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 ")
brfss2016=read.xport("~/Desktop/DataFolder/brfss2016.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='b-Married';5='a-Single';2:4='c-D/W/S';6='partner';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='a-Straight';2='b-Gay';3='Bisexual';4:7='Other';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$educ, ref='0Prim')
#Health Insurance; Do you have health care coverage? 1 Yes, 2 No, 7 Dont know, 9 Refuse
brfss2017$insurance<-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$CHECKUP1,recodes="1='1 year';2='2 years';3='5 years';4='5+ years';else=NA",as.factor=T)
#Mental Health; How many days during the past 30 days was your mental health not good? 1-30, 88 None, 77 Don't Know, 99 Refuse
#Interaction between marital status and sexual orientation
brfss2017$marXsxo<-interaction(brfss2017$marital,brfss2017$sxorient)
brfss2017$marXsxo<-as.factor(brfss2017$marXsxo)
#Interaction between marital status and sexual orientation recoded to gay people only (gay single and gay married)
#brfss2017$int1<-Recode(brfss2017$int,recodes="0.1=0.1;1.1=1.1;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="1:5='1-5';5:10='5-10';10:30='10-30';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; 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='aGood';4:5='Bad';else=NA",as.factor=T)
brfss2017$physh<-Recode(brfss2017$PHYSHLTH,recodes="88=0;77=NA;99=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",as.factor=T)
brfss2017$emosupport<-Recode(brfss2017$EMTSUPRT,recodes="1:2='a-Yes';3='b-Sometimes';4:5='c-No';else=NA",as.factor=T)
brfss2017$satisfed<-Recode(brfss2017$LSATISFY,recodes="1:2='a-Satisfied';3:4='b-Dissatisfied';else=NA",as.factor=T)
brfss2017$employ<-Recode(brfss2017$EMPLOY1,recodes="1:2='Yes';3:4='No';5:8='other';else=NA",as.factor=T)
brfss2017$personalDR<-Recode(brfss2017$PERSDOC2,recodes="1:2='Yes';3='No';else=NA",as.factor=T)
brfss2017$flushot<-Recode(brfss2017$FLUSHOT6,recode="1='Yes';2='No';else=NA",as.factor=T)
#Marital Status; Are you? (marital status): 1 Married, 2 Divorced, 3 Widowed, 4 Seprarated, 5 Never married, 6 Partner, 9 Refuse
brfss2016$marital<-Recode(brfss2016$MARITAL,recodes="1='b-Married';5='a-Single';2:4='c-D/W/S';6='partner';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
brfss2016$sxorient<-Recode(brfss2016$SXORIENT,recodes="1='a-Straight';2='b-Gay';3='Bisexual';4:7='Other';else=NA",as.factor=T)
#Income
brfss2016$income<-Recode(brfss2016$INCOME2,recodes="1:3='<$20k';4:5='$20k<$35k';6='35k<50k';7='50k<75';8='>75k';else=NA",as.factor=T)
#Age
brfss2016$age<-Recode(brfss2016$X_AGE_G, recodes="1='18-24';2='25-34';3='35-44';4='45-54';5='55-64';6='65+';else=NA",as.factor=T)
#Education
brfss2016$educ<-Recode(brfss2016$EDUCA, recodes="1:2='0Prim'; 3='1somehs'; 4='2hsgrad'; 5='3somecol'; 6='4colgrad';9=NA", as.factor=T)
brfss2016$educ<-relevel(brfss2016$educ, ref='0Prim')
#Health Insurance; Do you have health care coverage? 1 Yes, 2 No, 7 Dont know, 9 Refuse
brfss2016$insurance<-Recode(brfss2016$HLTHPLN1,recodes="1='Y';2='N';else=NA",as.factor=T)
#DR Checkup; Had a doctor checkup with the last...? 1 year, 2 years, 5 years, 5+ years
brfss2016$checkup<-Recode(brfss2016$CHECKUP1,recodes="1='1 year';2='2 years';3='5 years';4='5+ years';else=NA",as.factor=T)
#Mental Health; How many days during the past 30 days was your mental health not good? 1-30, 88 None, 77 Don't Know, 99 Refuse
#Interaction between marital status and sexual orientation
brfss2016$marXsxo<-interaction(brfss2016$marital,brfss2016$sxorient)
brfss2016$marXsxo<-as.factor(brfss2016$marXsxo)
#Interaction between marital status and sexual orientation recoded to gay people only (gay single and gay married)
#brfss2016$int1<-Recode(brfss2016$int,recodes="0.1=0.1;1.1=1.1;else=NA",as.factor=T)
#Interaction between marital status and sexual orientation recoded to married people only (married straight and married gay)
#brfss2016$int2<-Recode(brfss2016$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
brfss2016$ownhome<-Recode(brfss2016$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
brfss2016$smoke<-Recode(brfss2016$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
brfss2016$medcost<-Recode(brfss2016$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?
