R/g.triESS.r
In KironmoyDas/KD-STAT0035-GMupdate: Generalised Joint Regression Modelling
Defines functions
g.triESS
Documented in
g.triESS
g.triESS <- function(params, respvec, VC, TIn){
mean1 <- TIn$theta12 * TIn$mar1
mean2 <- TIn$theta13 * TIn$mar1
mean3 <- TIn$theta12 * TIn$mar2
mean4 <- TIn$theta23 * TIn$mar2
mean5 <- TIn$theta13 * TIn$mar3
mean6 <- TIn$theta23 * TIn$mar3
var1 <- 1 - TIn$theta12^2
var2 <- 1 - TIn$theta13^2
var3 <- 1 - TIn$theta23^2
cov1 <- TIn$theta23 - TIn$theta12 * TIn$theta13
cov2 <- TIn$theta13 - TIn$theta12 * TIn$theta23
cov3 <- TIn$theta12 - TIn$theta13 * TIn$theta23
cov1 <- mmf(cov1, max.pr = VC$max.pr)
cov2 <- mmf(cov2, max.pr = VC$max.pr)
cov3 <- mmf(cov3, max.pr = VC$max.pr)
d.1 <- dnorm(TIn$mar1)
d.2 <- dnorm(TIn$mar2)
d.3 <- dnorm(TIn$mar3)
p.1.11 <- mm(pbinorm( TIn$mar2, TIn$mar3, mean1 = mean1[VC$inde], mean2 = mean2[VC$inde], var1 = var1, var2 = var2, cov12 = cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.10 <- mm(pbinorm( TIn$mar2, -TIn$mar3, mean1 = mean1[VC$inde], mean2 = -mean2[VC$inde], var1 = var1, var2 = var2, cov12 = -cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.00 <- mm(pbinorm(-TIn$mar2, -TIn$mar3, mean1 = -mean1[VC$inde], mean2 = -mean2[VC$inde], var1 = var1, var2 = var2, cov12 = cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.01 <- mm(pbinorm(-TIn$mar2, TIn$mar3, mean1 = -mean1[VC$inde], mean2 = mean2[VC$inde], var1 = var1, var2 = var2, cov12 = -cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.2.11 <- mm(pbinorm( TIn$mar1[VC$inde], TIn$mar3, mean1 = mean3, mean2 = mean4, var1 = var1, var2 = var3, cov12 = cov2) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.2.10 <- mm(pbinorm( TIn$mar1[VC$inde], -TIn$mar3, mean1 = mean3, mean2 = -mean4, var1 = var1, var2 = var3, cov12 = -cov2) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.3.11 <- mm(pbinorm( TIn$mar1[VC$inde], TIn$mar2, mean1 = mean5, mean2 = mean6, var1 = var2, var2 = var3, cov12 = cov3) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.3.10 <- mm(pbinorm( TIn$mar1[VC$inde], -TIn$mar2, mean1 = mean5, mean2 = -mean6, var1 = var2, var2 = var3, cov12 = -cov3) , min.pr = VC$min.pr, max.pr = VC$max.pr)
dmar1 <- probm(TIn$eta1, VC$margins[1], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dmar2 <- probm(TIn$eta2, VC$margins[2], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dmar3 <- probm(TIn$eta3, VC$margins[3], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dF1.de1 <- (1/d.1) * dmar1
dF2.de2 <- (1/d.2) * dmar2
dF3.de3 <- (1/d.3) * dmar3
dl.dF1.1 <- - respvec$cy1/TIn$p0 * d.1
dl.dF1.1[VC$inde] <- respvec$y1.y2.y3/TIn$p111 * d.1[VC$inde] * p.1.11 +
respvec$y1.y2.cy3/TIn$p110 * d.1[VC$inde] * p.1.10 +
