BRFSS2
Doc Larry
9/12/2020
Read Manipulated Survey Data
y2003=read.csv("D:/Jose/y2003.csv")
y2004=read.csv("D:/Jose/y2004.csv")
y2005=read.csv("D:/Jose/y2005.csv")
y2006=read.csv("D:/Jose/y2006.csv")
y2007=read.csv("D:/Jose/y2007.csv")
y2008=read.csv("D:/Jose/y2008.csv")
y2009=read.csv("D:/Jose/y2009.csv")
y2010=read.csv("D:/Jose/y2010.csv")
y2011=read.csv("D:/Jose/y2011.csv")
y2012=read.csv("D:/Jose/y2012.csv")
y2013=read.csv("D:/Jose/y2013.csv")
y2014=read.csv("D:/Jose/y2014.csv")
y2015=read.csv("D:/Jose/y2015.csv")
y2016=read.csv("D:/Jose/y2016.csv")
y2017=read.csv("D:/Jose/y2017.csv")
y2018=read.csv("D:/Jose/y2018.csv")
y2019=read.csv("D:/Jose/y2019.csv")
y2003$Age[y2003$Age==0]=1Apply Survey Weights and Design
# Set options for allowing a single observation per stratum
options(survey.lonely.psu = "adjust")
makesurvey=function(surveydata){
mysurvey <- svydesign(
id=~1,
strata = ~Stratum,
weights = ~Weights,
data = surveydata)
return(mysurvey)
}
#Build complex weighted survey lists
svy2003=makesurvey(y2003)
svy2004=makesurvey(y2004)
svy2005=makesurvey(y2005)
svy2006=makesurvey(y2006)
svy2007=makesurvey(y2007)
svy2008=makesurvey(y2008)
svy2009=makesurvey(y2009)
svy2010=makesurvey(y2010)
svy2011=makesurvey(y2011)
svy2012=makesurvey(y2012)
svy2013=makesurvey(y2013)
svy2014=makesurvey(y2014)
svy2015=makesurvey(y2015)
svy2016=makesurvey(y2016)
svy2017=makesurvey(y2017)
svy2018=makesurvey(y2018)
svy2019=makesurvey(y2019)Plot Function
myplot=function(mydf,q){
mynames=c("Overweight+Obese","Heart Disease", "Stroke",
"Skin Cancer", "Cancer",
"COPD", "Arthritis",
"Mental Health", "Kidney Disease",
"Diabetes")
mydf$Year=round(mydf$Year,0)
z=ggplot(mydf, aes (x=Year))+
geom_line(aes(y=Veteran, col="Vet"))+
geom_line(aes(y=NonVeteran, col="Non-Vet"))+
geom_smooth(aes(y=Veteran, col="Vet"), size=.2)+
geom_smooth(aes(y=NonVeteran, col="Non-Vet"), size=.2)+
scale_fill_continuous(name = "Veteran", labels = c("Vet", "NonVet"))+
ylab("Proportion")+
xlab("")+
xlim(2003,2019)+
theme(legend.title = element_blank())+
theme(legend.text = element_text(size = 6))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ggtitle(mynames[q])+
theme(plot.title = element_text(size=11))+
theme(axis.text=element_text(size=6),
axis.title=element_text(size=6,face="bold"))+
theme(legend.key.size = unit(.5, "cm"))+
theme(legend.key.width = unit(0.2,"cm"))
return(z)
}Table Overweight + Obese
We build age-adjusted tables below to calculate proportions for vet and non-vet populations.
myf=function(svy){
myvet=ageadjust.direct(count=svytable(BMI~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(BMI~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(BMI~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(BMI~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
a1=myf(svy2003)
a2=myf(svy2004)
a3=myf(svy2005)
a4=myf(svy2006)
a5=myf(svy2007)
a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a1,a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16,a17)
mydf=as.data.frame(cbind(seq(2003,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot0=myplot(mydf, 1)
plot0## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Heart Disease
library(grid)
library(gridExtra)
library(ggplot2)
myf=function(svy){
myvet=ageadjust.direct(count=svytable(HeartDisease~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(HeartDisease~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(HeartDisease~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[,1],
stdpop=svytable(HeartDisease~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
a1=myf(svy2003)
a2=myf(svy2004)
a3=myf(svy2005)
a4=myf(svy2006)
a5=myf(svy2007)
a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a1,a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2003,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot1=myplot(mydf, 2)
plot1## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Stroke
myf=function(svy){
myvet=ageadjust.direct(count=svytable(Stroke~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(Stroke~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(Stroke~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(Stroke~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
a1=myf(svy2003)
a2=myf(svy2004)
a3=myf(svy2005)
a4=myf(svy2006)
a5=myf(svy2007)
a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a1,a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2003,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot2=myplot(mydf, 3)
plot2## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Skin Cancer
myf=function(svy){
myvet=ageadjust.direct(count=svytable(SkinCancer~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(SkinCancer~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(SkinCancer~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(SkinCancer~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
#a1=myf(svy2003)
#a2=myf(svy2004)
#a3=myf(svy2005)
#a4=myf(svy2006)
#a5=myf(svy2007)
#a6=myf(svy2008)
#a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2010,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot3=myplot(mydf, 4)
plot3## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Cancer
myf=function(svy){
myvet=ageadjust.direct(count=svytable(Cancer~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(Cancer~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(Cancer~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(Cancer~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
#a1=myf(svy2003)
#a2=myf(svy2004)
#a3=myf(svy2005)
#a4=myf(svy2006)
#a5=myf(svy2007)
#a6=myf(svy2008)
#a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2010,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot4=myplot(mydf, 5)
plot4## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table COPD
myf=function(svy){
myvet=ageadjust.direct(count=svytable(COPD~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(COPD~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(COPD~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(COPD~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
#a1=myf(svy2003)
#a2=myf(svy2004)
#a3=myf(svy2005)
#a4=myf(svy2006)
#a5=myf(svy2007)
#a6=myf(svy2008)
#a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2010,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot5=myplot(mydf, 6)
plot5## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Arthritis
myf=function(svy){
myvet=ageadjust.direct(count=svytable(Arthritis~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(Arthritis~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(Arthritis~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(Arthritis~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
#a1=myf(svy2003)
#a2=myf(svy2004)
#a3=myf(svy2005)
#a4=myf(svy2006)
#a5=myf(svy2007)
#a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2009,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot6=myplot(mydf, 7)
plot6## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Depression
myf=function(svy){
myvet=ageadjust.direct(count=svytable(Depression~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(Depression~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(Depression~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(Depression~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
a1=myf(svy2003)
a2=myf(svy2004)
a3=myf(svy2005)
a4=myf(svy2006)
a5=myf(svy2007)
a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2003,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot7=myplot(mydf, 8)
plot7## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Kidney Disease
myf=function(svy){
myvet=ageadjust.direct(count=svytable(KidneyDisease~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(KidneyDisease~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(KidneyDisease~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(KidneyDisease~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
#a1=myf(svy2003)
#a2=myf(svy2004)
#a3=myf(svy2005)
#a4=myf(svy2006)
#a5=myf(svy2007)
#a6=myf(svy2008)
#a7=myf(svy2009)
#a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a9, a10, a11, a12, a13, a14, a15, a17)
mydf=as.data.frame(cbind(seq(2011,2019),myval))## Warning in cbind(seq(2011, 2019), myval): number of rows of result is not a
## multiple of vector length (arg 1)colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot8=myplot(mydf, 9)
plot8## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Table Diabetes
myf=function(svy){
myvet=ageadjust.direct(count=svytable(Diabetes~Veteran+Age, svy)[2,],
pop=svytable(~Veteran+Age, svy)[2,],
stdpop=svytable(Diabetes~Age, svy),
conf.level=.95
)
mynonvet=ageadjust.direct(count=svytable(Diabetes~Veteran+Age, svy)[1,],
pop=svytable(~Veteran+Age, svy)[1,],
stdpop=svytable(Diabetes~Age, svy),
conf.level=.95
)
z=rbind(myvet,mynonvet)
return(z[,1])
}
a1=myf(svy2003)
a2=myf(svy2004)
a3=myf(svy2005)
a4=myf(svy2006)
a5=myf(svy2007)
a6=myf(svy2008)
a7=myf(svy2009)
a8=myf(svy2010)
a9=myf(svy2011)
a10=myf(svy2012)
a11=myf(svy2013)
a12=myf(svy2014)
a13=myf(svy2015)
a14=myf(svy2016)
a15=myf(svy2017)
a16=myf(svy2018)
a17=myf(svy2019)
myval=rbind(a1,a2,a3,a4,a5,a6,a7,a8,a9, a10, a11, a12, a13, a14, a15, a16, a17)
mydf=as.data.frame(cbind(seq(2003,2019),myval))
colnames(mydf)=c("Year", "Veteran", "NonVeteran")
plot9=myplot(mydf, 10)
plot9## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
All Plots Except Obesity
library(ggpubr)
ggarrange(plot1,plot2,plot3,plot4,plot5,plot6,plot7,plot8,plot9, nrow=3, ncol=3)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggarrange(plot2,plot3,plot4,plot5,plot8,plot9, nrow=3, ncol=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Model 1 BMI
model1<-svyglm(na.omit(BMI~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/obesity.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(BMI ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.28476 0.21737 1.31 0.19020