brfss2016$drinks<-Recode(brfss2016$AVEDRNK2,recodes="1:5='1-5';5:10='5-10';10:30='10-30';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
brfss2016$bmi<-brfss2016$X_BMI5CAT
#Not good mental health days: 1 (0 days), 2 (1-13 days), 3 (14-30 days), 9 Don't know
brfss2016$badmental<-Recode(brfss2016$X_MENT14D,recodes="1=0;2:3=1;9=NA;else=NA",as.factor=T)
#Race/Ethnicity; 1 White, 2 Black, 3 Other, 4 Multiracial, 5 Hispanic, 9 Don't know
brfss2016$race<-Recode(brfss2016$X_RACEGR3, recodes="2='black'; 1='awhite'; 3:4='other';5='hispanic'; else=NA",as.factor=T)
brfss2016$race<-relevel(brfss2016$race, ref='awhite')
#Sex; 1 Male, 2 Female
brfss2016$sex<-Recode(brfss2016$SEX,recodes="1=1;2=0;9=NA;else=NA",as.factor=T)
brfss2016$genh<-Recode(brfss2016$GENHLTH,recodes="1:3='aGood';4:5='Bad';else=NA",as.factor=T)
brfss2016$physh<-Recode(brfss2016$PHYSHLTH,recodes="88=0;77=NA;99=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
brfss2016$menth<-Recode(brfss2016$MENTHLTH,recodes="88=0;77=NA;99=NA",as.factor=T)
brfss2016$emosupport<-Recode(brfss2016$EMTSUPRT,recodes="1:2='a-Yes';3='b-Sometimes';4:5='c-No';else=NA",as.factor=T)
brfss2016$satisfed<-Recode(brfss2016$LSATISFY,recodes="1:2='a-Satisfied';3:4='b-Dissatisfied';else=NA",as.factor=T)
brfss2016$employ<-Recode(brfss2016$EMPLOY1,recodes="1:2='Yes';3:4='No';5:8='other';else=NA",as.factor=T)
brfss2016$personalDR<-Recode(brfss2016$PERSDOC2,recodes="1:2='Yes';3='No';else=NA",as.factor=T)
brfss2016$flushot<-Recode(brfss2016$FLUSHOT6,recode="1='Yes';2='No';else=NA",as.factor=T)
brfss2017<-brfss2017%>%
select(income,marital, sxorient,income, age, educ, checkup, insurance, marXsxo,ownhome, smoke, medcost, drinks, bmi, badmental, race, sex, genh, physh,menth,emosupport,satisfed,employ,personalDR,flushot,X_STRWT,X_LLCPWT)
brfss2016<-brfss2016%>%
select(income,marital, sxorient,income, age, educ, checkup, insurance, marXsxo,ownhome, smoke, medcost, drinks, bmi, badmental, race, sex, genh, physh,menth,emosupport,satisfed,employ,personalDR,flushot,X_STRWT,X_LLCPWT)
brfss1617<-rbind(brfss2017,brfss2016)
#Create a survey design
brfss1617<-brfss1617%>%
filter(complete.cases(.))
options(survey.lonely.psu = "adjust")
des<-svydesign(ids = ~1,strata=~X_STRWT,weights = ~X_LLCPWT,data = brfss1617)
#MODEL ONE
#General Health by Marital Status
fit.logit1<-svyglm(genh~marital,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit1)
##
## Call:
## svyglm(formula = genh ~ marital, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.8134 0.1265 -14.332 < 2e-16 ***
## maritalb-Married -0.2583 0.1448 -1.784 0.07441 .
## maritalc-D/W/S 0.4222 0.1636 2.581 0.00986 **
## maritalpartner 0.4496 0.2990 1.504 0.13272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.000108)
##
## Number of Fisher Scoring iterations: 5
fit.logit2<-svyglm(genh~sxorient,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit.logit2)
##
## Call:
## svyglm(formula = genh ~ sxorient, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.8449 0.0532 -34.680 < 2e-16 ***
## sxorientb-Gay 0.3909 0.5334 0.733 0.46367
## sxorientBisexual 0.4487 0.3635 1.234 0.21712
## sxorientOther 1.2609 0.4517 2.791 0.00526 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.000108)
##
## Number of Fisher Scoring iterations: 5
#MODEL ONE
#General Health by Marital Status (interaction)
fit1<-svyglm(genh~marXsxo,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit1)
##
## Call:
## svyglm(formula = genh ~ marXsxo, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.80302 0.13679 -13.180 < 2e-16 ***
