Nothing
R/jc.probs2.r
In JRM: Joint Regression Modelling
Defines functions
jc.probs2
Documented in
jc.probs2
jc.probs2 <- function(x, y1, y2, newdata, type, cond, intervals, n.sim, prob.lev, cont1par, cont2par, cont3par, bin.link){
epsilon <- 0.0000001
nu1 <- nu2 <- nu <- sigma2 <- 1
CIp12 <- NULL
dof <- x$dof
p12s <- NULL
if(type == "bivariate"){
if(!missing(newdata)){
nu <- sigma2 <- NA
p1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "response")
eta2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata)
if( !is.null(x$X3) && x$margins[2] %in% c(cont2par,cont3par) ){
sigma2 <- esp.tr(predict.SemiParBIV(x, eq = 3, newdata = newdata), x$margins[2])$vrb
if(x$margins[2] %in% cont2par) eq.th <- 4
if(x$margins[2] %in% cont3par){ eq.nu <- 4; eq.th <- 5}
if(x$margins[2] %in% cont3par) nu <- esp.tr(predict.SemiParBIV(x, eq = eq.nu, newdata = newdata), x$margins[2])$vrb
theta <- teta.tr(x$VC, predict.SemiParBIV(x, eq = eq.th, newdata = newdata))$teta
}
# new part for discrete
if( !is.null(x$X3) && x$margins[2] %in% cont1par ) theta <- teta.tr(x$VC, predict.SemiParBIV(x, eq = 3, newdata = newdata))$teta
if( is.null(x$X3) ){
sigma2 <- x$sigma2
nu <- x$nu
theta <- x$theta
}
}
if(missing(newdata)){
p1 <- x$p1
eta2 <- x$eta2
sigma2 <- x$sigma2
nu <- x$nu
theta <- x$theta
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){ ### joint prob of y1=0 and y2=x ###
p0 <- 1 - p1
p2 <- distrHsAT(y2, eta2, sigma2, nu, x$margins[2])$p2
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] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta[x$teta.ind1], dof)
if(length(theta) == 1) p12[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta, dof)
}
if( length(x$teta2) != 0){
if(length(theta) > 1) p12[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta[x$teta.ind2], dof)
if(length(theta) == 1) p12[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta, dof)
}
}
if(!(x$BivD %in% x$BivD2)) p12 <- BiCDF(p0, p2, x$nC, theta, dof)
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){ ### joint prob of y1=1 and y2=x ###
p12 <- p2 - p12
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0 <- 1 - p1
ppdf <- distrHsATDiscr(y2, eta2, sigma2, nu = 1, x$margins[2], x$VC$y2m)
p2 <- ppdf$p2
pdf2 <- ppdf$pdf2
if(x$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$Cop2),2]
C1 <- C2 <- NA
if( length(x$teta1) != 0){
if(length(theta) > 1){
C1[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta[x$teta.ind1], dof)
C2[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], mm(p2[x$teta.ind1]-pdf2[x$teta.ind1]), nC1, theta[x$teta.ind1], dof)
}
if(length(theta) == 1){
C1[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta, dof)
C2[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], mm(p2[x$teta.ind1]-pdf2[x$teta.ind1]), nC1, theta, dof)
}
}
if( length(x$teta2) != 0){
if(length(theta) > 1){
C1[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta[x$teta.ind2], dof)
C2[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], mm(p2[x$teta.ind2]-pdf2[x$teta.ind2]), nC2, theta[x$teta.ind2], dof)
}
if(length(theta) == 1){
C1[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta, dof)
C2[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], mm(p2[x$teta.ind2]-pdf2[x$teta.ind2]), nC2, theta, dof)
}
}
}
if(!(x$BivD %in% x$BivD2)){
C1 <- BiCDF(p0, p2, x$nC, theta, dof)
C2 <- BiCDF(p0, mm(p2-pdf2), x$nC, theta, dof)
}
A <- ifelse(C1 - C2 < epsilon, epsilon, C1 - C2)
if(y1 == 0){
p12 <- A
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){
p12 <- ifelse( pdf2 - A < epsilon, epsilon, pdf2 - A)
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(intervals == TRUE){
bs <- rMVN(n.sim, mean = x$coefficients, sigma = x$Vb)
#############
# etas
#############
if(!missing(newdata)){ X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2s <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix") }
if( missing(newdata)){ X1 <- x$X1
if(x$VC$ccss == "yes") X2s <- x$X2s else X2s <- x$X2