respvec$y1.cy2.cy3/TIn$p100 * d.1[VC$inde] * p.1.00 +
respvec$y1.cy2.y3/TIn$p101 * d.1[VC$inde] * p.1.01
dl.dF1 <- dl.dF1.1
dl.de1 <- dl.dF1 * dF1.de1
dl.dF2 <- respvec$y1.y2.y3/TIn$p111 * d.2 * p.2.11 +
respvec$y1.y2.cy3/TIn$p110 * d.2 * p.2.10 -
respvec$y1.cy2.cy3/TIn$p100 * d.2 * p.2.10 -
respvec$y1.cy2.y3/TIn$p101 * d.2 * p.2.11
dl.de2 <- dl.dF2 * dF2.de2
dl.dF3 <- respvec$y1.y2.y3/TIn$p111 * d.3 * p.3.11 -
respvec$y1.y2.cy3/TIn$p110 * d.3 * p.3.11 -
respvec$y1.cy2.cy3/TIn$p100 * d.3 * p.3.10 +
respvec$y1.cy2.y3/TIn$p101 * d.3 * p.3.10
dl.de3 <- dl.dF3 * dF3.de3
mean.12 <- ( TIn$mar1[VC$inde] * (TIn$theta13 - TIn$theta12 * TIn$theta23) + TIn$mar2 * (TIn$theta23 - TIn$theta12 * TIn$theta13) )/( 1 - TIn$theta12^2 )
mean.13 <- ( TIn$mar1[VC$inde] * (TIn$theta12 - TIn$theta13 * TIn$theta23) + TIn$mar3 * (TIn$theta23 - TIn$theta12 * TIn$theta13) )/( 1 - TIn$theta13^2 )
mean.23 <- ( TIn$mar2 * (TIn$theta12 - TIn$theta13 * TIn$theta23) + TIn$mar3 * (TIn$theta13 - TIn$theta12 * TIn$theta23) )/( 1 - TIn$theta23^2 )
deno <- 1 - TIn$theta12^2 - TIn$theta13^2 - TIn$theta23^2 + 2 * TIn$theta12 * TIn$theta13 * TIn$theta23
sd.12 <- sqrt( deno / ( 1 - TIn$theta12^2 ) )
sd.13 <- sqrt( deno / ( 1 - TIn$theta13^2 ) )
sd.23 <- sqrt( deno / ( 1 - TIn$theta23^2 ) )
p12.g <- mm( pnorm( (TIn$mar3 - mean.12)/sd.12) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p13.g <- mm( pnorm( (TIn$mar2 - mean.13)/sd.13) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p23.g <- mm( pnorm( (TIn$mar1[VC$inde] - mean.23)/sd.23) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p12.g.c <- mm(1 - p12.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
p13.g.c <- mm(1 - p13.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
p23.g.c <- mm(1 - p23.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
d11.12 <- dbinorm( TIn$mar1[VC$inde], TIn$mar2, cov12 = TIn$theta12)
d11.13 <- dbinorm( TIn$mar1[VC$inde], TIn$mar3, cov12 = TIn$theta13)
d11.23 <- dbinorm( TIn$mar2 , TIn$mar3, cov12 = TIn$theta23)
dl.dtheta12 <- respvec$y1.y2.y3/TIn$p111 * d11.12 * p12.g +
respvec$y1.y2.cy3/TIn$p110 * d11.12 * p12.g.c -
respvec$y1.cy2.cy3/TIn$p100 * d11.12 * p12.g.c -
respvec$y1.cy2.y3/TIn$p101 * d11.12 * p12.g
dl.dtheta13 <- respvec$y1.y2.y3/TIn$p111 * d11.13 * p13.g -
respvec$y1.y2.cy3/TIn$p110 * d11.13 * p13.g -
respvec$y1.cy2.cy3/TIn$p100 * d11.13 * p13.g.c +
respvec$y1.cy2.y3/TIn$p101 * d11.13 * p13.g.c
dl.dtheta23 <- respvec$y1.y2.y3/TIn$p111 * d11.23 * p23.g -
respvec$y1.y2.cy3/TIn$p110 * d11.23 * p23.g +
respvec$y1.cy2.cy3/TIn$p100 * d11.23 * p23.g -
respvec$y1.cy2.y3/TIn$p101 * d11.23 * p23.g
if(VC$Chol == FALSE){
dtheta12.dtheta12.st <- 4 * exp( 2 * TIn$theta12.st )/( exp(2 * TIn$theta12.st) + 1 )^2