## marXsxob-Married.a-Straight -0.26918 0.15432 -1.744 0.081148 .
## marXsxoc-D/W/S.a-Straight 0.29370 0.16651 1.764 0.077784 .
## marXsxopartner.a-Straight 0.50437 0.31520 1.600 0.109592
## marXsxoa-Single.b-Gay -0.02846 0.47628 -0.060 0.952355
## marXsxob-Married.b-Gay -12.99132 0.29592 -43.901 < 2e-16 ***
## marXsxoc-D/W/S.b-Gay 2.44921 0.90429 2.708 0.006773 **
## marXsxopartner.b-Gay -2.74313 0.79502 -3.450 0.000562 ***
## marXsxoa-Single.Bisexual -0.25295 0.54439 -0.465 0.642198
## marXsxob-Married.Bisexual -0.20856 0.60976 -0.342 0.732331
## marXsxoc-D/W/S.Bisexual 2.05660 0.67892 3.029 0.002459 **
## marXsxopartner.Bisexual 0.94631 0.99761 0.949 0.342860
## marXsxoa-Single.Other 0.59566 0.84044 0.709 0.478497
## marXsxob-Married.Other 1.27566 0.78505 1.625 0.104209
## marXsxoc-D/W/S.Other 1.77508 0.65808 2.697 0.007002 **
## marXsxopartner.Other -1.50559 1.22495 -1.229 0.219065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9949604)
##
## Number of Fisher Scoring iterations: 13
#MODEL TWO - includes biological demographic variables
#General Health by marital status and sex, age, race
fit2<-svyglm(genh~marXsxo+sex+age+race,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit2)
##
## Call:
## svyglm(formula = genh ~ marXsxo + sex + age + race, design = des,
## family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.9466 0.3435 -5.667 1.5e-08 ***
## marXsxob-Married.a-Straight -0.4199 0.1724 -2.437 0.014848 *
## marXsxoc-D/W/S.a-Straight 0.0478 0.1868 0.256 0.798100
## marXsxopartner.a-Straight 0.5515 0.3306 1.669 0.095250 .
## marXsxoa-Single.b-Gay -0.1026 0.4586 -0.224 0.823013
## marXsxob-Married.b-Gay -13.1685 0.3118 -42.238 < 2e-16 ***
## marXsxoc-D/W/S.b-Gay 2.1870 0.9244 2.366 0.018009 *
## marXsxopartner.b-Gay -2.6822 0.7655 -3.504 0.000461 ***
## marXsxoa-Single.Bisexual -0.1780 0.6157 -0.289 0.772489
## marXsxob-Married.Bisexual -0.2180 0.6525 -0.334 0.738288
## marXsxoc-D/W/S.Bisexual 1.6734 0.6037 2.772 0.005582 **
## marXsxopartner.Bisexual 1.1562 0.9802 1.180 0.238202
## marXsxoa-Single.Other 0.5225 0.8378 0.624 0.532849
## marXsxob-Married.Other 0.8710 0.7115 1.224 0.220917
## marXsxoc-D/W/S.Other 1.4732 0.6777 2.174 0.029750 *
## marXsxopartner.Other -2.3923 1.3378 -1.788 0.073775 .
## sex1 0.1401 0.1066 1.314 0.188863
## age25-34 -0.4944 0.3939 -1.255 0.209416
## age35-44 -0.2234 0.3972 -0.562 0.573816
## age45-54 0.2888 0.3867 0.747 0.455194
## age55-64 0.4541 0.3879 1.171 0.241764
## age65+ 0.2506 0.3840 0.653 0.513925
## raceblack 0.7601 0.2119 3.588 0.000335 ***
## racehispanic 0.5949 0.4610 1.291 0.196887
## raceother 0.5775 0.2288 2.524 0.011619 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 0.9945309)
##
## Number of Fisher Scoring iterations: 13
regTermTest(fit2, test.terms = ~sex+age+race, method="Wald", df = NULL)
## Wald test for sex age race
## in svyglm(formula = genh ~ marXsxo + sex + age + race, design = des,
## family = binomial)
## F = 6.180301 on 9 and 9094 df: p= 9.8885e-09
####MODEL THREE - includes biological demographic variables and social demographic variables
#General Health by Marital Status plus sex, age, race, plus educ, income
fit3<-svyglm(genh~marXsxo+sex+age+race+educ+income,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit3)
