R/jc.probs4.r

In KironmoyDas/KD-STAT0035-GMupdate: Generalised Joint Regression Modelling

jc.probs4 <- function(x, y1, y2, newdata, type, cond, intervals, n.sim, prob.lev, cont1par, cont2par, cont3par, bin.link, min.pr, max.pr){

######################################################################################################

CIp12 <- p12s <- NULL
CIkt <- tau <- NULL

######################################################################################################


if(type == "joint"){ 


######

if(!missing(newdata)){ 

X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix")
if( !is.null(x$X3) ) X3 <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")


eta1 <- X1%*%x$coef.t[1:x$X1.d2]
eta2 <- X2%*%x$coef.t[(x$X1.d2+1):(x$X1.d2+x$X2.d2)]


if( !is.null(x$X3) ) theta <- teta.tr(x$VC, predict.SemiParBIV(x, eq = 3, newdata = newdata))$teta
if( is.null(x$X3) )  theta <- x$theta 

                     }


if(missing(newdata)){

X1 <- x$X1 
X2 <- x$X2 
if( !is.null(x$X3) ) X3 <- x$X3

eta1  <- x$eta1
eta2  <- x$eta2
theta <- x$theta 
                     }


######

p1 <- as.numeric(probmS(eta1, x$VC$margins[1], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr) 
p2 <- as.numeric(probmS(eta2, x$VC$margins[2], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr)  
theta <- as.numeric(theta)
###### 


if(cond == 0){


if(!(x$BivD %in% x$BivD2)) p12 <- mm(BiCDF(p1, p2, x$nC, theta, 3), min.pr = min.pr, max.pr = max.pr  )

if(x$BivD %in% x$BivD2){

nC1 <- x$VC$ct[which(x$VC$ct[,1] == x$Cop1),2] 
nC2 <- x$VC$ct[which(x$VC$ct[,1] == x$Cop2),2]

p12 <- NA
 
if( length(x$teta1) != 0){
if(length(theta) > 1)  p12[x$teta.ind1] <- mm(BiCDF(p1[x$teta.ind1], p2[x$teta.ind1], nC1, theta[x$teta.ind1], 3), min.pr = min.pr, max.pr = max.pr  )
if(length(theta) == 1) p12[x$teta.ind1] <- mm(BiCDF(p1[x$teta.ind1], p2[x$teta.ind1], nC1, theta, 3), min.pr = min.pr, max.pr = max.pr  )
                         }  
                          
if( length(x$teta2) != 0){
if(length(theta) > 1)  p12[x$teta.ind2] <- mm(BiCDF(p1[x$teta.ind2], p2[x$teta.ind2], nC2, theta[x$teta.ind2], 3), min.pr = min.pr, max.pr = max.pr  )
if(length(theta) == 1) p12[x$teta.ind2] <- mm(BiCDF(p1[x$teta.ind2], p2[x$teta.ind2], nC2, theta, 3), min.pr = min.pr, max.pr = max.pr  )
                         }                            
                                                                           
           }


}







if(cond == 1){

if(!(x$BivD %in% x$BivD2)) p12 <- copgHsCond(p1, p2, theta, dof = 3, x$BivD, min.pr = min.pr, max.pr = max.pr)$c.copula.be1


if(x$BivD %in% x$BivD2){

p12 <- NA
 
if( length(x$teta1) != 0){
if(length(theta) > 1)  p12[x$teta.ind1] <- copgHsCond(p1[x$teta.ind1], p2[x$teta.ind1], theta[x$teta.ind1], dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be1
if(length(theta) == 1) p12[x$teta.ind1] <- copgHsCond(p1[x$teta.ind1], p2[x$teta.ind1], theta, dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be1
                         }  
                          
if( length(x$teta2) != 0){
if(length(theta) > 1)  p12[x$teta.ind2] <- copgHsCond(p1[x$teta.ind2], p2[x$teta.ind2], theta[x$teta.ind2], dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be1
if(length(theta) == 1) p12[x$teta.ind2] <- copgHsCond(p1[x$teta.ind2], p2[x$teta.ind2], theta, dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be1
                         }                            
                                                                           
}



}



if(cond == 2){

if(!(x$BivD %in% x$BivD2)) p12 <- copgHsCond(p1, p2, theta, dof = 3, x$BivD, min.pr = min.pr, max.pr = max.pr)$c.copula.be2


if(x$BivD %in% x$BivD2){

p12 <- NA
 
if( length(x$teta1) != 0){
if(length(theta) > 1)  p12[x$teta.ind1] <- copgHsCond(p1[x$teta.ind1], p2[x$teta.ind1], theta[x$teta.ind1], dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be2
if(length(theta) == 1) p12[x$teta.ind1] <- copgHsCond(p1[x$teta.ind1], p2[x$teta.ind1], theta, dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be2
                         }  
                          
if( length(x$teta2) != 0){
if(length(theta) > 1)  p12[x$teta.ind2] <- copgHsCond(p1[x$teta.ind2], p2[x$teta.ind2], theta[x$teta.ind2], dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be2
if(length(theta) == 1) p12[x$teta.ind2] <- copgHsCond(p1[x$teta.ind2], p2[x$teta.ind2], theta, dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be2
                         }                            
                                                                           
