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)
brfss2017$genh<-Recode(brfss2017$GENHLTH,recodes="1:3=0;4:5=1;else=NA")
#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
## [385] genh
## <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$sxorient)
##
## 0 1
## 190310 3167
####MODEL ONE
#Mental Health by Interaction between Mental Health and Sexual Orientation
fit.logit3<-svyglm(badmental~int,design=des,family=binomial)
## Warning in eval(family$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
fit.logit4<-svyglm(badmental~int+sex,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit4)
##
## Call:
## svyglm(formula = badmental ~ int + sex, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11223 0.02797 4.012 6.02e-05 ***
## int1.0 -0.79768 0.02856 -27.927 < 2e-16 ***
## int0.1 0.55957 0.10754 5.203 1.96e-07 ***
## int1.1 -0.42976 0.13520 -3.179 0.00148 **
## sex1 -0.53554 0.02566 -20.870 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.423894)
##
## Number of Fisher Scoring iterations: 4
regTermTest(fit.logit4, test.terms = ~sex, method="Wald", df = NULL)
## Wald test for sex
## in svyglm(formula = badmental ~ int + sex, design = des, family = binomial)
## F = 435.543 on 1 and 128057 df: p= < 2.22e-16
fit.logit5<-svyglm(badmental~int+sex+age,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit5)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age, 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) 0.28168 0.04275 6.589 4.44e-11 ***
## int1.0 -0.50347 0.03342 -15.063 < 2e-16 ***
## int0.1 0.62346 0.10824 5.760 8.43e-09 ***
## int1.1 -0.18807 0.13982 -1.345 0.1786
## sex1 -0.53272 0.02592 -20.556 < 2e-16 ***
## age25-34 -0.13649 0.05155 -2.648 0.0081 **
## age35-44 -0.23541 0.05497 -4.282 1.85e-05 ***
## age45-54 -0.35428 0.05438 -6.515 7.31e-11 ***
## age55-64 -0.55609 0.05470 -10.166 < 2e-16 ***
## age65+ -0.96722 0.05563 -17.388 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.425918)
##
## Number of Fisher Scoring iterations: 4
regTermTest(fit.logit5, test.terms = ~sex+age, method="Wald", df = NULL)
## Wald test for sex age
## in svyglm(formula = badmental ~ int + sex + age, design = des, family = binomial)
## F = 154.2013 on 6 and 128052 df: p= < 2.22e-16
fit.logit6<-svyglm(badmental~int+sex+age+race,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit6)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss2017)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.43657 0.04455 9.800 < 2e-16 ***
## int1.0 -0.54225 0.03466 -15.645 < 2e-16 ***
## int0.1 0.61051 0.11168 5.466 4.60e-08 ***
## int1.1 -0.27427 0.14042 -1.953 0.0508 .
## sex1 -0.53674 0.02622 -20.472 < 2e-16 ***
## age25-34 -0.12738 0.05202 -2.448 0.0143 *
## age35-44 -0.23185 0.05562 -4.169 3.06e-05 ***
## age45-54 -0.37647 0.05477 -6.874 6.29e-12 ***
## age55-64 -0.59680 0.05552 -10.749 < 2e-16 ***
## age65+ -1.03365 0.05644 -18.314 < 2e-16 ***
## raceblack -0.26398 0.04445 -5.938 2.89e-09 ***
## racehispanic -0.32125 0.04270 -7.524 5.35e-14 ***
## raceother -0.22588 0.05537 -4.080 4.51e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.429427)
##
## Number of Fisher Scoring iterations: 4
regTermTest(fit.logit6, test.terms = ~sex+age+race, method="Wald", df = NULL)
## Wald test for sex age race
## in svyglm(formula = badmental ~ int + sex + age + race, design = des,
## family = binomial)
## F = 110.9692 on 9 and 126216 df: p= < 2.22e-16
fit.logit7<-svyglm(badmental~int+sex+age+race+educ,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit7)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + educ, 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) 0.28224 0.10420 2.709 0.006757 **