##
## Call:
## svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income,
## design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.63112 0.59322 -1.064 0.28741
## marXsxob-Married.a-Straight 0.13647 0.18531 0.736 0.46149
## marXsxoc-D/W/S.a-Straight 0.06411 0.18415 0.348 0.72772
## marXsxopartner.a-Straight 0.72972 0.28366 2.573 0.01011 *
## marXsxoa-Single.b-Gay -0.03656 0.45866 -0.080 0.93647
## marXsxob-Married.b-Gay -12.61173 0.38124 -33.081 < 2e-16 ***
## marXsxoc-D/W/S.b-Gay 1.97938 1.04850 1.888 0.05908 .
## marXsxopartner.b-Gay -1.96533 0.82453 -2.384 0.01717 *
## marXsxoa-Single.Bisexual -0.32380 0.69336 -0.467 0.64052
## marXsxob-Married.Bisexual 0.18610 0.61178 0.304 0.76099
## marXsxoc-D/W/S.Bisexual 1.63706 0.54416 3.008 0.00263 **
## marXsxopartner.Bisexual 1.52734 1.04142 1.467 0.14252
## marXsxoa-Single.Other 0.37515 0.79201 0.474 0.63575
## marXsxob-Married.Other 0.61086 0.87599 0.697 0.48561
## marXsxoc-D/W/S.Other 1.34229 0.75625 1.775 0.07594 .
## marXsxopartner.Other -2.72030 1.33110 -2.044 0.04102 *
## sex1 0.15493 0.11121 1.393 0.16363
## age25-34 -0.40826 0.37251 -1.096 0.27312
## age35-44 -0.01647 0.37158 -0.044 0.96465
## age45-54 0.40664 0.36138 1.125 0.26052
## age55-64 0.52923 0.35873 1.475 0.14016
## age65+ 0.20899 0.35527 0.588 0.55638
## raceblack 0.24394 0.23492 1.038 0.29911
## racehispanic 0.23540 0.48987 0.481 0.63086
## raceother 0.49533 0.24984 1.983 0.04744 *
## educ1somehs -0.29249 0.49471 -0.591 0.55438
## educ2hsgrad -0.74900 0.46793 -1.601 0.10949
## educ3somecol -0.75427 0.45933 -1.642 0.10060
## educ4colgrad -1.25015 0.46470 -2.690 0.00715 **
## income>75k -1.54579 0.20860 -7.410 1.37e-13 ***
## income$20k<$35k -0.40152 0.17148 -2.341 0.01923 *
## income35k<50k -0.79903 0.19498 -4.098 4.20e-05 ***
## income50k<75 -1.05491 0.19586 -5.386 7.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.014183)
##
## Number of Fisher Scoring iterations: 13
regTermTest(fit3, test.terms = ~sex+age+race+educ+income, method="Wald", df = NULL)
## Wald test for sex age race educ income
## in svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income,
## design = des, family = binomial)
## F = 10.82161 on 17 and 9086 df: p= < 2.22e-16
#MODEL FOUR -includes biological and social demographic variables plus marriage behavior benefit variables
fit4<-svyglm(genh~marXsxo+sex+age+race+educ+income+smoke+drinks+bmi,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit4)
