library(car)
## Loading required package: carData
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(survey)
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
##
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
##
## dotchart
library(questionr)
library(foreign)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
brfss2017=read.xport("~/Desktop/DataFolder/brfss.XPT ")
#Marital Status; Are you? (marital status): 1 Married, 2 Divorced, 3 Widowed, 4 Seprarated, 5 Never married, 6 Partner, 9 Refuse
brfss2017$marital<-Recode(brfss2017$MARITAL, recodes="1=1;5=0;else=NA",as.factor=T)
#Sexual Orientation; Do you consider yourself to be? (sexual orientation): 1 Straight, 2 Gay, 3 Bisexual, 4 Other, 7 Don't know, 9 Refuse
brfss2017$sxorient<-Recode(brfss2017$SXORIENT,recodes="1=0;2=1;else=NA",as.factor=T)
#Income
brfss2017$income<-Recode(brfss2017$INCOME2,recodes="1:3='<$20k';4:5='$20k<$35k';6='35k<50k';7='50k<75';8='>75k';else=NA",as.factor=T)
#Age
brfss2017$age<-Recode(brfss2017$X_AGE_G, recodes="1='18-24';2='25-34';3='35-44';4='45-54';5='55-64';6='65+';else=NA",as.factor=T)
#Education
brfss2017$educ<-Recode(brfss2017$EDUCA, recodes="1:2='0Prim'; 3='1somehs'; 4='2hsgrad'; 5='3somecol'; 6='4colgrad';9=NA", as.factor=T)
brfss2017$educ<-relevel(brfss2017$educ, ref='0Prim')
#Cholesterol; Have you EVER been told by a doctor that your cholesterol is high? 1 Yes, 2 No, 7 Dont know, 9 Refuse
brfss2017$choles<-Recode(brfss2017$TOLDHI2,recodes="1='Y';2='N';else=NA",as.factor=T)
#Blood Pressure; Are you currently taking medicine for blood Pressure? 1 Yes, 2 No, 7 Dont know, 9 Refuse
brfss2017$blpressure<-Recode(brfss2017$BPMEDS,recodes="1='Y';2='N';else=NA",as.factor=T)
#Health Insurance; Do you have health care coverage? 1 Yes, 2 No, 7 Dont know, 9 Refuse
brfss2017$hlthinsur<-Recode(brfss2017$HLTHPLN1,recodes="1='Y';2='N';else=NA",as.factor=T)
#DR Checkup; Had a doctor checkup with the last...? 1 year, 2 years, 5 years, 5+ years
brfss2017$checkup<-Recode(brfss2017$CHECKUP1,recodes="1='1 year';2='2 years';3='5 years';4='5+ years';else=NA",as.factor=T)
#Live in a Safe Neighborhood; How safe from crime do you consider your neighborhood to be? 1 Extremely Safe, 2 Safe, 3 Unsafe, 4 Extremely Unsafe, 7 Don't know, 9 Refuse
brfss2017$safeneigh<-Recode(brfss2017$HOWSAFE1,recodes="1:2=0;3:4=1;else=NA",as.factor=T)
#Mental Health; How many days during the past 30 days was your mental health not good? 1-30, 88 None, 77 Don't Know, 99 Refuse
brfss2017$menth<-Recode(brfss2017$MENTHLTH,recodes="88=0;77=NA;99=NA")
#Interaction between marital status and sexual orientation
brfss2017$int<-interaction(brfss2017$marital,brfss2017$sxorient)
brfss2017$int<-as.factor(brfss2017$int)
#Interaction between marital status and sexual orientation recoded to gay people only (gay single and gay married)
brfss2017$int1<-Recode(brfss2017$int,recodes="'M.G'='M.G';'S.G'='S.G';else=NA",as.factor=T)
#Interaction between marital status and sexual orientation recoded to married people only (married straight and married gay)
brfss2017$int2<-Recode(brfss2017$int,recodes="'M.G'='M.G';'M.S'='M.S';else=NA",as.factor=T)
#Homeowner; Do you own or rent your home? 1 Own, 2 Rent, 3 Other, 7 Don't know, 9 Refuse
brfss2017$ownhome<-Recode(brfss2017$RENTHOM1,recodes="1='Y';2:3='N';else=NA",as.factor=T)
#Smoking; Do you now smoke cigarettes every day, some days, or not at all? 1 Everyday, 2 Some days, 3 Not at all, 7 Don't know, 9 Refuse
brfss2017$smoke<-Recode(brfss2017$SMOKDAY2,recodes="1:2='Y';3='N';else=NA",as.factor=T)
#Was there a time in the past 12 months when you needed to see a doctor but could not because of cost? 1 Yes, 2 No, 7 Don't know, 9 Refuse
brfss2017$medcost<-Recode(brfss2017$MEDCOST,recodes="1='Y';2='N';else=NA",as.factor=T)
#During the past 30 days, on the days when you drank, about how many drinks did you drink on the average?