}
p1s <- probm( X1%*%t(bs[,1:x$X1.d2]), x$VC$margins[1])$pr
eta2s <- eta.tr( X2s%*%t(bs[,(x$X1.d2+1):(x$X1.d2+x$X2.d2)]) , x$VC$margins[2])
#############
# thetas
#############
if( is.null(x$X3) ) epds <- bs[,length(x$coefficients)]
if( !is.null(x$X3) ){
if(x$VC$margins[2] %in% cont1par){
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
epds <- X3s%*%t(bs[,(x$X1.d2+x$X2.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2)])
}
if(x$VC$margins[2] %in% cont2par){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
epds <- X4s%*%t(bs[,(x$X1.d2+x$X2.d2+x$X3.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2)])
}
if(x$VC$margins[2] %in% cont3par){
if(!missing(newdata)){ X5s <- predict.SemiParBIV(x, eq = 5, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X5s <- x$X5s else X5s <- x$X5}
epds <- X5s%*%t(bs[,(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2+x$X5.d2)])
}
}
est.RHOb <- teta.tr(x$VC, epds)$teta
#############
# sigmas
#############
if( x$VC$margins[2] %in% cont2par ){
if( is.null(x$X3) ) sigma2.star <- bs[, x$X1.d2 + x$X2.d2 + 1]
if( !is.null(x$X3) ) {
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
sigma2.star <- X3s%*%t(bs[,(x$X1.d2+x$X2.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2)])
}
sigma2 <- esp.tr(sigma2.star, x$VC$margins[2])$vrb
}
#############
# NUs
#############
if( x$VC$margins[2] %in% cont3par ){
if( is.null(x$X3) ) nu.st <- bs[, x$X1.d2 + x$X2.d2 + 2] # t(as.matrix(bs[, x$X1.d2 + x$X2.d2 + 2]))
if( !is.null(x$X3) ){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
nu.st <- X4s%*%t(bs[,(x$X1.d2 + x$X2.d2 + x$X3.d2 + 1):(x$X1.d2 + x$X2.d2 + x$X3.d2 + x$X4.d2)])
}
nu <- esp.tr(nu.st, x$VC$margins[2])$vrb
}
#################
if( is.null(x$X3) ){
est.RHOb <- matrix(rep(est.RHOb, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
sigma2 <- matrix(rep(sigma2, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
nu <- matrix(rep(nu, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0s <- 1 - p1s
p2s <- distrHsAT(y2, eta2s, sigma2, nu, x$margins[2])$p2
if(x$VC$BivD %in% c("N","T")) p12s <- matrix(BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
p12s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) p12s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], p2s[x$teta.ind1,], nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) p12s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], p2s[x$teta.ind2,], nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) p12s <- BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE)
}
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- p2s - p12s
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0s <- 1 - p1s
ppdf <- distrHsATDiscr(y2, eta2s, sigma2, nu = 1, x$margins[2], x$VC$y2m) # not sure about y2m
p2s <- ppdf$p2
pdf2s <- ppdf$pdf2
if(x$VC$BivD %in% c("N","T")) C1s <- matrix(BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
C1s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) C1s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], p2s[x$teta.ind1,], nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) C1s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], p2s[x$teta.ind2,], nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) C1s <- BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE)
}
if(x$VC$BivD %in% c("N","T")) C2s <- matrix(BiCDF(p0s, mm(p2s-pdf2s), x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
C2s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) C2s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], mm(p2s[x$teta.ind1,]-pdf2s[x$teta.ind1,]), nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) C2s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], mm(p2s[x$teta.ind2,]-pdf2s[x$teta.ind2,]), nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) C2s <- BiCDF(p0s, mm(p2s-pdf2s), x$nC, est.RHOb, dof, test = FALSE)
}
As <- ifelse(C1s - C2s < epsilon, epsilon, C1s - C2s)