## as.factor(Age)2 0.59482 0.03238 18.37 < 2e-16 ***
## as.factor(Age)3 0.91423 0.03525 25.93 < 2e-16 ***
## as.factor(Age)4 1.13422 0.03585 31.64 < 2e-16 ***
## as.factor(Age)5 1.06612 0.03614 29.50 < 2e-16 ***
## as.factor(Age)6 0.85451 0.03888 21.98 < 2e-16 ***
## as.factor(Race)2 0.39806 0.02853 13.95 < 2e-16 ***
## as.factor(Race)3 -0.50650 0.04516 -11.22 < 2e-16 ***
## as.factor(Race)4 0.24721 0.06359 3.89 0.00010 ***
## as.factor(Race)5 0.52116 0.03055 17.06 < 2e-16 ***
## as.factor(Race)6 0.12682 0.03946 3.21 0.00131 **
## as.factor(Gender)1 0.29387 0.01601 18.35 < 2e-16 ***
## as.factor(Married)2 -0.15772 0.02284 -6.90 5.1e-12 ***
## as.factor(Married)3 -0.26499 0.02573 -10.30 < 2e-16 ***
## as.factor(Married)4 -0.09713 0.05288 -1.84 0.06626 .
## as.factor(Married)5 -0.22747 0.02370 -9.60 < 2e-16 ***
## as.factor(Married)6 -0.13990 0.03652 -3.83 0.00013 ***
## as.factor(Income)2 0.04620 0.05234 0.88 0.37744
## as.factor(Income)3 0.05761 0.04625 1.25 0.21289
## as.factor(Income)4 0.08019 0.04540 1.77 0.07731 .
## as.factor(Income)5 0.02640 0.04570 0.58 0.56346
## as.factor(Income)6 0.08033 0.04316 1.86 0.06270 .
## as.factor(Income)7 0.05755 0.04324 1.33 0.18322
## as.factor(Income)8 -0.07048 0.04194 -1.68 0.09286 .
## as.factor(Income)9 0.00766 0.04107 0.19 0.85208
## as.factor(Education)2 -0.04424 0.21235 -0.21 0.83496
## as.factor(Education)3 -0.12456 0.21016 -0.59 0.55340
## as.factor(Education)4 -0.12775 0.20895 -0.61 0.54095
## as.factor(Education)5 -0.14373 0.20911 -0.69 0.49187
## as.factor(Education)6 -0.43755 0.20946 -2.09 0.03671 *
## as.factor(Employed)2 -0.26007 0.02654 -9.80 < 2e-16 ***
## as.factor(Employed)3 -0.11216 0.05406 -2.07 0.03801 *
## as.factor(Employed)4 -0.08387 0.04841 -1.73 0.08316 .
## as.factor(Employed)5 -0.13409 0.03393 -3.95 7.7e-05 ***
## as.factor(Employed)6 -0.34727 0.04076 -8.52 < 2e-16 ***
## as.factor(Employed)7 -0.11788 0.02610 -4.52 6.3e-06 ***
## as.factor(Employed)8 0.03284 0.03248 1.01 0.31195
## as.factor(State)2 -0.08773 0.07397 -1.19 0.23558
## as.factor(State)4 -0.17704 0.05550 -3.19 0.00142 **
## as.factor(State)5 0.03213 0.05996 0.54 0.59204
## as.factor(State)6 -0.28131 0.04689 -6.00 2.0e-09 ***
## as.factor(State)8 -0.42402 0.04623 -9.17 < 2e-16 ***
## as.factor(State)9 -0.07850 0.04977 -1.58 0.11474
## as.factor(State)10 0.02512 0.06632 0.38 0.70480
## as.factor(State)11 -0.45664 0.06675 -6.84 7.9e-12 ***
## as.factor(State)12 -0.24495 0.05302 -4.62 3.8e-06 ***
## as.factor(State)13 -0.10471 0.05683 -1.84 0.06537 .
## as.factor(State)15 -0.33449 0.05374 -6.22 4.9e-10 ***
## as.factor(State)16 -0.15753 0.06081 -2.59 0.00958 **
## as.factor(State)17 -0.16857 0.05222 -3.23 0.00125 **
## as.factor(State)18 0.05225 0.04901 1.07 0.28640
## as.factor(State)19 0.03937 0.04630 0.85 0.39509
## as.factor(State)20 0.07363 0.04654 1.58 0.11363
## as.factor(State)21 0.12339 0.05682 2.17 0.02987 *
## as.factor(State)22 0.03747 0.05708 0.66 0.51152
## as.factor(State)23 -0.11290 0.05117 -2.21 0.02735 *
## as.factor(State)24 -0.08779 0.04633 -1.89 0.05812 .
## as.factor(State)25 -0.21481 0.04951 -4.34 1.4e-05 ***
## as.factor(State)26 0.07665 0.04818 1.59 0.11162
## as.factor(State)27 -0.05868 0.04346 -1.35 0.17697
## as.factor(State)28 0.09505 0.05889 1.61 0.10649
## as.factor(State)29 -0.03748 0.05281 -0.71 0.47781
## as.factor(State)30 -0.17153 0.05040 -3.40 0.00067 ***
## as.factor(State)31 0.02706 0.04614 0.59 0.55753
## as.factor(State)32 -0.11054 0.07117 -1.55 0.12037
## as.factor(State)33 0.01102 0.05747 0.19 0.84792
## as.factor(State)35 -0.34256 0.05790 -5.92 3.3e-09 ***
## as.factor(State)36 -0.17158 0.04681 -3.67 0.00025 ***
## as.factor(State)37 0.01597 0.05583 0.29 0.77483
## as.factor(State)38 0.13191 0.05941 2.22 0.02640 *
## as.factor(State)39 0.03690 0.04932 0.75 0.45433
## as.factor(State)40 0.11753 0.05401 2.18 0.02956 *
## as.factor(State)41 -0.16999 0.05148 -3.30 0.00096 ***
## as.factor(State)42 0.00638 0.05189 0.12 0.90216
## as.factor(State)44 -0.15160 0.05731 -2.65 0.00817 **
## as.factor(State)45 -0.03172 0.05249 -0.60 0.54570
## as.factor(State)46 0.14500 0.06849 2.12 0.03425 *
## as.factor(State)47 0.00708 0.05577 0.13 0.89904
## as.factor(State)48 -0.04012 0.05550 -0.72 0.46973
## as.factor(State)49 -0.15077 0.04570 -3.30 0.00097 ***
## as.factor(State)50 -0.31849 0.05577 -5.71 1.1e-08 ***
## as.factor(State)51 -0.05979 0.04919 -1.22 0.22421
## as.factor(State)53 -0.13792 0.04564 -3.02 0.00251 **
## as.factor(State)54 0.13062 0.05710 2.29 0.02215 *
## as.factor(State)55 0.05403 0.05773 0.94 0.34937
## as.factor(State)56 -0.19197 0.05989 -3.21 0.00135 **
## as.factor(State)66 0.08060 0.08968 0.90 0.36879
## as.factor(State)72 -0.37964 0.06055 -6.27 3.6e-10 ***
## as.factor(Veteran)1 0.11359 0.02588 4.39 1.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.994)
##
## Number of Fisher Scoring iterations: 5Model 2
model1<-svyglm(na.omit(HeartDisease~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/heartdisease.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(HeartDisease ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.98044 0.41516 -12.00 < 2e-16 ***
