Nothing
interCov <-
function(d1,n1,d2,n2,rho1,rho2,B=0,DB=c(0,0), JC=FALSE, CI_Boot, type="bca", plot=FALSE){
if(is.numeric(d1)){d1=d1}else{stop("d1 is not numeric")}
if(is.numeric(n1)){n1=n1}else{stop("n1 is not numeric")}
if(is.numeric(d2)){d2=d2}else{stop("d2 is not numeric")}
if(is.numeric(n2)){n2=n2}else{stop("n2 is not numeric")}
if(is.numeric(rho1)){rho1=rho1}else{stop("rho1 is not numeric")}
if(is.numeric(rho2)){rho2=rho2}else{stop("rho1 is not numeric")}
if(B==0&& plot==TRUE){stop("please select a number of bootstrap repititions for the plot")}
if(B%%1==0){B=B}else{stop("B is not an integer")}
if(DB[1]%%1==0 && DB[2]%%1==0 ){DB=DB}else{stop("At least one entry in DB is not an integer")}
if(length(d1)==length(n1) && length(d2)==length(n2) && length(d1)==length(d2)){}else{stop("Input vectors do not have the same length")}
def1<- (d1/n1)
def2<- (d2/n2)
estimate=function(def1,def2){
cov_est<-cov(def1,def2)
probOneDefault1<- mean(def1)
probOneDefault2<- mean(def2)
Inter_Est=function(R2){
corr=matrix(c(1,R2,R2,1),2)
integrand=function(u){
pnorm((qnorm(probOneDefault2)-R2*sqrt(rho1* rho2)*u)/sqrt(1-R2^2*rho1* rho2))*dnorm(u)
}
E_D=integrate(integrand,-Inf,qnorm(probOneDefault1))$value
return(abs(E_D-probOneDefault1*probOneDefault2-cov_est))
}
InterCor <-optimise(Inter_Est, interval = c(-1, 1), maximum = FALSE)$minimum
Est<-list(Original =InterCor)}
Estimate_Standard<-estimate(def1,def2)
DEF<-rbind(def1,def2)
if(DB[1]!=0){
IN=DB[1]
OUT=DB[2]
theta1=NULL
theta2=matrix(ncol = OUT, nrow=IN)
for(i in 1:OUT){
N<-length(d1)
Ib<-sample(N,N,replace=TRUE) ## sampling with replacement
Db1<-def1[Ib]
Db2<-def2[Ib]
try(theta1[i]<-estimate(Db1,Db2)$Original, silent = TRUE)
for(c in 1:IN){
Ic<-sample(N,N,replace=TRUE) ## sampling with replacement
Db3<-Db1[Ic]
Db4<-Db2[Ic]
try( theta2[c,i]<-estimate(Db3,Db4)$Original, silent = TRUE)
}
}
Boot1<- mean(theta1, na.rm = TRUE)
Boot2<- mean(theta2, na.rm = TRUE)
BC<- 2*Estimate_Standard$Original -Boot1
DBC<- (3*Estimate_Standard$Original-3*Boot1+Boot2)
Estimate_DoubleBootstrap<-list(Original = Estimate_Standard$Original, Bootstrap=BC, Double_Bootstrap=DBC, oValues=theta1, iValues=theta2)
}
if(B>0){
N<-length(n1)
convert=function(d){
G=length(d)
y1=list()
for (y in 1:G){
y1[[y]]=as.matrix((c(d[y])))
}
return(y1)
}
d1<-convert(def1)
d2<-convert(def2)
DEF_JC<-cbind(d1,d2)
estimate2=function(X){
def1=NULL
N=length(X)/2
for(t in 1:N){
def1[t]<-X[[t]]
}
N1=2*N
def2=NULL
for(p in N:N1){
def2[p]<-X[[p]]
}
def2<-def2[-(1:(N))]
cov_est<-cov(def1,def2)
probOneDefault1<- mean(def1)
probOneDefault2<- mean(def2)
Inter_Est=function(R2){
corr=matrix(c(1,R2,R2,1),2)
integrand=function(u){
pnorm((qnorm(probOneDefault2)-R2*sqrt(rho1* rho2)*u)/sqrt(1-R2^2*rho1* rho2))*dnorm(u)