}


}


# kendalls' tau

if(x$BivD %in% x$BivD2)    {x$SemiParFit <- x; tau <- Reg2Copost(x$SemiParFit, x$VC, theta)$tau } 
if(!(x$BivD %in% x$BivD2)) tau <- ass.ms(x$VC$BivD, x$VC$nCa, theta)$tau


############################## 


if(intervals == TRUE){

bs <- rMVN(n.sim, mean = x$coef.t, sigma = x$Vb.t)
if(!is.null(x$VC$mono.sm.pos)) mono.sm.pos <- x$VC$mono.sm.pos else mono.sm.pos <- c(x$VC$mono.sm.pos1, x$VC$mono.sm.pos2 + x$VC$X1.d2)  
bs[, mono.sm.pos] <- ifelse(bs[, mono.sm.pos] < 0, 0, bs[, mono.sm.pos]) 

lf <- length(x$coefficients)

                       
p1s <- probmS( X1%*%t(bs[,1:x$X1.d2])                     , x$VC$margins[1], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr 
p2s <- probmS( X2%*%t(bs[,(x$X1.d2+1):(x$X1.d2+x$X2.d2)]) , x$VC$margins[2], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr

  
if( !is.null(x$X3) ) epds <- X3%*%t(bs[,(x$X1.d2 + x$X2.d2 + 1):(x$X1.d2 + x$X2.d2 + x$X3.d2)])
if( is.null(x$X3)  ) epds <- bs[, lf]
       
                         
est.RHOb <- teta.tr(x$VC, epds)$teta
if( is.null(x$X3) ) est.RHOb <- matrix(rep(est.RHOb, each = dim(p1s)[1]), ncol = n.sim, byrow = FALSE)

###########################   



if(cond == 0){


if(x$VC$BivD %in% c("N","T")) p12s <- matrix(mm(BiCDF(p1s, p2s, x$nC, est.RHOb, 3, test = FALSE), min.pr = min.pr, max.pr = max.pr  ), dim(p1s)[1], n.sim) else{

if(!(x$BivD %in% x$BivD2)) p12s <- matrix(mm(BiCDF(p1s, p2s, x$nC, est.RHOb, par2 = 3, test = FALSE), min.pr = min.pr, max.pr = max.pr  ), dim(p1s)[1], n.sim)

if(x$BivD %in% x$BivD2){

p12s <- matrix(NA, ncol = n.sim, nrow = dim(p1s)[1])

if( length(x$teta1) != 0) p12s[x$teta.ind1,] <- mm(BiCDF(p1s[x$teta.ind1,], p2s[x$teta.ind1,], nC1,  est.RHOb[x$teta.ind1,]), min.pr = min.pr, max.pr = max.pr  )                  
if( length(x$teta2) != 0) p12s[x$teta.ind2,] <- mm(BiCDF(p1s[x$teta.ind2,], p2s[x$teta.ind2,], nC2, -est.RHOb[x$teta.ind2,]), min.pr = min.pr, max.pr = max.pr  )
                      
                        }
      
 }
         
}         
         
         
         
         
         
if(cond == 1){


if(!(x$BivD %in% x$BivD2)) p12s <- matrix( copgHsCond(p1s, p2s, est.RHOb, dof = 3, x$BivD, min.pr = min.pr, max.pr = max.pr)$c.copula.be1, dim(p1s)[1], n.sim)

if(x$BivD == "T") p12s <- matrix(p12s, dim(p1s)[1], n.sim)


if(x$BivD %in% x$BivD2){

p12s <- matrix(NA, ncol = n.sim, nrow = dim(p1s)[1])
 
if( length(x$teta1) != 0) p12s[x$teta.ind1,] <- copgHsCond(p1s[x$teta.ind1,], p2s[x$teta.ind1,],  est.RHOb[x$teta.ind1,], dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be1                                               
if( length(x$teta2) != 0) p12s[x$teta.ind2,] <- copgHsCond(p1s[x$teta.ind2,], p2s[x$teta.ind2,], -est.RHOb[x$teta.ind2,], dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be1
                                                                                                     