## int1.0 -0.53328 0.03473 -15.353 < 2e-16 ***
## int0.1 0.62304 0.11141 5.592 2.25e-08 ***
## int1.1 -0.25104 0.14039 -1.788 0.073748 .
## sex1 -0.54058 0.02624 -20.600 < 2e-16 ***
## age25-34 -0.10885 0.05222 -2.084 0.037134 *
## age35-44 -0.20843 0.05592 -3.727 0.000194 ***
## age45-54 -0.35408 0.05504 -6.433 1.26e-10 ***
## age55-64 -0.58300 0.05563 -10.480 < 2e-16 ***
## age65+ -1.01691 0.05644 -18.016 < 2e-16 ***
## raceblack -0.27851 0.04466 -6.236 4.51e-10 ***
## racehispanic -0.33408 0.04569 -7.311 2.66e-13 ***
## raceother -0.20231 0.05549 -3.646 0.000266 ***
## educ1somehs 0.30997 0.10528 2.944 0.003239 **
## educ2hsgrad 0.12606 0.09461 1.332 0.182727
## educ3somecol 0.18944 0.09454 2.004 0.045099 *
## educ4colgrad 0.06061 0.09344 0.649 0.516561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.429457)
##
## Number of Fisher Scoring iterations: 4
regTermTest(fit.logit7, test.terms = ~sex+age+race+educ, method="Wald", df = NULL)
## Wald test for sex age race educ
## in svyglm(formula = badmental ~ int + sex + age + race + educ, design = des,
## family = binomial)
## F = 78.07223 on 13 and 126001 df: p= < 2.22e-16
####MODEL TWO
fit.logit8<-svyglm(badmental~int+sex+age+race+educ+income,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit8)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + educ +
## income, 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) 0.48453 0.11661 4.155 3.25e-05 ***
## int1.0 -0.38715 0.03852 -10.050 < 2e-16 ***
## int0.1 0.57751 0.12019 4.805 1.55e-06 ***
## int1.1 -0.16304 0.14571 -1.119 0.263184
## sex1 -0.52757 0.02805 -18.808 < 2e-16 ***
## age25-34 -0.19024 0.05789 -3.286 0.001017 **
## age35-44 -0.30578 0.06108 -5.006 5.55e-07 ***
## age45-54 -0.44642 0.06056 -7.371 1.70e-13 ***
## age55-64 -0.69649 0.06108 -11.402 < 2e-16 ***
## age65+ -1.17440 0.06295 -18.656 < 2e-16 ***
## raceblack -0.30010 0.04879 -6.151 7.71e-10 ***
## racehispanic -0.37591 0.04986 -7.540 4.75e-14 ***
## raceother -0.16216 0.05957 -2.722 0.006488 **
## educ1somehs 0.36799 0.11640 3.161 0.001571 **
## educ2hsgrad 0.25871 0.10523 2.459 0.013952 *
## educ3somecol 0.37469 0.10578 3.542 0.000397 ***
## educ4colgrad 0.32319 0.10649 3.035 0.002408 **
## income>75k -0.52706 0.05386 -9.787 < 2e-16 ***
## income$20k<$35k -0.26022 0.05325 -4.887 1.02e-06 ***
## income35k<50k -0.34742 0.05721 -6.072 1.26e-09 ***
## income50k<75 -0.35922 0.05815 -6.178 6.52e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.423009)
##
## Number of Fisher Scoring iterations: 4
regTermTest(fit.logit8, test.terms = ~sex+age+race+educ+income, method="Wald", df = NULL)
## Wald test for sex age race educ income
## in svyglm(formula = badmental ~ int + sex + age + race + educ +
## income, design = des, family = binomial)
## F = 61.8658 on 17 and 109397 df: p= < 2.22e-16
####MODEL FIVE
#Mental Health by demographics and "marriage" variables
