth$H4PR,recodes="999=NA;997=NA;996=NA")
addhealth$pulserate<-ifelse(addhealth$H4PR>=85,1,0)
#4 pulse pressure
addhealth$H4PP<-Recode(addhealth$H4PP,recodes="999=NA;997=NA;996=NA")
addhealth$pulsepressure<-ifelse(addhealth$H4PP>=60,1,0)
#5 TOTAL CHOLESTEROL
addhealth$TC<-Recode(addhealth$TC,recodes="99=NA")
addhealth$totalchol<-ifelse(addhealth$TC>=8,1,0)
#6 HDL, HIGH-DENSITY LIPOPROTEIN CHOLESTEROL
addhealth$HDL<-Recode(addhealth$HDL,recodes="99=NA")
addhealth$hidensity_lip<-ifelse(addhealth$HDL<=2,1,0)
#7 TRIGLYCERIDES
addhealth$TG<-Recode(addhealth$TG,recodes="99=NA")
addhealth$triglyceride<-ifelse(addhealth$TG>=8,1,0)
#8 HEMOGLOBIN
addhealth$HBA1C<-Recode(addhealth$HBA1C,recodes="99=NA")
addhealth$hemoglobin<-ifelse(addhealth$HBA1C>=6.4,1,0)
#9 glucose
addhealth$GLUCOSE<-Recode(addhealth$GLUCOSE,recodes="999=NA")
addhealth$glucose<-ifelse(addhealth$GLUCOSE>=200,1,0)
#10 BMI
addhealth$H4BMI<-Recode(addhealth$H4BMI,recodes="999=NA;888=NA;889=NA;996=NA;997=NA")
addhealth$BMI<-ifelse(addhealth$H4BMI>=30,1,0)
# 11 high-sensitivity C-reactive protein test.
addhealth$CRPa<-Recode(addhealth$CRP,recodes="999=NA;998=NA")
addhealth$hsCRP<-ifelse(addhealth$CRPa>=3,1,0)
#EPSTEIN BARR
#quantile(addhealth$EBVa, c(.80), na.rm = T)
#addhealth$EBVa<-Recode(addhealth$EBV,recodes="9999=NA;9998=NA")
#addhealth$EpsteinBar<-ifelse(addhealth$EBVa>=243,1,0)
#12 waist circumference
addhealth$H4WAIST[addhealth$H4WAIST == 996] <- NA
addhealth$H4WAIST[addhealth$H4WAIST == 997] <- NA
addhealth$H4WAIST[addhealth$H4WAIST == 998] <- NA
addhealth$waist<-ifelse(addhealth$H5OD2A==2&addhealth$H4WAIST>88,1,
ifelse(addhealth$H5OD2A==2&addhealth$H4WAIST<89,0,
ifelse(addhealth$H5OD2A==1&addhealth$H4WAIST>102,1,
ifelse(addhealth$H5OD2A==1&addhealth$H4WAIST<103,0,NA))))
#combine biomarkers
addhealth$scoreW4<-addhealth$hsCRP+addhealth$BMI+addhealth$triglyceride+addhealth$hemoglobin+addhealth$hidensity_lip+addhealth$totalchol+addhealth$pulserate+addhealth$systolicbp+addhealth$pulsepressure+addhealth$diastolicbp+addhealth$glucose+addhealth$waist
summary(addhealth$HHincomeW3)
## Length Class Mode
## 0 NULL NULL
#household income wave 3
#addhealth$HHincomeW3<-Recode(addhealth$H3EC6)
addhealth$HHincomeW3<-ifelse(addhealth$H3EC6>0&addhealth$H3EC6<30000,'a_<30K',
ifelse(addhealth$H3EC6>29999&addhealth$H3EC6<50000,'b_30k<50k',
ifelse(addhealth$H3EC6>49999&addhealth$H3EC6<100000,'c_50k>100k',
ifelse(addhealth$H3EC6>99999,'d_>100k',NA))))
addhealth$HHincomeW3<-as.factor(addhealth$HHincomeW3)
#household income wave 5
addhealth$HHincomeW5<- Recode(addhealth$H5EC2,recodes="1:6='a_<30K';7:8='b_30k<50k';9:10='c_50k>100k';11:13='d_>100k';else=NA")
#household income wave 4
addhealth$HHincomeW4<- Recode(addhealth$H4EC1,recodes="1:6='a_<30K';7:8='b_30k<50k';9:10='c_50k>100k';11:12='d_>100k';else=NA")
#percieved closeness mom WAVE 1
addhealth$close.momW1<-Recode(addhealth$H1WP9,recodes="1=1;2=2;3=3;4=4;5=5;else=NA")
#percieved closeness mom WAVE 3
addhealth$close.momW3<-Recode(addhealth$H3WP20,recodes="5=1;4=2;3=3;2=4;1=5;else=NA")
#percieved closeness mom WAVE 4
addhealth$close.momW4<-Recode(addhealth$H4WP24,recodes="1=1;2=2;3=3;4=4;5=5;else=NA")
#percieved closeness mom WAVE 5
addhealth$close.momW5<-Recode(addhealth$H5WP14,recodes="1=1;2=2;3=3;4=4;5=5;else=NA")
#parent SUPPORT/SUPERVISION involvement
addhealth$curfew<-Recode(addhealth$H1WP1,recodes="0=0;1=1;else=NA")
addhealth$hang<-Recode(addhealth$H1WP2,recodes="0=0;1=1;else=NA")
addhealth$wear<-Recode(addhealth$H1WP3,recodes="0=0;1=1;else=NA")
addhealth$tv<-Recode(addhealth$H1WP4,recodes="0=0;1=1;else=NA")
addhealth$prog<-Recode(addhealth$H1WP5,recodes="0=0;1=1;else=NA")
addhealth$bed<-Recode(addhealth$H1WP6,recodes="0=0;1=1;else=NA")
addhealth$eat<-Recode(addhealth$H1WP7,recodes="0=0;1=1;else=NA")
addhealth$parentSupervise<-addhealth$curfew+addhealth$hang+addhealth$wear+addhealth$tv+addhealth$prog+addhealth$bed+addhealth$eat
#mothers education
addhealth$mom.educ<-Recode(addhealth$H1RM1,recodes="1:3=0;4:7=1;8:9=2;else=NA")
addhealth$mom.educ2<-Recode(addhealth$H1RM1,recodes="1:3=0;4:9=1;else=NA")
#mothers nativity
addhealth$mom.nativity<-Recode(addhealth$H1RM2,recodes="0=0;1=1;else=NA")
#mother employed
addhealth$mom.employed<-Recode(addhealth$H1RM4,recodes="16=0;1:15=1;else=NA")
#mother accecpt public assistance
addhealth$mom.pubass<-Recode(addhealth$H1RM9,recodes="0=0;1=1;else=NA")
addhealth$motherInequity<-addhealth$mom.educ2+addhealth$mom.nativity+addhealth$mom.employed+addhealth$mom.pubass
addhealth$relw1<-Recode(addhealth$H1RE1,recodes="0=0;1:28=1;else=NA")
addhealth$religionW1<-ifelse(addhealth$relw1==0,0,
ifelse(addhealth$relw1<2&addhealth$H1RE3==4,0,
ifelse(addhealth$relw1<2&addhealth$H1RE3==3,0,
ifelse(addhealth$relw1<2&addhealth$H1RE3==2,0,
ifelse(addhealth$relw1<2&addhealth$H1RE3==1,1,NA)))))
addhealth$religionW3<-Recode(addhealth$H3RE24,recodes="0:4=0;5:6=1;else=NA")
addhealth$religionW4<-Recode(addhealth$H4RE7,recodes="0:3=0;4:5=1;else=NA")
addhealth$religionW5<-Recode(addhealth$H5RE2,recodes="1:4=0;5:6=1")
#school prestige friends
addhealth$pre<-addhealth$prxprest
addhealth$schoolSeg<-addhealth$SEG1RCE5
#education level of 0=less high school, 1=highschool,
#education wave 3
addhealth$educW3<-ifelse(addhealth$H3ED2==1,1,
ifelse(addhealth$H3ED3==1,1,
ifelse(addhealth$H3ED5==1,2,
ifelse(addhealth$H3ED6==1&addhealth$H3ED5!=1,2,
ifelse(addhealth$H3ED7==1&addhealth$H3ED5!=1,2,
ifelse(addhealth$H3ED8==1&addhealth$H3ED5!=1,2,
ifelse(addhealth$H3ED9==1,0,NA)))))))
#education wave 4
addhealth$educW4<-Recode(addhealth$H4ED2,recodes="1:2=0;3:6=1;7:13=2;else=NA")
#education wave 5
addhealth$educW5<-Recode(addhealth$H5OD11,recodes="1:2=0;3:9=1;10:16=2;else=NA")
#education wave 1
addhealth$educW1<-addhealth$H1GI18
addhealth$educW1<-as.factor(addhealth$educW1)
addhealth$educW3<-as.factor(addhealth$educW3)
addhealth$educW4<-as.factor(addhealth$educW4)
addhealth$educW5<-as.factor(addhealth$educW5)
#employment
#employment binary scaled
addhealth$currently.employed<-Recode(addhealth$H5LM5,recodes="1=1;2:3=0;else=NA")
#employer provides insurance, benefits, sick leave
addhealth$H5LM13A<-Recode(addhealth$H5LM13A,recodes="0=0;1=1;else=NA")
addhealth$H5LM13B<-Recode(addhealth$H5LM13B,recodes="0=0;1=1;else=NA")
addhealth$H5LM13C<-Recode(addhealth$H5LM13C,recodes="0=0;1=1;else=NA")
#scale benefits
addhealth$benefitscore<-addhealth$H5LM13A+addhealth$H5LM13B+addhealth$H5LM13C
addhealth$beneScore<-Recode(addhealth$benefitscore,recodes="0=0;1:2=1;3=2")
table(addhealth$benefitscore)
##
## 0 1 2 3
## 604 380 351 3406
table(addhealth$beneScore)
##
## 0 1 2
## 604 731 3406
addhealth$employment<-ifelse(addhealth$currently.employed==0,"unemployed",