## as.factor(Age)2 0.02026 0.20765 0.10 0.92229
## as.factor(Age)3 0.75483 0.21370 3.53 0.00041 ***
## as.factor(Age)4 1.65222 0.18775 8.80 < 2e-16 ***
## as.factor(Age)5 2.14713 0.18683 11.49 < 2e-16 ***
## as.factor(Age)6 2.68106 0.19171 13.98 < 2e-16 ***
## as.factor(Race)2 -0.31259 0.05528 -5.65 1.6e-08 ***
## as.factor(Race)3 0.12744 0.16801 0.76 0.44814
## as.factor(Race)4 0.31732 0.12344 2.57 0.01015 *
## as.factor(Race)5 -0.33083 0.09813 -3.37 0.00075 ***
## as.factor(Race)6 0.25812 0.08898 2.90 0.00372 **
## as.factor(Gender)1 0.55112 0.03448 15.99 < 2e-16 ***
## as.factor(Married)2 0.04950 0.04288 1.15 0.24833
## as.factor(Married)3 0.15283 0.04182 3.65 0.00026 ***
## as.factor(Married)4 0.17062 0.09376 1.82 0.06881 .
## as.factor(Married)5 -0.17573 0.05931 -2.96 0.00305 **
## as.factor(Married)6 -0.00276 0.10351 -0.03 0.97871
## as.factor(Income)2 0.00125 0.08391 0.01 0.98809
## as.factor(Income)3 -0.03480 0.07831 -0.44 0.65675
## as.factor(Income)4 0.02345 0.08177 0.29 0.77428
## as.factor(Income)5 -0.10056 0.08156 -1.23 0.21760
## as.factor(Income)6 -0.20333 0.07940 -2.56 0.01044 *
## as.factor(Income)7 -0.22759 0.08027 -2.84 0.00458 **
## as.factor(Income)8 -0.34699 0.08258 -4.20 2.7e-05 ***
## as.factor(Income)9 -0.28642 0.07717 -3.71 0.00021 ***
## as.factor(Education)2 -0.51787 0.36143 -1.43 0.15190
## as.factor(Education)3 -0.36940 0.36090 -1.02 0.30605
## as.factor(Education)4 -0.49321 0.36020 -1.37 0.17092
## as.factor(Education)5 -0.43106 0.36095 -1.19 0.23238
## as.factor(Education)6 -0.56632 0.36534 -1.55 0.12111
## as.factor(Employed)2 0.22455 0.07005 3.21 0.00135 **
## as.factor(Employed)3 0.71223 0.09857 7.23 5.0e-13 ***
## as.factor(Employed)4 0.46522 0.12298 3.78 0.00015 ***
## as.factor(Employed)5 0.38388 0.08592 4.47 7.9e-06 ***
## as.factor(Employed)6 -0.46519 0.24650 -1.89 0.05913 .
## as.factor(Employed)7 0.69207 0.06053 11.43 < 2e-16 ***
## as.factor(Employed)8 1.48937 0.05587 26.66 < 2e-16 ***
## as.factor(State)2 -0.82154 0.16635 -4.94 7.9e-07 ***
## as.factor(State)4 -0.21383 0.10487 -2.04 0.04144 *
## as.factor(State)5 0.11497 0.09470 1.21 0.22475
## as.factor(State)6 -0.39410 0.09822 -4.01 6.0e-05 ***
## as.factor(State)8 -0.59354 0.09731 -6.10 1.1e-09 ***
## as.factor(State)9 -0.39988 0.09287 -4.31 1.7e-05 ***
## as.factor(State)10 -0.12047 0.12004 -1.00 0.31558
## as.factor(State)11 -0.05614 0.13780 -0.41 0.68372
## as.factor(State)12 -0.12580 0.10191 -1.23 0.21704
## as.factor(State)13 -0.10147 0.10143 -1.00 0.31712
## as.factor(State)15 -0.70452 0.13359 -5.27 1.3e-07 ***
## as.factor(State)16 -0.27989 0.11605 -2.41 0.01588 *
## as.factor(State)17 -0.17184 0.10271 -1.67 0.09430 .
## as.factor(State)18 -0.05401 0.08580 -0.63 0.52902
## as.factor(State)19 -0.25483 0.08692 -2.93 0.00337 **
## as.factor(State)20 -0.24725 0.08633 -2.86 0.00418 **
## as.factor(State)21 0.08182 0.10002 0.82 0.41333
## as.factor(State)22 0.01027 0.10017 0.10 0.91834
## as.factor(State)23 -0.10917 0.08765 -1.25 0.21294
## as.factor(State)24 -0.27845 0.08259 -3.37 0.00075 ***
## as.factor(State)25 -0.14964 0.10048 -1.49 0.13642
## as.factor(State)26 -0.11904 0.08559 -1.39 0.16428
## as.factor(State)27 -0.29161 0.08234 -3.54 0.00040 ***
## as.factor(State)28 -0.06039 0.09942 -0.61 0.54355
## as.factor(State)29 -0.16578 0.09797 -1.69 0.09062 .
## as.factor(State)30 -0.34369 0.09743 -3.53 0.00042 ***
## as.factor(State)31 -0.22280 0.08316 -2.68 0.00738 **
## as.factor(State)32 0.01865 0.16027 0.12 0.90736
## as.factor(State)33 -0.31905 0.10181 -3.13 0.00173 **
## as.factor(State)35 -0.53810 0.11962 -4.50 6.8e-06 ***
## as.factor(State)36 -0.11986 0.09346 -1.28 0.19966
## as.factor(State)37 -0.07737 0.10428 -0.74 0.45813
## as.factor(State)38 -0.26926 0.09977 -2.70 0.00696 **
## as.factor(State)39 -0.08975 0.08665 -1.04 0.30026
## as.factor(State)40 0.03684 0.09336 0.39 0.69312
## as.factor(State)41 -0.34416 0.11449 -3.01 0.00265 **
## as.factor(State)42 -0.20002 0.10181 -1.96 0.04945 *
## as.factor(State)44 -0.18939 0.10381 -1.82 0.06809 .
## as.factor(State)45 -0.07580 0.09204 -0.82 0.41021
## as.factor(State)46 -0.10203 0.13880 -0.74 0.46227
## as.factor(State)47 -0.06143 0.09807 -0.63 0.53107
## as.factor(State)48 -0.25461 0.10477 -2.43 0.01509 *
## as.factor(State)49 -0.49071 0.09663 -5.08 3.8e-07 ***
## as.factor(State)50 -0.31329 0.10710 -2.93 0.00344 **
## as.factor(State)51 -0.28049 0.08933 -3.14 0.00169 **
## as.factor(State)53 -0.34254 0.08737 -3.92 8.8e-05 ***
## as.factor(State)54 0.18911 0.09109 2.08 0.03788 *
## as.factor(State)55 -0.30704 0.10525 -2.92 0.00353 **
## as.factor(State)56 -0.42023 0.12227 -3.44 0.00059 ***
## as.factor(State)66 -0.37118 0.18135 -2.05 0.04068 *
## as.factor(State)72 0.60215 0.13081 4.60 4.2e-06 ***
## as.factor(Veteran)1 0.29184 0.04527 6.45 1.1e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.977)
##
## Number of Fisher Scoring iterations: 8Model 3
model1<-svyglm(na.omit(Stroke~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/stroke.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(Stroke ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.36787 0.34590 -15.52 < 2e-16 ***