if(y1 == 0){
p12s <- As
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- ifelse( pdf2s - As < epsilon, epsilon, pdf2s - As)
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
} # int
} # biv
###########################################
###########################################
###########################################
if(type == "independence"){
if(!missing(newdata)){
nu <- sigma2 <- NA
p1 <- probm(predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")%*%x$gam1$coefficients, x$VC$margins[1])$pr
eta2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[1:x$X2.d2]
if( !(x$VC$margins[2] %in% cont1par) ){
if( !is.null(x$X3) ){
sigma2 <- esp.tr(predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[(x$X2.d2+1):(x$X2.d2+x$X3.d2)], x$margins[2])$vrb
if(x$margins[2] %in% cont3par) nu <- esp.tr(predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[(x$X2.d2 + x$X3.d2 + 1):(x$X2.d2 + x$X3.d2 + x$X4.d2)], x$margins[2])$vrb
}
if( is.null(x$X3) ){
sigma2 <- x$gamlss$sigma2
nu <- x$gamlss$nu
}
}
}
if(missing(newdata)){
p1 <- probm(predict.SemiParBIV(x, eq = 1, type = "lpmatrix")%*%x$gam1$coefficients, x$VC$margins[1])$pr
eta2 <- x$gamlss$eta1
sigma2 <- x$gamlss$sigma2
nu <- x$gamlss$nu
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0 <- 1 - p1
p2 <- distrHsAT(y2, eta2, sigma2, nu, x$margins[2])$p2
p12 <- p0*p2
if(cond == 1) p12 <- p2
if(cond == 2) p12 <- p0
}
if(y1 == 1){
p12 <- p1*p2
if(cond == 1) p12 <- p2
if(cond == 2) p12 <- p1
}
} #*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0 <- 1 - p1
p2 <- distrHsATDiscr(y2, eta2, sigma2, nu = 1, x$margins[2], x$VC$y2m)$pdf2 # note pdf2 here!
if(y1 == 0){
p12 <- p0*p2
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){
p12 <- p1*p2
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(intervals == TRUE){
bs1 <- rMVN(n.sim, mean = x$gam1$coefficients, sigma=x$gam1$Vp)
bs2 <- rMVN(n.sim, mean = x$gamlss$coefficients, sigma=x$gamlss$Vb)
#############
# etas
#############
if(!missing(newdata)){ X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2s <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix") }
if( missing(newdata)){ X1 <- x$X1
if(x$VC$ccss == "yes") X2s <- x$X2s else X2s <- x$X2
}
p1s <- probm( X1%*%t(bs1[,1:x$X1.d2]) , x$VC$margins[1])$pr
eta2s <- eta.tr( X2s%*%t(bs2[,1:x$X2.d2]) , x$VC$margins[2])
#############
# sigmas
#############
if( x$VC$margins[2] %in% cont2par ){
if( is.null(x$X3) ) sigma2.star <- bs2[, x$X2.d2 + 1]
if( !is.null(x$X3) ){
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
sigma2.star <- X3s%*%t(bs2[,(x$X2.d2+1):(x$X2.d2+x$X3.d2)])
}
sigma2 <- esp.tr(sigma2.star, x$VC$margins[2])$vrb
}
#############
# NUs
#############
if( x$VC$margins[2] %in% cont3par ){
if( is.null(x$X3) ) nu.st <- bs2[, x$X2.d2 + 2] # t(as.matrix(bs2[, x$X2.d2 + 2]))
if( !is.null(x$X3) ){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
nu.st <- X4s%*%t(bs2[,(x$X2.d2 + x$X3.d2 + 1):(x$X2.d2 + x$X3.d2 + x$X4.d2)])
}
nu <- esp.tr(nu.st, x$VC$margins[2])$vrb
}
#################
if( is.null(x$X3) ){
sigma2 <- matrix(rep(sigma2, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
nu <- matrix(rep(nu, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0s <- 1 - p1s
p2s <- distrHsAT(y2, eta2s, sigma2, nu, x$margins[2])$p2
p12s <- p0s*p2s
if(cond == 1) p12s <- p2s
if(cond == 2) p12s <- p0s
}
if(y1 == 1){
p12s <- p1s*p2s
if(cond == 1) p12s <- p2s
if(cond == 2) p12s <- p1s
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0s <- 1 - p1s
pdf2s <- distrHsATDiscr(y2, eta2s, sigma2, nu = 1, x$margins[2], x$VC$y2m)$pdf2 # not sure about y2m
if(y1 == 0){
p12s <- p0s*pdf2s
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- p1s*pdf2s
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
}# int
} # indep
list(p12 = p12, p12s = p12s, p1 = p1, p2 = p2)
}
Try the JRM package in your browser
Any scripts or data that you put into this service are public.