ifelse(addhealth$currently.employed==1&addhealth$beneScore==0,"empNoBene",
ifelse(addhealth$currently.employed==1&addhealth$beneScore==1,"empPartBene",
ifelse(addhealth$currently.employed==1&addhealth$beneScore==2,"empFullBene",NA))))
table(addhealth$currently.employed)
##
## 0 1
## 1084 4783
table(addhealth$benefitscore)
##
## 0 1 2 3
## 604 380 351 3406
table(addhealth$beneScore)
##
## 0 1 2
## 604 731 3406
addhealth$employment<-as.factor(addhealth$employment)
summary(addhealth$employment)
## empFullBene empNoBene empPartBene unemployed NA's
## 3406 604 731 1084 62
#install.packages("magrittr")
#library(data.table)
library(magrittr)
addhealthSub<-addhealth%>%
dplyr::select(aid,W5BIOWGT,raceW5min,sexW5,scoreW4,scoreW5,ageW1,ageW3,ageW4,ageW5,sexualOrientationW3,sexualOrientationW4,sexualOrientationW5,educW1,educW3,educW4,educW5,employment,maritalW1,maritalW3,maritalW4,maritalW5,religionW1,religionW3,religionW4,religionW5,HHincomeW3,HHincomeW4,HHincomeW5,livesParent2,close.momW1,close.momW3,close.momW4,close.momW5,parentSupervise,mom.educ,mom.educ2,mom.nativity,mom.employed,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,scoreW4,scoreW5)
out<-melt(setDT(addhealthSub), id = "aid",
measure.vars = list(age=c("ageW1","ageW3","ageW4","ageW5"),
sexualorientation=c("sexualOrientationW3","sexualOrientationW3", "sexualOrientationW4", "sexualOrientationW5"),
education=c("educW1","educW3", "educW4", "educW5"),
hhincome=c("HHincomeW3","HHincomeW4", "HHincomeW4", "HHincomeW5"),
marriage=c("maritalW1","maritalW3","maritalW4","maritalW5"),
religion=c("religionW1","religionW3","religionW4","religionW5"),
closeMom=c("close.momW1","close.momW3","close.momW4","close.momW5"),
score=c("scoreW4","scoreW4","scoreW4","scoreW5")))%>%
setorder(aid)
#library(dplyr)
addhealthLong<-addhealthSub%>%
dplyr::select(aid, W5BIOWGT,raceW5min,sexW5,livesParent2,parentSupervise,mom.educ,mom.educ2,mom.nativity,mom.employed,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,employment)%>%
left_join(., out, "aid")
addhealthLong$wave<-Recode(addhealthLong$variable,recodes="1=1;2=3;3=4;4=5")
addhealthS1long <- addhealthLong[,c(27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,19,20,21,22,23,24,25,26)]
addhealthS1long$sexualorientation <- factor(addhealthS1long$sexualorientation, levels = c("Straight", "Gay", "Bisexual"))
addhealthS1long$raceXsexo<-interaction(addhealthS1long$raceW5min,addhealthS1long$sexualorientation,drop=TRUE)
addhealthS1long$education<-as.factor(addhealthS1long$education)
addhealthS1long$hhincome<-as.factor(addhealthS1long$hhincome)
#addhealthS1long$employment<-as.factor(addhealthS1long$employment)
addhealthS1long$marriage<-as.factor(addhealthS1long$marriage)
addhealthS1long$mom.educ<-as.factor(addhealthS1long$mom.educ)
addhealthS1long$mom.educ2<-as.factor(addhealthS1long$mom.educ2)
addhealthS1long$mom.employed<-as.factor(addhealthS1long$mom.employed)
addhealthS1long$mom.pubass<-as.factor(addhealthS1long$mom.pubass)
addhealthS1long$mom.nativity<-as.factor(addhealthS1long$mom.nativity)
addhealthS1long$religion<-as.factor(addhealthS1long$religion)
addhealthS1long$livesParent2<-as.factor(addhealthS1long$livesParent2)
addhealthS1long$schoolClubJoin<-as.factor(addhealthS1long$schoolClubJoin)
addhealthS1long$teacherTrouble<-as.factor(addhealthS1long$teacherTrouble)
addhealthS1long$studentTrouble<-as.factor(addhealthS1long$studentTrouble)
addhealthS1long$parentSupervise<-as.factor(addhealthS1long$parentSupervise)
addhealthS1long$closeMom<-as.factor(addhealthS1long$closeMom)
addhealthexp<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo)
#library(tidyr)
addhealthBOMB<-addhealthexp%>%
filter(complete.cases(.))
addhealthBOMB$livesParent2<-factor(addhealthBOMB$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthBOMB$closeMom<-as.numeric(addhealthBOMB$closeMom)
addhealthBOMB$closeMom2<-Recode(addhealthBOMB$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthBOMB$parentSupervise<-as.numeric(addhealthBOMB$parentSupervise)
#table(addhealthBOMB$parentSupervise)
addhealthBOMB$parentSupervise2<-Recode(addhealthBOMB$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthBOMB$teacherTrouble2<-as.numeric(addhealthBOMB$teacherTrouble)
addhealthBOMB$studentTrouble2<-as.numeric(addhealthBOMB$studentTrouble)
addhealthBOMB$schoolconflict<-addhealthBOMB$studentTrouble2+addhealthBOMB$teacherTrouble2
addhealthBOMB$schoolconflict<-as.factor(addhealthBOMB$schoolconflict)
#install.packages("geepack")
library(geepack)
#wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,,,,,,,,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,
fit<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBOMB, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fit)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage, data = addhealthBOMB,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 3.4649189 0.6168953 31.547 1.95e-08 ***
## age -0.0875666 0.0379377 5.328 0.02099 *
## I(age^2) 0.0020074 0.0007525 7.116 0.00764 **
## sexW5Female 0.1581419 0.1211654 1.703 0.19183
## raceXsexob_black.Straight 0.3806230 0.1539909 6.109 0.01345 *
## raceXsexoc_hispanic.Straight 0.2365964 0.2456997 0.927 0.33557
## raceXsexoa_white.Gay -0.3981976 0.1595432 6.229 0.01257 *
## raceXsexob_black.Gay 0.1747051 0.2207857 0.626 0.42878
## raceXsexoc_hispanic.Gay 0.8971712 0.6982601 1.651 0.19884
## raceXsexoa_white.Bisexual 0.3043039 0.1867727 2.655 0.10326
## raceXsexob_black.Bisexual -0.1947599 0.3981531 0.239 0.62473
## raceXsexoc_hispanic.Bisexual 0.2228296 0.3274918 0.463 0.49624
## education1 -0.1437687 0.2037183 0.498 0.48036
## education2 -0.4305850 0.2211951 3.789 0.05158 .
## employmentempNoBene 0.0930432 0.1954456 0.227 0.63403
## employmentempPartBene 0.1428496 0.1765695 0.655 0.41850
## employmentunemployed 0.4651464 0.1819475 6.536 0.01057 *
## hhincomeb_30k<50k -0.3015014 0.1581869 3.633 0.05665 .
## hhincomec_50k>100k -0.0190452 0.1552813 0.015 0.90238
## hhincomed_>100k -0.0955309 0.1868809 0.261 0.60922
## marriage1 0.0170306 0.1052989 0.026 0.87151
## marriage2 0.2332339 0.2060918 1.281 0.25776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.602 0.2629
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.7911 0.1541
## Number of clusters: 2063 Maximum cluster size: 450
#education+employment+hhincome+marriage
#fit2<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBOMB, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fit2)
fit3<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthBOMB, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fit3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage + livesParent2 +