## as.factor(Age)2 0.77738 0.18926 4.11 4.0e-05 ***
## as.factor(Age)3 1.31336 0.18246 7.20 6.1e-13 ***
## as.factor(Age)4 1.93261 0.17909 10.79 < 2e-16 ***
## as.factor(Age)5 2.20223 0.17971 12.25 < 2e-16 ***
## as.factor(Age)6 2.56595 0.18028 14.23 < 2e-16 ***
## as.factor(Race)2 0.24674 0.05027 4.91 9.2e-07 ***
## as.factor(Race)3 0.18008 0.20374 0.88 0.37677
## as.factor(Race)4 0.26047 0.10246 2.54 0.01102 *
## as.factor(Race)5 -0.29474 0.08519 -3.46 0.00054 ***
## as.factor(Race)6 0.33633 0.08833 3.81 0.00014 ***
## as.factor(Gender)1 0.17113 0.03864 4.43 9.5e-06 ***
## as.factor(Married)2 0.16895 0.04659 3.63 0.00029 ***
## as.factor(Married)3 0.23175 0.04750 4.88 1.1e-06 ***
## as.factor(Married)4 0.07212 0.08585 0.84 0.40088
## as.factor(Married)5 0.07493 0.06268 1.20 0.23187
## as.factor(Married)6 0.01877 0.10161 0.18 0.85341
## as.factor(Income)2 -0.00499 0.08746 -0.06 0.95452
## as.factor(Income)3 0.05551 0.07777 0.71 0.47538
## as.factor(Income)4 -0.01112 0.08035 -0.14 0.88993
## as.factor(Income)5 -0.22325 0.08117 -2.75 0.00595 **
## as.factor(Income)6 -0.32701 0.08189 -3.99 6.5e-05 ***
## as.factor(Income)7 -0.47073 0.08594 -5.48 4.3e-08 ***
## as.factor(Income)8 -0.71847 0.08982 -8.00 1.3e-15 ***
## as.factor(Income)9 -0.28493 0.07446 -3.83 0.00013 ***
## as.factor(Education)2 -0.05033 0.28289 -0.18 0.85878
## as.factor(Education)3 0.00360 0.28008 0.01 0.98974
## as.factor(Education)4 -0.21060 0.27834 -0.76 0.44928
## as.factor(Education)5 -0.18756 0.27865 -0.67 0.50089
## as.factor(Education)6 -0.32441 0.28060 -1.16 0.24764
## as.factor(Employed)2 -0.07469 0.08214 -0.91 0.36317
## as.factor(Employed)3 0.77803 0.12929 6.02 1.8e-09 ***
## as.factor(Employed)4 0.56006 0.13360 4.19 2.8e-05 ***
## as.factor(Employed)5 0.35127 0.09015 3.90 9.8e-05 ***
## as.factor(Employed)6 -0.29169 0.26738 -1.09 0.27530
## as.factor(Employed)7 0.78315 0.06435 12.17 < 2e-16 ***
## as.factor(Employed)8 1.73930 0.06688 26.00 < 2e-16 ***
## as.factor(State)2 -0.52476 0.16156 -3.25 0.00116 **
## as.factor(State)4 -0.09666 0.11884 -0.81 0.41602
## as.factor(State)5 -0.05612 0.09826 -0.57 0.56793
## as.factor(State)6 -0.22912 0.09885 -2.32 0.02046 *
## as.factor(State)8 -0.37111 0.10205 -3.64 0.00028 ***
## as.factor(State)9 -0.34815 0.11158 -3.12 0.00181 **
## as.factor(State)10 -0.07753 0.12103 -0.64 0.52179
## as.factor(State)11 -0.08969 0.13994 -0.64 0.52159
## as.factor(State)12 -0.23835 0.09977 -2.39 0.01689 *
## as.factor(State)13 -0.13014 0.10988 -1.18 0.23628
## as.factor(State)15 -0.31603 0.14167 -2.23 0.02570 *
## as.factor(State)16 -0.19912 0.13651 -1.46 0.14467
## as.factor(State)17 -0.21726 0.11100 -1.96 0.05031 .
## as.factor(State)18 -0.08374 0.09073 -0.92 0.35604
## as.factor(State)19 -0.15370 0.09229 -1.67 0.09584 .
## as.factor(State)20 -0.23479 0.09162 -2.56 0.01038 *
## as.factor(State)21 0.05559 0.10521 0.53 0.59726
## as.factor(State)22 -0.08463 0.11354 -0.75 0.45605
## as.factor(State)23 -0.18468 0.09359 -1.97 0.04847 *
## as.factor(State)24 -0.16648 0.08928 -1.86 0.06221 .
## as.factor(State)25 -0.41800 0.11007 -3.80 0.00015 ***
## as.factor(State)26 -0.18734 0.09453 -1.98 0.04750 *
## as.factor(State)27 -0.27475 0.08653 -3.18 0.00150 **
## as.factor(State)28 -0.09707 0.09933 -0.98 0.32846
## as.factor(State)29 -0.06984 0.09804 -0.71 0.47624
## as.factor(State)30 -0.28151 0.10993 -2.56 0.01045 *
## as.factor(State)31 -0.22192 0.09141 -2.43 0.01520 *
## as.factor(State)32 -0.15923 0.16789 -0.95 0.34292
## as.factor(State)33 -0.33434 0.11211 -2.98 0.00286 **
## as.factor(State)35 -0.36984 0.12062 -3.07 0.00217 **
## as.factor(State)36 -0.25324 0.09953 -2.54 0.01095 *
## as.factor(State)37 0.02199 0.11282 0.19 0.84545
## as.factor(State)38 -0.27440 0.12058 -2.28 0.02287 *
## as.factor(State)39 -0.13795 0.09210 -1.50 0.13416
## as.factor(State)40 -0.00122 0.09713 -0.01 0.99000
## as.factor(State)41 -0.20029 0.10428 -1.92 0.05478 .
## as.factor(State)42 -0.07336 0.10552 -0.70 0.48690
## as.factor(State)44 -0.44384 0.11508 -3.86 0.00011 ***
## as.factor(State)45 -0.05078 0.09803 -0.52 0.60448
## as.factor(State)46 -0.27257 0.15736 -1.73 0.08326 .
## as.factor(State)47 -0.05661 0.10103 -0.56 0.57528
## as.factor(State)48 0.08666 0.11046 0.78 0.43271
## as.factor(State)49 -0.24045 0.09879 -2.43 0.01494 *
## as.factor(State)50 -0.29513 0.11406 -2.59 0.00967 **
## as.factor(State)51 -0.15505 0.09288 -1.67 0.09504 .
## as.factor(State)53 -0.22928 0.09118 -2.51 0.01192 *
## as.factor(State)54 -0.09477 0.09869 -0.96 0.33693
## as.factor(State)55 -0.41882 0.12331 -3.40 0.00068 ***
## as.factor(State)56 -0.10760 0.12845 -0.84 0.40221
## as.factor(State)66 -0.50681 0.20418 -2.48 0.01306 *
## as.factor(State)72 -0.60367 0.14820 -4.07 4.6e-05 ***
## as.factor(Veteran)1 0.21899 0.04690 4.67 3.0e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.962)
##
## Number of Fisher Scoring iterations: 7Model 4
model1<-svyglm(na.omit(SkinCancer~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/skin.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(SkinCancer ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.8998 0.3980 -12.31 < 2e-16 ***
## as.factor(Age)2 -0.1424 0.1952 -0.73 0.46572
## as.factor(Age)3 0.8686 0.1842 4.72 2.4e-06 ***
## as.factor(Age)4 1.6379 0.1723 9.50 < 2e-16 ***
## as.factor(Age)5 2.2803 0.1713 13.31 < 2e-16 ***
## as.factor(Age)6 2.8690 0.1730 16.58 < 2e-16 ***
## as.factor(Race)2 -2.7966 0.1045 -26.75 < 2e-16 ***
## as.factor(Race)3 -2.6986 0.2943 -9.17 < 2e-16 ***
## as.factor(Race)4 -0.8078 0.1452 -5.56 2.6e-08 ***
## as.factor(Race)5 -1.5455 0.1434 -10.78 < 2e-16 ***
## as.factor(Race)6 -0.7415 0.0811 -9.14 < 2e-16 ***
## as.factor(Gender)1 0.0868 0.0266 3.27 0.00108 **
## as.factor(Married)2 -0.1099 0.0361 -3.04 0.00235 **
## as.factor(Married)3 0.0706 0.0315 2.24 0.02515 *
## as.factor(Married)4 -0.0742 0.1038 -0.72 0.47452
## as.factor(Married)5 -0.2221 0.0510 -4.36 1.3e-05 ***
## as.factor(Married)6 -0.0757 0.0931 -0.81 0.41621
## as.factor(Income)2 -0.0654 0.1063 -0.62 0.53839
## as.factor(Income)3 0.0482 0.1077 0.45 0.65488
## as.factor(Income)4 0.1003 0.1028 0.97 0.32965
## as.factor(Income)5 0.1268 0.1018 1.25 0.21296
## as.factor(Income)6 0.1962 0.0979 2.00 0.04511 *
## as.factor(Income)7 0.1838 0.0973 1.89 0.05889 .
## as.factor(Income)8 0.2933 0.0974 3.01 0.00261 **
## as.factor(Income)9 0.1642 0.0971 1.69 0.09070 .
## as.factor(Education)2 0.2360 0.3483 0.68 0.49813
## as.factor(Education)3 0.1477 0.3461 0.43 0.66964
## as.factor(Education)4 0.2376 0.3451 0.69 0.49118
## as.factor(Education)5 0.4250 0.3460 1.23 0.21933
## as.factor(Education)6 0.5923 0.3472 1.71 0.08808 .
## as.factor(Employed)2 0.1982 0.0448 4.42 9.9e-06 ***
## as.factor(Employed)3 0.2264 0.0950 2.38 0.01712 *
## as.factor(Employed)4 0.1129 0.1190 0.95 0.34270
## as.factor(Employed)5 0.2030 0.0577 3.52 0.00044 ***
## as.factor(Employed)6 -0.5459 0.2210 -2.47 0.01353 *
## as.factor(Employed)7 0.4197 0.0414 10.14 < 2e-16 ***
## as.factor(Employed)8 0.4491 0.0539 8.33 < 2e-16 ***
## as.factor(State)2 -0.9463 0.1404 -6.74 1.6e-11 ***
## as.factor(State)4 -0.0224 0.0726 -0.31 0.75753
## as.factor(State)5 -0.2010 0.0764 -2.63 0.00852 **
## as.factor(State)6 -0.0178 0.0707 -0.25 0.80126
## as.factor(State)8 -0.2229 0.0660 -3.38 0.00074 ***
## as.factor(State)9 -0.5018 0.0683 -7.34 2.1e-13 ***
## as.factor(State)10 -0.2207 0.0873 -2.53 0.01145 *
## as.factor(State)11 -0.0760 0.1215 -0.63 0.53186
## as.factor(State)12 0.2658 0.0729 3.65 0.00026 ***
## as.factor(State)13 -0.2023 0.0788 -2.57 0.01026 *
## as.factor(State)15 -0.0277 0.0856 -0.32 0.74601
## as.factor(State)16 -0.2129 0.0881 -2.42 0.01573 *
## as.factor(State)17 -0.4979 0.0819 -6.08 1.2e-09 ***
## as.factor(State)18 -0.4589 0.0661 -6.95 3.7e-12 ***
## as.factor(State)19 -0.4921 0.0653 -7.54 4.8e-14 ***
## as.factor(State)20 -0.4280 0.0641 -6.67 2.5e-11 ***
## as.factor(State)21 -0.3066 0.0803 -3.82 0.00013 ***
## as.factor(State)22 -0.2567 0.0847 -3.03 0.00245 **
## as.factor(State)23 -0.4775 0.0690 -6.92 4.7e-12 ***
## as.factor(State)24 -0.3602 0.0613 -5.88 4.1e-09 ***
## as.factor(State)25 -0.3769 0.0718 -5.25 1.5e-07 ***
## as.factor(State)26 -0.3680 0.0667 -5.51 3.5e-08 ***
## as.factor(State)27 -0.5974 0.0612 -9.75 < 2e-16 ***
## as.factor(State)28 -0.1436 0.0845 -1.70 0.08941 .
## as.factor(State)29 -0.2374 0.0756 -3.14 0.00169 **
## as.factor(State)30 -0.4600 0.0719 -6.40 1.5e-10 ***
## as.factor(State)31 -0.3954 0.0630 -6.28 3.5e-10 ***
## as.factor(State)32 -0.1930 0.1074 -1.80 0.07238 .
## as.factor(State)33 -0.4831 0.0756 -6.39 1.7e-10 ***
## as.factor(State)35 -0.1153 0.0842 -1.37 0.17095
## as.factor(State)36 -0.6364 0.0694 -9.17 < 2e-16 ***
## as.factor(State)37 -0.1069 0.0833 -1.28 0.19930
## as.factor(State)38 -0.6167 0.0832 -7.41 1.3e-13 ***
## as.factor(State)39 -0.4721 0.0685 -6.90 5.4e-12 ***
## as.factor(State)40 -0.4136 0.0746 -5.54 2.9e-08 ***
## as.factor(State)41 -0.3122 0.0764 -4.09 4.3e-05 ***
## as.factor(State)42 -0.4198 0.0771 -5.45 5.1e-08 ***
## as.factor(State)44 -0.3632 0.0766 -4.74 2.1e-06 ***
## as.factor(State)45 0.0174 0.0708 0.25 0.80583
## as.factor(State)46 -0.4852 0.0978 -4.96 6.9e-07 ***
## as.factor(State)47 -0.1715 0.0808 -2.12 0.03385 *
## as.factor(State)48 -0.3139 0.0798 -3.94 8.3e-05 ***
## as.factor(State)49 -0.0169 0.0638 -0.27 0.79086
## as.factor(State)50 -0.4897 0.0768 -6.38 1.8e-10 ***
## as.factor(State)51 -0.2969 0.0670 -4.43 9.2e-06 ***
## as.factor(State)53 -0.4114 0.0633 -6.50 8.1e-11 ***
## as.factor(State)54 -0.2152 0.0746 -2.88 0.00393 **
## as.factor(State)55 -0.5717 0.0824 -6.93 4.1e-12 ***
## as.factor(State)56 -0.3563 0.0833 -4.28 1.9e-05 ***
## as.factor(State)66 -0.9420 0.2428 -3.88 0.00010 ***
## as.factor(State)72 -0.6190 0.1848 -3.35 0.00081 ***
## as.factor(Veteran)1 0.2932 0.0357 8.21 2.3e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.22)
##
## Number of Fisher Scoring iterations: 7Model 5
model1<-svyglm(na.omit(Cancer~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/cancer.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(Cancer ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.910602 0.374434 -13.11 < 2e-16 ***