##
## Call:
## svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income +
## smoke + drinks + bmi, design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.70418 0.62404 -2.731 0.006329 **
## marXsxob-Married.a-Straight 0.17232 0.18576 0.928 0.353638
## marXsxoc-D/W/S.a-Straight 0.09297 0.18597 0.500 0.617156
## marXsxopartner.a-Straight 0.69110 0.27806 2.485 0.012958 *
## marXsxoa-Single.b-Gay -0.17572 0.47313 -0.371 0.710344
## marXsxob-Married.b-Gay -12.58468 0.35442 -35.508 < 2e-16 ***
## marXsxoc-D/W/S.b-Gay 2.08443 1.09877 1.897 0.057852 .
## marXsxopartner.b-Gay -2.06391 0.85723 -2.408 0.016076 *
## marXsxoa-Single.Bisexual -0.31610 0.68643 -0.461 0.645166
## marXsxob-Married.Bisexual 0.06565 0.64728 0.101 0.919216
## marXsxoc-D/W/S.Bisexual 1.69338 0.55619 3.045 0.002337 **
## marXsxopartner.Bisexual 1.31825 0.95663 1.378 0.168235
## marXsxoa-Single.Other 0.36961 0.80564 0.459 0.646405
## marXsxob-Married.Other 0.77683 0.81231 0.956 0.338933
## marXsxoc-D/W/S.Other 1.24000 0.75791 1.636 0.101856
## marXsxopartner.Other -2.73227 1.26282 -2.164 0.030520 *
## sex1 0.11368 0.11400 0.997 0.318687
## age25-34 -0.41804 0.37131 -1.126 0.260258
## age35-44 -0.01576 0.37157 -0.042 0.966167
## age45-54 0.38257 0.36306 1.054 0.292022
## age55-64 0.56789 0.35875 1.583 0.113461
## age65+ 0.36736 0.35534 1.034 0.301251
## raceblack 0.14706 0.23416 0.628 0.530004
## racehispanic 0.19459 0.49390 0.394 0.693596
## raceother 0.48208 0.25011 1.927 0.053954 .
## educ1somehs -0.32420 0.51273 -0.632 0.527214
## educ2hsgrad -0.78017 0.48731 -1.601 0.109415
## educ3somecol -0.78166 0.47866 -1.633 0.102499
## educ4colgrad -1.19213 0.48375 -2.464 0.013745 *
## income>75k -1.46190 0.21152 -6.911 5.12e-12 ***
## income$20k<$35k -0.38289 0.17309 -2.212 0.026990 *
## income35k<50k -0.75656 0.19447 -3.890 0.000101 ***
## income50k<75 -1.00468 0.19906 -5.047 4.57e-07 ***
## smokeY 0.44598 0.11253 3.963 7.45e-05 ***
## drinks10-30 0.36842 0.37234 0.989 0.322462
## drinks5-10 0.14658 0.22829 0.642 0.520823
## bmi 0.26463 0.07013 3.773 0.000162 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.010352)
##
## Number of Fisher Scoring iterations: 13
regTermTest(fit4, test.terms = ~sex+age+race+educ+income, method="Wald", df = NULL)
## Wald test for sex age race educ income
## in svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income +
## smoke + drinks + bmi, design = des, family = binomial)
## F = 9.438308 on 17 and 9082 df: p= < 2.22e-16
#MODEL FIVE - includes biological and social demographic variables plus marriage behavior benefit variables marriage benefit financial variables
fit5<-svyglm(genh~marXsxo+sex+age+race+educ+income+smoke+drinks+bmi+medcost+insurance+checkup+ownhome,design=des,family=binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial
## glm!
summary(fit5)
##
## Call:
## svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income +
## smoke + drinks + bmi + medcost + insurance + checkup + ownhome,
## design = des, family = binomial)
##
## Survey design:
## svydesign(ids = ~1, strata = ~X_STRWT, weights = ~X_LLCPWT, data = brfss1617)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.25817 0.65068 -3.470 0.000522 ***