dtheta13.dtheta13.st <- 4 * exp( 2 * TIn$theta13.st )/( exp(2 * TIn$theta13.st) + 1 )^2
dtheta23.dtheta23.st <- 4 * exp( 2 * TIn$theta23.st )/( exp(2 * TIn$theta23.st) + 1 )^2
dl.dtheta12.st <- dl.dtheta12 * dtheta12.dtheta12.st
dl.dtheta13.st <- dl.dtheta13 * dtheta13.dtheta13.st
dl.dtheta23.st <- dl.dtheta23 * dtheta23.dtheta23.st
}
if(VC$Chol == TRUE){
dl.dtheta <- matrix(c ( dl.dtheta12,
dl.dtheta13,
dl.dtheta23), length(which(VC$inde==TRUE)), 3)
dth12.dth12.st <- 1/(1 + TIn$theta12.st^2)^(3/2)
dth12.dth13.st <- 0
dth12.dth23.st <- 0
dth13.dth12.st <- 0
dth13.dth13.st <- (1 + TIn$theta23.st^2)/(1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2)
dth13.dth23.st <- - (TIn$theta13.st * TIn$theta23.st)/(1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2)
dth23.dth12.st <- TIn$theta13.st/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta12.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/((1 + TIn$theta12.st^2)^(3/2) * sqrt(1 + TIn$theta13.st^2 + TIn$theta23.st^2))
dth23.dth13.st <- TIn$theta12.st/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta13.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/(sqrt(1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2))
dth23.dth23.st <- 1/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta23.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/(sqrt(1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2))
dtheta.theta.st <- matrix( c( dth12.dth12.st, dth13.dth12.st, dth23.dth12.st,
dth12.dth13.st, dth13.dth13.st, dth23.dth13.st,
dth12.dth23.st, dth13.dth23.st, dth23.dth23.st ), 3 , 3)
dl.dtheta.st <- dl.dtheta %*% dtheta.theta.st
dl.dtheta12.st <- dl.dtheta.st[, 1]
dl.dtheta13.st <- dl.dtheta.st[, 2]
dl.dtheta23.st <- dl.dtheta.st[, 3]
}
GTRIVec <- list(p12.g = p12.g, p13.g = p13.g, p23.g = p23.g,
p12.g.c = p12.g.c, p13.g.c = p13.g.c,
d.1 = d.1, d.2 = d.2, d.3 = d.3,
dmar1 = dmar1, dmar2 = dmar2, dmar3 = dmar3,
d11.12 = d11.12, d11.13 = d11.13, d11.23 = d11.23,
p.1.11 = p.1.11, p.1.10 = p.1.10, p.1.00 = p.1.00, p.1.01 = p.1.01,
p.2.11 = p.2.11, p.2.10 = p.2.10,
p.3.11 = p.3.11, p.3.10 = p.3.10,
dF1.de1 = dF1.de1, dF2.de2 = dF2.de2, dF3.de3 = dF3.de3,
dl.dF1 = dl.dF1, dl.dF2 = dl.dF2, dl.dF3 = dl.dF3,
dl.de1 = VC$weights*dl.de1, dl.de2 = VC$weights[VC$inde]*dl.de2, dl.de3 = VC$weights[VC$inde]*dl.de3,
dl.dtheta12.st = VC$weights[VC$inde]*dl.dtheta12.st, dl.dtheta13.st = VC$weights[VC$inde]*dl.dtheta13.st,
dl.dtheta23.st = VC$weights[VC$inde]*dl.dtheta23.st,
mean.12 = mean.12,
mean.13 = mean.13,
mean.23 = mean.23,
sd.12 =sd.12,
sd.13 =sd.13,
sd.23 =sd.23,
dl.dtheta12 =dl.dtheta12, dl.dtheta13 = dl.dtheta13, dl.dtheta23 = dl.dtheta23)
GTRIVec
}
KironmoyDas/KD-STAT0035-GMupdate documentation built on Feb. 15, 2021, 12:17 a.m.
