#Chapter 5
#5.1 
install.packages("mma")
library(mma)

#data organization and variable identification
data("weight_behavior")
#binary predictor x
#binary y
x=weight_behavior[,c(2,4:14)]
pred=weight_behavior[,"sex"]
y=weight_behavior[,"overweigh"]
data.b.b.1<-data.org(x,y,mediator=5:12,jointm=list(n=1,j1=7:9),
                     pred=pred,predref="M", alpha=0.4,
                     alpha2=0.4)
summary(data.b.b.1)

#Alterantive to define potential third-variables
data.b.b.2<-data.org(x,y,pred=pred,contmed=c(7:9,11:12),
                     binmed=c(6,10),binref=c(1,1),catmed=5,
                     catref=1, predref="M",jointm=
                     list(n=1,j1=7:9),alpha=0.4,alpha2=0.4) 
summary(data.b.b.2)

#5.1.2 third-variable analysis
med.b.b.1<-med(data=data.b.b.2,n=50)
med.b.b.1
med.b.b.2<-med(data=data.b.b.2,n=50,nonlinear=TRUE)
med.b.b.2

#5.1.3 third-variable effect inference
bootmed.b.b.1<-boot.med(data=data.b.b.2,n=2,n2=50)
bootmed.b.b.2<-boot.med(data=data.b.b.2,n=2,n2=40,
                        nu=0.05,nonlinear=TRUE)

#three functions in one
x=weight_behavior[,c(2,4:14)]
pred=weight_behavior[,3]
y=weight_behavior[,15]
mma.b.b.glm<-mma(x,y,pred=pred,contmed=c(7:9,11:12),
                 binmed=c(6,10),binref=c(1,1), catmed=5, 
                 catref=1,predref="M",alpha=0.4,
                 alpha2=0.4, n=2,n2=50)				
mma.b.b.mart<-mma(x,y,pred=pred,contmed=c(7:9,11:12),
                  binmed=c(6,10),binref=c(1,1), catmed=5,
                  catref=1,predref="M",alpha=0.4,alpha2=0.4,
                  nonlinear=TRUE,n=2,n2=40)								 
summary(bootmed.b.b.2,alpha=0.2)
summary(bootmed.b.b.2,RE=T,alpha=0.2,quant=T)