fit.logit9<-svyglm(genh~int+sex+age+race+educ+income+smoke+drinks+choles+blpressure+bmi+medcost+hlthinsur+checkup+ownhome+safeneigh,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit9)
##
## Call:
## svyglm(formula = genh ~ int + sex + age + race + educ + income +
## 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) -6.54594 1.52878 -4.282 1.92e-05 ***
## int1.0 0.09611 0.24675 0.390 0.696928
## int0.1 0.39086 0.78373 0.499 0.618020
## int1.1 0.14695 0.69952 0.210 0.833632
## sex1 0.22722 0.17645 1.288 0.197949
## age25-34 3.71460 1.19395 3.111 0.001883 **
## age35-44 3.58254 1.19863 2.989 0.002825 **
## age45-54 4.21270 1.18367 3.559 0.000379 ***
## age55-64 3.83916 1.18408 3.242 0.001200 **
## age65+ 3.48992 1.17616 2.967 0.003031 **
## raceblack 0.22373 0.30691 0.729 0.466073
## racehispanic -0.06138 0.54549 -0.113 0.910414
## raceother 0.47597 0.38522 1.236 0.216718
## educ1somehs -0.16512 0.70630 -0.234 0.815169
## educ2hsgrad -0.52406 0.63570 -0.824 0.409794
## educ3somecol -0.65440 0.65708 -0.996 0.319376
## educ4colgrad -0.79005 0.66258 -1.192 0.233215
## income>75k -1.72490 0.35433 -4.868 1.19e-06 ***
## income$20k<$35k -0.27823 0.32234 -0.863 0.388131
## income35k<50k -1.00559 0.35075 -2.867 0.004176 **
## income50k<75 -1.43253 0.34962 -4.097 4.30e-05 ***
## smokeY 0.39896 0.20754 1.922 0.054666 .
## drinks1 1.11594 0.54561 2.045 0.040920 *
## cholesY 0.68532 0.17716 3.868 0.000112 ***
## blpressureY 0.40686 0.29174 1.395 0.163241
## bmi 0.21274 0.11512 1.848 0.064707 .
## medcostY 1.05094 0.24326 4.320 1.61e-05 ***
## hlthinsurY 1.01807 0.39478 2.579 0.009965 **
## checkup2 years -0.14232 0.32420 -0.439 0.660704
## checkup5 years 0.08322 0.53938 0.154 0.877390
## checkup5+ years 0.78750 0.42535 1.851 0.064218 .
## ownhomeY 0.40841 0.27407 1.490 0.136297
## safeneigh1 -0.59306 0.53729 -1.104 0.269781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.215212)
##
## Number of Fisher Scoring iterations: 6
regTermTest(fit.logit9, test.terms = ~sex+age+race+educ+income+smoke+drinks+choles+blpressure+bmi+medcost+hlthinsur+checkup+ownhome+safeneigh, method="Wald", df = NULL)
## Wald test for sex age race educ income smoke drinks choles blpressure bmi medcost hlthinsur checkup ownhome safeneigh
## in svyglm(formula = genh ~ int + sex + age + race + educ + income +
## smoke + drinks + choles + blpressure + bmi + medcost + hlthinsur +
## checkup + ownhome + safeneigh, design = des, family = binomial)
## F = 5.400662 on 29 and 2743 df: p= < 2.22e-16
####MODEL THREE
fit.logit10<-svyglm(badmental~int+sex+age+race+educ+income+smoke+drinks+choles+blpressure+bmi,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit10)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + educ +
## income + smoke + drinks + choles + blpressure + bmi, 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) 0.98284 0.67519 1.456 0.145524