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Defines functions g.triESS
Documented in g.triESS
g.triESS <- function(params, respvec, VC, TIn){
mean1 <- TIn$theta12 * TIn$mar1
mean2 <- TIn$theta13 * TIn$mar1
mean3 <- TIn$theta12 * TIn$mar2
mean4 <- TIn$theta23 * TIn$mar2
mean5 <- TIn$theta13 * TIn$mar3
mean6 <- TIn$theta23 * TIn$mar3
var1 <- 1 - TIn$theta12^2
var2 <- 1 - TIn$theta13^2
var3 <- 1 - TIn$theta23^2
cov1 <- TIn$theta23 - TIn$theta12 * TIn$theta13
cov2 <- TIn$theta13 - TIn$theta12 * TIn$theta23
cov3 <- TIn$theta12 - TIn$theta13 * TIn$theta23
cov1 <- mmf(cov1, max.pr = VC$max.pr)
cov2 <- mmf(cov2, max.pr = VC$max.pr)
cov3 <- mmf(cov3, max.pr = VC$max.pr)
d.1 <- dnorm(TIn$mar1)
d.2 <- dnorm(TIn$mar2)
d.3 <- dnorm(TIn$mar3)
p.1.11 <- mm(pbinorm( TIn$mar2, TIn$mar3, mean1 = mean1[VC$inde], mean2 = mean2[VC$inde], var1 = var1, var2 = var2, cov12 = cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.10 <- mm(pbinorm( TIn$mar2, -TIn$mar3, mean1 = mean1[VC$inde], mean2 = -mean2[VC$inde], var1 = var1, var2 = var2, cov12 = -cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.00 <- mm(pbinorm(-TIn$mar2, -TIn$mar3, mean1 = -mean1[VC$inde], mean2 = -mean2[VC$inde], var1 = var1, var2 = var2, cov12 = cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.1.01 <- mm(pbinorm(-TIn$mar2, TIn$mar3, mean1 = -mean1[VC$inde], mean2 = mean2[VC$inde], var1 = var1, var2 = var2, cov12 = -cov1), min.pr = VC$min.pr, max.pr = VC$max.pr )
p.2.11 <- mm(pbinorm( TIn$mar1[VC$inde], TIn$mar3, mean1 = mean3, mean2 = mean4, var1 = var1, var2 = var3, cov12 = cov2) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.2.10 <- mm(pbinorm( TIn$mar1[VC$inde], -TIn$mar3, mean1 = mean3, mean2 = -mean4, var1 = var1, var2 = var3, cov12 = -cov2) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.3.11 <- mm(pbinorm( TIn$mar1[VC$inde], TIn$mar2, mean1 = mean5, mean2 = mean6, var1 = var2, var2 = var3, cov12 = cov3) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p.3.10 <- mm(pbinorm( TIn$mar1[VC$inde], -TIn$mar2, mean1 = mean5, mean2 = -mean6, var1 = var2, var2 = var3, cov12 = -cov3) , min.pr = VC$min.pr, max.pr = VC$max.pr)
dmar1 <- probm(TIn$eta1, VC$margins[1], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dmar2 <- probm(TIn$eta2, VC$margins[2], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dmar3 <- probm(TIn$eta3, VC$margins[3], only.pr = FALSE, min.dn = VC$min.dn, min.pr = VC$min.pr, max.pr = VC$max.pr)$d.n
dF1.de1 <- (1/d.1) * dmar1
dF2.de2 <- (1/d.2) * dmar2
dF3.de3 <- (1/d.3) * dmar3
dl.dF1.1 <- - respvec$cy1/TIn$p0 * d.1
dl.dF1.1[VC$inde] <- respvec$y1.y2.y3/TIn$p111 * d.1[VC$inde] * p.1.11 +
respvec$y1.y2.cy3/TIn$p110 * d.1[VC$inde] * p.1.10 +