                       }

}




if(cond == 2){


if(!(x$BivD %in% x$BivD2)) p12s <- matrix( copgHsCond(p1s, p2s, est.RHOb, dof = 3, x$BivD, min.pr = min.pr, max.pr = max.pr)$c.copula.be2, dim(p1s)[1], n.sim)

if(x$BivD == "T") p12s <- matrix(p12s, dim(p1s)[1], n.sim)


if(x$BivD %in% x$BivD2){

p12s <- matrix(NA, ncol = n.sim, nrow = dim(p1s)[1])
 
if( length(x$teta1) != 0) p12s[x$teta.ind1,] <- copgHsCond(p1s[x$teta.ind1,], p2s[x$teta.ind1,],  est.RHOb[x$teta.ind1,], dof = 3, x$Cop1, min.pr = min.pr, max.pr = max.pr)$c.copula.be2                                               
if( length(x$teta2) != 0) p12s[x$teta.ind2,] <- copgHsCond(p1s[x$teta.ind2,], p2s[x$teta.ind2,], -est.RHOb[x$teta.ind2,], dof = 3, x$Cop2, min.pr = min.pr, max.pr = max.pr)$c.copula.be2
                                                                                                     
                       }

}








nCa   <- x$VC$nCa
BivDt <- x$VC$BivD

  if(x$BivD %in% x$BivD2){
  
  if(x$BivD %in% x$BivD2[c(1:4,13:16)]) { BivDt <- "C0"; nCa <- 3} 
  if(x$BivD %in% x$BivD2[5:8]) { BivDt <- "J0"; nCa <- 6}
  if(x$BivD %in% x$BivD2[9:12]){ BivDt <- "G0"; nCa <- 4}
  
                         }
  
ass.msR <- ass.ms(BivDt, nCa, est.RHOb)
taus    <- ass.msR$tau
if(!is.null(x$X3) && BivDt %in% c("AMH", "FGM")) taus <- matrix(taus, nrow(x$X3), nrow(bs))
CIkt <- rowQuantiles(taus, probs = c(prob.lev/2,1-prob.lev/2), na.rm = TRUE)
#if( is.null(x$X3) ) CIkt <- t(CIkt) 



 if(x$BivD %in% x$BivD2){ 
 
   if(length(x$theta) > 1){
 
     if( length(x$teta2) != 0) CIkt[x$teta.ind2, ] <- -CIkt[x$teta.ind2, ]; CIkt[x$teta.ind2, c(1,2)] <- CIkt[x$teta.ind2, c(2,1)] 
                                 
                          }else{
 
     if( length(x$teta2) != 0) CIkt <- -CIkt; CIkt[, c(1,2)] <- CIkt[, c(2,1)]
                                 
                                }
 }









} # interv


}## biv
                        
######################################################################################################
######################################################################################################

if(type == "independence"){

if(!missing(newdata)){

X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix")

eta1 <- X1%*%x$gamlss1$coef.t[1:x$X1.d2]
eta2 <- X2%*%x$gamlss2$coef.t[1:x$X2.d2]

}

if(missing(newdata)){

X1 <- x$X1
X2 <- x$X2

eta1 <- x$gamlss1$eta1
eta2 <- x$gamlss2$eta1

}


p1 <- as.numeric(probmS(eta1, x$VC$margins[1], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr) 
p2 <- as.numeric(probmS(eta2, x$VC$margins[2], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr) 

p12 <- p1*p2

if(cond == 1) p12 <- p2
if(cond == 2) p12 <- p1


if(intervals == TRUE){

bs1 <- rMVN(n.sim, mean = x$gamlss1$coef.t, sigma=x$gamlss1$Vb.t)
mono.sm.pos <- x$gamlss1$VC$mono.sm.pos 
bs1[, mono.sm.pos] <- ifelse(bs1[, mono.sm.pos] < 0, 0, bs1[, mono.sm.pos]) 

bs2 <- rMVN(n.sim, mean = x$gamlss2$coef.t, sigma=x$gamlss2$Vb.t)
mono.sm.pos <- x$gamlss2$VC$mono.sm.pos 
bs2[, mono.sm.pos] <- ifelse(bs2[, mono.sm.pos] < 0, 0, bs2[, mono.sm.pos]) 

#############  
# etas
############# 

p1s <- probmS( X1%*%t(bs1[,1:x$X1.d2]), x$VC$margins[1], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr 
p2s <- probmS( X2%*%t(bs2[,1:x$X2.d2]), x$VC$margins[2], min.dn = min.pr, min.pr = min.pr, max.pr = max.pr)$pr 

p12s <- p1s*p2s

if(cond == 1) p12s <- p2s
if(cond == 2) p12s <- p1s

                      } # intervals


} # independence


list(p12 = p12, p12s = p12s, p1 = p1, p2 = p2, p3 = NULL, CIkt = CIkt, tau = tau)


}