brfss2017$drinks<-Recode(brfss2017$AVEDRNK2,recodes="1:10=0;10:30=1;77=NA;99=NA;else=NA",as.factor=T)
#BMI; Four-categories of Body Mass Index (BMI): 1 Underweight, 2 Normal Weight, 3 Overweight, 4 Obese
brfss2017$bmi<-brfss2017$X_BMI5CAT
#Not good mental health days: 1 (0 days), 2 (1-13 days), 3 (14-30 days), 9 Don't know
brfss2017$badmental<-Recode(brfss2017$X_MENT14D,recodes="1=0;2:3=1;9=NA;else=NA",as.factor=T)
#Race/Ethnicity
brfss2017$black<-Recode(brfss2017$racegr3, recodes="2=1; 9=NA; else=0")
brfss2017$white<-Recode(brfss2017$racegr3, recodes="1=1; 9=NA; else=0")
brfss2017$other<-Recode(brfss2017$racegr3, recodes="3:4=1; 9=NA; else=0")
brfss2017$hispanic<-Recode(brfss2017$racegr3, recodes="5=1; 9=NA; else=0")
#Race/Ethnicity; 1 White, 2 Black, 3 Other, 4 Multiracial, 5 Hispanic, 9 Don't know
brfss2017$race<-Recode(brfss2017$X_RACEGR3, recodes="2='black'; 1='awhite'; 3:4='other';5='hispanic'; else=NA",as.factor=T)
brfss2017$race<-relevel(brfss2017$race, ref='awhite')
#Sex; 1 Male, 2 Female
brfss2017$sex<-Recode(brfss2017$SEX,recodes="1=1;2=0;9=NA;else=NA",as.factor=T)
#Create a survey design
brfss2017%>%
filter(complete.cases(.))
## [1] X_STATE FMONTH IDATE IMONTH IDAY IYEAR
## [7] DISPCODE SEQNO X_PSU CTELENM1 PVTRESD1 COLGHOUS
## [13] STATERE1 CELLFON4 LADULT NUMADULT NUMMEN NUMWOMEN
## [19] SAFETIME CTELNUM1 CELLFON5 CADULT PVTRESD3 CCLGHOUS
## [25] CSTATE1 LANDLINE HHADULT GENHLTH PHYSHLTH MENTHLTH
## [31] POORHLTH HLTHPLN1 PERSDOC2 MEDCOST CHECKUP1 BPHIGH4
## [37] BPMEDS CHOLCHK1 TOLDHI2 CHOLMED1 CVDINFR4 CVDCRHD4
## [43] CVDSTRK3 ASTHMA3 ASTHNOW CHCSCNCR CHCOCNCR CHCCOPD1
## [49] HAVARTH3 ADDEPEV2 CHCKIDNY DIABETE3 DIABAGE2 LMTJOIN3
## [55] ARTHDIS2 ARTHSOCL JOINPAI1 SEX MARITAL EDUCA
## [61] RENTHOM1 NUMHHOL2 NUMPHON2 CPDEMO1A VETERAN3 EMPLOY1
## [67] CHILDREN INCOME2 INTERNET WEIGHT2 HEIGHT3 PREGNANT
## [73] DEAF BLIND DECIDE DIFFWALK DIFFDRES DIFFALON
## [79] SMOKE100 SMOKDAY2 STOPSMK2 LASTSMK2 USENOW3 ECIGARET
## [85] ECIGNOW ALCDAY5 AVEDRNK2 DRNK3GE5 MAXDRNKS FRUIT2
## [91] FRUITJU2 FVGREEN1 FRENCHF1 POTATOE1 VEGETAB2 EXERANY2
## [97] EXRACT11 EXEROFT1 EXERHMM1 EXRACT21 EXEROFT2 EXERHMM2
## [103] STRENGTH SEATBELT FLUSHOT6 FLSHTMY2 PNEUVAC3 SHINGLE2
## [109] HIVTST6 HIVTSTD3 HIVRISK5 PDIABTST PREDIAB1 INSULIN
## [115] BLDSUGAR FEETCHK2 DOCTDIAB CHKHEMO3 FEETCHK EYEEXAM
## [121] DIABEYE DIABEDU COPDCOGH COPDFLEM COPDBRTH COPDBTST
## [127] COPDSMOK HAREHAB1 STREHAB1 CVDASPRN ASPUNSAF RLIVPAIN
## [133] RDUCHART RDUCSTRK BPEATHBT BPSALT BPALCHOL BPEXER
## [139] BPEATADV BPSLTADV BPALCADV BPEXRADV BPMEDADV BPHI2MR
## [145] ARTTODAY ARTHWGT ARTHEXER ARTHEDU ASTHMAGE ASATTACK
## [151] ASERVIST ASDRVIST ASRCHKUP ASACTLIM ASYMPTOM ASNOSLEP
## [157] ASTHMED3 ASINHALR PAINACT2 QLMENTL2 QLSTRES2 QLHLTH2
## [163] SLEPTIM1 ADSLEEP SLEPDAY1 SLEPSNO2 SLEPBRTH MEDICARE
## [169] HLTHCVR1 DELAYMED DLYOTHER NOCOV121 LSTCOVRG DRVISITS
## [175] MEDSCOS1 