JRM documentation built on July 13, 2017, 5:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions jc.probs2
Documented in jc.probs2
jc.probs2 <- function(x, y1, y2, newdata, type, cond, intervals, n.sim, prob.lev, cont1par, cont2par, cont3par, bin.link){
epsilon <- 0.0000001
nu1 <- nu2 <- nu <- sigma2 <- 1
CIp12 <- NULL
dof <- x$dof
p12s <- NULL
if(type == "bivariate"){
if(!missing(newdata)){
nu <- sigma2 <- NA
p1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "response")
eta2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata)
if( !is.null(x$X3) && x$margins[2] %in% c(cont2par,cont3par) ){
sigma2 <- esp.tr(predict.SemiParBIV(x, eq = 3, newdata = newdata), x$margins[2])$vrb
if(x$margins[2] %in% cont2par) eq.th <- 4
if(x$margins[2] %in% cont3par){ eq.nu <- 4; eq.th <- 5}
if(x$margins[2] %in% cont3par) nu <- esp.tr(predict.SemiParBIV(x, eq = eq.nu, newdata = newdata), x$margins[2])$vrb
theta <- teta.tr(x$VC, predict.SemiParBIV(x, eq = eq.th, newdata = newdata))$teta
}
# new part for discrete
if( !is.null(x$X3) && x$margins[2] %in% cont1par ) theta <- teta.tr(x$VC, predict.SemiParBIV(x, eq = 3, newdata = newdata))$teta
if( is.null(x$X3) ){
sigma2 <- x$sigma2
nu <- x$nu
theta <- x$theta
}
}
if(missing(newdata)){
p1 <- x$p1
eta2 <- x$eta2
sigma2 <- x$sigma2
nu <- x$nu
theta <- x$theta
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){ ### joint prob of y1=0 and y2=x ###
p0 <- 1 - p1
p2 <- distrHsAT(y2, eta2, sigma2, nu, x$margins[2])$p2
if(x$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$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta[x$teta.ind1], dof)
if(length(theta) == 1) p12[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta, dof)
}
if( length(x$teta2) != 0){
if(length(theta) > 1) p12[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta[x$teta.ind2], dof)
if(length(theta) == 1) p12[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta, dof)
}
}
if(!(x$BivD %in% x$BivD2)) p12 <- BiCDF(p0, p2, x$nC, theta, dof)
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){ ### joint prob of y1=1 and y2=x ###
p12 <- p2 - p12
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0 <- 1 - p1
ppdf <- distrHsATDiscr(y2, eta2, sigma2, nu = 1, x$margins[2], x$VC$y2m)
p2 <- ppdf$p2
pdf2 <- ppdf$pdf2
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]
C1 <- C2 <- NA
if( length(x$teta1) != 0){
if(length(theta) > 1){
C1[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta[x$teta.ind1], dof)
C2[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], mm(p2[x$teta.ind1]-pdf2[x$teta.ind1]), nC1, theta[x$teta.ind1], dof)
}
if(length(theta) == 1){
C1[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], p2[x$teta.ind1], nC1, theta, dof)
C2[x$teta.ind1] <- BiCDF(p0[x$teta.ind1], mm(p2[x$teta.ind1]-pdf2[x$teta.ind1]), nC1, theta, dof)
}
}
if( length(x$teta2) != 0){
if(length(theta) > 1){
C1[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta[x$teta.ind2], dof)
C2[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], mm(p2[x$teta.ind2]-pdf2[x$teta.ind2]), nC2, theta[x$teta.ind2], dof)
}
if(length(theta) == 1){
C1[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], p2[x$teta.ind2], nC2, theta, dof)