## mom.educ + mom.pubass + closeMom + parentSupervise + religion +
## schoolconflict + schoolClubJoin + scale(schoolSeg) + scale(pre),
## data = addhealthBOMB, weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T),
## id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 3.510446 0.710784 24.39 7.9e-07 ***
## age -0.094999 0.040990 5.37 0.0205 *
## I(age^2) 0.002116 0.000793 7.12 0.0076 **
## sexW5Female 0.123144 0.119761 1.06 0.3038
## raceXsexob_black.Straight 0.213006 0.163122 1.71 0.1916
## raceXsexoc_hispanic.Straight -0.047661 0.268801 0.03 0.8593
## raceXsexoa_white.Gay -0.393744 0.167590 5.52 0.0188 *
## raceXsexob_black.Gay -0.030754 0.233047 0.02 0.8950
## raceXsexoc_hispanic.Gay 0.531514 0.706117 0.57 0.4516
## raceXsexoa_white.Bisexual 0.318376 0.181094 3.09 0.0787 .
## raceXsexob_black.Bisexual -0.386330 0.399730 0.93 0.3338
## raceXsexoc_hispanic.Bisexual -0.158454 0.332452 0.23 0.6336
## education1 -0.156093 0.200158 0.61 0.4355
## education2 -0.446832 0.223275 4.01 0.0454 *
## employmentempNoBene 0.084296 0.192590 0.19 0.6616
## employmentempPartBene 0.071741 0.171427 0.18 0.6756
## employmentunemployed 0.416257 0.177641 5.49 0.0191 *
## hhincomeb_30k<50k -0.273597 0.150162 3.32 0.0685 .
## hhincomec_50k>100k -0.015090 0.151758 0.01 0.9208
## hhincomed_>100k -0.088484 0.180598 0.24 0.6242
## marriage1 0.030670 0.109289 0.08 0.7790
## marriage2 0.252877 0.209135 1.46 0.2266
## livesParent2lives_1_parent 0.244244 0.161862 2.28 0.1313
## livesParent2lives_other -0.119122 0.328301 0.13 0.7167
## mom.educ1 -0.360046 0.170907 4.44 0.0351 *
## mom.educ2 -0.484507 0.189160 6.56 0.0104 *
## mom.pubass1 0.347122 0.234055 2.20 0.1381
## closeMom 0.018181 0.038036 0.23 0.6327
## parentSupervise 0.060637 0.037326 2.64 0.1043
## religion1 -0.143936 0.120553 1.43 0.2325
## schoolconflict3 0.049418 0.133185 0.14 0.7106
## schoolconflict4 -0.033291 0.155031 0.05 0.8300
## schoolClubJoin1 0.297985 0.185828 2.57 0.1088
## scale(schoolSeg) 0.092086 0.059905 2.36 0.1242
## scale(pre) -0.087763 0.043465 4.08 0.0435 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.58 0.252
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.797 0.125
## Number of clusters: 2063 Maximum cluster size: 450
#fit4<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBOMB, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fit4)
library(huxtable)
##
## Attaching package: 'huxtable'
## The following object is masked from 'package:StatMeasures':
##
## contents
## The following object is masked from 'package:dplyr':
##
## add_rownames
allmodels<-huxreg(fit,fit3,statistics = character(0))
allmodels
| (1) | (2) |
| (Intercept) | 3.465 *** | 3.510 *** |
| (0.617) | (0.711) |
| age | -0.088 * | -0.095 * |
| (0.038) | (0.041) |
| I(age^2) | 0.002 ** | 0.002 ** |
| (0.001) | (0.001) |
| sexW5Female | 0.158 | 0.123 |
| (0.121) | (0.120) |
| raceXsexob_black.Straight | 0.381 * | 0.213 |
| (0.154) | (0.163) |
| raceXsexoc_hispanic.Straight | 0.237 | -0.048 |
| (0.246) | (0.269) |
| raceXsexoa_white.Gay | -0.398 * | -0.394 * |
| (0.160) | (0.168) |
| raceXsexob_black.Gay | 0.175 | -0.031 |
| (0.221) | (0.233) |
| raceXsexoc_hispanic.Gay | 0.897 | 0.532 |
| (0.698) | (0.706) |
| raceXsexoa_white.Bisexual | 0.304 | 0.318 |
| (0.187) | (0.181) |
| raceXsexob_black.Bisexual | -0.195 | -0.386 |
| (0.398) | (0.400) |
| raceXsexoc_hispanic.Bisexual | 0.223 | -0.158 |
| (0.327) | (0.332) |
| education1 | -0.144 | -0.156 |
| (0.204) | (0.200) |
| education2 | -0.431 | -0.447 * |
| (0.221) | (0.223) |
| employmentempNoBene | 0.093 | 0.084 |
| (0.195) | (0.193) |
| employmentempPartBene | 0.143 | 0.072 |
| (0.177) | (0.171) |
| employmentunemployed | 0.465 * | 0.416 * |
| (0.182) | (0.178) |
| hhincomeb_30k<50k | -0.302 | -0.274 |
| (0.158) | (0.150) |
| hhincomec_50k>100k | -0.019 | -0.015 |
| (0.155) | (0.152) |
| hhincomed_>100k | -0.096 | -0.088 |
| (0.187) | (0.181) |
| marriage1 | 0.017 | 0.031 |
| (0.105) | (0.109) |
| marriage2 | 0.233 | 0.253 |
| (0.206) | (0.209) |
| livesParent2lives_1_parent | | 0.244 |
| | (0.162) |
| livesParent2lives_other | | -0.119 |
| | (0.328) |
| mom.educ1 | | -0.360 * |
| | (0.171) |
| mom.educ2 | | -0.485 * |
| | (0.189) |
| mom.pubass1 | | 0.347 |
| | (0.234) |
| closeMom | | 0.018 |
| | (0.038) |
| parentSupervise | | 0.061 |
| | (0.037) |
| religion1 | | -0.144 |
| | (0.121) |
| schoolconflict3 | | 0.049 |
| | (0.133) |
| schoolconflict4 | | -0.033 |
| | (0.155) |
| schoolClubJoin1 | | 0.298 |
| | (0.186) |
| scale(schoolSeg) | | 0.092 |
| | (0.060) |
| scale(pre) | | -0.088 * |
| | (0.043) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
nobs(fit3)
## [1] 24657
addhealthwh<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
addhealthwhi<-addhealthwh%>%
subset(raceW5min=='a_white')
addhealthWhite<-addhealthwhi%>%
filter(complete.cases(.))
addhealthWhite$livesParent2<-factor(addhealthWhite$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthWhite$closeMom<-as.numeric(addhealthWhite$closeMom)
addhealthWhite$closeMom2<-Recode(addhealthWhite$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthWhite$parentSupervise<-as.numeric(addhealthWhite$parentSupervise)
#table(addhealthWhite$parentSupervise)
addhealthWhite$parentSupervise2<-Recode(addhealthWhite$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthWhite$teacherTrouble2<-as.numeric(addhealthWhite$teacherTrouble)
addhealthWhite$studentTrouble2<-as.numeric(addhealthWhite$studentTrouble)
addhealthWhite$schoolconflict<-addhealthWhite$studentTrouble2+addhealthWhite$teacherTrouble2
addhealthWhite$schoolconflict<-as.factor(addhealthWhite$schoolconflict)
addhealthWhite$raceXsexo<-as.factor(addhealthWhite$raceXsexo)
addhealthWhite$raceXsexo <- droplevels(addhealthWhite$raceXsexo)
table(addhealthWhite$raceXsexo)
##
## a_white.Straight a_white.Gay a_white.Bisexual
## 15312 490 609
fitWh<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthWhite, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitWh)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage, data = addhealthWhite,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 2.810140 0.567034 24.56 7.2e-07 ***
## age -0.031307 0.030707 1.04 0.308
## I(age^2) 0.000898 0.000649 1.91 0.166
## sexW5Female 0.100711 0.135129 0.56 0.456
## raceXsexoa_white.Gay -0.350094 0.174113 4.04 0.044 *
## raceXsexoa_white.Bisexual 0.223013 0.187780 1.41 0.235
## education1 -0.177601 0.269628 0.43 0.510
## education2 -0.532091 0.285953 3.46 0.063 .
## employmentempNoBene 0.083545 0.224884 0.14 0.710
## employmentempPartBene 0.135111 0.194388 0.48 0.487
## employmentunemployed 0.444388 0.201968 4.84 0.028 *
## hhincomeb_30k<50k -0.150562 0.180686 0.69 0.405
## hhincomec_50k>100k 0.160655 0.145681 1.22 0.270
## hhincomed_>100k 0.071093 0.180332 0.16 0.693
## marriage1 -0.021352 0.127952 0.03 0.867
## marriage2 0.128264 0.259592 0.24 0.621
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.63 0.321
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.557 0.0845
## Number of clusters: 1532 Maximum cluster size: 392
#education+employment+hhincome+marriage
#fit2<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthWhite, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fit2)
fitWh3<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthWhite, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitWh3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage + livesParent2 +
