Unique contribution of SSS to each model (semi-partial correlations)

load(file="~/Desktop/Research/HANDLS Data/MeasurementData1.rdata")
library(ppcor)

## Loading required package: MASS

Make Variables Numeric

##Make Sex Numeric
MeasurementData1$SexNum <- ifelse(MeasurementData1$Sex == "Men",1,0)

##Make Race Numeric
MeasurementData1$RaceNum<- ifelse(MeasurementData1$Race == "White",1,0)

Create Interaction Variables

#Create Interaction Variables
MeasurementData1$RaceNumEduc = (MeasurementData1$RaceNum * MeasurementData1$Educ)

MeasurementData1$RaceNumEmployment01 = (MeasurementData1$RaceNum * MeasurementData1$Employment01)

MeasurementData1$SexNumEmployment01 = (MeasurementData1$SexNum * MeasurementData1$Employment01)

save(MeasurementData1,file="~/Desktop/Research/HANDLS Data/MeasurementData1.rdata")

Unique Contribution of self-rated health to the Model

spcor.test(MeasurementData1$sHealthNum,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","Educ","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate        p.value   statistic    n gp  Method
## 1 0.07117361293 0.001203682806 3.242513813 2077 10 pearson

Unique Contribution of Race to the Model

spcor.test(MeasurementData1$RaceNum,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##          estimate      p.value     statistic    n gp  Method
## 1 -0.004112542337 0.8517692808 -0.1868848441 2077 10 pearson

Unique Contribution of Education to the Model

spcor.test(MeasurementData1$Educ,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate      p.value    statistic    n gp  Method
## 1 0.01605512236 0.4656714586 0.7296752715 2077 10 pearson

Unique Contribution of Employment to the Model

spcor.test(MeasurementData1$Employment01,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","acasiIncomx01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate      p.value  statistic    n gp  Method
## 1 0.03499958326 0.1116646421 1.59143558 2077 10 pearson

Unique Contribution of Income to the Model

spcor.test(MeasurementData1$acasiIncomx01,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##         estimate       p.value    statistic    n gp  Method
## 1 -0.06358339124 0.00382858361 -2.895232119 2077 10 pearson

Unique Contribution of Neighborhood to the Model

spcor.test(MeasurementData1$Neighborhood02,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","CES","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##         estimate        p.value    statistic    n gp  Method
## 1 -0.06608996462 0.002645336148 -3.009858637 2077 10 pearson

Unique Contribution of CES to the Model

spcor.test(MeasurementData1$CES,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate         p.value   statistic    n gp  Method
## 1 -0.1854845183 1.868468605e-17 -8.57768472 2077 10 pearson

Unique Contribution of Race*Education Interaction to the Model

spcor.test(MeasurementData1$RaceNumEduc,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate      p.value    statistic    n gp  Method
## 1 0.01136119571 0.6056918974 0.5163118583 2077 10 pearson

Unique Contribution of Race*Employment Interaction to the Model

spcor.test(MeasurementData1$RaceNumEmployment01,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEduc", "SexNumEmployment01")])

##         estimate      p.value     statistic    n gp  Method
## 1 -0.01862837648 0.3972812889 -0.8466626491 2077 10 pearson

Unique Contribution of Sex*Employment Interaction to the Model

spcor.test(MeasurementData1$SexNumEmployment01,MeasurementData1$MCLcountry,MeasurementData1[,c("SexNum","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","CES","RaceNumEduc","RaceNumEmployment01")])

##         estimate      p.value    statistic    n gp  Method
## 1 -0.02726817171 0.2152684574 -1.239588586 2077 10 pearson

Unique Contribution of Sex to the Model

spcor.test(MeasurementData1$SexNum,MeasurementData1$MCLcountry,MeasurementData1[,c("CES","RaceNum","sHealthNum","Educ","Employment01","acasiIncomx01","Neighborhood02","RaceNumEduc","RaceNumEmployment01", "SexNumEmployment01")])

##        estimate      p.value   statistic    n gp  Method
## 1 0.02581170936 0.2407973614 1.173333622 2077 10 pearson