## int1.0 -0.22461 0.14829 -1.515 0.129898
## int0.1 0.75257 0.38476 1.956 0.050499 .
## int1.1 -0.29170 0.43598 -0.669 0.503467
## sex1 -0.63487 0.09424 -6.737 1.72e-11 ***
## age25-34 -0.07978 0.51037 -0.156 0.875783
## age35-44 -0.70169 0.50898 -1.379 0.168049
## age45-54 -0.87644 0.50914 -1.721 0.085213 .
## age55-64 -1.02547 0.50678 -2.023 0.043052 *
## age65+ -1.70987 0.51221 -3.338 0.000846 ***
## raceblack -0.06522 0.16020 -0.407 0.683938
## racehispanic 0.16986 0.21315 0.797 0.425516
## raceother -0.18073 0.23222 -0.778 0.436428
## educ1somehs 0.08364 0.48112 0.174 0.862000
## educ2hsgrad 0.26355 0.46411 0.568 0.570141
## educ3somecol 0.45376 0.46529 0.975 0.329470
## educ4colgrad 0.63438 0.46777 1.356 0.175078
## income>75k -1.16181 0.19770 -5.877 4.34e-09 ***
## income$20k<$35k -0.59774 0.20576 -2.905 0.003682 **
## income35k<50k -0.89411 0.20170 -4.433 9.41e-06 ***
## income50k<75 -0.91421 0.20093 -4.550 5.44e-06 ***
## smokeY 0.14551 0.11299 1.288 0.197845
## drinks1 -0.22664 0.33030 -0.686 0.492633
## cholesY 0.36974 0.09412 3.928 8.61e-05 ***
## blpressureY -0.31228 0.11611 -2.690 0.007167 **
## bmi 0.09478 0.05903 1.605 0.108426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.174513)
##
## Number of Fisher Scoring iterations: 4
####MODEL FOUR
fit.logit11<-svyglm(badmental~int+sex+age+race+educ+income+smoke+drinks+choles+blpressure+bmi+medcost+hlthinsur+checkup,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit11)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + educ +