## mom.educ + mom.pubass + closeMom + parentSupervise + religion +
## schoolconflict + schoolClubJoin + scale(schoolSeg) + scale(pre),
## data = addhealthWhite, weights = W5BIOWGT/mean(W5BIOWGT,
## na.rm = T), id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 2.616554 0.652674 16.07 6.1e-05 ***
## age -0.031241 0.030760 1.03 0.3098
## I(age^2) 0.000896 0.000654 1.88 0.1708
## sexW5Female 0.082918 0.133961 0.38 0.5359
## raceXsexoa_white.Gay -0.367379 0.184855 3.95 0.0469 *
## raceXsexoa_white.Bisexual 0.232803 0.190112 1.50 0.2207
## education1 -0.152057 0.266101 0.33 0.5677
## education2 -0.485693 0.285829 2.89 0.0893 .
## employmentempNoBene 0.117340 0.221241 0.28 0.5959
## employmentempPartBene 0.057171 0.189720 0.09 0.7632
## employmentunemployed 0.395037 0.199120 3.94 0.0473 *
## hhincomeb_30k<50k -0.145013 0.183905 0.62 0.4304
## hhincomec_50k>100k 0.170580 0.148942 1.31 0.2521
## hhincomed_>100k 0.079809 0.181147 0.19 0.6595
## marriage1 -0.017018 0.129100 0.02 0.8951
## marriage2 0.132122 0.262653 0.25 0.6149
## livesParent2lives_1_parent 0.146458 0.197842 0.55 0.4591
## livesParent2lives_other 0.114116 0.395390 0.08 0.7729
## mom.educ1 -0.434468 0.200679 4.69 0.0304 *
## mom.educ2 -0.568908 0.215076 7.00 0.0082 **
## mom.pubass1 0.512910 0.327836 2.45 0.1177
## closeMom 0.014817 0.042193 0.12 0.7255
## parentSupervise 0.076303 0.042331 3.25 0.0715 .
## religion1 0.007071 0.069796 0.01 0.9193
## schoolconflict3 -0.027417 0.152315 0.03 0.8572
## schoolconflict4 0.023652 0.175534 0.02 0.8928
## schoolClubJoin1 0.313127 0.211909 2.18 0.1395
## scale(schoolSeg) 0.113061 0.060863 3.45 0.0632 .
## scale(pre) -0.109855 0.040076 7.51 0.0061 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.51 0.296
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.546 0.11
## Number of clusters: 1532 Maximum cluster size: 392
#fit4<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthWhite, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fit4)
#library(huxtable)
allmodelsWh<-huxreg(fitWh,fitWh3,statistics = character(0))
allmodelsWh
| (1) | (2) |
| (Intercept) | 2.810 *** | 2.617 *** |
| (0.567) | (0.653) |
| age | -0.031 | -0.031 |
| (0.031) | (0.031) |
| I(age^2) | 0.001 | 0.001 |
| (0.001) | (0.001) |
| sexW5Female | 0.101 | 0.083 |
| (0.135) | (0.134) |
| raceXsexoa_white.Gay | -0.350 * | -0.367 * |
| (0.174) | (0.185) |
| raceXsexoa_white.Bisexual | 0.223 | 0.233 |
| (0.188) | (0.190) |
| education1 | -0.178 | -0.152 |
| (0.270) | (0.266) |
| education2 | -0.532 | -0.486 |
| (0.286) | (0.286) |
| employmentempNoBene | 0.084 | 0.117 |
| (0.225) | (0.221) |
| employmentempPartBene | 0.135 | 0.057 |
| (0.194) | (0.190) |
| employmentunemployed | 0.444 * | 0.395 * |
| (0.202) | (0.199) |
| hhincomeb_30k<50k | -0.151 | -0.145 |
| (0.181) | (0.184) |
| hhincomec_50k>100k | 0.161 | 0.171 |
| (0.146) | (0.149) |
| hhincomed_>100k | 0.071 | 0.080 |
| (0.180) | (0.181) |
| marriage1 | -0.021 | -0.017 |
| (0.128) | (0.129) |
| marriage2 | 0.128 | 0.132 |
| (0.260) | (0.263) |
| livesParent2lives_1_parent | | 0.146 |
| | (0.198) |
| livesParent2lives_other | | 0.114 |
| | (0.395) |
| mom.educ1 | | -0.434 * |
| | (0.201) |
| mom.educ2 | | -0.569 ** |
| | (0.215) |
| mom.pubass1 | | 0.513 |
| | (0.328) |
| closeMom | | 0.015 |
| | (0.042) |
| parentSupervise | | 0.076 |
| | (0.042) |
| religion1 | | 0.007 |
| | (0.070) |
| schoolconflict3 | | -0.027 |
| | (0.152) |
| schoolconflict4 | | 0.024 |
| | (0.176) |
| schoolClubJoin1 | | 0.313 |
| | (0.212) |
| scale(schoolSeg) | | 0.113 |
| | (0.061) |
| scale(pre) | | -0.110 ** |
| | (0.040) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
addhealthM<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
addhealthMa<-addhealthM%>%
subset(sexW5=='Male')
addhealthMale<-addhealthMa%>%
filter(complete.cases(.))
addhealthMale$livesParent2<-factor(addhealthMale$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthMale$closeMom<-as.numeric(addhealthMale$closeMom)
addhealthMale$closeMom2<-Recode(addhealthMale$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthMale$parentSupervise<-as.numeric(addhealthMale$parentSupervise)
#table(addhealthMale$parentSupervise)
addhealthMale$parentSupervise2<-Recode(addhealthMale$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthMale$teacherTrouble2<-as.numeric(addhealthMale$teacherTrouble)
addhealthMale$studentTrouble2<-as.numeric(addhealthMale$studentTrouble)
addhealthMale$schoolconflict<-addhealthMale$studentTrouble2+addhealthMale$teacherTrouble2
addhealthMale$schoolconflict<-as.factor(addhealthMale$schoolconflict)
addhealthMale$raceXsexo<-factor(addhealthMale$raceXsexo)
table(addhealthMale$raceXsexo)
##
## a_white.Straight b_black.Straight c_hispanic.Straight a_white.Gay
## 5846 2887 643 188
## b_black.Gay c_hispanic.Gay a_white.Bisexual
## 92 13 48
fitM<-geeglm(score~age+I(age^2)+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthMale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitM)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + raceXsexo + education +
## employment + hhincome + marriage, data = addhealthMale, weights = W5BIOWGT/mean(W5BIOWGT,
## na.rm = T), id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 4.22685 0.87293 23.45 1.3e-06 ***
## age -0.15694 0.06021 6.79 0.0091 **
## I(age^2) 0.00344 0.00117 8.63 0.0033 **
## raceXsexob_black.Straight -0.01165 0.30750 0.00 0.9698
## raceXsexoc_hispanic.Straight 0.15088 0.41525 0.13 0.7163
## raceXsexoa_white.Gay -0.50843 0.34139 2.22 0.1364
## raceXsexob_black.Gay -0.09098 0.71681 0.02 0.8990
## raceXsexoc_hispanic.Gay 1.01029 0.93637 1.16 0.2806
## raceXsexoa_white.Bisexual 0.31074 0.47721 0.42 0.5150
## education1 0.19883 0.13938 2.03 0.1537
## education2 -0.14912 0.15676 0.90 0.3415
## employmentempNoBene -0.15896 0.30748 0.27 0.6052
## employmentempPartBene 0.02251 0.29305 0.01 0.9388
## employmentunemployed 0.14147 0.40020 0.12 0.7237
## hhincomeb_30k<50k -0.42724 0.23161 3.40 0.0651 .
## hhincomec_50k>100k -0.10488 0.24239 0.19 0.6652
## hhincomed_>100k -0.37208 0.25952 2.06 0.1516
## marriage1 -0.09106 0.18998 0.23 0.6317
## marriage2 0.11396 0.26340 0.19 0.6653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.21 0.306
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.703 0.0991
## Number of clusters: 737 Maximum cluster size: 450
#education+employment+hhincome+marriage
#fitM2<-geeglm(score~age+I(age^2)+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthMale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitM2)
fitM3<-geeglm(score~age+I(age^2)+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthMale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitM3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + raceXsexo + education +
## employment + hhincome + marriage + livesParent2 + mom.educ +
## mom.pubass + closeMom + parentSupervise + religion + schoolconflict +
## schoolClubJoin + scale(schoolSeg) + scale(pre), data = addhealthMale,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 4.89408 1.24504 15.45 8.5e-05 ***