## as.factor(Age)2 0.687388 0.231452 2.97 0.0030 **
## as.factor(Age)3 1.062017 0.238249 4.46 8.3e-06 ***
## as.factor(Age)4 1.645389 0.236173 6.97 3.2e-12 ***
## as.factor(Age)5 2.090960 0.236371 8.85 < 2e-16 ***
## as.factor(Age)6 2.631739 0.238523 11.03 < 2e-16 ***
## as.factor(Race)2 -0.344446 0.043440 -7.93 2.2e-15 ***
## as.factor(Race)3 -0.809051 0.137356 -5.89 3.9e-09 ***
## as.factor(Race)4 0.116235 0.113034 1.03 0.3038
## as.factor(Race)5 -0.362979 0.063012 -5.76 8.4e-09 ***
## as.factor(Race)6 -0.028282 0.070851 -0.40 0.6898
## as.factor(Gender)1 -0.399942 0.026604 -15.03 < 2e-16 ***
## as.factor(Married)2 0.031507 0.033169 0.95 0.3422
## as.factor(Married)3 0.049223 0.033723 1.46 0.1444
## as.factor(Married)4 0.076118 0.078261 0.97 0.3307
## as.factor(Married)5 -0.110617 0.052131 -2.12 0.0338 *
## as.factor(Married)6 -0.029701 0.084340 -0.35 0.7247
## as.factor(Income)2 0.093077 0.076504 1.22 0.2237
## as.factor(Income)3 0.083236 0.072055 1.16 0.2480
## as.factor(Income)4 0.140630 0.072123 1.95 0.0512 .
## as.factor(Income)5 0.119789 0.074526 1.61 0.1080
## as.factor(Income)6 0.132814 0.069713 1.91 0.0568 .
## as.factor(Income)7 0.125318 0.070076 1.79 0.0737 .
## as.factor(Income)8 0.107375 0.070827 1.52 0.1295
## as.factor(Income)9 0.029165 0.065497 0.45 0.6561
## as.factor(Education)2 0.131185 0.301422 0.44 0.6634
## as.factor(Education)3 0.350024 0.300940 1.16 0.2448
## as.factor(Education)4 0.263921 0.296836 0.89 0.3739
## as.factor(Education)5 0.328818 0.297197 1.11 0.2686
## as.factor(Education)6 0.312578 0.297556 1.05 0.2935
## as.factor(Employed)2 0.138612 0.056427 2.46 0.0140 *
## as.factor(Employed)3 0.426206 0.088586 4.81 1.5e-06 ***
## as.factor(Employed)4 0.166923 0.095435 1.75 0.0803 .
## as.factor(Employed)5 0.189993 0.057960 3.28 0.0010 **
## as.factor(Employed)6 -0.256383 0.190252 -1.35 0.1778
## as.factor(Employed)7 0.481078 0.036409 13.21 < 2e-16 ***
## as.factor(Employed)8 0.936747 0.043860 21.36 < 2e-16 ***
## as.factor(State)2 -0.207985 0.117648 -1.77 0.0771 .
## as.factor(State)4 0.096774 0.083768 1.16 0.2480
## as.factor(State)5 -0.012684 0.078894 -0.16 0.8723
## as.factor(State)6 0.058852 0.072270 0.81 0.4155
## as.factor(State)8 0.053996 0.068161 0.79 0.4283
## as.factor(State)9 0.065444 0.068056 0.96 0.3362
## as.factor(State)10 0.146144 0.093298 1.57 0.1173
## as.factor(State)11 0.099240 0.100168 0.99 0.3218
## as.factor(State)12 0.110698 0.071404 1.55 0.1211
## as.factor(State)13 0.045653 0.082138 0.56 0.5783
## as.factor(State)15 0.153732 0.091947 1.67 0.0945 .
## as.factor(State)16 -0.064862 0.091868 -0.71 0.4802
## as.factor(State)17 0.015728 0.078294 0.20 0.8408
## as.factor(State)18 0.040719 0.068176 0.60 0.5503
## as.factor(State)19 -0.034061 0.066169 -0.51 0.6067
## as.factor(State)20 0.138512 0.066265 2.09 0.0366 *
## as.factor(State)21 0.237248 0.079802 2.97 0.0029 **
## as.factor(State)22 0.188040 0.085369 2.20 0.0276 *
## as.factor(State)23 0.187627 0.066886 2.81 0.0050 **
## as.factor(State)24 0.168150 0.064002 2.63 0.0086 **
## as.factor(State)25 0.158971 0.073280 2.17 0.0301 *
## as.factor(State)26 0.176231 0.066773 2.64 0.0083 **
## as.factor(State)27 -0.011054 0.062045 -0.18 0.8586
## as.factor(State)28 -0.045193 0.084014 -0.54 0.5906
## as.factor(State)29 0.132164 0.075972 1.74 0.0819 .
## as.factor(State)30 0.096866 0.072104 1.34 0.1791
## as.factor(State)31 0.033913 0.063807 0.53 0.5951
## as.factor(State)32 -0.094115 0.111843 -0.84 0.4001
## as.factor(State)33 0.082500 0.075460 1.09 0.2743
## as.factor(State)35 0.064821 0.084797 0.76 0.4446
## as.factor(State)36 0.126208 0.066557 1.90 0.0579 .
## as.factor(State)37 0.124212 0.082346 1.51 0.1315
## as.factor(State)38 0.104590 0.078090 1.34 0.1805
## as.factor(State)39 0.130751 0.067676 1.93 0.0534 .
## as.factor(State)40 0.046421 0.073560 0.63 0.5280
## as.factor(State)41 0.000445 0.075493 0.01 0.9953
## as.factor(State)42 0.117579 0.076138 1.54 0.1225
## as.factor(State)44 0.154691 0.076865 2.01 0.0442 *
## as.factor(State)45 0.144291 0.072475 1.99 0.0465 *
## as.factor(State)46 0.054546 0.096929 0.56 0.5736
## as.factor(State)47 0.110484 0.078305 1.41 0.1583
## as.factor(State)48 0.105644 0.082560 1.28 0.2007
## as.factor(State)49 -0.049624 0.069478 -0.71 0.4751
## as.factor(State)50 -0.058724 0.077977 -0.75 0.4514
## as.factor(State)51 0.077123 0.068505 1.13 0.2603
## as.factor(State)53 0.143840 0.065896 2.18 0.0291 *
## as.factor(State)54 0.183899 0.077052 2.39 0.0170 *
## as.factor(State)55 0.098252 0.084469 1.16 0.2448
## as.factor(State)56 0.020270 0.088685 0.23 0.8192
## as.factor(State)66 -0.408388 0.196951 -2.07 0.0381 *
## as.factor(State)72 0.027688 0.102613 0.27 0.7873
## as.factor(Veteran)1 0.339197 0.032506 10.43 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.966)
##
## Number of Fisher Scoring iterations: 7Model 6
model1<-svyglm(na.omit(COPD~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/COPD.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(COPD ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.207613 0.332203 -9.66 < 2e-16 ***