CARERCVD MEDBILL1 ASBIALCH ASBIDRNK ASBIBING
## [181] ASBIADVC ASBIRDUC CNCRDIFF CNCRAGE CNCRTYP1 CSRVTRT2
## [187] CSRVDOC1 CSRVSUM CSRVRTRN CSRVINST CSRVINSR CSRVDEIN
## [193] CSRVCLIN CSRVPAIN CSRVCTL1 SSBSUGR2 SSBFRUT3 WTCHSALT
## [199] DRADVISE MARIJANA USEMRJN1 RSNMRJNA PFPPRVN2 TYPCNTR7
## [205] NOBCUSE6 IMFVPLAC HPVADVC2 HPVADSHT TETANUS LCSFIRST
## [211] LCSLAST LCSNUMCG LCSCTSCN CAREGIV1 CRGVREL2 CRGVLNG1
## [217] CRGVHRS1 CRGVPRB2 CRGVPERS CRGVHOUS CRGVMST2 CRGVEXPT
## [223] CIMEMLOS CDHOUSE CDASSIST CDHELP CDSOCIAL CDDISCUS
## [229] EMTSUPRT LSATISFY SDHBILLS SDHMOVE HOWSAFE1 SDHFOOD
## [235] SDHMEALS SDHMONEY SDHSTRES SXORIENT TRNSGNDR FIREARM4
## [241] GUNLOAD LOADULK2 RCSGENDR RCSRLTN2 CASTHDX2 CASTHNO2
## [247] QSTVER QSTLANG MSCODE X_STSTR X_STRWT X_RAWRAKE
## [253] X_WT2RAKE X_IMPRACE X_CHISPNC X_CRACE1 X_CPRACE X_CLLCPWT
## [259] X_DUALUSE X_DUALCOR X_LLCPWT2 X_LLCPWT X_RFHLTH X_PHYS14D
## [265] X_MENT14D X_HCVU651 X_RFHYPE5 X_CHOLCH1 X_RFCHOL1 X_MICHD
## [271] X_LTASTH1 X_CASTHM1 X_ASTHMS1 X_DRDXAR1 X_LMTACT1 X_LMTWRK1
## [277] X_LMTSCL1 X_PRACE1 X_MRACE1 X_HISPANC X_RACE X_RACEG21
## [283] X_RACEGR3 X_RACE_G1 X_AGEG5YR X_AGE65YR X_AGE80 X_AGE_G
## [289] HTIN4 HTM4 WTKG3 X_BMI5 X_BMI5CAT X_RFBMI5
## [295] X_CHLDCNT X_EDUCAG X_INCOMG X_SMOKER3 X_RFSMOK3 X_ECIGSTS
## [301] X_CURECIG DRNKANY5 DROCDY3_ X_RFBING5 X_DRNKWEK X_RFDRHV5
## [307] FTJUDA2_ FRUTDA2_ GRENDA1_ FRNCHDA_ POTADA1_ VEGEDA2_
## [313] X_MISFRT1 X_MISVEG1 X_FRTRES1 X_VEGRES1 X_FRUTSU1 X_VEGESU1
## [319] X_FRTLT1A X_VEGLT1A X_FRT16A X_VEG23A X_FRUITE1 X_VEGETE1
## [325] X_TOTINDA METVL11_ METVL21_ MAXVO2_ FC60_ ACTIN11_
## [331] ACTIN21_ PADUR1_ PADUR2_ PAFREQ1_ PAFREQ2_ X_MINAC11
## [337] X_MINAC21 STRFREQ_ PAMISS1_ PAMIN11_ PAMIN21_ PA1MIN_
## [343] PAVIG11_ PAVIG21_ PA1VIGM_ X_PACAT1 X_PAINDX1 X_PA150R2
## [349] X_PA300R2 X_PA30021 X_PASTRNG X_PAREC1 X_PASTAE1 X_RFSEAT2
## [355] X_RFSEAT3 X_FLSHOT6 X_PNEUMO2 X_AIDTST3 marital sxorient
## [361] income age educ choles blpressure hlthinsur
## [367] checkup safeneigh menth int int1 int2
## [373] ownhome smoke medcost drinks bmi badmental
## [379] black white other hispanic race sex
## <0 rows> (or 0-length row.names)
options(survey.lonely.psu = "adjust")
des<-svydesign(ids = ~1,strata=~X_STRWT,weights = ~X_LLCPWT,data = brfss2017)
#Mental Health by Marital Status
fit.logit1<-svyglm(badmental~marital,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit1)
##
## Call:
## svyglm(formula = badmental ~ marital, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.14876 0.01506 -9.877 <2e-16 ***
## marital1 -0.75125 0.01809 -41.528 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.088388)
##
## Number of Fisher Scoring iterations: 4
#Mental Health by Sexual Orientation
fit.logit2<-svyglm(badmental~sxorient,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit2)