## age -0.17396 0.06265 7.71 0.0055 **
## I(age^2) 0.00366 0.00118 9.63 0.0019 **
## raceXsexob_black.Straight -0.07358 0.31467 0.05 0.8151
## raceXsexoc_hispanic.Straight 0.05239 0.44042 0.01 0.9053
## raceXsexoa_white.Gay -0.53342 0.35785 2.22 0.1361
## raceXsexob_black.Gay -0.04096 0.73847 0.00 0.9558
## raceXsexoc_hispanic.Gay 0.78890 1.03395 0.58 0.4455
## raceXsexoa_white.Bisexual 0.21068 0.45279 0.22 0.6417
## education1 0.11304 0.17231 0.43 0.5118
## education2 -0.27092 0.20012 1.83 0.1758
## employmentempNoBene -0.15388 0.30473 0.25 0.6136
## employmentempPartBene 0.05431 0.28196 0.04 0.8473
## employmentunemployed 0.14372 0.36246 0.16 0.6917
## hhincomeb_30k<50k -0.33481 0.21120 2.51 0.1129
## hhincomec_50k>100k -0.10252 0.22246 0.21 0.6449
## hhincomed_>100k -0.37378 0.23604 2.51 0.1133
## marriage1 -0.05906 0.20750 0.08 0.7759
## marriage2 0.15954 0.28029 0.32 0.5692
## livesParent2lives_1_parent -0.05584 0.28728 0.04 0.8459
## livesParent2lives_other -0.23381 0.69942 0.11 0.7382
## mom.educ1 -0.61197 0.34954 3.07 0.0800 .
## mom.educ2 -0.60836 0.36604 2.76 0.0965 .
## mom.pubass1 -0.23801 0.35196 0.46 0.4989
## closeMom -0.02296 0.05861 0.15 0.6953
## parentSupervise 0.07596 0.06372 1.42 0.2332
## religion1 -0.30569 0.18808 2.64 0.1041
## schoolconflict3 -0.03964 0.21790 0.03 0.8557
## schoolconflict4 -0.19978 0.25949 0.59 0.4414
## schoolClubJoin1 0.35567 0.32657 1.19 0.2761
## scale(schoolSeg) 0.19222 0.09354 4.22 0.0399 *
## scale(pre) 0.03601 0.09228 0.15 0.6964
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.11 0.338
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.694 0.163
## Number of clusters: 737 Maximum cluster size: 450
#fitM4<-geeglm(score~age+I(age^2)+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthMale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitM4)
#library(huxtable)
allmodelsM<-huxreg(fitM,fitM3,statistics = character(0))
allmodelsM
| (1) | (2) |
| (Intercept) | 4.227 *** | 4.894 *** |
| (0.873) | (1.245) |
| age | -0.157 ** | -0.174 ** |
| (0.060) | (0.063) |
| I(age^2) | 0.003 ** | 0.004 ** |
| (0.001) | (0.001) |
| raceXsexob_black.Straight | -0.012 | -0.074 |
| (0.307) | (0.315) |
| raceXsexoc_hispanic.Straight | 0.151 | 0.052 |
| (0.415) | (0.440) |
| raceXsexoa_white.Gay | -0.508 | -0.533 |
| (0.341) | (0.358) |
| raceXsexob_black.Gay | -0.091 | -0.041 |
| (0.717) | (0.738) |
| raceXsexoc_hispanic.Gay | 1.010 | 0.789 |
| (0.936) | (1.034) |
| raceXsexoa_white.Bisexual | 0.311 | 0.211 |
| (0.477) | (0.453) |
| education1 | 0.199 | 0.113 |
| (0.139) | (0.172) |
| education2 | -0.149 | -0.271 |
| (0.157) | (0.200) |
| employmentempNoBene | -0.159 | -0.154 |
| (0.307) | (0.305) |
| employmentempPartBene | 0.023 | 0.054 |
| (0.293) | (0.282) |
| employmentunemployed | 0.141 | 0.144 |
| (0.400) | (0.362) |
| hhincomeb_30k<50k | -0.427 | -0.335 |
| (0.232) | (0.211) |
| hhincomec_50k>100k | -0.105 | -0.103 |
| (0.242) | (0.222) |
| hhincomed_>100k | -0.372 | -0.374 |
| (0.260) | (0.236) |
| marriage1 | -0.091 | -0.059 |
| (0.190) | (0.207) |
| marriage2 | 0.114 | 0.160 |
| (0.263) | (0.280) |
| livesParent2lives_1_parent | | -0.056 |
| | (0.287) |
| livesParent2lives_other | | -0.234 |
| | (0.699) |
| mom.educ1 | | -0.612 |
| | (0.350) |
| mom.educ2 | | -0.608 |
| | (0.366) |
| mom.pubass1 | | -0.238 |
| | (0.352) |
| closeMom | | -0.023 |
| | (0.059) |
| parentSupervise | | 0.076 |
| | (0.064) |
| religion1 | | -0.306 |
| | (0.188) |
| schoolconflict3 | | -0.040 |
| | (0.218) |
| schoolconflict4 | | -0.200 |
| | (0.259) |
| schoolClubJoin1 | | 0.356 |
| | (0.327) |
| scale(schoolSeg) | | 0.192 * |
| | (0.094) |
| scale(pre) | | 0.036 |
| | (0.092) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
addhealthFem<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
addhealthFema<-addhealthFem%>%
subset(sexW5=='Female')
addhealthFemale<-addhealthFema%>%
filter(complete.cases(.))
addhealthFemale$livesParent2<-factor(addhealthFemale$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthFemale$closeMom<-as.numeric(addhealthFemale$closeMom)
addhealthFemale$closeMom2<-Recode(addhealthFemale$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthFemale$parentSupervise<-as.numeric(addhealthFemale$parentSupervise)
#table(addhealthFemale$parentSupervise)
addhealthFemale$parentSupervise2<-Recode(addhealthFemale$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthFemale$teacherTrouble2<-as.numeric(addhealthFemale$teacherTrouble)
addhealthFemale$studentTrouble2<-as.numeric(addhealthFemale$studentTrouble)
addhealthFemale$schoolconflict<-addhealthFemale$studentTrouble2+addhealthFemale$teacherTrouble2
addhealthFemale$schoolconflict<-as.factor(addhealthFemale$schoolconflict)
addhealthFemale$raceXsexo<-factor(addhealthFemale$raceXsexo)
table(addhealthFemale$raceXsexo)
##
## a_white.Straight b_black.Straight c_hispanic.Straight a_white.Gay
## 9466 3282 1081 302
## b_black.Gay c_hispanic.Gay a_white.Bisexual b_black.Bisexual
## 120 30 561 70
## c_hispanic.Bisexual
## 28
fitF<-geeglm(score~age+I(age^2)+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthFemale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitF)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + raceXsexo + education +
## employment + hhincome + marriage, data = addhealthFemale,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 3.078619 0.639292 23.19 1.5e-06 ***
## age -0.026879 0.032320 0.69 0.40561
## I(age^2) 0.000722 0.000679 1.13 0.28794
## raceXsexob_black.Straight 0.643001 0.160909 15.97 6.4e-05 ***
## raceXsexoc_hispanic.Straight 0.259608 0.260898 0.99 0.31971
## raceXsexoa_white.Gay -0.389102 0.286384 1.85 0.17425
## raceXsexob_black.Gay 0.651857 0.309356 4.44 0.03511 *
## raceXsexoc_hispanic.Gay 2.241957 0.448893 24.94 5.9e-07 ***
## raceXsexoa_white.Bisexual 0.268287 0.206382 1.69 0.19362
## raceXsexob_black.Bisexual 0.159300 0.386485 0.17 0.68021
## raceXsexoc_hispanic.Bisexual 0.108363 0.332551 0.11 0.74453
## education1 -0.636224 0.370124 2.95 0.08562 .
## education2 -0.884396 0.408509 4.69 0.03039 *
## employmentempNoBene 0.319713 0.246741 1.68 0.19506
## employmentempPartBene 0.287783 0.226474 1.61 0.20383
## employmentunemployed 0.679409 0.190497 12.72 0.00036 ***
## hhincomeb_30k<50k -0.076757 0.173049 0.20 0.65736
## hhincomec_50k>100k 0.201772 0.141433 2.04 0.15369
## hhincomed_>100k 0.258528 0.192910 1.80 0.18020
## marriage1 0.102510 0.117089 0.77 0.38131
## marriage2 0.313539 0.302708 1.07 0.30030
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.79 0.361
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.65 0.0897
## Number of clusters: 1326 Maximum cluster size: 324
#education+employment+hhincome+marriage
#fitF2<-geeglm(score~age+I(age^2)+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthFemale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitF2)
fitF3<-geeglm(score~age+I(age^2)+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthFemale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitF3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + raceXsexo + education +
## employment + hhincome + marriage + livesParent2 + mom.educ +
## mom.pubass + closeMom + parentSupervise + religion + schoolconflict +
## schoolClubJoin + scale(schoolSeg) + scale(pre), data = addhealthFemale,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 2.713856 0.670535 16.38 5.2e-05 ***