## as.factor(Age)2 0.355372 0.108184 3.28 0.00102 **
## as.factor(Age)3 0.679936 0.105482 6.45 1.1e-10 ***
## as.factor(Age)4 1.062742 0.102389 10.38 < 2e-16 ***
## as.factor(Age)5 1.367657 0.101064 13.53 < 2e-16 ***
## as.factor(Age)6 1.508100 0.104137 14.48 < 2e-16 ***
## as.factor(Race)2 -0.510510 0.043846 -11.64 < 2e-16 ***
## as.factor(Race)3 -0.529762 0.172925 -3.06 0.00219 **
## as.factor(Race)4 0.038761 0.081384 0.48 0.63388
## as.factor(Race)5 -0.793530 0.067662 -11.73 < 2e-16 ***
## as.factor(Race)6 0.211164 0.062062 3.40 0.00067 ***
## as.factor(Gender)1 -0.224331 0.029371 -7.64 2.2e-14 ***
## as.factor(Married)2 0.316733 0.032373 9.78 < 2e-16 ***
## as.factor(Married)3 0.210102 0.034860 6.03 1.7e-09 ***
## as.factor(Married)4 0.341988 0.066315 5.16 2.5e-07 ***
## as.factor(Married)5 0.140434 0.043712 3.21 0.00131 **
## as.factor(Married)6 0.225472 0.067022 3.36 0.00077 ***
## as.factor(Income)2 0.025022 0.058200 0.43 0.66724
## as.factor(Income)3 -0.015497 0.056851 -0.27 0.78517
## as.factor(Income)4 -0.099274 0.059076 -1.68 0.09287 .
## as.factor(Income)5 -0.183440 0.060455 -3.03 0.00241 **
## as.factor(Income)6 -0.321542 0.060458 -5.32 1.0e-07 ***
## as.factor(Income)7 -0.577180 0.060516 -9.54 < 2e-16 ***
## as.factor(Income)8 -0.813589 0.063435 -12.83 < 2e-16 ***
## as.factor(Income)9 -0.435583 0.053016 -8.22 < 2e-16 ***
## as.factor(Education)2 -0.070670 0.304724 -0.23 0.81660
## as.factor(Education)3 0.224968 0.303969 0.74 0.45924
## as.factor(Education)4 -0.103713 0.303314 -0.34 0.73240
## as.factor(Education)5 -0.137409 0.304088 -0.45 0.65136
## as.factor(Education)6 -0.683521 0.304414 -2.25 0.02475 *
## as.factor(Employed)2 0.001326 0.063564 0.02 0.98335
## as.factor(Employed)3 0.619513 0.075319 8.23 < 2e-16 ***
## as.factor(Employed)4 0.413038 0.087695 4.71 2.5e-06 ***
## as.factor(Employed)5 0.096123 0.063408 1.52 0.12953
## as.factor(Employed)6 -0.245631 0.153131 -1.60 0.10870
## as.factor(Employed)7 0.506706 0.040675 12.46 < 2e-16 ***
## as.factor(Employed)8 1.445719 0.039394 36.70 < 2e-16 ***
## as.factor(State)2 -0.529314 0.128052 -4.13 3.6e-05 ***
## as.factor(State)4 -0.145290 0.087793 -1.65 0.09794 .
## as.factor(State)5 0.058752 0.083121 0.71 0.47968
## as.factor(State)6 -0.310844 0.082071 -3.79 0.00015 ***
## as.factor(State)8 -0.375881 0.079747 -4.71 2.4e-06 ***
## as.factor(State)9 -0.339553 0.083569 -4.06 4.8e-05 ***
## as.factor(State)10 0.080939 0.100700 0.80 0.42153
## as.factor(State)11 -0.175373 0.125773 -1.39 0.16321
## as.factor(State)12 -0.089937 0.077549 -1.16 0.24616
## as.factor(State)13 -0.023950 0.087559 -0.27 0.78445
## as.factor(State)15 -0.520575 0.115358 -4.51 6.4e-06 ***
## as.factor(State)16 -0.528737 0.097890 -5.40 6.6e-08 ***
## as.factor(State)17 -0.205561 0.087462 -2.35 0.01876 *
## as.factor(State)18 0.000431 0.071663 0.01 0.99520
## as.factor(State)19 -0.289280 0.074386 -3.89 0.00010 ***
## as.factor(State)20 -0.224629 0.072715 -3.09 0.00201 **
## as.factor(State)21 0.125205 0.079119 1.58 0.11354
## as.factor(State)22 -0.053483 0.089231 -0.60 0.54892
## as.factor(State)23 -0.033946 0.074543 -0.46 0.64884
## as.factor(State)24 -0.179401 0.072123 -2.49 0.01287 *
## as.factor(State)25 -0.352218 0.083583 -4.21 2.5e-05 ***
## as.factor(State)26 -0.028569 0.072926 -0.39 0.69524
## as.factor(State)27 -0.542466 0.071925 -7.54 4.6e-14 ***
## as.factor(State)28 -0.028586 0.085614 -0.33 0.73846
## as.factor(State)29 0.013637 0.079766 0.17 0.86425
## as.factor(State)30 -0.311420 0.081751 -3.81 0.00014 ***
## as.factor(State)31 -0.326796 0.072834 -4.49 7.2e-06 ***
## as.factor(State)32 0.076260 0.116645 0.65 0.51325
## as.factor(State)33 -0.353429 0.087719 -4.03 5.6e-05 ***
## as.factor(State)35 -0.325787 0.095396 -3.42 0.00064 ***
## as.factor(State)36 -0.221777 0.074514 -2.98 0.00292 **
## as.factor(State)37 -0.088817 0.090186 -0.98 0.32471
## as.factor(State)38 -0.376436 0.093938 -4.01 6.1e-05 ***
## as.factor(State)39 -0.006607 0.070982 -0.09 0.92584
## as.factor(State)40 -0.063552 0.077734 -0.82 0.41361
## as.factor(State)41 -0.348208 0.088242 -3.95 7.9e-05 ***
## as.factor(State)42 -0.147374 0.083667 -1.76 0.07817 .
## as.factor(State)44 -0.175739 0.087418 -2.01 0.04440 *
## as.factor(State)45 -0.098968 0.079258 -1.25 0.21178
## as.factor(State)46 -0.342419 0.121665 -2.81 0.00489 **
## as.factor(State)47 0.023898 0.079326 0.30 0.76321
## as.factor(State)48 -0.220490 0.087500 -2.52 0.01174 *
## as.factor(State)49 -0.401701 0.079892 -5.03 5.0e-07 ***
## as.factor(State)50 -0.311601 0.088037 -3.54 0.00040 ***
## as.factor(State)51 -0.100707 0.076448 -1.32 0.18773
## as.factor(State)53 -0.396818 0.075280 -5.27 1.4e-07 ***
## as.factor(State)54 0.042661 0.076495 0.56 0.57705
## as.factor(State)55 -0.408593 0.098354 -4.15 3.3e-05 ***
## as.factor(State)56 -0.182240 0.099174 -1.84 0.06612 .
## as.factor(State)66 -1.047370 0.207085 -5.06 4.2e-07 ***
## as.factor(State)72 -0.222259 0.116180 -1.91 0.05574 .
## as.factor(Veteran)1 0.379134 0.042664 8.89 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 1.03)
##
## Number of Fisher Scoring iterations: 6Model 7
model1<-svyglm(na.omit(Arthritis~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/arthritis.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(Arthritis ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.9929 0.2061 -14.53 < 2e-16 ***
## as.factor(Age)2 0.6917 0.0695 9.96 < 2e-16 ***
## as.factor(Age)3 1.3546 0.0682 19.87 < 2e-16 ***
## as.factor(Age)4 1.9805 0.0675 29.32 < 2e-16 ***
## as.factor(Age)5 2.4453 0.0674 36.28 < 2e-16 ***
## as.factor(Age)6 2.7702 0.0689 40.20 < 2e-16 ***
## as.factor(Race)2 -0.2020 0.0268 -7.54 4.6e-14 ***
## as.factor(Race)3 -0.6095 0.0744 -8.19 2.5e-16 ***
## as.factor(Race)4 0.0961 0.0678 1.42 0.15636
## as.factor(Race)5 -0.5120 0.0381 -13.44 < 2e-16 ***
## as.factor(Race)6 0.0174 0.0455 0.38 0.70246
## as.factor(Gender)1 -0.4318 0.0172 -25.09 < 2e-16 ***
## as.factor(Married)2 0.0970 0.0230 4.21 2.5e-05 ***
## as.factor(Married)3 0.1205 0.0252 4.78 1.7e-06 ***
## as.factor(Married)4 0.1989 0.0472 4.22 2.5e-05 ***
## as.factor(Married)5 -0.0852 0.0285 -2.98 0.00286 **
## as.factor(Married)6 0.0598 0.0445 1.34 0.17886
## as.factor(Income)2 0.0621 0.0513 1.21 0.22572
## as.factor(Income)3 0.0340 0.0471 0.72 0.46984
## as.factor(Income)4 0.0019 0.0467 0.04 0.96747
## as.factor(Income)5 -0.0588 0.0458 -1.29 0.19857
## as.factor(Income)6 -0.0558 0.0444 -1.26 0.20924
## as.factor(Income)7 -0.0740 0.0449 -1.65 0.09979 .
## as.factor(Income)8 -0.1951 0.0447 -4.37 1.2e-05 ***
## as.factor(Income)9 -0.2461 0.0423 -5.82 5.8e-09 ***
## as.factor(Education)2 0.3317 0.1901 1.75 0.08096 .
## as.factor(Education)3 0.5202 0.1885 2.76 0.00580 **
## as.factor(Education)4 0.4602 0.1870 2.46 0.01384 *
## as.factor(Education)5 0.5551 0.1870 2.97 0.00300 **
## as.factor(Education)6 0.2604 0.1869 1.39 0.16362
## as.factor(Employed)2 -0.0341 0.0291 -1.17 0.24184
## as.factor(Employed)3 0.5152 0.0538 9.58 < 2e-16 ***
## as.factor(Employed)4 0.3068 0.0546 5.62 1.9e-08 ***
## as.factor(Employed)5 0.0246 0.0357 0.69 0.49164
## as.factor(Employed)6 -0.2712 0.0813 -3.34 0.00085 ***
## as.factor(Employed)7 0.4054 0.0241 16.80 < 2e-16 ***
## as.factor(Employed)8 1.3634 0.0303 45.04 < 2e-16 ***
## as.factor(State)2 -0.4481 0.0775 -5.78 7.3e-09 ***
## as.factor(State)4 -0.4101 0.0548 -7.49 6.9e-14 ***
## as.factor(State)5 -0.1343 0.0557 -2.41 0.01581 *
## as.factor(State)6 -0.3917 0.0487 -8.04 9.2e-16 ***
## as.factor(State)8 -0.3417 0.0469 -7.29 3.2e-13 ***
## as.factor(State)9 -0.3941 0.0479 -8.23 < 2e-16 ***
## as.factor(State)10 -0.2308 0.0637 -3.62 0.00029 ***
## as.factor(State)11 -0.4944 0.0734 -6.74 1.6e-11 ***
## as.factor(State)12 -0.4105 0.0522 -7.86 3.8e-15 ***
## as.factor(State)13 -0.3483 0.0570 -6.11 9.9e-10 ***
## as.factor(State)15 -0.3921 0.0617 -6.35 2.1e-10 ***
## as.factor(State)16 -0.3396 0.0636 -5.34 9.1e-08 ***
## as.factor(State)17 -0.2721 0.0532 -5.12 3.1e-07 ***
## as.factor(State)18 -0.2779 0.0469 -5.92 3.2e-09 ***
## as.factor(State)19 -0.3146 0.0453 -6.95 3.8e-12 ***
## as.factor(State)20 -0.2775 0.0455 -6.10 1.1e-09 ***
## as.factor(State)21 0.0729 0.0552 1.32 0.18661
## as.factor(State)22 -0.2438 0.0561 -4.34 1.4e-05 ***
## as.factor(State)23 -0.1714 0.0494 -3.47 0.00052 ***
## as.factor(State)24 -0.2776 0.0433 -6.42 1.4e-10 ***
## as.factor(State)25 -0.2844 0.0505 -5.63 1.8e-08 ***
## as.factor(State)26 -0.0798 0.0460 -1.73 0.08282 .
## as.factor(State)27 -0.5300 0.0429 -12.36 < 2e-16 ***
## as.factor(State)28 -0.2474 0.0553 -4.47 7.8e-06 ***
## as.factor(State)29 -0.3055 0.0509 -6.00 2.0e-09 ***
## as.factor(State)30 -0.2263 0.0501 -4.52 6.3e-06 ***
## as.factor(State)31 -0.4324 0.0443 -9.76 < 2e-16 ***
## as.factor(State)32 -0.4475 0.0826 -5.42 6.0e-08 ***
## as.factor(State)33 -0.3630 0.0541 -6.71 1.9e-11 ***
## as.factor(State)35 -0.2160 0.0574 -3.77 0.00017 ***
## as.factor(State)36 -0.4174 0.0460 -9.08 < 2e-16 ***
## as.factor(State)37 -0.2788 0.0569 -4.90 9.7e-07 ***
## as.factor(State)38 -0.2132 0.0579 -3.68 0.00023 ***
## as.factor(State)39 -0.1105 0.0473 -2.34 0.01941 *
## as.factor(State)40 -0.3159 0.0512 -6.17 6.7e-10 ***
## as.factor(State)41 -0.2968 0.0520 -5.70 1.2e-08 ***
## as.factor(State)42 -0.1580 0.0514 -3.07 0.00212 **
## as.factor(State)44 -0.2425 0.0553 -4.39 1.2e-05 ***
## as.factor(State)45 -0.2626 0.0510 -5.16 2.5e-07 ***
## as.factor(State)46 -0.2456 0.0730 -3.36 0.00077 ***
## as.factor(State)47 -0.1355 0.0539 -2.51 0.01192 *
## as.factor(State)48 -0.3837 0.0552 -6.95 3.6e-12 ***
## as.factor(State)49 -0.1625 0.0454 -3.58 0.00034 ***
## as.factor(State)50 -0.3180 0.0544 -5.85 4.9e-09 ***
## as.factor(State)51 -0.1834 0.0480 -3.82 0.00013 ***
## as.factor(State)53 -0.2882 0.0449 -6.42 1.4e-10 ***
## as.factor(State)54 0.2544 0.0525 4.84 1.3e-06 ***
## as.factor(State)55 -0.2419 0.0556 -4.35 1.4e-05 ***
## as.factor(State)56 -0.3718 0.0609 -6.11 1.0e-09 ***
## as.factor(State)66 -0.5560 0.1121 -4.96 7.0e-07 ***
## as.factor(State)72 -0.2841 0.0630 -4.51 6.4e-06 ***
## as.factor(Veteran)1 0.2117 0.0241 8.77 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.977)
##
## Number of Fisher Scoring iterations: 5Model 8
model1<-svyglm(na.omit(Depression~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/mentalhealth.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(Depression ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15365 0.18615 0.83 0.40914