##
## Call:
## svyglm(formula = badmental ~ sxorient, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.65585 0.01057 -62.020 <2e-16 ***
## sxorient1 0.70461 0.07080 9.952 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.295568)
##
## Number of Fisher Scoring iterations: 4
table(brfss2017$sxorient)
##
## 0 1
## 190310 3167
#Mental Health by Interaction between Mental Health and Sexual Orientation
fit.logit3<-svyglm(badmental~int,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit3)
##
## Call:
## svyglm(formula = badmental ~ int, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.18154 0.02400 -7.565 3.89e-14 ***
## int1.0 -0.75810 0.02828 -26.808 < 2e-16 ***
## int0.1 0.48298 0.10399 4.644 3.41e-06 ***
## int1.1 -0.39163 0.13634 -2.872 0.00407 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.425193)
##
## Number of Fisher Scoring iterations: 4
#Mental Health by a slew of variables
fit.logit4<-svyglm(badmental~int+sex+age+race+income+educ+smoke+drinks+choles+blpressure+bmi+medcost+hlthinsur+checkup+ownhome+safeneigh,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit4)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + income +
## educ + smoke + drinks + choles + blpressure + bmi + medcost +
## hlthinsur + checkup + ownhome + safeneigh, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.54830 1.25089 0.438 0.661181
## int1.0 -0.16169 0.24001 -0.674 0.500556
## int0.1 0.37280 0.58463 0.638 0.523737
## int1.1 1.13669 0.98863 1.150 0.250345
## sex1 -0.45078 0.15885 -2.838 0.004576 **
## age25-34 -0.57685 0.94754 -0.609 0.542716
## age35-44 -1.12223 0.96125 -1.167 0.243122
## age45-54 -0.79643 0.94962 -0.839 0.401719
## age55-64 -1.11845 0.95800 -1.167 0.243121
## age65+ -1.74400 0.95456 -1.827 0.067807 .
## raceblack -0.11213 0.31341 -0.358 0.720531
## racehispanic 0.14686 0.47654 0.308 0.757972
## raceother -0.06902 0.42516 -0.162 0.871045
## income>75k -0.72867 0.35497 -2.053 0.040190 *
## income$20k<$35k -0.62400 0.35315 -1.767 0.077347 .
## income35k<50k -0.50571 0.36094 -1.401 0.161301
## income50k<75 -0.79036 0.36071 -2.191 0.028528 *
## educ1somehs -0.04017 0.79056 -0.051 0.959474
## educ2hsgrad 0.19865 0.74582 0.266 0.789989
## educ3somecol 0.23813 0.76295 0.312 0.754971
## educ4colgrad 0.41696 0.76405 0.546 0.585305
## smokeY 0.10633 0.18426 0.577 0.563952
## drinks1 -0.96876 0.54821 -1.767 0.077322 .
## cholesY 0.60338 0.15683 3.847 0.000122 ***
## blpressureY -0.15075 0.22526 -0.669 0.503415
## bmi 0.04895 0.10158 0.482 0.629935
## medcostY 0.59763 0.24935 2.397 0.016608 *
## hlthinsurY 0.62896 0.36508 1.723 0.085036 .
## checkup2 years -0.10696 0.27540 -0.388 0.697756
## checkup5 years 0.32157 0.37913 0.848 0.396415
## checkup5+ years 0.81412 0.45872 1.775 0.076050 .
## ownhomeY -0.49428 0.24551 -2.013 0.044184 *
## safeneigh1 0.93952 0.42573 2.207 0.027409 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.187565)
##
## Number of Fisher Scoring iterations: 4