## age -0.017887 0.034476 0.27 0.60389
## I(age^2) 0.000568 0.000724 0.62 0.43270
## raceXsexob_black.Straight 0.429745 0.181143 5.63 0.01767 *
## raceXsexoc_hispanic.Straight -0.042884 0.313175 0.02 0.89108
## raceXsexoa_white.Gay -0.477348 0.320072 2.22 0.13586
## raceXsexob_black.Gay 0.718911 0.263547 7.44 0.00638 **
## raceXsexoc_hispanic.Gay 1.802898 0.842873 4.58 0.03244 *
## raceXsexoa_white.Bisexual 0.335023 0.220206 2.31 0.12816
## raceXsexob_black.Bisexual -0.517748 0.453800 1.30 0.25391
## raceXsexoc_hispanic.Bisexual -0.117935 0.416777 0.08 0.77720
## education1 -0.659821 0.384451 2.95 0.08611 .
## education2 -0.836935 0.433242 3.73 0.05338 .
## employmentempNoBene 0.329643 0.238160 1.92 0.16632
## employmentempPartBene 0.101225 0.206244 0.24 0.62356
## employmentunemployed 0.539713 0.197750 7.45 0.00635 **
## hhincomeb_30k<50k -0.070950 0.179715 0.16 0.69300
## hhincomec_50k>100k 0.249148 0.147010 2.87 0.09012 .
## hhincomed_>100k 0.344520 0.205465 2.81 0.09359 .
## marriage1 0.120576 0.122367 0.97 0.32444
## marriage2 0.336581 0.310623 1.17 0.27856
## livesParent2lives_1_parent 0.424961 0.167894 6.41 0.01137 *
## livesParent2lives_other -0.256504 0.377995 0.46 0.49740
## mom.educ1 -0.173575 0.194065 0.80 0.37110
## mom.educ2 -0.462578 0.219494 4.44 0.03508 *
## mom.pubass1 0.743083 0.264443 7.90 0.00495 **
## closeMom 0.005114 0.051268 0.01 0.92054
## parentSupervise 0.045179 0.043005 1.10 0.29345
## religion1 0.037083 0.081015 0.21 0.64715
## schoolconflict3 0.211844 0.167829 1.59 0.20685
## schoolconflict4 0.095816 0.181679 0.28 0.59792
## schoolClubJoin1 0.276558 0.208396 1.76 0.18448
## scale(schoolSeg) -0.016519 0.073833 0.05 0.82297
## scale(pre) -0.149921 0.043409 11.93 0.00055 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.94 0.387
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 1.09 0.255
## Number of clusters: 1326 Maximum cluster size: 324
#fitF4<-geeglm(score~age+I(age^2)+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthFemale, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitF4)
#library(huxtable)
allmodelsF<-huxreg(fitF,fitF3,statistics = character(0))
allmodelsF
| (1) | (2) |
| (Intercept) | 3.079 *** | 2.714 *** |
| (0.639) | (0.671) |
| age | -0.027 | -0.018 |
| (0.032) | (0.034) |
| I(age^2) | 0.001 | 0.001 |
| (0.001) | (0.001) |
| raceXsexob_black.Straight | 0.643 *** | 0.430 * |
| (0.161) | (0.181) |
| raceXsexoc_hispanic.Straight | 0.260 | -0.043 |
| (0.261) | (0.313) |
| raceXsexoa_white.Gay | -0.389 | -0.477 |
| (0.286) | (0.320) |
| raceXsexob_black.Gay | 0.652 * | 0.719 ** |
| (0.309) | (0.264) |
| raceXsexoc_hispanic.Gay | 2.242 *** | 1.803 * |
| (0.449) | (0.843) |
| raceXsexoa_white.Bisexual | 0.268 | 0.335 |
| (0.206) | (0.220) |
| raceXsexob_black.Bisexual | 0.159 | -0.518 |
| (0.386) | (0.454) |
| raceXsexoc_hispanic.Bisexual | 0.108 | -0.118 |
| (0.333) | (0.417) |
| education1 | -0.636 | -0.660 |
| (0.370) | (0.384) |
| education2 | -0.884 * | -0.837 |
| (0.409) | (0.433) |
| employmentempNoBene | 0.320 | 0.330 |
| (0.247) | (0.238) |
| employmentempPartBene | 0.288 | 0.101 |
| (0.226) | (0.206) |
| employmentunemployed | 0.679 *** | 0.540 ** |
| (0.190) | (0.198) |
| hhincomeb_30k<50k | -0.077 | -0.071 |
| (0.173) | (0.180) |
| hhincomec_50k>100k | 0.202 | 0.249 |
| (0.141) | (0.147) |
| hhincomed_>100k | 0.259 | 0.345 |
| (0.193) | (0.205) |
| marriage1 | 0.103 | 0.121 |
| (0.117) | (0.122) |
| marriage2 | 0.314 | 0.337 |
| (0.303) | (0.311) |
| livesParent2lives_1_parent | | 0.425 * |
| | (0.168) |
| livesParent2lives_other | | -0.257 |
| | (0.378) |
| mom.educ1 | | -0.174 |
| | (0.194) |
| mom.educ2 | | -0.463 * |
| | (0.219) |
| mom.pubass1 | | 0.743 ** |
| | (0.264) |
| closeMom | | 0.005 |
| | (0.051) |
| parentSupervise | | 0.045 |
| | (0.043) |
| religion1 | | 0.037 |
| | (0.081) |
| schoolconflict3 | | 0.212 |
| | (0.168) |
| schoolconflict4 | | 0.096 |
| | (0.182) |
| schoolClubJoin1 | | 0.277 |
| | (0.208) |
| scale(schoolSeg) | | -0.017 |
| | (0.074) |
| scale(pre) | | -0.150 *** |
| | (0.043) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
addhealthBl<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
addhealthBla<-addhealthBl%>%
subset(raceW5min=='b_black')
addhealthBlack<-addhealthBla%>%
filter(complete.cases(.))
addhealthBlack$livesParent2<-factor(addhealthBlack$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthBlack$closeMom<-as.numeric(addhealthBlack$closeMom)
addhealthBlack$closeMom2<-Recode(addhealthBlack$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthBlack$parentSupervise<-as.numeric(addhealthBlack$parentSupervise)
#table(addhealthBlack$parentSupervise)
addhealthBlack$parentSupervise2<-Recode(addhealthBlack$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthBlack$teacherTrouble2<-as.numeric(addhealthBlack$teacherTrouble)
addhealthBlack$studentTrouble2<-as.numeric(addhealthBlack$studentTrouble)
addhealthBlack$schoolconflict<-addhealthBlack$studentTrouble2+addhealthBlack$teacherTrouble2
addhealthBlack$schoolconflict<-as.factor(addhealthBlack$schoolconflict)
addhealthBlack$raceXsexo<-factor(addhealthBlack$raceXsexo)
table(addhealthBlack$raceXsexo)
##
## b_black.Straight b_black.Gay b_black.Bisexual
## 6169 212 70
fitB<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBlack, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitB)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage, data = addhealthBlack,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 5.66537 1.37253 17.04 3.7e-05 ***
## age -0.25985 0.08813 8.69 0.0032 **
## I(age^2) 0.00531 0.00164 10.50 0.0012 **
## sexW5Female 0.64609 0.31170 4.30 0.0382 *
## raceXsexob_black.Gay -0.33687 0.19282 3.05 0.0806 .
## raceXsexob_black.Bisexual -0.56838 0.47390 1.44 0.2304
## education1 -0.17947 0.10226 3.08 0.0793 .
## education2 -0.15006 0.16925 0.79 0.3753
## employmentempNoBene 0.10722 0.40546 0.07 0.7914
## employmentempPartBene 0.49690 0.52581 0.89 0.3446
## employmentunemployed 0.83273 0.46445 3.21 0.0730 .
## hhincomeb_30k<50k -0.45002 0.26110 2.97 0.0848 .
## hhincomec_50k>100k -0.44935 0.28290 2.52 0.1122
## hhincomed_>100k -0.44841 0.37636 1.42 0.2335
## marriage1 0.20755 0.23772 0.76 0.3826
## marriage2 0.40471 0.38797 1.09 0.2969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.52 0.631
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.991 0.262
## Number of clusters: 367 Maximum cluster size: 450
#education+employment+hhincome+marriage
#fitB2<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBlack, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitB2)
fitB3<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthBlack, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitB3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage + livesParent2 +
## mom.educ + mom.pubass + closeMom + parentSupervise + religion +
## schoolconflict + schoolClubJoin + scale(schoolSeg) + scale(pre),
## data = addhealthBlack, weights = W5BIOWGT/mean(W5BIOWGT,
## na.rm = T), id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 5.88242 1.36066 18.69 1.5e-05 ***
## age -0.25675 0.08454 9.22 0.00239 **
## I(age^2) 0.00515 0.00152 11.51 0.00069 ***
## sexW5Female 0.53456 0.30287 3.12 0.07757 .
## raceXsexob_black.Gay -0.44335 0.20348 4.75 0.02934 *
## raceXsexob_black.Bisexual -0.54684 0.45495 1.44 0.22937
## education1 -0.33077 0.16231 4.15 0.04156 *
## education2 -0.32161 0.28327 1.29 0.25623
## employmentempNoBene -0.16980 0.40870 0.17 0.67780
## employmentempPartBene 0.36385 0.53327 0.47 0.49506
## employmentunemployed 0.65870 0.49973 1.74 0.18746
## hhincomeb_30k<50k -0.41690 0.23746 3.08 0.07914 .
## hhincomec_50k>100k -0.44460 0.25908 2.94 0.08615 .
## hhincomed_>100k -0.42108 0.34947 1.45 0.22823
## marriage1 0.22272 0.24663 0.82 0.36649
## marriage2 0.43838 0.37559 1.36 0.24314
## livesParent2lives_1_parent 0.39516 0.28252 1.96 0.16190
## livesParent2lives_other -0.78873 0.52457 2.26 0.13269
## mom.educ1 -0.46033 0.42895 1.15 0.28320
## mom.educ2 -0.26542 0.46733 0.32 0.57007
## mom.pubass1 0.24991 0.37810 0.44 0.50863
## closeMom 0.00985 0.08514 0.01 0.90790
## parentSupervise 0.01692 0.07891 0.05 0.83019
## religion1 -0.26865 0.21513 1.56 0.21174
## schoolconflict3 0.36945 0.33732 1.20 0.27341
## schoolconflict4 0.11827 0.34471 0.12 0.73153
## schoolClubJoin1 0.96260 0.46458 4.29 0.03827 *
## scale(schoolSeg) -0.08236 0.14231 0.33 0.56277
## scale(pre) -0.02931 0.14565 0.04 0.84050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.59 0.68
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.943 0.399
## Number of clusters: 367 Maximum cluster size: 450
#fitB4<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthBlack, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitB4)
#library(huxtable)
allmodelsB<-huxreg(fitB,fitB3,statistics = character(0))
allmodelsB
| (1) | (2) |
| (Intercept) | 5.665 *** | 5.882 *** |
| (1.373) | (1.361) |
| age | -0.260 ** | -0.257 ** |
| (0.088) | (0.085) |
| I(age^2) | 0.005 ** | 0.005 *** |
| (0.002) | (0.002) |
| sexW5Female | 0.646 * | 0.535 |
| (0.312) | (0.303) |
| raceXsexob_black.Gay | -0.337 | -0.443 * |
| (0.193) | (0.203) |
| raceXsexob_black.Bisexual | -0.568 | -0.547 |
| (0.474) | (0.455) |
| education1 | -0.179 | -0.331 * |
| (0.102) | (0.162) |
| education2 | -0.150 | -0.322 |
| (0.169) | (0.283) |
| employmentempNoBene | 0.107 | -0.170 |
| (0.405) | (0.409) |
| employmentempPartBene | 0.497 | 0.364 |
| (0.526) | (0.533) |
| employmentunemployed | 0.833 | 0.659 |
| (0.464) | (0.500) |
| hhincomeb_30k<50k | -0.450 | -0.417 |
| (0.261) | (0.237) |
| hhincomec_50k>100k | -0.449 | -0.445 |
| (0.283) | (0.259) |
| hhincomed_>100k | -0.448 | -0.421 |
| (0.376) | (0.349) |
| marriage1 | 0.208 | 0.223 |
| (0.238) | (0.247) |
| marriage2 | 0.405 | 0.438 |
| (0.388) | (0.376) |
| livesParent2lives_1_parent | | 0.395 |
| | (0.283) |
| livesParent2lives_other | | -0.789 |
| | (0.525) |
| mom.educ1 | | -0.460 |
| | (0.429) |
| mom.educ2 | | -0.265 |
| | (0.467) |
| mom.pubass1 | | 0.250 |
| | (0.378) |
| closeMom | | 0.010 |
| | (0.085) |
| parentSupervise | | 0.017 |
| | (0.079) |
| religion1 | | -0.269 |
| | (0.215) |
| schoolconflict3 | | 0.369 |
| | (0.337) |
| schoolconflict4 | | 0.118 |
| | (0.345) |
| schoolClubJoin1 | | 0.963 * |
| | (0.465) |
| scale(schoolSeg) | | -0.082 |
| | (0.142) |
| scale(pre) | | -0.029 |
| | (0.146) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
addhealthHi<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
addhealthHis<-addhealthHi%>%
subset(raceW5min=='c_hispanic')
addhealthHispanic<-addhealthHis%>%
filter(complete.cases(.))