respvec$y1.cy2.cy3/TIn$p100 * d.1[VC$inde] * p.1.00 +
respvec$y1.cy2.y3/TIn$p101 * d.1[VC$inde] * p.1.01
dl.dF1 <- dl.dF1.1
dl.de1 <- dl.dF1 * dF1.de1
dl.dF2 <- respvec$y1.y2.y3/TIn$p111 * d.2 * p.2.11 +
respvec$y1.y2.cy3/TIn$p110 * d.2 * p.2.10 -
respvec$y1.cy2.cy3/TIn$p100 * d.2 * p.2.10 -
respvec$y1.cy2.y3/TIn$p101 * d.2 * p.2.11
dl.de2 <- dl.dF2 * dF2.de2
dl.dF3 <- respvec$y1.y2.y3/TIn$p111 * d.3 * p.3.11 -
respvec$y1.y2.cy3/TIn$p110 * d.3 * p.3.11 -
respvec$y1.cy2.cy3/TIn$p100 * d.3 * p.3.10 +
respvec$y1.cy2.y3/TIn$p101 * d.3 * p.3.10
dl.de3 <- dl.dF3 * dF3.de3
mean.12 <- ( TIn$mar1[VC$inde] * (TIn$theta13 - TIn$theta12 * TIn$theta23) + TIn$mar2 * (TIn$theta23 - TIn$theta12 * TIn$theta13) )/( 1 - TIn$theta12^2 )
mean.13 <- ( TIn$mar1[VC$inde] * (TIn$theta12 - TIn$theta13 * TIn$theta23) + TIn$mar3 * (TIn$theta23 - TIn$theta12 * TIn$theta13) )/( 1 - TIn$theta13^2 )
mean.23 <- ( TIn$mar2 * (TIn$theta12 - TIn$theta13 * TIn$theta23) + TIn$mar3 * (TIn$theta13 - TIn$theta12 * TIn$theta23) )/( 1 - TIn$theta23^2 )
deno <- 1 - TIn$theta12^2 - TIn$theta13^2 - TIn$theta23^2 + 2 * TIn$theta12 * TIn$theta13 * TIn$theta23
sd.12 <- sqrt( deno / ( 1 - TIn$theta12^2 ) )
sd.13 <- sqrt( deno / ( 1 - TIn$theta13^2 ) )
sd.23 <- sqrt( deno / ( 1 - TIn$theta23^2 ) )
p12.g <- mm( pnorm( (TIn$mar3 - mean.12)/sd.12) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p13.g <- mm( pnorm( (TIn$mar2 - mean.13)/sd.13) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p23.g <- mm( pnorm( (TIn$mar1[VC$inde] - mean.23)/sd.23) , min.pr = VC$min.pr, max.pr = VC$max.pr)
p12.g.c <- mm(1 - p12.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
p13.g.c <- mm(1 - p13.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
p23.g.c <- mm(1 - p23.g, min.pr = VC$min.pr, max.pr = VC$max.pr)
d11.12 <- dbinorm( TIn$mar1[VC$inde], TIn$mar2, cov12 = TIn$theta12)
d11.13 <- dbinorm( TIn$mar1[VC$inde], TIn$mar3, cov12 = TIn$theta13)
d11.23 <- dbinorm( TIn$mar2 , TIn$mar3, cov12 = TIn$theta23)
dl.dtheta12 <- respvec$y1.y2.y3/TIn$p111 * d11.12 * p12.g +
respvec$y1.y2.cy3/TIn$p110 * d11.12 * p12.g.c -
respvec$y1.cy2.cy3/TIn$p100 * d11.12 * p12.g.c -
respvec$y1.cy2.y3/TIn$p101 * d11.12 * p12.g
dl.dtheta13 <- respvec$y1.y2.y3/TIn$p111 * d11.13 * p13.g -
respvec$y1.y2.cy3/TIn$p110 * d11.13 * p13.g -
respvec$y1.cy2.cy3/TIn$p100 * d11.13 * p13.g.c +
respvec$y1.cy2.y3/TIn$p101 * d11.13 * p13.g.c
dl.dtheta23 <- respvec$y1.y2.y3/TIn$p111 * d11.23 * p23.g -
respvec$y1.y2.cy3/TIn$p110 * d11.23 * p23.g +
respvec$y1.cy2.cy3/TIn$p100 * d11.23 * p23.g -
respvec$y1.cy2.y3/TIn$p101 * d11.23 * p23.g
if(VC$Chol == FALSE){
dtheta12.dtheta12.st <- 4 * exp( 2 * TIn$theta12.st )/( exp(2 * TIn$theta12.st) + 1 )^2