C2[x$teta.ind2] <- BiCDF(p0[x$teta.ind2], mm(p2[x$teta.ind2]-pdf2[x$teta.ind2]), nC2, theta, dof)
}
}
}
if(!(x$BivD %in% x$BivD2)){
C1 <- BiCDF(p0, p2, x$nC, theta, dof)
C2 <- BiCDF(p0, mm(p2-pdf2), x$nC, theta, dof)
}
A <- ifelse(C1 - C2 < epsilon, epsilon, C1 - C2)
if(y1 == 0){
p12 <- A
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){
p12 <- ifelse( pdf2 - A < epsilon, epsilon, pdf2 - A)
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(intervals == TRUE){
bs <- rMVN(n.sim, mean = x$coefficients, sigma = x$Vb)
#############
# etas
#############
if(!missing(newdata)){ X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2s <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix") }
if( missing(newdata)){ X1 <- x$X1
if(x$VC$ccss == "yes") X2s <- x$X2s else X2s <- x$X2
}
p1s <- probm( X1%*%t(bs[,1:x$X1.d2]), x$VC$margins[1])$pr
eta2s <- eta.tr( X2s%*%t(bs[,(x$X1.d2+1):(x$X1.d2+x$X2.d2)]) , x$VC$margins[2])
#############
# thetas
#############
if( is.null(x$X3) ) epds <- bs[,length(x$coefficients)]
if( !is.null(x$X3) ){
if(x$VC$margins[2] %in% cont1par){
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
epds <- X3s%*%t(bs[,(x$X1.d2+x$X2.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2)])
}
if(x$VC$margins[2] %in% cont2par){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
epds <- X4s%*%t(bs[,(x$X1.d2+x$X2.d2+x$X3.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2)])
}
if(x$VC$margins[2] %in% cont3par){
if(!missing(newdata)){ X5s <- predict.SemiParBIV(x, eq = 5, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X5s <- x$X5s else X5s <- x$X5}
epds <- X5s%*%t(bs[,(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2+x$X4.d2+x$X5.d2)])
}
}
est.RHOb <- teta.tr(x$VC, epds)$teta
#############
# sigmas
#############
if( x$VC$margins[2] %in% cont2par ){
if( is.null(x$X3) ) sigma2.star <- bs[, x$X1.d2 + x$X2.d2 + 1]
if( !is.null(x$X3) ) {
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
sigma2.star <- X3s%*%t(bs[,(x$X1.d2+x$X2.d2+1):(x$X1.d2+x$X2.d2+x$X3.d2)])
}
sigma2 <- esp.tr(sigma2.star, x$VC$margins[2])$vrb
}
#############
# NUs
#############
if( x$VC$margins[2] %in% cont3par ){
if( is.null(x$X3) ) nu.st <- bs[, x$X1.d2 + x$X2.d2 + 2] # t(as.matrix(bs[, x$X1.d2 + x$X2.d2 + 2]))
if( !is.null(x$X3) ){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
nu.st <- X4s%*%t(bs[,(x$X1.d2 + x$X2.d2 + x$X3.d2 + 1):(x$X1.d2 + x$X2.d2 + x$X3.d2 + x$X4.d2)])
}
nu <- esp.tr(nu.st, x$VC$margins[2])$vrb
}
#################
if( is.null(x$X3) ){
est.RHOb <- matrix(rep(est.RHOb, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
sigma2 <- matrix(rep(sigma2, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
nu <- matrix(rep(nu, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0s <- 1 - p1s
p2s <- distrHsAT(y2, eta2s, sigma2, nu, x$margins[2])$p2
if(x$VC$BivD %in% c("N","T")) p12s <- matrix(BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
p12s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) p12s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], p2s[x$teta.ind1,], nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) p12s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], p2s[x$teta.ind2,], nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) p12s <- BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE)