## income + smoke + drinks + choles + blpressure + bmi + medcost +
## hlthinsur + checkup, 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) 0.12833 0.70395 0.182 0.855349
## int1.0 -0.30473 0.14936 -2.040 0.041356 *
## int0.1 0.65561 0.38580 1.699 0.089293 .
## int1.1 -0.35716 0.41814 -0.854 0.393031
## sex1 -0.61905 0.09636 -6.424 1.39e-10 ***
## age25-34 -0.22879 0.52711 -0.434 0.664265
## age35-44 -0.89147 0.52499 -1.698 0.089527 .
## age45-54 -1.04501 0.52308 -1.998 0.045767 *
## age55-64 -1.14827 0.52190 -2.200 0.027821 *
## age65+ -1.78846 0.52713 -3.393 0.000695 ***
## raceblack -0.08733 0.16369 -0.534 0.593671
## racehispanic 0.19045 0.21601 0.882 0.377991
## raceother -0.13518 0.23611 -0.573 0.566977
## educ1somehs 0.23652 0.46352 0.510 0.609870
## educ2hsgrad 0.44638 0.44533 1.002 0.316194
## educ3somecol 0.61900 0.44616 1.387 0.165360
## educ4colgrad 0.80495 0.44974 1.790 0.073519 .
## income>75k -1.06880 0.20346 -5.253 1.53e-07 ***
## income$20k<$35k -0.55837 0.20985 -2.661 0.007808 **
## income35k<50k -0.86685 0.20964 -4.135 3.58e-05 ***
## income50k<75 -0.89454 0.20651 -4.332 1.50e-05 ***
## smokeY 0.13394 0.11579 1.157 0.247412
## drinks1 -0.13966 0.35522 -0.393 0.694204
## cholesY 0.35814 0.09623 3.722 0.000199 ***
## blpressureY -0.25316 0.12259 -2.065 0.038948 *
## bmi 0.10224 0.06036 1.694 0.090351 .
## medcostY 0.99378 0.15320 6.487 9.22e-11 ***
## hlthinsurY 0.66238 0.21173 3.128 0.001763 **
## checkup2 years -0.01155 0.15868 -0.073 0.941957
## checkup5 years 0.37006 0.20844 1.775 0.075868 .
## checkup5+ years 0.09625 0.23112 0.416 0.677106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.176246)
##
## Number of Fisher Scoring iterations: 4
fit.logit9<-svyglm(badmental~int+sex+age+race+educ+income+smoke+drinks+choles+blpressure+bmi+medcost+hlthinsur+checkup+ownhome+safeneigh,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit9)
##
## Call:
## svyglm(formula = badmental ~ int + sex + age + race + educ +
## income + 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
## 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
## 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 *
## 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
stargazer(fit.logit3, fit.logit8, fit.logit10,fit.logit11,fit.logit9,type = "html", style="demography", covariate.labels =c("SM","GS" ,"GM","Male", "25-34", "35-44","45-54", "55-64", "65+", "Back", "Hisp","Other","someHS","HSgrad","someCol","Colgrad","75+","20-35","35-50","50-75","smokeY","drinks1","cholesY","blpressY","bmi","medcostY","hlthinsY","CU2","CU5","CU5+","ownhome","safeneigh"), ci = T )
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badmental
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Model 1
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Model 2
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Model 3
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Model 4
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Model 5
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SM
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-0.758***
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-0.387***
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-0.225
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-0.305*
|
-0.162
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|
|
(-0.814, -0.703)
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(-0.463, -0.312)
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(-0.515, 0.066)
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(-0.597, -0.012)
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(-0.632, 0.309)
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GS
|
0.483***
|
0.578***
|
0.753
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0.656
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0.373
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|
|
(0.279, 0.687)
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(0.342, 0.813)
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(-0.002, 1.507)
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(-0.101, 1.412)
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(-0.773, 1.519)
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GM
|
-0.392**
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-0.163
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-0.292
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-0.357
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1.137
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|
|
(-0.659, -0.124)
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(-0.449, 0.123)
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(-1.146, 0.563)
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(-1.177, 0.462)
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(-0.801, 3.074)
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Male
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-0.528***
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-0.635***
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-0.619***
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-0.451**
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|
(-0.583, -0.473)
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(-0.820, -0.450)
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(-0.808, -0.430)
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(-0.762, -0.139)
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25-34
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-0.190**
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-0.080
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-0.229
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-0.577
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(-0.304, -0.077)
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(-1.080, 0.921)
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(-1.262, 0.804)
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(-2.434, 1.280)
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35-44
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-0.306***
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-0.702
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-0.891
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-1.122
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|
|
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(-0.425, -0.186)
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(-1.699, 0.296)
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(-1.920, 0.137)
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(-3.006, 0.762)
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45-54
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-0.446***
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-0.876
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-1.045*
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-0.796
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(-0.565, -0.328)
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(-1.874, 0.121)
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(-2.070, -0.020)
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(-2.658, 1.065)
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55-64
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-0.696***
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-1.025*
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-1.148*
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-1.118
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(-0.816, -0.577)
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(-2.019, -0.032)
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(-2.171, -0.125)
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(-2.996, 0.759)
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65+
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-1.174***
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-1.710***
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-1.788***
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-1.744
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(-1.298, -1.051)
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(-2.714, -0.706)
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(-2.822, -0.755)
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(-3.615, 0.127)
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Back
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-0.300***
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-0.065
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-0.087
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-0.112
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(-0.396, -0.204)
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(-0.379, 0.249)
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(-0.408, 0.233)
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(-0.726, 0.502)
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Hisp
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-0.376***