## marXsxob-Married.a-Straight 0.21540 0.19698 1.093 0.274207
## marXsxoc-D/W/S.a-Straight 0.08108 0.19009 0.427 0.669739
## marXsxopartner.a-Straight 0.61441 0.27182 2.260 0.023823 *
## marXsxoa-Single.b-Gay -0.15039 0.45084 -0.334 0.738712
## marXsxob-Married.b-Gay -13.48381 0.35942 -37.516 < 2e-16 ***
## marXsxoc-D/W/S.b-Gay 2.30088 1.09613 2.099 0.035836 *
## marXsxopartner.b-Gay -2.36430 0.93748 -2.522 0.011686 *
## marXsxoa-Single.Bisexual -0.29171 0.67253 -0.434 0.664481
## marXsxob-Married.Bisexual -0.07154 0.60961 -0.117 0.906577
## marXsxoc-D/W/S.Bisexual 1.56992 0.61741 2.543 0.011015 *
## marXsxopartner.Bisexual 0.95687 0.84354 1.134 0.256679
## marXsxoa-Single.Other 0.45527 0.77433 0.588 0.556585
## marXsxob-Married.Other 0.99116 0.85548 1.159 0.246650
## marXsxoc-D/W/S.Other 1.31411 0.80662 1.629 0.103313
## marXsxopartner.Other -2.77710 1.32246 -2.100 0.035760 *
## sex1 0.16245 0.10934 1.486 0.137400
## age25-34 -0.25584 0.34829 -0.735 0.462632
## age35-44 0.14649 0.35348 0.414 0.678569
## age45-54 0.59985 0.34848 1.721 0.085226 .
## age55-64 0.86562 0.33923 2.552 0.010735 *
## age65+ 0.75176 0.33731 2.229 0.025860 *
## raceblack 0.03729 0.22792 0.164 0.870052
## racehispanic 0.26287 0.44389 0.592 0.553740
## raceother 0.48943 0.26578 1.841 0.065587 .
## educ1somehs -0.21758 0.50780 -0.428 0.668318
## educ2hsgrad -0.62993 0.47984 -1.313 0.189288
## educ3somecol -0.65708 0.47482 -1.384 0.166438
## educ4colgrad -1.08295 0.48094 -2.252 0.024362 *
## income>75k -1.25712 0.21298 -5.903 3.71e-09 ***
## income$20k<$35k -0.38392 0.17162 -2.237 0.025313 *
## income35k<50k -0.66920 0.19202 -3.485 0.000494 ***
## income50k<75 -0.86276 0.20233 -4.264 2.03e-05 ***
## smokeY 0.40392 0.11378 3.550 0.000387 ***
## drinks10-30 0.36794 0.40782 0.902 0.366964
## drinks5-10 0.11568 0.22703 0.510 0.610381
## bmi 0.26011 0.07075 3.677 0.000238 ***
## medcostY 0.97847 0.15593 6.275 3.65e-10 ***
## insuranceY 0.22270 0.21683 1.027 0.304411
## checkup2 years -0.08671 0.18155 -0.478 0.632951
## checkup5 years 0.17879 0.20655 0.866 0.386733
## checkup5+ years -0.25007 0.21187 -1.180 0.237914
## ownhomeY -0.36399 0.15354 -2.371 0.017774 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1.003035)
##
## Number of Fisher Scoring iterations: 14
regTermTest(fit5, test.terms = ~sex+age+race+educ+income, method="Wald", df = NULL)
## Wald test for sex age race educ income
## in svyglm(formula = genh ~ marXsxo + sex + age + race + educ + income +
## smoke + drinks + bmi + medcost + insurance + checkup + ownhome,
## design = des, family = binomial)
## F = 8.738684 on 17 and 9076 df: p= < 2.22e-16
car::vif(fit5)
