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### biasCoxSnell.R ---
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: aug 2 2019 (10:20)
## Version:
## Last-Updated: Jan 11 2022 (17:36)
## By: Brice Ozenne
## Update #: 177
##----------------------------------------------------------------------
##
### Commentary:
##
### Change Log:
##----------------------------------------------------------------------
##
### Code:
## * .init_sscCoxSnell
.init_sscCoxSnell <- function(object,...){
return(object,...)
}
## * .sscCoxSnell
.sscCoxSnell <- function(object, ssc){
param <- ssc$param0
name.param <- names(param)
## ** compute JJK
object$sCorrect$skeleton$grid.2varD2.1varD1
JJK <- .calcJJK(object)
## print(range(.old_calcJJK(object)-JJK))
## print(sum(.old_calcJJK(object))-sum(JJK))
## ** least squares
Y <- (1/2) * sapply(name.param, function(iP){sum(JJK[,,iP] * object$sCorrect$vcov.param)})
X <- object$sCorrect$information
## information(e.lvm2)
## dd <- as.data.frame(cbind(Y = Y,X))
## names(dd) <- c("Y",paste0("X",1:9))
## e.lm <- lm(Y ~ -1+X1+X2+X3+X4+X5+X6+X7+X8+X9, data = dd)
e.lm <- stats::lm.fit(y = Y, x = X)
newparam <- param - e.lm$coefficient
## print(e.lm$coefficient)
## ** export
attr(newparam,"JJK") <- JJK
attr(newparam,"lm") <- e.lm
return(newparam)
}
## * .calcJJK
.calcJJK <- function(object){
## ** extract information
dmu <- object$sCorrect$dmoment$dmu
d2mu <- object$sCorrect$d2moment$d2mu
dOmega <- object$sCorrect$dmoment$dOmega
d2Omega <- object$sCorrect$d2moment$d2Omega
missing.pattern <- object$sCorrect$missing$pattern
name.pattern <- object$sCorrect$missing$name.pattern
unique.pattern <- object$sCorrect$missing$unique.pattern
n.pattern <- length(name.pattern)
OmegaM1 <- object$sCorrect$moment$OmegaM1.missing.pattern
name.param <- names(object$sCorrect$param)
n.param <- length(name.param)
n.cluster <- object$sCorrect$cluster$n.cluster
grid.2meanD1.1varD1 <- object$sCorrect$skeleton$grid.2meanD1.1varD1
grid.2meanD2.1meanD1 <- object$sCorrect$skeleton$grid.2meanD2.1meanD1
grid.2varD2.1varD1 <- object$sCorrect$skeleton$grid.2varD2.1varD1
n.grid.2meanD1.1varD1 <- NROW(grid.2meanD1.1varD1)
n.grid.2meanD2.1meanD1 <- NROW(grid.2meanD2.1meanD1)
n.grid.2varD2.1varD1 <- NROW(grid.2varD2.1varD1)
## ** prepare output
JJK <- array(0, dim = c(n.param,n.param,n.param),
dimnames = list(name.param,name.param,name.param))
## ** loop over missing data pattern
for(iP in 1:n.pattern){ ## iP <- 1
iPattern <- name.pattern[iP]
iIndex <- missing.pattern[[iPattern]]
iY <- which(unique.pattern[iP,]==1)
iOmegaM1 <- OmegaM1[[iPattern]]
idmu <- .subsetList(dmu, indexRow = iIndex, indexCol = iY)
idOmega <- .subsetList(dOmega, indexRow = iY, indexCol = iY)
id2mu <- .subsetList2(d2mu, indexRow = iIndex, indexCol = iY)
id2Omega <- .subsetList2(d2Omega, indexRow = iY, indexCol = iY)
## *** 1 second derivative and 1 first derivative regarding the variance
if(n.grid.2varD2.1varD1>0){
for(iGrid in 1:n.grid.2varD2.1varD1){ # iGrid <- 1
iName1 <- grid.2varD2.1varD1[iGrid,"X"]
iName2 <- grid.2varD2.1varD1[iGrid,"Y"]
iName3 <- grid.2varD2.1varD1[iGrid,"Z"]
## term 1
if(grid.2varD2.1varD1[iGrid,"d2XY"]){
d2.Var1 <- grid.2varD2.1varD1[iGrid,"d2XY.Var1"]
d2.Var2 <- grid.2varD2.1varD1[iGrid,"d2XY.Var2"]
iDiag <- diag(iOmegaM1 %*% id2Omega[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 %*% idOmega[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
## cat("a: ",iName1," ",iName2," ",iName3,"\n")
}
## term 2
if(grid.2varD2.1varD1[iGrid,"d2XZ"]){
d2.Var1 <- grid.2varD2.1varD1[iGrid,"d2XZ.Var1"]
d2.Var2 <- grid.2varD2.1varD1[iGrid,"d2XZ.Var2"]
iDiag <- diag(iOmegaM1 %*% id2Omega[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 %*% idOmega[[iName2]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
## cat("b: ",iName1," ",iName2," ",iName3,"\n")
}
## term 3
if(grid.2varD2.1varD1[iGrid,"d2YZ"]){
d2.Var1 <- grid.2varD2.1varD1[iGrid,"d2YZ.Var1"]