dtheta13.dtheta13.st <- 4 * exp( 2 * TIn$theta13.st )/( exp(2 * TIn$theta13.st) + 1 )^2
dtheta23.dtheta23.st <- 4 * exp( 2 * TIn$theta23.st )/( exp(2 * TIn$theta23.st) + 1 )^2
dl.dtheta12.st <- dl.dtheta12 * dtheta12.dtheta12.st
dl.dtheta13.st <- dl.dtheta13 * dtheta13.dtheta13.st
dl.dtheta23.st <- dl.dtheta23 * dtheta23.dtheta23.st
}
if(VC$Chol == TRUE){
dl.dtheta <- matrix(c ( dl.dtheta12,
dl.dtheta13,
dl.dtheta23), length(which(VC$inde==TRUE)), 3)
dth12.dth12.st <- 1/(1 + TIn$theta12.st^2)^(3/2)
dth12.dth13.st <- 0
dth12.dth23.st <- 0
dth13.dth12.st <- 0
dth13.dth13.st <- (1 + TIn$theta23.st^2)/(1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2)
dth13.dth23.st <- - (TIn$theta13.st * TIn$theta23.st)/(1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2)
dth23.dth12.st <- TIn$theta13.st/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta12.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/((1 + TIn$theta12.st^2)^(3/2) * sqrt(1 + TIn$theta13.st^2 + TIn$theta23.st^2))
dth23.dth13.st <- TIn$theta12.st/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta13.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/(sqrt(1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2))
dth23.dth23.st <- 1/sqrt((1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)) - (TIn$theta23.st * (TIn$theta12.st * TIn$theta13.st + TIn$theta23.st))/(sqrt(1 + TIn$theta12.st^2) * (1 + TIn$theta13.st^2 + TIn$theta23.st^2)^(3/2))
dtheta.theta.st <- matrix( c( dth12.dth12.st, dth13.dth12.st, dth23.dth12.st,
dth12.dth13.st, dth13.dth13.st, dth23.dth13.st,
dth12.dth23.st, dth13.dth23.st, dth23.dth23.st ), 3 , 3)
dl.dtheta.st <- dl.dtheta %*% dtheta.theta.st
dl.dtheta12.st <- dl.dtheta.st[, 1]
dl.dtheta13.st <- dl.dtheta.st[, 2]
dl.dtheta23.st <- dl.dtheta.st[, 3]
}
GTRIVec <- list(p12.g = p12.g, p13.g = p13.g, p23.g = p23.g,
p12.g.c = p12.g.c, p13.g.c = p13.g.c,
d.1 = d.1, d.2 = d.2, d.3 = d.3,
dmar1 = dmar1, dmar2 = dmar2, dmar3 = dmar3,
d11.12 = d11.12, d11.13 = d11.13, d11.23 = d11.23,
p.1.11 = p.1.11, p.1.10 = p.1.10, p.1.00 = p.1.00, p.1.01 = p.1.01,
p.2.11 = p.2.11, p.2.10 = p.2.10,
p.3.11 = p.3.11, p.3.10 = p.3.10,
dF1.de1 = dF1.de1, dF2.de2 = dF2.de2, dF3.de3 = dF3.de3,
dl.dF1 = dl.dF1, dl.dF2 = dl.dF2, dl.dF3 = dl.dF3,
dl.de1 = VC$weights*dl.de1, dl.de2 = VC$weights[VC$inde]*dl.de2, dl.de3 = VC$weights[VC$inde]*dl.de3,
dl.dtheta12.st = VC$weights[VC$inde]*dl.dtheta12.st, dl.dtheta13.st = VC$weights[VC$inde]*dl.dtheta13.st,
dl.dtheta23.st = VC$weights[VC$inde]*dl.dtheta23.st,
mean.12 = mean.12,
mean.13 = mean.13,
mean.23 = mean.23,
sd.12 =sd.12,
sd.13 =sd.13,
sd.23 =sd.23,
dl.dtheta12 =dl.dtheta12, dl.dtheta13 = dl.dtheta13, dl.dtheta23 = dl.dtheta23)
GTRIVec
}
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