## as.factor(Age)2 -0.22894 0.03201 -7.15 8.6e-13 ***
## as.factor(Age)3 -0.45211 0.03430 -13.18 < 2e-16 ***
## as.factor(Age)4 -0.69575 0.03448 -20.18 < 2e-16 ***
## as.factor(Age)5 -0.98712 0.03563 -27.70 < 2e-16 ***
## as.factor(Age)6 -1.35257 0.03878 -34.88 < 2e-16 ***
## as.factor(Race)2 -0.34807 0.02498 -13.94 < 2e-16 ***
## as.factor(Race)3 -0.48025 0.04730 -10.15 < 2e-16 ***
## as.factor(Race)4 -0.07538 0.06203 -1.22 0.22429
## as.factor(Race)5 -0.47205 0.02820 -16.74 < 2e-16 ***
## as.factor(Race)6 0.12001 0.03714 3.23 0.00123 **
## as.factor(Gender)1 -0.48861 0.01541 -31.70 < 2e-16 ***
## as.factor(Married)2 0.35567 0.02284 15.57 < 2e-16 ***
## as.factor(Married)3 0.22482 0.02638 8.52 < 2e-16 ***
## as.factor(Married)4 0.56202 0.04737 11.87 < 2e-16 ***
## as.factor(Married)5 0.37453 0.02208 16.96 < 2e-16 ***
## as.factor(Married)6 0.38287 0.03364 11.38 < 2e-16 ***
## as.factor(Income)2 -0.02529 0.05018 -0.50 0.61425
## as.factor(Income)3 -0.02415 0.04459 -0.54 0.58810
## as.factor(Income)4 -0.03622 0.04390 -0.83 0.40932
## as.factor(Income)5 -0.05520 0.04355 -1.27 0.20490
## as.factor(Income)6 -0.08476 0.04163 -2.04 0.04177 *
## as.factor(Income)7 -0.11705 0.04269 -2.74 0.00611 **
## as.factor(Income)8 -0.23975 0.04096 -5.85 4.8e-09 ***
## as.factor(Income)9 -0.30775 0.03989 -7.71 1.2e-14 ***
## as.factor(Education)2 0.22357 0.18038 1.24 0.21519
## as.factor(Education)3 0.31328 0.17818 1.76 0.07872 .
## as.factor(Education)4 0.24052 0.17652 1.36 0.17303
## as.factor(Education)5 0.45390 0.17660 2.57 0.01016 *
## as.factor(Education)6 0.43341 0.17678 2.45 0.01422 *
## as.factor(Employed)2 -0.07718 0.02631 -2.93 0.00335 **
## as.factor(Employed)3 0.51488 0.04943 10.42 < 2e-16 ***
## as.factor(Employed)4 0.41769 0.04686 8.91 < 2e-16 ***
## as.factor(Employed)5 -0.08936 0.03571 -2.50 0.01233 *
## as.factor(Employed)6 0.32341 0.04104 7.88 3.3e-15 ***
## as.factor(Employed)7 0.00947 0.02595 0.36 0.71512
## as.factor(Employed)8 1.15147 0.02947 39.08 < 2e-16 ***
## as.factor(State)2 -0.16756 0.07347 -2.28 0.02257 *
## as.factor(State)4 0.06759 0.05380 1.26 0.20898
## as.factor(State)5 -0.01888 0.05583 -0.34 0.73523
## as.factor(State)6 -0.02417 0.04518 -0.54 0.59264
## as.factor(State)8 0.02364 0.04498 0.53 0.59916
## as.factor(State)9 -0.09345 0.04824 -1.94 0.05274 .
## as.factor(State)10 0.00965 0.06032 0.16 0.87292
## as.factor(State)11 0.15002 0.06552 2.29 0.02205 *
## as.factor(State)12 0.00281 0.05092 0.06 0.95592
## as.factor(State)13 0.11062 0.05467 2.02 0.04305 *
## as.factor(State)15 -0.11491 0.05412 -2.12 0.03371 *
## as.factor(State)16 -0.00253 0.05809 -0.04 0.96522
## as.factor(State)17 -0.03915 0.05017 -0.78 0.43520
## as.factor(State)18 -0.04050 0.04612 -0.88 0.37989
## as.factor(State)19 -0.14099 0.04406 -3.20 0.00138 **
## as.factor(State)20 -0.04962 0.04409 -1.13 0.26037
## as.factor(State)21 0.04914 0.05300 0.93 0.35384
## as.factor(State)22 0.08009 0.05377 1.49 0.13637
## as.factor(State)23 -0.19429 0.04977 -3.90 9.5e-05 ***
## as.factor(State)24 -0.00847 0.04421 -0.19 0.84817
## as.factor(State)25 -0.08365 0.04772 -1.75 0.07961 .
## as.factor(State)26 0.06985 0.04528 1.54 0.12295
## as.factor(State)27 -0.03912 0.04133 -0.95 0.34394
## as.factor(State)28 -0.15948 0.05451 -2.93 0.00344 **
## as.factor(State)29 -0.12301 0.05059 -2.43 0.01505 *
## as.factor(State)30 -0.01932 0.04862 -0.40 0.69102
## as.factor(State)31 -0.19929 0.04394 -4.54 5.7e-06 ***
## as.factor(State)32 -0.02532 0.06643 -0.38 0.70314
## as.factor(State)33 -0.10065 0.05472 -1.84 0.06586 .
## as.factor(State)35 -0.01766 0.05606 -0.32 0.75271
## as.factor(State)36 -0.13258 0.04509 -2.94 0.00328 **
## as.factor(State)37 -0.20751 0.05339 -3.89 0.00010 ***
## as.factor(State)38 -0.31914 0.05708 -5.59 2.3e-08 ***
## as.factor(State)39 0.00075 0.04678 0.02 0.98720
## as.factor(State)40 -0.07930 0.05027 -1.58 0.11471
## as.factor(State)41 0.01341 0.04845 0.28 0.78196
## as.factor(State)42 -0.09339 0.04976 -1.88 0.06053 .
## as.factor(State)44 -0.08559 0.05576 -1.53 0.12480
## as.factor(State)45 -0.06390 0.05035 -1.27 0.20435
## as.factor(State)46 -0.34339 0.06795 -5.05 4.3e-07 ***
## as.factor(State)47 -0.03001 0.05235 -0.57 0.56651
## as.factor(State)48 -0.08716 0.05298 -1.65 0.09995 .
## as.factor(State)49 0.20389 0.04326 4.71 2.4e-06 ***
## as.factor(State)50 0.06916 0.05457 1.27 0.20501
## as.factor(State)51 -0.12692 0.04705 -2.70 0.00699 **
## as.factor(State)53 0.07035 0.04377 1.61 0.10803
## as.factor(State)54 0.01872 0.05269 0.36 0.72241
## as.factor(State)55 0.00224 0.05524 0.04 0.96759
## as.factor(State)56 -0.29794 0.06010 -4.96 7.1e-07 ***
## as.factor(State)66 -0.30773 0.08706 -3.53 0.00041 ***
## as.factor(State)72 -0.66256 0.06112 -10.84 < 2e-16 ***
## as.factor(Veteran)1 -0.00971 0.02470 -0.39 0.69411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.997)
##
## Number of Fisher Scoring iterations: 4Model 9
model1<-svyglm(na.omit(KidneyDisease~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/kidneydisease.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(KidneyDisease ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.69564 0.30682 -15.30 < 2e-16 ***