addhealthHispanic$livesParent2<-factor(addhealthHispanic$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthHispanic$closeMom<-as.numeric(addhealthHispanic$closeMom)
addhealthHispanic$closeMom2<-Recode(addhealthHispanic$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthHispanic$parentSupervise<-as.numeric(addhealthHispanic$parentSupervise)
#table(addhealthHispanic$parentSupervise)
addhealthHispanic$parentSupervise2<-Recode(addhealthHispanic$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthHispanic$teacherTrouble2<-as.numeric(addhealthHispanic$teacherTrouble)
addhealthHispanic$studentTrouble2<-as.numeric(addhealthHispanic$studentTrouble)
addhealthHispanic$schoolconflict<-addhealthHispanic$studentTrouble2+addhealthHispanic$teacherTrouble2
addhealthHispanic$schoolconflict<-as.factor(addhealthHispanic$schoolconflict)
addhealthHispanic$raceXsexo<-as.factor(addhealthHispanic$raceXsexo)
addhealthHispanic$raceXsexo <- droplevels(addhealthHispanic$raceXsexo)
table(addhealthHispanic$raceXsexo)
##
## c_hispanic.Straight c_hispanic.Gay c_hispanic.Bisexual
## 1724 43 28
fitH<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthHispanic, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitH)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage, data = addhealthHispanic,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 2.454657 0.876583 7.84 0.0051 **
## age 0.023512 0.044766 0.28 0.5994
## I(age^2) -0.000475 0.000913 0.27 0.6031
## sexW5Female 0.045089 0.516764 0.01 0.9305
## raceXsexoc_hispanic.Gay 0.565429 0.491550 1.32 0.2500
## raceXsexoc_hispanic.Bisexual 0.035445 0.209377 0.03 0.8656
## education1 0.272502 0.179368 2.31 0.1287
## education2 -0.017844 0.292044 0.00 0.9513
## employmentempNoBene -0.144671 0.777441 0.03 0.8524
## employmentempPartBene -0.865062 0.536248 2.60 0.1067
## employmentunemployed 0.670840 0.852456 0.62 0.4313
## hhincomeb_30k<50k -0.192146 0.224673 0.73 0.3924
## hhincomec_50k>100k -0.103171 0.136918 0.57 0.4511
## hhincomed_>100k -0.314455 0.176674 3.17 0.0751 .
## marriage1 0.161644 0.385867 0.18 0.6753
## marriage2 0.203663 0.263185 0.60 0.4390
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.18 0.447
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.855 0.25
## Number of clusters: 164 Maximum cluster size: 144
#education+employment+hhincome+marriage
#fitH2<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthHispanic, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitH2)
fitH3<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthHispanic, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitH3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage + livesParent2 +
## mom.educ + mom.pubass + closeMom + parentSupervise + religion +
## schoolconflict + schoolClubJoin + scale(schoolSeg) + scale(pre),
## data = addhealthHispanic, weights = W5BIOWGT/mean(W5BIOWGT,
## na.rm = T), id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 3.104386 1.274192 5.94 0.015 *
## age 0.019011 0.043153 0.19 0.660
## I(age^2) -0.000513 0.000895 0.33 0.567
## sexW5Female -0.532197 0.461412 1.33 0.249
## raceXsexoc_hispanic.Gay 0.905780 0.843065 1.15 0.283
## raceXsexoc_hispanic.Bisexual 0.721257 0.643032 1.26 0.262
## education1 0.212933 0.175720 1.47 0.226
## education2 -0.121321 0.285889 0.18 0.671
## employmentempNoBene 0.514646 0.642000 0.64 0.423
## employmentempPartBene -0.917241 0.894171 1.05 0.305
## employmentunemployed 1.247867 0.700588 3.17 0.075 .
## hhincomeb_30k<50k -0.099950 0.229453 0.19 0.663
## hhincomec_50k>100k -0.076310 0.153875 0.25 0.620
## hhincomed_>100k -0.329258 0.176977 3.46 0.063 .
## marriage1 0.252930 0.375516 0.45 0.501
## marriage2 0.267995 0.265022 1.02 0.312
## livesParent2lives_1_parent -0.571748 0.536681 1.13 0.287
## livesParent2lives_other -0.896069 0.907206 0.98 0.323
## mom.educ1 0.684990 0.482822 2.01 0.156
## mom.educ2 -0.640828 0.604393 1.12 0.289
## mom.pubass1 -0.679167 0.637028 1.14 0.286
## closeMom -0.099078 0.079702 1.55 0.214
## parentSupervise 0.003106 0.163876 0.00 0.985
## religion1 -0.058741 0.121131 0.24 0.628
## schoolconflict3 1.317662 0.536915 6.02 0.014 *
## schoolconflict4 -0.763895 0.615631 1.54 0.215
## schoolClubJoin1 -0.161329 0.484262 0.11 0.739
## scale(schoolSeg) 0.065268 0.284526 0.05 0.819
## scale(pre) 0.196067 0.156813 1.56 0.211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 2.61 0.519
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.559 0.394
## Number of clusters: 164 Maximum cluster size: 144
#fitH4<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoi#n+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthHispanic, #weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitH4)
#library(huxtable)
allmodelsH<-huxreg(fitH,fitH3,statistics = character(0))
allmodelsH
| (1) | (2) |
| (Intercept) | 2.455 ** | 3.104 * |
| (0.877) | (1.274) |
| age | 0.024 | 0.019 |
| (0.045) | (0.043) |
| I(age^2) | -0.000 | -0.001 |
| (0.001) | (0.001) |
| sexW5Female | 0.045 | -0.532 |
| (0.517) | (0.461) |
| raceXsexoc_hispanic.Gay | 0.565 | 0.906 |
| (0.492) | (0.843) |
| raceXsexoc_hispanic.Bisexual | 0.035 | 0.721 |
| (0.209) | (0.643) |
| education1 | 0.273 | 0.213 |
| (0.179) | (0.176) |
| education2 | -0.018 | -0.121 |
| (0.292) | (0.286) |
| employmentempNoBene | -0.145 | 0.515 |
| (0.777) | (0.642) |
| employmentempPartBene | -0.865 | -0.917 |
| (0.536) | (0.894) |
| employmentunemployed | 0.671 | 1.248 |
| (0.852) | (0.701) |
| hhincomeb_30k<50k | -0.192 | -0.100 |
| (0.225) | (0.229) |
| hhincomec_50k>100k | -0.103 | -0.076 |
| (0.137) | (0.154) |
| hhincomed_>100k | -0.314 | -0.329 |
| (0.177) | (0.177) |
| marriage1 | 0.162 | 0.253 |
| (0.386) | (0.376) |
| marriage2 | 0.204 | 0.268 |
| (0.263) | (0.265) |
| livesParent2lives_1_parent | | -0.572 |
| | (0.537) |
| livesParent2lives_other | | -0.896 |
| | (0.907) |
| mom.educ1 | | 0.685 |
| | (0.483) |
| mom.educ2 | | -0.641 |
| | (0.604) |
| mom.pubass1 | | -0.679 |
| | (0.637) |
| closeMom | | -0.099 |
| | (0.080) |
| parentSupervise | | 0.003 |
| | (0.164) |
| religion1 | | -0.059 |
| | (0.121) |
| schoolconflict3 | | 1.318 * |
| | (0.537) |
| schoolconflict4 | | -0.764 |
| | (0.616) |
| schoolClubJoin1 | | -0.161 |
| | (0.484) |
| scale(schoolSeg) | | 0.065 |
| | (0.285) |
| scale(pre) | | 0.196 |
| | (0.157) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |
addhealthL<-addhealthS1long%>%
dplyr::select(wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,livesParent2,parentSupervise,mom.educ2,mom.educ,mom.employed,mom.nativity,mom.pubass,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,sexualorientation,religion,closeMom,score,raceXsexo,raceW5min)
#library(tidyr)
addhealthLG<-addhealthL%>%
subset(sexualorientation=='Gay'|sexualorientation=="Bisexual")
addhealthLG$sexualorientation<-factor(addhealthLG$sexualorientation)
addhealthLG$raceXsexo<-interaction(addhealthLG$raceW5min,addhealthLG$sexualorientation)
addhealthLG$raceXsexo<-factor(addhealthLG$raceXsexo)
addhealthLGBTQ<-addhealthLG%>%
filter(complete.cases(.))
