logistic 回归results
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k<-18
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚
x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
logit_1<-glm(y~x-1,family=binomial(link=logit)) summary(logit_1)
#####################################
#####################################
#############加入社会公平感###############
k<-20
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚
x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
x[,19]<-ifelse(rd$In_1>=1,1,0)
x[,20]<-ifelse(rd$gongping_2==5|rd$gongping_2==4,1,0) logit_2<-glm(y~x-1,family=binomial(link=logit)) summary(logit_2)
#####################################
#####################################
#########政治地位、阶级认同############
k<-22
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚
x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
x[,19]<-ifelse(rd$In_1>=1,1,0)
x[,20]<-ifelse(rd$gongping_2==5|rd$gongping_2==4,1,0)
x[,21]<-scale(rd$jieji)
x[,22]<-rd$dangyuan
logit_3<-glm(y~x-1,family=binomial(link=logit)) summary(logit_3)
#####################################
#####################################
#########社会公平感、信任度############
k<-24
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚
x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
x[,19]<-ifelse(rd$In_1>=1,1,0)
x[,20]<-ifelse(rd$gongping_2==5|rd$gongping_2==4,1,0)
x[,21]<-scale(rd$jieji)
x[,22]<-rd$dangyuan
x[,23]<-ifelse(rd$gongping==5|rd$gongping==4,1,0) x[,24]<-ifelse(rd$believe==5|rd$believe==4,1,0) logit_4<-glm(y~x-1,family=binomial(link=logit)) summary(logit_4)
#####################################
#####################################
#########政治参与度###################
k<-26
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
x[,19]<-ifelse(rd$In_1>=1,1,0)
x[,20]<-ifelse(rd$gongping_2==5|rd$gongping_2==4,1,0)
x[,21]<-scale(rd$jieji)
x[,22]<-rd$dangyuan
x[,23]<-ifelse(rd$gongping==5|rd$gongping==4,1,0)
x[,24]<-ifelse(rd$believe==5|rd$believe==4,1,0)
x[,25]<-ifelse(rd$toupiao==3,1,0)#主动投票
x[,26]<-ifelse(rd$toupiao==2|rd$toupiao==1,1,0)#被动投票logit_5<-glm(y~x-1,family=binomial(link=logit)) summary(logit_5)
#只对相对收入较高的人群分析政治参与度对幸福的影响rd<-rd[rd$In_1>=1,]
dim(rd)
y<-ifelse(rd$xingfu==4|rd$xingfu==5,1,0)
k<-25
nr <- length(y)
x<-matrix(0,nr,k)
x[,1]<-1
x[,2]<-rd$gender
x[,3]<-ifelse(rd$age>=18&rd$age<30,1,0)
x[,4]<-ifelse(rd$age>=30&rd$age<45,1,0)
x[,5]<-ifelse(rd$age>=60&rd$age<70,1,0)
x[,6]<-ifelse(rd$age>=70,1,0)
x[,7]<-rd$yiliao
x[,8]<-rd$nation
x[,9]<-rd$zongjiao
x[,10]<-scale(rd$edu)
x[,11]<-scale(rd$fangzi)
x[,12]<-ifelse(rd$marry==2|rd$marry==3,1,0)#已婚
x[,13]<-ifelse(rd$work==11,1,0)#工作
x[,14]<-ifelse(rd$work==7,1,0)#退休
x[,15]<-ifelse(rd$work==8,1,0)#家务
x[,16]<-ifelse(rd$health==5|rd$health==4,1,0)
x[,17]<-ifelse(rd$jiating==1|rd$jiating==3|rd$jiating==2,1,0) x[,18]<-scale(log(rd$income))
x[,19]<-ifelse(rd$toupiao==2|rd$toupiao==1,1,0)#被动投票#ifelse(rd$In_1>=1,1,0)
x[,20]<-ifelse(rd$gongping_2==5|rd$gongping_2==4,1,0)
x[,21]<-scale(rd$jieji)
x[,22]<-rd$dangyuan
x[,23]<-ifelse(rd$gongping==5|rd$gongping==4,1,0)
x[,24]<-ifelse(rd$believe==5|rd$believe==4,1,0)
x[,25]<-ifelse(rd$toupiao==3,1,0)#主动投票
#x[,26]<-ifelse(rd$toupiao==2|rd$toupiao==1,1,0)#被动投票logit_6<-glm(y~x-1,family=binomial(link=logit)) summary(logit_6)
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