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0.170
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0.190
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0.147
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(-0.474, -0.278)
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(-0.248, 0.588)
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(-0.233, 0.614)
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(-0.787, 1.081)
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Other
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-0.162**
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-0.181
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-0.135
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-0.069
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(-0.279, -0.045)
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(-0.636, 0.274)
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(-0.598, 0.328)
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(-0.902, 0.764)
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someHS
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0.368**
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0.084
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0.237
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-0.040
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(0.140, 0.596)
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(-0.859, 1.027)
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(-0.672, 1.145)
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(-1.590, 1.509)
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HSgrad
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0.259*
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0.264
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0.446
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0.199
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(0.052, 0.465)
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(-0.646, 1.173)
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(-0.426, 1.319)
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(-1.263, 1.660)
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someCol
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0.375***
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0.454
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0.619
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0.238
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(0.167, 0.582)
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(-0.458, 1.366)
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(-0.255, 1.493)
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(-1.257, 1.733)
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Colgrad
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0.323**
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0.634
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0.805
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0.417
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(0.114, 0.532)
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(-0.282, 1.551)
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(-0.077, 1.686)
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(-1.081, 1.914)
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75+
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-0.527***
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-1.162***
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-1.069***
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-0.729*
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(-0.633, -0.422)
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(-1.549, -0.774)
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(-1.468, -0.670)
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(-1.424, -0.033)
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20-35
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-0.260***
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-0.598**
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-0.558**
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-0.624
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(-0.365, -0.156)
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(-1.001, -0.194)
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(-0.970, -0.147)
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(-1.316, 0.068)
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35-50
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-0.347***
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-0.894***
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-0.867***
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-0.506
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(-0.460, -0.235)
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(-1.289, -0.499)
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(-1.278, -0.456)
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(-1.213, 0.202)
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50-75
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-0.359***
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-0.914***
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-0.895***
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-0.790*
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(-0.473, -0.245)
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(-1.308, -0.520)
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(-1.299, -0.490)
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(-1.497, -0.083)
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smokeY
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0.146
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0.134
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0.106
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(-0.076, 0.367)
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(-0.093, 0.361)
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(-0.255, 0.467)
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drinks1
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-0.227
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-0.140
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-0.969
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(-0.874, 0.421)
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(-0.836, 0.557)
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(-2.043, 0.106)
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cholesY
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0.370***
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0.358***
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0.603***
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(0.185, 0.554)
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(0.170, 0.547)
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(0.296, 0.911)
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blpressY
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-0.312**
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-0.253*
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-0.151
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(-0.540, -0.085)
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(-0.493, -0.013)
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(-0.592, 0.291)
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bmi
|
|
|
0.095
|
0.102
|
0.049
|
|
|
|
|
(-0.021, 0.210)
|
(-0.016, 0.221)
|
(-0.150, 0.248)
|
|
medcostY
|
|
|
|
0.994***
|
0.598*
|
|
|
|
|
|
(0.694, 1.294)
|
(0.109, 1.086)
|
|
hlthinsY
|
|
|
|
0.662**
|
0.629
|
|
|
|
|
|
(0.247, 1.077)
|
(-0.087, 1.344)
|
|
CU2
|
|
|
|
-0.012
|
-0.107
|
|
|
|
|
|
(-0.323, 0.299)
|
(-0.647, 0.433)
|
|
CU5
|
|
|
|
0.370
|
0.322
|
|
|
|
|
|
(-0.038, 0.779)
|
(-0.422, 1.065)
|
|
CU5+
|
|
|
|
0.096
|
0.814
|
|
|
|
|
|
(-0.357, 0.549)
|
(-0.085, 1.713)
|
|
ownhome
|
|
|
|
|
-0.494*
|
|
|
|
|
|
|
(-0.975, -0.013)
|
|
safeneigh
|
|
|
|
|
0.940*
|
|
|
|
|
|
|
(0.105, 1.774)
|
|
Constant
|
-0.182***
|
0.485***
|
0.983
|
0.128
|
0.548
|
|
|
(-0.229, -0.135)
|
(0.256, 0.713)
|
(-0.341, 2.306)
|
(-1.251, 1.508)
|
(-1.903, 3.000)
|
|
N
|
129,075
|
110,388
|
9,878
|
9,793
|
3,059
|
|
Log Likelihood
|
-110,251.300
|
-91,350.100
|
-5,993.179
|
-5,823.441
|
-1,944.332
|
|
AIC
|
220,510.600
|
182,742.200
|
12,038.360
|
11,708.880
|
3,954.664
|
|
|
|
p < .05; p < .01; p < .001
|
car::vif(fit.logit9)
## GVIF Df GVIF^(1/(2*Df))
## int 1.767976 3 1.099629
## sex 1.134080 1 1.064932
## age 2.699401 5 1.104401
## race 1.829708 3 1.105937
## educ 2.669204 4 1.130571
## income 3.076330 4 1.150811
## smoke 1.470649 1 1.212703
## drinks 1.069380 1 1.034108
## choles 1.196838 1 1.094001
## blpressure 1.739478 1 1.318893
## bmi 1.248594 1 1.117405
## medcost 1.299942 1 1.140150
## hlthinsur 1.392280 1 1.179949
## checkup 1.838199 3 1.106790
## ownhome 1.740114 1 1.319134
## safeneigh 1.106986 1 1.052134