## as.factor(Age)2 0.42083 0.16224 2.59 0.00949 **
## as.factor(Age)3 0.58894 0.14529 4.05 5.0e-05 ***
## as.factor(Age)4 1.01995 0.14036 7.27 3.7e-13 ***
## as.factor(Age)5 1.22425 0.14059 8.71 < 2e-16 ***
## as.factor(Age)6 1.59411 0.14042 11.35 < 2e-16 ***
## as.factor(Race)2 0.21643 0.05747 3.77 0.00017 ***
## as.factor(Race)3 -0.02866 0.18337 -0.16 0.87580
## as.factor(Race)4 0.07420 0.10227 0.73 0.46813
## as.factor(Race)5 0.26297 0.07721 3.41 0.00066 ***
## as.factor(Race)6 0.15945 0.10836 1.47 0.14118
## as.factor(Gender)1 0.08540 0.04078 2.09 0.03625 *
## as.factor(Married)2 0.08425 0.05502 1.53 0.12575
## as.factor(Married)3 0.11185 0.04875 2.29 0.02177 *
## as.factor(Married)4 0.07470 0.11429 0.65 0.51340
## as.factor(Married)5 -0.09498 0.07162 -1.33 0.18475
## as.factor(Married)6 0.02524 0.10762 0.23 0.81458
## as.factor(Income)2 -0.17509 0.09479 -1.85 0.06472 .
## as.factor(Income)3 -0.00955 0.09318 -0.10 0.91836
## as.factor(Income)4 -0.08551 0.09644 -0.89 0.37528
## as.factor(Income)5 -0.18659 0.09652 -1.93 0.05322 .
## as.factor(Income)6 -0.25183 0.09766 -2.58 0.00992 **
## as.factor(Income)7 -0.37072 0.10289 -3.60 0.00031 ***
## as.factor(Income)8 -0.42894 0.10319 -4.16 3.2e-05 ***
## as.factor(Income)9 -0.33196 0.09184 -3.61 0.00030 ***
## as.factor(Education)2 -0.22627 0.25708 -0.88 0.37877
## as.factor(Education)3 -0.32349 0.25220 -1.28 0.19960
## as.factor(Education)4 -0.26962 0.24974 -1.08 0.28032
## as.factor(Education)5 -0.17250 0.24863 -0.69 0.48782
## as.factor(Education)6 -0.29654 0.24984 -1.19 0.23525
## as.factor(Employed)2 0.06895 0.08914 0.77 0.43924
## as.factor(Employed)3 0.86986 0.13903 6.26 3.9e-10 ***
## as.factor(Employed)4 0.23379 0.16639 1.41 0.16001
## as.factor(Employed)5 0.46068 0.10223 4.51 6.6e-06 ***
## as.factor(Employed)6 0.03872 0.18183 0.21 0.83139
## as.factor(Employed)7 0.82448 0.06322 13.04 < 2e-16 ***
## as.factor(Employed)8 1.54157 0.06264 24.61 < 2e-16 ***
## as.factor(State)2 -0.33096 0.18123 -1.83 0.06783 .
## as.factor(State)4 0.37219 0.11460 3.25 0.00116 **
## as.factor(State)5 0.18138 0.11713 1.55 0.12148
## as.factor(State)6 0.10626 0.11011 0.97 0.33453
## as.factor(State)8 -0.34627 0.11817 -2.93 0.00339 **
## as.factor(State)9 -0.13067 0.11280 -1.16 0.24670
## as.factor(State)10 0.40605 0.13761 2.95 0.00317 **
## as.factor(State)11 -0.21393 0.18234 -1.17 0.24069
## as.factor(State)12 0.18451 0.11484 1.61 0.10812
## as.factor(State)13 0.27879 0.12596 2.21 0.02688 *
## as.factor(State)15 0.05617 0.14622 0.38 0.70085
## as.factor(State)16 0.05232 0.15280 0.34 0.73204
## as.factor(State)17 -0.02643 0.12112 -0.22 0.82729
## as.factor(State)18 0.19486 0.10368 1.88 0.06019 .
## as.factor(State)19 -0.16261 0.10875 -1.50 0.13484
## as.factor(State)20 0.00675 0.10575 0.06 0.94908
## as.factor(State)21 0.22911 0.11439 2.00 0.04519 *
## as.factor(State)22 0.21386 0.12052 1.77 0.07598 .
## as.factor(State)23 -0.00499 0.10824 -0.05 0.96321
## as.factor(State)24 0.04074 0.10487 0.39 0.69766
## as.factor(State)25 -0.16080 0.12752 -1.26 0.20733
## as.factor(State)26 0.15445 0.10219 1.51 0.13068
## as.factor(State)27 -0.03125 0.10141 -0.31 0.75799
## as.factor(State)28 -0.18027 0.12732 -1.42 0.15681
## as.factor(State)29 0.02235 0.11757 0.19 0.84926
## as.factor(State)30 -0.16181 0.12201 -1.33 0.18476
## as.factor(State)31 -0.08294 0.10720 -0.77 0.43914
## as.factor(State)32 0.01239 0.16042 0.08 0.93846
## as.factor(State)33 -0.03930 0.13157 -0.30 0.76517
## as.factor(State)35 0.09880 0.12863 0.77 0.44245
## as.factor(State)36 -0.16055 0.10739 -1.49 0.13492
## as.factor(State)37 0.23250 0.12448 1.87 0.06181 .
## as.factor(State)38 0.14414 0.12705 1.13 0.25657
## as.factor(State)39 0.08080 0.10563 0.76 0.44431
## as.factor(State)40 0.28580 0.11251 2.54 0.01108 *
## as.factor(State)41 0.09815 0.12613 0.78 0.43647
## as.factor(State)42 0.07694 0.12145 0.63 0.52640
## as.factor(State)44 -0.19083 0.13399 -1.42 0.15438
## as.factor(State)45 -0.10004 0.11708 -0.85 0.39282
## as.factor(State)46 0.12059 0.15293 0.79 0.43038
## as.factor(State)47 0.15966 0.11998 1.33 0.18328
## as.factor(State)48 0.16235 0.12422 1.31 0.19122
## as.factor(State)49 0.15169 0.10941 1.39 0.16561
## as.factor(State)50 -0.15051 0.14957 -1.01 0.31428
## as.factor(State)51 -0.02700 0.10947 -0.25 0.80515
## as.factor(State)53 0.01026 0.10595 0.10 0.92286
## as.factor(State)54 0.17205 0.11232 1.53 0.12558
## as.factor(State)55 -0.00787 0.14078 -0.06 0.95541
## as.factor(State)56 -0.16363 0.14870 -1.10 0.27116
## as.factor(State)66 0.23746 0.19896 1.19 0.23267
## as.factor(State)72 -0.22869 0.14005 -1.63 0.10250
## as.factor(Veteran)1 0.08049 0.05328 1.51 0.13086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.974)
##
## Number of Fisher Scoring iterations: 7Model 10
model1<-svyglm(na.omit(Diabetes~as.factor(Age)+
as.factor(Race)+as.factor(Gender)+
as.factor(Married)+as.factor(Income)+
as.factor(Education)+
as.factor(Employed)+
as.factor(State)+
as.factor(Veteran)),
design=svy2019,
family=quasibinomial)
#model1$oddsratios=exp(model1$coefficients)
#model1$lower=exp(log(model1$oddsratios)-qnorm(.975)*summary(model1)$coefficients[,2])
#model1$upper=exp(log(model1$oddsratios)+qnorm(.975)*summary(model1)$coefficients[,2])
options(digits=3)
write.csv(summary(model1)$coefficients,"d:/jose/diabetes.csv")
summary(model1)##
## Call:
## svyglm(formula = na.omit(Diabetes ~ as.factor(Age) + as.factor(Race) +
## as.factor(Gender) + as.factor(Married) + as.factor(Income) +
## as.factor(Education) + as.factor(Employed) + as.factor(State) +
## as.factor(Veteran)), design = svy2019, family = quasibinomial)
##
## Survey design:
## mysurvey <- svydesign(
## id=~1,
## strata = ~Stratum,
## weights = ~Weights,
## data = surveydata)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.539715 0.227472 -19.96 < 2e-16 ***
## as.factor(Age)2 0.654635 0.117634 5.56 2.6e-08 ***
## as.factor(Age)3 1.644197 0.111227 14.78 < 2e-16 ***
## as.factor(Age)4 2.500356 0.107721 23.21 < 2e-16 ***
## as.factor(Age)5 2.891709 0.107860 26.81 < 2e-16 ***
## as.factor(Age)6 3.188382 0.109200 29.20 < 2e-16 ***
## as.factor(Race)2 0.501504 0.032508 15.43 < 2e-16 ***
## as.factor(Race)3 0.427390 0.083536 5.12 3.1e-07 ***
## as.factor(Race)4 0.473272 0.088412 5.35 8.7e-08 ***
## as.factor(Race)5 0.544722 0.040873 13.33 < 2e-16 ***
## as.factor(Race)6 0.434414 0.056128 7.74 1.0e-14 ***
## as.factor(Gender)1 0.217650 0.022818 9.54 < 2e-16 ***
## as.factor(Married)2 -0.005060 0.032936 -0.15 0.8779
## as.factor(Married)3 0.015337 0.030544 0.50 0.6156
## as.factor(Married)4 0.050112 0.062061 0.81 0.4194
## as.factor(Married)5 0.000375 0.036202 0.01 0.9917
## as.factor(Married)6 -0.057830 0.067097 -0.86 0.3887
## as.factor(Income)2 0.142400 0.060028 2.37 0.0177 *
## as.factor(Income)3 0.174286 0.055476 3.14 0.0017 **
## as.factor(Income)4 0.113413 0.058003 1.96 0.0506 .
## as.factor(Income)5 -0.002286 0.056293 -0.04 0.9676
## as.factor(Income)6 0.015829 0.056043 0.28 0.7776
## as.factor(Income)7 -0.079656 0.057891 -1.38 0.1688
## as.factor(Income)8 -0.261752 0.055757 -4.69 2.7e-06 ***
## as.factor(Income)9 -0.104948 0.051075 -2.05 0.0399 *
## as.factor(Education)2 0.035161 0.191452 0.18 0.8543
## as.factor(Education)3 -0.105317 0.190027 -0.55 0.5794
## as.factor(Education)4 -0.189724 0.187726 -1.01 0.3122
## as.factor(Education)5 -0.176976 0.188246 -0.94 0.3471
## as.factor(Education)6 -0.511670 0.188487 -2.71 0.0066 **
## as.factor(Employed)2 -0.253721 0.044786 -5.67 1.5e-08 ***
## as.factor(Employed)3 0.345297 0.073428 4.70 2.6e-06 ***
## as.factor(Employed)4 0.169590 0.101407 1.67 0.0945 .
## as.factor(Employed)5 0.230882 0.054492 4.24 2.3e-05 ***
## as.factor(Employed)6 0.002865 0.142209 0.02 0.9839
## as.factor(Employed)7 0.358572 0.031727 11.30 < 2e-16 ***
## as.factor(Employed)8 0.992852 0.035471 27.99 < 2e-16 ***
## as.factor(State)2 -0.500088 0.110134 -4.54 5.6e-06 ***
## as.factor(State)4 -0.178579 0.071433 -2.50 0.0124 *
## as.factor(State)5 0.023371 0.069025 0.34 0.7349
## as.factor(State)6 -0.260950 0.062003 -4.21 2.6e-05 ***
## as.factor(State)8 -0.482231 0.064525 -7.47 7.8e-14 ***
## as.factor(State)9 -0.250474 0.063454 -3.95 7.9e-05 ***
## as.factor(State)10 0.021175 0.081294 0.26 0.7945
## as.factor(State)11 -0.276195 0.089331 -3.09 0.0020 **
## as.factor(State)12 -0.251319 0.064444 -3.90 9.6e-05 ***
## as.factor(State)13 -0.040037 0.069447 -0.58 0.5643
## as.factor(State)15 -0.332281 0.078904 -4.21 2.5e-05 ***
## as.factor(State)16 -0.118569 0.077821 -1.52 0.1276
## as.factor(State)17 -0.082574 0.066947 -1.23 0.2174
## as.factor(State)18 0.067994 0.058790 1.16 0.2475
## as.factor(State)19 -0.075013 0.058619 -1.28 0.2007
## as.factor(State)20 -0.037247 0.057206 -0.65 0.5150
## as.factor(State)21 0.100252 0.067391 1.49 0.1369
## as.factor(State)22 -0.076549 0.068574 -1.12 0.2643
## as.factor(State)23 -0.179308 0.062904 -2.85 0.0044 **
## as.factor(State)24 -0.095870 0.056412 -1.70 0.0892 .
## as.factor(State)25 -0.350302 0.069177 -5.06 4.1e-07 ***
## as.factor(State)26 -0.127057 0.059357 -2.14 0.0323 *
## as.factor(State)27 -0.221573 0.055792 -3.97 7.1e-05 ***
## as.factor(State)28 0.052537 0.064974 0.81 0.4188
## as.factor(State)29 -0.202898 0.065955 -3.08 0.0021 **
## as.factor(State)30 -0.518424 0.069421 -7.47 8.2e-14 ***
## as.factor(State)31 -0.088231 0.057013 -1.55 0.1217
## as.factor(State)32 -0.234587 0.102864 -2.28 0.0226 *
## as.factor(State)33 -0.222057 0.072598 -3.06 0.0022 **
## as.factor(State)35 -0.237044 0.073556 -3.22 0.0013 **
## as.factor(State)36 -0.208739 0.060810 -3.43 0.0006 ***
## as.factor(State)37 -0.096348 0.071548 -1.35 0.1781
## as.factor(State)38 -0.143407 0.072018 -1.99 0.0465 *
## as.factor(State)39 -0.042793 0.059438 -0.72 0.4715
## as.factor(State)40 -0.024892 0.063232 -0.39 0.6938
## as.factor(State)41 -0.372176 0.071765 -5.19 2.1e-07 ***
## as.factor(State)42 -0.130599 0.068816 -1.90 0.0577 .
## as.factor(State)44 -0.171314 0.072754 -2.35 0.0185 *
## as.factor(State)45 0.020983 0.063977 0.33 0.7429
## as.factor(State)46 -0.033782 0.094867 -0.36 0.7218
## as.factor(State)47 0.097234 0.065634 1.48 0.1385
## as.factor(State)48 -0.011320 0.070229 -0.16 0.8719
## as.factor(State)49 -0.144401 0.062086 -2.33 0.0200 *
## as.factor(State)50 -0.314457 0.078267 -4.02 5.9e-05 ***
## as.factor(State)51 -0.086904 0.060570 -1.43 0.1514
## as.factor(State)53 -0.191489 0.058936 -3.25 0.0012 **
## as.factor(State)54 0.152804 0.064662 2.36 0.0181 *
## as.factor(State)55 -0.364235 0.077291 -4.71 2.4e-06 ***
## as.factor(State)56 -0.432975 0.083650 -5.18 2.3e-07 ***
## as.factor(State)66 -0.058809 0.114038 -0.52 0.6061
## as.factor(State)72 -0.161688 0.074074 -2.18 0.0291 *
## as.factor(Veteran)1 0.082708 0.028605 2.89 0.0038 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasibinomial family taken to be 0.989)
##
## Number of Fisher Scoring iterations: 6