}
E_D=integrate(integrand,-Inf,qnorm( probOneDefault1))$value
return(abs(E_D-probOneDefault1*probOneDefault2-cov_est))
}
InterCor <-optimise(Inter_Est, interval = c(-1, 1), maximum = FALSE)$minimum
return(InterCor)}
BCA=function(data, indices){
d <- data[indices,]
tryCatch(estimate2(d),error=function(e)NA)
#try(estimate2(d))
}
boot1<- boot(data = DEF_JC, statistic = BCA, R=B)
Estimate_Bootstrap<-list(Original = boot1$t0, Bootstrap=2*boot1$t0 - mean(boot1$t,na.rm = TRUE),bValues=boot1$t )
if(missing(CI_Boot)){Estimate_Bootstrap=Estimate_Bootstrap}else{
if(type=="norm"){Conf=(boot.ci(boot1,conf=CI_Boot,type = type)$normal[2:3])}
if(type=="basic"){Conf=(boot.ci(boot1,conf=CI_Boot,type = type)$basic[4:5])}
if(type=="perc"){Conf=(boot.ci(boot1,conf=CI_Boot,type = type))$percent[4:5]}
if(type=="bca"){Conf=(boot.ci(boot1,conf=CI_Boot,type = type))$bca[4:5]}
if(type=="all"){Conf=(boot.ci(boot1,conf=CI_Boot,type = type))}
Estimate_Bootstrap<-list(Original = boot1$t0, Bootstrap=2*boot1$t0 - mean(boot1$t,na.rm = TRUE),CI_Boot=Conf,bValues=boot1$t )
}
if(plot==TRUE){
Dens<-density(boot1$t, na.rm = TRUE)
XY<-cbind(Dens$x,Dens$y)
label<-data.frame(rep("Bootstrap density",times=length(Dens$x)))
Plot<-cbind(XY,label)
colnames(Plot)<-c("Estimate","Density","Label")
SD<-cbind(rep(boot1$t0,times=length(Dens$x)), Dens$y,rep("Standard estimate",times=length(Dens$x)))
colnames(SD)<-c("Estimate","Density","Label")
BC<-cbind(rep(Estimate_Bootstrap$Bootstrap,times=length(Dens$x)), Dens$y,rep("Bootstrap corrected estimate",times=length(Dens$x)))
colnames(BC)<-c("Estimate","Density","Label")
Plot<-rbind(Plot,SD, BC)
Plot$Estimate<-as.numeric(Plot$Estimate)
Plot$Density<- as.numeric(Plot$Density)
Estimate<-Plot$Estimate
Density<-Plot$Density
Label<-Plot$Label
P<-ggplot()
P<-P+with(Plot, aes(x=Estimate, y=Density, colour=Label)) +
geom_line()+
scale_colour_manual(values = c("black", "red", "orange"))+
theme_minimal(base_size = 15) +
ggtitle("Bootstrap Density" )+
theme(plot.title = element_text(hjust = 0.5),legend.position="bottom",legend.text = element_text(size = 12),legend.title = element_text( size = 12), legend.justification = "center",axis.text.x= element_text(face = "bold", size = 12))
print(P)
}
}
if(JC==TRUE){
N<-length(n1)
Test=NULL
for(v in 1:N){
d1<-def1[-v]
d2<-def2[-v]
try(Test[v]<-estimate(d1,d2)$Original)
}
Estimate_Jackknife<-list(Original = Estimate_Standard$Original, Jackknife=(N*Estimate_Standard$Original-(N-1)*mean(Test)))
}
if(B>0){return(Estimate_Bootstrap)}
if(JC==TRUE){return(Estimate_Jackknife)}
if(DB[1]!=0){return(Estimate_DoubleBootstrap)}
if(B==0 && JC==FALSE && DB[1]==0){return(Estimate_Standard)}
}
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