}
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- p2s - p12s
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0s <- 1 - p1s
ppdf <- distrHsATDiscr(y2, eta2s, sigma2, nu = 1, x$margins[2], x$VC$y2m) # not sure about y2m
p2s <- ppdf$p2
pdf2s <- ppdf$pdf2
if(x$VC$BivD %in% c("N","T")) C1s <- matrix(BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
C1s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) C1s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], p2s[x$teta.ind1,], nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) C1s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], p2s[x$teta.ind2,], nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) C1s <- BiCDF(p0s, p2s, x$nC, est.RHOb, dof, test = FALSE)
}
if(x$VC$BivD %in% c("N","T")) C2s <- matrix(BiCDF(p0s, mm(p2s-pdf2s), x$nC, est.RHOb, dof, test = FALSE), dim(p1s)[1], n.sim) else{
if(x$BivD %in% x$BivD2){
C2s <- matrix(NA, ncol = n.sim, nrow = dim(p0s)[1])
if( length(x$teta1) != 0) C2s[x$teta.ind1,] <- BiCDF(p0s[x$teta.ind1,], mm(p2s[x$teta.ind1,]-pdf2s[x$teta.ind1,]), nC1, est.RHOb[x$teta.ind1,])
if( length(x$teta2) != 0) C2s[x$teta.ind2,] <- BiCDF(p0s[x$teta.ind2,], mm(p2s[x$teta.ind2,]-pdf2s[x$teta.ind2,]), nC2, -est.RHOb[x$teta.ind2,])
}
if(!(x$BivD %in% x$BivD2)) C2s <- BiCDF(p0s, mm(p2s-pdf2s), x$nC, est.RHOb, dof, test = FALSE)
}
As <- ifelse(C1s - C2s < epsilon, epsilon, C1s - C2s)
if(y1 == 0){
p12s <- As
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- ifelse( pdf2s - As < epsilon, epsilon, pdf2s - As)
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
} # int
} # biv
###########################################
###########################################
###########################################
if(type == "independence"){
if(!missing(newdata)){
nu <- sigma2 <- NA
p1 <- probm(predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")%*%x$gam1$coefficients, x$VC$margins[1])$pr
eta2 <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[1:x$X2.d2]
if( !(x$VC$margins[2] %in% cont1par) ){
if( !is.null(x$X3) ){
sigma2 <- esp.tr(predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[(x$X2.d2+1):(x$X2.d2+x$X3.d2)], x$margins[2])$vrb
if(x$margins[2] %in% cont3par) nu <- esp.tr(predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")%*%x$gamlss$coefficients[(x$X2.d2 + x$X3.d2 + 1):(x$X2.d2 + x$X3.d2 + x$X4.d2)], x$margins[2])$vrb
}
if( is.null(x$X3) ){
sigma2 <- x$gamlss$sigma2
nu <- x$gamlss$nu
}
}
}
if(missing(newdata)){
p1 <- probm(predict.SemiParBIV(x, eq = 1, type = "lpmatrix")%*%x$gam1$coefficients, x$VC$margins[1])$pr
eta2 <- x$gamlss$eta1
sigma2 <- x$gamlss$sigma2
nu <- x$gamlss$nu
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0 <- 1 - p1
p2 <- distrHsAT(y2, eta2, sigma2, nu, x$margins[2])$p2
p12 <- p0*p2
if(cond == 1) p12 <- p2
if(cond == 2) p12 <- p0
}
if(y1 == 1){
p12 <- p1*p2
if(cond == 1) p12 <- p2
if(cond == 2) p12 <- p1
}
} #*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0 <- 1 - p1
p2 <- distrHsATDiscr(y2, eta2, sigma2, nu = 1, x$margins[2], x$VC$y2m)$pdf2 # note pdf2 here!