d2.Var2 <- grid.2varD2.1varD1[iGrid,"d2YZ.Var2"]
iDiag <- diag(iOmegaM1 %*% id2Omega[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 %*% idOmega[[iName1]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + 1/2 * sum(iDiag * n.cluster)
## cat(iGrid,") c: ",iName1," ",iName2," ",iName3," = ", 1/2 * sum(iDiag * n.cluster),"\n")
}
}
}
## *** 1 second derivative and 1 first derivative regarding the mean
if(n.grid.2meanD2.1meanD1>0){
for(iGrid in 1:n.grid.2meanD2.1meanD1){ # iGrid <- 1
iName1 <- grid.2meanD2.1meanD1[iGrid,"X"]
iName2 <- grid.2meanD2.1meanD1[iGrid,"Y"]
iName3 <- grid.2meanD2.1meanD1[iGrid,"Z"]
## term 4
if(grid.2meanD2.1meanD1[iGrid,"d2XY"]){
d2.Var1 <- grid.2meanD2.1meanD1[iGrid,"d2XY.Var1"]
d2.Var2 <- grid.2meanD2.1meanD1[iGrid,"d2XY.Var2"]
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 * idmu[[iName3]])
}
## term 5
if(grid.2meanD2.1meanD1[iGrid,"d2XZ"]){
d2.Var1 <- grid.2meanD2.1meanD1[iGrid,"d2XZ.Var1"]
d2.Var2 <- grid.2meanD2.1meanD1[iGrid,"d2XZ.Var2"]
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 * idmu[[iName2]])
}
## term 6
if(grid.2meanD2.1meanD1[iGrid,"d2YZ"]){
d2.Var1 <- grid.2meanD2.1meanD1[iGrid,"d2YZ.Var1"]
d2.Var2 <- grid.2meanD2.1meanD1[iGrid,"d2YZ.Var2"]
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + sum(id2mu[[d2.Var1]][[d2.Var2]] %*% iOmegaM1 * idmu[[iName1]])
}
}
}
## *** 2 first derivative regarding the mean and one regarding the variance
if(n.grid.2meanD1.1varD1>0){
for(iGrid in 1:n.grid.2meanD1.1varD1){ # iGrid <- 1
## term 7
iName1 <- grid.2meanD1.1varD1[iGrid,"Z"]
iName2 <- grid.2meanD1.1varD1[iGrid,"X"]
iName3 <- grid.2meanD1.1varD1[iGrid,"Y"]
value <- sum(idmu[[iName2]] %*% iOmegaM1 %*% idOmega[[iName1]] %*% iOmegaM1 * idmu[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + value
## term 8
iName1 <- grid.2meanD1.1varD1[iGrid,"X"]
iName2 <- grid.2meanD1.1varD1[iGrid,"Z"]
iName3 <- grid.2meanD1.1varD1[iGrid,"Y"]
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - value
## term 9
iName1 <- grid.2meanD1.1varD1[iGrid,"X"]
iName2 <- grid.2meanD1.1varD1[iGrid,"Y"]
iName3 <- grid.2meanD1.1varD1[iGrid,"Z"]
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - value
}
}
}
## sum(abs(JJK)>0)
return(JJK)
}
## * .old_calcJJK
.old_calcJJK <- function(object){
## ** extract information
dmu <- object$sCorrect$dmoment$dmu
d2mu <- object$sCorrect$d2moment$d2mu
dOmega <- object$sCorrect$dmoment$dOmega
d2Omega <- object$sCorrect$d2moment$d2Omega
missing.pattern <- object$sCorrect$missing$pattern
name.pattern <- object$sCorrect$missing$name.pattern
unique.pattern <- object$sCorrect$missing$unique.pattern
n.pattern <- length(name.pattern)
OmegaM1 <- object$sCorrect$moment$OmegaM1.missing.pattern
name.param <- names(object$sCorrect$param)
n.param <- length(name.param)
n.cluster <- object$sCorrect$cluster$n.cluster
grid.2meanD1.1varD1 <- object$sCorrect$skeleton$grid.2meanD1.1varD1
grid.2meanD2.1meanD1 <- object$sCorrect$skeleton$grid.2meanD2.1meanD1
grid.2varD2.1varD1 <- object$sCorrect$skeleton$grid.2varD2.1varD1
n.grid.2meanD1.1varD1 <- NROW(grid.2meanD1.1varD1)
n.grid.2meanD2.1meanD1 <- NROW(grid.2meanD2.1meanD1)
n.grid.2varD2.1varD1 <- NROW(grid.2varD2.1varD1)
## ** prepare output
JJK <- array(0, dim = c(n.param,n.param,n.param),
dimnames = list(name.param,name.param,name.param))
## ** loop over missing data pattern
for(iP in 1:n.pattern){ ## iP <- 1
iPattern <- name.pattern[iP]
iIndex <- missing.pattern[[iPattern]]
iY <- which(unique.pattern[iP,]==1)
iOmegaM1 <- OmegaM1[[iPattern]]
idmu <- .subsetList(dmu, indexRow = iIndex, indexCol = iY)
idOmega <- .subsetList(dOmega, indexRow = iY, indexCol = iY)
id2mu <- .subsetList2(d2mu, indexRow = iIndex, indexCol = iY)
id2Omega <- .subsetList2(d2Omega, indexRow = iY, indexCol = iY)
for(iParam1 in 1:n.param){ ## iParam1 <- 1
for(iParam2 in 1:n.param){ ## iParam2 <- 1
for(iParam3 in 1:n.param){ ## iParam3 <- 1
iName1 <- name.param[iParam1]