## GVIF Df GVIF^(1/(2*Df))
## marXsxo 6.448420 15 1.064098
## sex 1.297648 1 1.139143
## age 5.037189 5 1.175490
## race 2.505569 3 1.165425
## educ 2.025355 4 1.092226
## income 3.382894 4 1.164558
## smoke 1.335152 1 1.155488
## drinks 1.370919 2 1.082064
## bmi 1.168850 1 1.081134
## medcost 1.410815 1 1.187777
## insurance 1.505556 1 1.227011
## checkup 2.207601 3 1.141091
## ownhome 1.992533 1 1.411571
stargazer(fit1, fit2, fit3,fit4,fit5,type = "html", style="demography", covariate.labels =c("MarriedStraight","D/W/SStraight","partnerStraight","SingleGay","MarriedGay","D/W/Sb-Gay","partnerGay ","SingleBisexual","MarriedBisexual","D/W/SBisexual","singleoth","marriedother","DWSother","partnerother", "Male","25-34","35-44","45-54","55-64","65+","Black","Hisp","Other","someHS","HSgrad","someCol","Colgrad","75+","20-35","35-50","50-75","smokeY","drinks10-30","drinks5-10","bmi","medcostY","insuranceyes","Chup2","ChupU5","Chup5+","ownhome"), ci = T )
|
|
|
|
genh
|
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
|
|
|
MarriedStraight
|
-0.269
|
-0.420*
|
0.136
|
0.172
|
0.215
|
|
|
(-0.572, 0.033)
|
(-0.758, -0.082)
|
(-0.227, 0.500)
|
(-0.192, 0.536)
|
(-0.171, 0.601)
|
|
D/W/SStraight
|
0.294
|
0.048
|
0.064
|
0.093
|
0.081
|
|
|
(-0.033, 0.620)
|
(-0.318, 0.414)
|
(-0.297, 0.425)
|
(-0.272, 0.457)
|
(-0.291, 0.454)
|
|
partnerStraight
|
0.504
|
0.552
|
0.730*
|
0.691*
|
0.614*
|
|
|
(-0.113, 1.122)
|
(-0.096, 1.199)
|
(0.174, 1.286)
|
(0.146, 1.236)
|
(0.082, 1.147)
|
|
SingleGay
|
-0.028
|
-0.103
|
-0.037
|
-0.176
|
-0.150
|
|
|
(-0.962, 0.905)
|
(-1.001, 0.796)
|
(-0.936, 0.862)
|
(-1.103, 0.752)
|
(-1.034, 0.733)
|
|
MarriedGay
|
-12.991***
|
-13.169***
|
-12.612***
|
-12.585***
|
-13.484***
|
|
|
(-13.571, -12.411)
|
(-13.780, -12.557)
|
(-13.359, -11.865)
|
(-13.279, -11.890)
|
(-14.188, -12.779)
|
|
D/W/Sb-Gay
|
2.449**
|
2.187*
|
1.979
|
2.084
|
2.301*
|
|
|
(0.677, 4.222)
|
(0.375, 3.999)
|
(-0.076, 4.034)
|
(-0.069, 4.238)
|
(0.153, 4.449)
|
|
partnerGay
|
-2.743***
|
-2.682***
|
-1.965*
|
-2.064*
|
-2.364*
|
|
|
(-4.301, -1.185)
|
(-4.183, -1.182)
|
(-3.581, -0.349)
|
(-3.744, -0.384)
|
(-4.202, -0.527)
|
|
SingleBisexual
|
-0.253
|
-0.178
|
-0.324
|
-0.316
|
-0.292
|
|
|
(-1.320, 0.814)
|
(-1.385, 1.029)
|
(-1.683, 1.035)
|
(-1.661, 1.029)
|
(-1.610, 1.026)
|
|
MarriedBisexual
|
-0.209
|
-0.218
|
0.186
|
0.066
|
-0.072
|
|
|
(-1.404, 0.987)
|
(-1.497, 1.061)
|
(-1.013, 1.385)
|
(-1.203, 1.334)
|
(-1.266, 1.123)
|
|
D/W/SBisexual
|
2.057**
|
1.673**
|
1.637**
|
1.693**
|
1.570*
|
|
|
(0.726, 3.387)
|
(0.490, 2.857)
|
(0.571, 2.704)
|
(0.603, 2.783)
|
(0.360, 2.780)
|
|
singleoth
|
0.946
|
1.156
|
1.527
|
1.318
|
0.957
|
|
|
(-1.009, 2.902)
|
(-0.765, 3.077)
|
(-0.514, 3.568)
|
(-0.557, 3.193)
|
(-0.696, 2.610)
|
|
marriedother
|
0.596
|
0.523
|
0.375
|
0.370
|
0.455
|
|
|
(-1.052, 2.243)
|
(-1.119, 2.164)
|
(-1.177, 1.927)
|
(-1.209, 1.949)
|
(-1.062, 1.973)
|
|
DWSother
|
1.276
|
0.871
|
0.611
|
0.777
|
0.991
|
|
|
(-0.263, 2.814)
|
(-0.523, 2.265)
|
(-1.106, 2.328)
|
(-0.815, 2.369)
|
(-0.686, 2.668)
|
|
partnerother
|
1.775**
|
1.473*
|
1.342
|
1.240
|
1.314
|
|
|
(0.485, 3.065)
|
(0.145, 2.802)
|
(-0.140, 2.825)
|
(-0.245, 2.725)
|
(-0.267, 2.895)
|
|
Male
|
-1.506
|
-2.392
|
-2.720*
|
-2.732*
|
-2.777*
|
|
|
(-3.906, 0.895)
|
(-5.014, 0.230)
|
(-5.329, -0.111)
|
(-5.207, -0.257)
|
(-5.369, -0.185)
|
|
25-34
|
|
0.140
|
0.155
|
0.114
|
0.162