addhealthLGBTQ$livesParent2<-factor(addhealthLGBTQ$livesParent2,levels=c("lives_2_parents","lives_1_parent","lives_other"))
addhealthLGBTQ$closeMom<-as.numeric(addhealthLGBTQ$closeMom)
addhealthLGBTQ$closeMom2<-Recode(addhealthLGBTQ$closeMom,recodes="1:3='a_notClose';4:5='b_close'")
addhealthLGBTQ$parentSupervise<-as.numeric(addhealthLGBTQ$parentSupervise)
#table(addhealthLGBTQ$parentSupervise)
addhealthLGBTQ$parentSupervise2<-Recode(addhealthLGBTQ$parentSupervise,recodes="1:3='a_easy';4:6='b_moderate';7:8='c_strict'")
addhealthLGBTQ$teacherTrouble2<-as.numeric(addhealthLGBTQ$teacherTrouble)
addhealthLGBTQ$studentTrouble2<-as.numeric(addhealthLGBTQ$studentTrouble)
addhealthLGBTQ$schoolconflict<-addhealthLGBTQ$studentTrouble2+addhealthLGBTQ$teacherTrouble2
addhealthLGBTQ$schoolconflict<-as.factor(addhealthLGBTQ$schoolconflict)
#install.packages("geepack")
#library(geepack)
#wave,aid,W5BIOWGT,age,sexW5,education,hhincome,employment,marriage,,,,,,,,pre,teacherTrouble,studentTrouble,schoolClubJoin,schoolSeg,
fitL<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthLGBTQ, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitL)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage, data = addhealthLGBTQ,
## weights = W5BIOWGT/mean(W5BIOWGT, na.rm = T), id = aid, waves = wave,
## corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) 1.57212 2.28545 0.47 0.4915
## age 0.07130 0.14311 0.25 0.6183
## I(age^2) -0.00127 0.00253 0.25 0.6147
## sexW5Female 0.00792 0.61050 0.00 0.9896
## raceXsexob_black.Gay 1.70967 0.87584 3.81 0.0509 .
## raceXsexoc_hispanic.Gay 1.43045 1.66016 0.74 0.3889
## raceXsexoa_white.Bisexual 0.52741 0.29871 3.12 0.0775 .
## raceXsexob_black.Bisexual 3.35179 1.18462 8.01 0.0047 **
## raceXsexoc_hispanic.Bisexual 1.20103 1.48668 0.65 0.4192
## education1 -0.55057 1.12586 0.24 0.6248
## education2 -1.08734 1.20407 0.82 0.3665
## employmentempNoBene -0.66919 0.86869 0.59 0.4411
## employmentempPartBene 0.68322 0.73119 0.87 0.3501
## employmentunemployed -1.60702 0.71602 5.04 0.0248 *
## hhincomeb_30k<50k -0.23942 0.34515 0.48 0.4879
## hhincomec_50k>100k 0.66625 0.25613 6.77 0.0093 **
## hhincomed_>100k 0.06250 0.36483 0.03 0.8640
## marriage1 0.48378 0.54472 0.79 0.3745
## marriage2 -0.42727 0.42582 1.01 0.3157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 3.58 0.849
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.464 0.251
## Number of clusters: 107 Maximum cluster size: 108
#education+employment+hhincome+marriage
#fitL2<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion,id=aid , wave = wave, corstr ="exchangeable", data=addhealthLGBTQ, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitL2)
fitL3<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+education+employment+hhincome+marriage+livesParent2+mom.educ+mom.pubass+closeMom+parentSupervise+religion+schoolconflict+schoolClubJoin+scale(schoolSeg)+scale(pre),id=aid , wave = wave, corstr ="exchangeable", data=addhealthLGBTQ, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
summary(fitL3)
##
## Call:
## geeglm(formula = score ~ age + I(age^2) + sexW5 + raceXsexo +
## education + employment + hhincome + marriage + livesParent2 +
## mom.educ + mom.pubass + closeMom + parentSupervise + religion +
## schoolconflict + schoolClubJoin + scale(schoolSeg) + scale(pre),
## data = addhealthLGBTQ, weights = W5BIOWGT/mean(W5BIOWGT,
## na.rm = T), id = aid, waves = wave, corstr = "exchangeable")
##
## Coefficients:
## Estimate Std.err Wald Pr(>|W|)
## (Intercept) -1.49494 2.85539 0.27 0.6006
## age 0.07640 0.14128 0.29 0.5887
## I(age^2) -0.00123 0.00249 0.24 0.6232
## sexW5Female 0.04740 0.58722 0.01 0.9357
## raceXsexob_black.Gay 1.19851 0.95570 1.57 0.2098
## raceXsexoc_hispanic.Gay 0.85910 1.59535 0.29 0.5902
## raceXsexoa_white.Bisexual 0.17678 0.36867 0.23 0.6316
## raceXsexob_black.Bisexual 3.44033 1.12022 9.43 0.0021 **
## raceXsexoc_hispanic.Bisexual -0.02877 1.36694 0.00 0.9832
## education1 -0.88524 1.41582 0.39 0.5318
## education2 -1.31648 1.50790 0.76 0.3826
## employmentempNoBene -1.68722 0.77474 4.74 0.0294 *
## employmentempPartBene 0.54170 0.61893 0.77 0.3815
## employmentunemployed -1.34672 0.82643 2.66 0.1032
## hhincomeb_30k<50k -0.18042 0.45041 0.16 0.6887
## hhincomec_50k>100k 0.67145 0.24065 7.78 0.0053 **
## hhincomed_>100k -0.20798 0.37551 0.31 0.5797
## marriage1 0.45947 0.36391 1.59 0.2067
## marriage2 -0.18438 0.38379 0.23 0.6309
## livesParent2lives_1_parent 0.16774 0.57984 0.08 0.7724
## livesParent2lives_other 2.48493 0.94418 6.93 0.0085 **
## mom.educ1 0.94091 0.70676 1.77 0.1831
## mom.educ2 0.85359 0.74858 1.30 0.2542
## mom.pubass1 0.09401 1.01127 0.01 0.9259
## closeMom -0.06225 0.09768 0.41 0.5239
## parentSupervise 0.44100 0.18368 5.76 0.0164 *
## religion1 1.11343 0.18262 37.17 1.1e-09 ***
## schoolconflict3 -0.23951 0.65018 0.14 0.7126
## schoolconflict4 0.46511 0.68561 0.46 0.4975
## schoolClubJoin1 -0.13494 0.96830 0.02 0.8892
## scale(schoolSeg) -0.06728 0.30982 0.05 0.8281
## scale(pre) -0.71197 0.26115 7.43 0.0064 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation structure = exchangeable
## Estimated Scale Parameters:
##
## Estimate Std.err
## (Intercept) 1.86 0.552
## Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 0.244 0.139
## Number of clusters: 107 Maximum cluster size: 108
#fitL4<-geeglm(score~age+I(age^2)+sexW5+raceXsexo+livesParent2+mom.educ+mom.pubass+closeMom2+parentSupervise2+religion+schoolconflict+schoolClubJoin+schoolSeg+pre+education+employment+hhincome+marriage,id=aid , wave = wave, corstr ="exchangeable", data=addhealthLGBTQ, weights=W5BIOWGT/mean(W5BIOWGT,na.rm=T))
#summary(fitL4)
#library(huxtable)
allmodelsL<-huxreg(fitL,fitL3,statistics = character(0))
allmodelsL
| (1) | (2) |
| (Intercept) | 1.572 | -1.495 |
| (2.285) | (2.855) |
| age | 0.071 | 0.076 |
| (0.143) | (0.141) |
| I(age^2) | -0.001 | -0.001 |
| (0.003) | (0.002) |
| sexW5Female | 0.008 | 0.047 |
| (0.611) | (0.587) |
| raceXsexob_black.Gay | 1.710 | 1.199 |
| (0.876) | (0.956) |
| raceXsexoc_hispanic.Gay | 1.430 | 0.859 |
| (1.660) | (1.595) |
| raceXsexoa_white.Bisexual | 0.527 | 0.177 |
| (0.299) | (0.369) |
| raceXsexob_black.Bisexual | 3.352 ** | 3.440 ** |
| (1.185) | (1.120) |
| raceXsexoc_hispanic.Bisexual | 1.201 | -0.029 |
| (1.487) | (1.367) |
| education1 | -0.551 | -0.885 |
| (1.126) | (1.416) |
| education2 | -1.087 | -1.316 |
| (1.204) | (1.508) |
| employmentempNoBene | -0.669 | -1.687 * |
| (0.869) | (0.775) |
| employmentempPartBene | 0.683 | 0.542 |
| (0.731) | (0.619) |
| employmentunemployed | -1.607 * | -1.347 |
| (0.716) | (0.826) |
| hhincomeb_30k<50k | -0.239 | -0.180 |
| (0.345) | (0.450) |
| hhincomec_50k>100k | 0.666 ** | 0.671 ** |
| (0.256) | (0.241) |
| hhincomed_>100k | 0.062 | -0.208 |
| (0.365) | (0.376) |
| marriage1 | 0.484 | 0.459 |
| (0.545) | (0.364) |
| marriage2 | -0.427 | -0.184 |
| (0.426) | (0.384) |
| livesParent2lives_1_parent | | 0.168 |
| | (0.580) |
| livesParent2lives_other | | 2.485 ** |
| | (0.944) |
| mom.educ1 | | 0.941 |
| | (0.707) |
| mom.educ2 | | 0.854 |
| | (0.749) |
| mom.pubass1 | | 0.094 |
| | (1.011) |
| closeMom | | -0.062 |
| | (0.098) |
| parentSupervise | | 0.441 * |
| | (0.184) |
| religion1 | | 1.113 *** |
| | (0.183) |
| schoolconflict3 | | -0.240 |
| | (0.650) |
| schoolconflict4 | | 0.465 |
| | (0.686) |
| schoolClubJoin1 | | -0.135 |
| | (0.968) |
| scale(schoolSeg) | | -0.067 |
| | (0.310) |
| scale(pre) | | -0.712 ** |
| | (0.261) |
| *** p < 0.001; ** p < 0.01; * p < 0.05. |