if(y1 == 0){
p12 <- p0*p2
if(cond == 1) p12 <- p12/p0
if(cond == 2) p12 <- p12/p2
}
if(y1 == 1){
p12 <- p1*p2
if(cond == 1) p12 <- p12/p1
if(cond == 2) p12 <- p12/p2
}
}#*#
if(intervals == TRUE){
bs1 <- rMVN(n.sim, mean = x$gam1$coefficients, sigma=x$gam1$Vp)
bs2 <- rMVN(n.sim, mean = x$gamlss$coefficients, sigma=x$gamlss$Vb)
#############
# etas
#############
if(!missing(newdata)){ X1 <- predict.SemiParBIV(x, eq = 1, newdata = newdata, type = "lpmatrix")
X2s <- predict.SemiParBIV(x, eq = 2, newdata = newdata, type = "lpmatrix") }
if( missing(newdata)){ X1 <- x$X1
if(x$VC$ccss == "yes") X2s <- x$X2s else X2s <- x$X2
}
p1s <- probm( X1%*%t(bs1[,1:x$X1.d2]) , x$VC$margins[1])$pr
eta2s <- eta.tr( X2s%*%t(bs2[,1:x$X2.d2]) , x$VC$margins[2])
#############
# sigmas
#############
if( x$VC$margins[2] %in% cont2par ){
if( is.null(x$X3) ) sigma2.star <- bs2[, x$X2.d2 + 1]
if( !is.null(x$X3) ){
if(!missing(newdata)){ X3s <- predict.SemiParBIV(x, eq = 3, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X3s <- x$X3s else X3s <- x$X3}
sigma2.star <- X3s%*%t(bs2[,(x$X2.d2+1):(x$X2.d2+x$X3.d2)])
}
sigma2 <- esp.tr(sigma2.star, x$VC$margins[2])$vrb
}
#############
# NUs
#############
if( x$VC$margins[2] %in% cont3par ){
if( is.null(x$X3) ) nu.st <- bs2[, x$X2.d2 + 2] # t(as.matrix(bs2[, x$X2.d2 + 2]))
if( !is.null(x$X3) ){
if(!missing(newdata)){ X4s <- predict.SemiParBIV(x, eq = 4, newdata = newdata, type = "lpmatrix")}
if( missing(newdata)){ if(x$VC$ccss == "yes") X4s <- x$X4s else X4s <- x$X4}
nu.st <- X4s%*%t(bs2[,(x$X2.d2 + x$X3.d2 + 1):(x$X2.d2 + x$X3.d2 + x$X4.d2)])
}
nu <- esp.tr(nu.st, x$VC$margins[2])$vrb
}
#################
if( is.null(x$X3) ){
sigma2 <- matrix(rep(sigma2, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
nu <- matrix(rep(nu, each = dim(eta2s)[1]), ncol = n.sim, byrow=FALSE)
}
if(x$margins[2] %in% c(x$VC$m2, x$VC$m3)){#*#
if(y1 == 0 || y1 == 1){
p0s <- 1 - p1s
p2s <- distrHsAT(y2, eta2s, sigma2, nu, x$margins[2])$p2
p12s <- p0s*p2s
if(cond == 1) p12s <- p2s
if(cond == 2) p12s <- p0s
}
if(y1 == 1){
p12s <- p1s*p2s
if(cond == 1) p12s <- p2s
if(cond == 2) p12s <- p1s
}
}#*#
if(x$margins[2] %in% c(x$VC$m2d, x$VC$m1d)){#*#
p0s <- 1 - p1s
pdf2s <- distrHsATDiscr(y2, eta2s, sigma2, nu = 1, x$margins[2], x$VC$y2m)$pdf2 # not sure about y2m
if(y1 == 0){
p12s <- p0s*pdf2s
if(cond == 1) p12s <- p12s/p0s
if(cond == 2) p12s <- p12s/p2s
}
if(y1 == 1){
p12s <- p1s*pdf2s
if(cond == 1) p12s <- p12s/p1s
if(cond == 2) p12s <- p12s/p2s
}
}#*#
}# int
} # indep
list(p12 = p12, p12s = p12s, p1 = p1, p2 = p2)
}
Try the JRM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.