|
|
|
|
(-0.069, 0.349)
|
(-0.063, 0.373)
|
(-0.110, 0.337)
|
(-0.052, 0.377)
|
|
35-44
|
|
-0.494
|
-0.408
|
-0.418
|
-0.256
|
|
|
|
(-1.266, 0.278)
|
(-1.138, 0.322)
|
(-1.146, 0.310)
|
(-0.938, 0.427)
|
|
45-54
|
|
-0.223
|
-0.016
|
-0.016
|
0.146
|
|
|
|
(-1.002, 0.555)
|
(-0.745, 0.712)
|
(-0.744, 0.713)
|
(-0.546, 0.839)
|
|
55-64
|
|
0.289
|
0.407
|
0.383
|
0.600
|
|
|
|
(-0.469, 1.047)
|
(-0.302, 1.115)
|
(-0.329, 1.094)
|
(-0.083, 1.283)
|
|
65+
|
|
0.454
|
0.529
|
0.568
|
0.866*
|
|
|
|
(-0.306, 1.214)
|
(-0.174, 1.232)
|
(-0.135, 1.271)
|
(0.201, 1.530)
|
|
Black
|
|
0.251
|
0.209
|
0.367
|
0.752*
|
|
|
|
(-0.502, 1.003)
|
(-0.487, 0.905)
|
(-0.329, 1.064)
|
(0.091, 1.413)
|
|
Hisp
|
|
0.760***
|
0.244
|
0.147
|
0.037
|
|
|
|
(0.345, 1.175)
|
(-0.216, 0.704)
|
(-0.312, 0.606)
|
(-0.409, 0.484)
|
|
Other
|
|
0.595
|
0.235
|
0.195
|
0.263
|
|
|
|
(-0.309, 1.498)
|
(-0.725, 1.196)
|
(-0.773, 1.163)
|
(-0.607, 1.133)
|
|
someHS
|
|
0.577*
|
0.495*
|
0.482
|
0.489
|
|
|
|
(0.129, 1.026)
|
(0.006, 0.985)
|
(-0.008, 0.972)
|
(-0.031, 1.010)
|
|
HSgrad
|
|
|
-0.292
|
-0.324
|
-0.218
|
|
|
|
|
(-1.262, 0.677)
|
(-1.329, 0.681)
|
(-1.213, 0.778)
|
|
someCol
|
|
|
-0.749
|
-0.780
|
-0.630
|
|
|
|
|
(-1.666, 0.168)
|
(-1.735, 0.175)
|
(-1.570, 0.311)
|
|
Colgrad
|
|
|
-0.754
|
-0.782
|
-0.657
|
|
|
|
|
(-1.655, 0.146)
|
(-1.720, 0.156)
|
(-1.588, 0.274)
|
|
75+
|
|
|
-1.250**
|
-1.192*
|
-1.083*
|
|
|
|
|
(-2.161, -0.339)
|
(-2.140, -0.244)
|
(-2.026, -0.140)
|
|
20-35
|
|
|
-1.546***
|
-1.462***
|
-1.257***
|
|
|
|
|
(-1.955, -1.137)
|
(-1.876, -1.047)
|
(-1.675, -0.840)
|
|
35-50
|
|
|
-0.402*
|
-0.383*
|
-0.384*
|
|
|
|
|
(-0.738, -0.065)
|
(-0.722, -0.044)
|
(-0.720, -0.048)
|
|
50-75
|
|
|
-0.799***
|
-0.757***
|
-0.669***
|
|
|
|
|
(-1.181, -0.417)
|
(-1.138, -0.375)
|
(-1.046, -0.293)
|
|
smokeY
|
|
|
-1.055***
|
-1.005***
|
-0.863***
|
|
|
|
|
(-1.439, -0.671)
|
(-1.395, -0.615)
|
(-1.259, -0.466)
|
|
drinks10-30
|
|
|
|
0.446***
|
0.404***
|
|
|
|
|
|
(0.225, 0.667)
|
(0.181, 0.627)
|
|
drinks5-10
|
|
|
|
0.368
|
0.368
|
|
|
|
|
|
(-0.361, 1.098)
|
(-0.431, 1.167)
|
|
bmi
|
|
|
|
0.147
|
0.116
|
|
|
|
|
|
(-0.301, 0.594)
|
(-0.329, 0.561)
|
|
medcostY
|
|
|
|
0.265***
|
0.260***
|
|
|
|
|
|
(0.127, 0.402)
|
(0.121, 0.399)
|
|
insuranceyes
|
|
|
|
|
0.978***
|
|
|
|
|
|
|
(0.673, 1.284)
|
|
Chup2
|
|
|
|
|
0.223
|
|
|
|
|
|
|
(-0.202, 0.648)
|
|
ChupU5
|
|
|
|
|
-0.087
|
|
|
|
|
|
|
(-0.443, 0.269)
|
|
Chup5+
|
|
|
|
|
0.179
|
|
|
|
|
|
|
(-0.226, 0.584)
|
|
ownhome
|
|
|
|
|
-0.250
|
|
|
|
|
|
|
(-0.665, 0.165)
|
|
ownhomeY
|
|
|
|
|
-0.364*
|
|
|
|
|
|
|
(-0.665, -0.063)
|
|
Constant
|
-1.803***
|
-1.947***
|
-0.631
|
-1.704**
|
-2.258***
|
|
|
(-2.071, -1.535)
|
(-2.620, -1.273)
|
(-1.794, 0.532)
|
(-2.927, -0.481)
|
(-3.533, -0.983)
|
|
N
|
9,226
|
9,226
|
9,226
|
9,226
|
9,226
|
|
Log Likelihood
|
-3,433.255
|
-3,362.642
|
-3,186.169
|
-3,150.093
|
-3,072.187
|
|
AIC
|
6,898.509
|
6,775.283
|
6,438.338
|
6,374.185
|
6,230.374
|
|
|
|
p < .05; p < .01; p < .001
|