iName2 <- name.param[iParam2]
iName3 <- name.param[iParam3]
## *** 1 second derivative and 1 first derivative regarding the variance
## term 1
if(!is.null(idOmega[[iName3]]) && !is.null(id2Omega[[iName1]][[iName2]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName1]][[iName2]] %*% iOmegaM1 %*% idOmega[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
}else if(!is.null(idOmega[[iName3]]) && !is.null(id2Omega[[iName2]][[iName1]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName2]][[iName1]] %*% iOmegaM1 %*% idOmega[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
}
## term 2
if(!is.null(idOmega[[iName2]]) && !is.null(id2Omega[[iName1]][[iName3]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName1]][[iName3]] %*% iOmegaM1 %*% idOmega[[iName2]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
}else if(!is.null(idOmega[[iName2]]) && !is.null(id2Omega[[iName3]][[iName1]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName3]][[iName1]] %*% iOmegaM1 %*% idOmega[[iName2]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - 1/2 * sum(iDiag * n.cluster)
}
## term 3
if(!is.null(idOmega[[iName1]]) && !is.null(id2Omega[[iName2]][[iName3]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName2]][[iName3]] %*% iOmegaM1 %*% idOmega[[iName1]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + 1/2 * sum(iDiag * n.cluster)
## cat("c: ",iName1," ",iName2," ",iName3," = ", 1/2 * sum(iDiag * n.cluster),"\n")
}else if(!is.null(idOmega[[iName1]]) && !is.null(id2Omega[[iName3]][[iName2]])){
iDiag <- diag(iOmegaM1 %*% id2Omega[[iName3]][[iName2]] %*% iOmegaM1 %*% idOmega[[iName1]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + 1/2 * sum(iDiag * n.cluster)
## cat("c: ",iName1," ",iName2," ",iName3," = ", 1/2 * sum(iDiag * n.cluster),"\n")
}
## *** 1 second derivative and 1 first derivative regarding the mean
## term 4
if(!is.null(idmu[[iName3]]) && !is.null(id2mu[[iName1]][[iName2]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[iName1]][[iName2]] %*% iOmegaM1 * idmu[[iName3]])
}else if(!is.null(idmu[[iName3]]) && !is.null(id2mu[[iName2]][[iName1]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[iName2]][[iName1]] %*% iOmegaM1 * idmu[[iName3]])
}
## term 5
if(!is.null(idmu[[iName2]]) && !is.null(id2mu[[iName1]][[iName3]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[iName1]][[iName3]] %*% iOmegaM1 * idmu[[iName2]])
}else if(!is.null(idmu[[iName2]]) && !is.null(id2mu[[iName3]][[iName1]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - sum(id2mu[[iName3]][[iName1]] %*% iOmegaM1 * idmu[[iName2]])
}
## term 6
if(!is.null(idmu[[iName1]]) && !is.null(id2mu[[iName2]][[iName3]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + sum(id2mu[[iName2]][[iName3]] %*% iOmegaM1 * idmu[[iName1]])
}else if(!is.null(idmu[[iName1]]) && !is.null(id2mu[[iName3]][[iName2]])){
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + sum(id2mu[[iName3]][[iName2]] %*% iOmegaM1 * idmu[[iName1]])
}
## *** 2 first derivative regarding the mean and one regarding the variance
## term 7
if(!is.null(idmu[[iName2]]) && !is.null(idOmega[[iName1]]) && !is.null(idmu[[iName3]])){
value <- sum(idmu[[iName2]] %*% iOmegaM1 %*% idOmega[[iName1]] %*% iOmegaM1 * idmu[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] + value
}
## term 8
if(!is.null(idmu[[iName1]]) && !is.null(idOmega[[iName2]]) && !is.null(idmu[[iName3]])){
value <- sum(idmu[[iName1]] %*% iOmegaM1 %*% idOmega[[iName2]] %*% iOmegaM1 * idmu[[iName3]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - value
}
## term 9
if(!is.null(idmu[[iName2]]) && !is.null(idOmega[[iName3]]) && !is.null(idmu[[iName1]])){
value <- sum(idmu[[iName2]] %*% iOmegaM1 %*% idOmega[[iName3]] %*% iOmegaM1 * idmu[[iName1]])
JJK[iName1,iName2,iName3] <- JJK[iName1,iName2,iName3] - value
}
}
}
}
}
return(JJK)
}
######################################################################
### biasCoxSnell.R ends here
|
