Nothing
####################################################
#### 2 sample, discrete hazards, q constraints. #####
#### right censored, left truncated data. #####
####################################################
emplikHs.disc2 <- function(x1, d1, y1 = -Inf, x2, d2, y2 = -Inf,
theta, fun1, fun2, maxit = 25, tola = 1e-6, itertrace=FALSE) {
theta <- as.vector(theta)
q <- length(theta)
########Sample One########
x1 <- as.vector(x1)
n1 <- length(x1)
if (n1 <= 2*q+1)
stop("Need more observations in x1")
if (length(d1) != n1)
stop("length of x1 and d1 must agree")
if (any((d1 != 0) & (d1 != 1)))
stop("d1 must be 0/1's for censor/not-censor")
if (!is.numeric(x1))
stop("x1 must be numeric -- observed times")
newdata1 <- Wdataclean2(z=x1, d=d1)
temp1 <- DnR(newdata1$value, newdata1$dd, newdata1$weight, y=y1)
jump1 <- (temp1$n.event)/temp1$n.risk
k1 <- temp1$n.event - temp1$n.risk
index1 <- (jump1 < 1)
k1 <- k1[index1]
eve1 <- temp1$n.event[index1]
tm1 <- temp1$times[index1]
rsk1 <- temp1$n.risk[index1]
jmp1 <- jump1[index1]
funtime1 <- as.matrix(fun1(tm1))
if( ncol(funtime1) != q ) stop("check the output dim of fun1")
## funh1 <- funtime1/rsk1
########Sample two########
x2 <- as.vector(x2)
n2 <- length(x2)
if (n2 <= 2*q+1)
stop("Need more observations for sample 2")
if (length(d2) != n2)
stop("length of x2 and d2 must agree")
if (any((d2 != 0) & (d2 != 1)))
stop("d2 must be 0/1's for censor/not-censor")
if (!is.numeric(x2))
stop("x2 must be numeric -- observed times")
newdata2 <- Wdataclean2(z=x2, d=d2)
temp2 <- DnR(newdata2$value, newdata2$dd, newdata2$weight, y=y2)
jump2 <- (temp2$n.event)/temp2$n.risk
k2 <- temp2$n.event - temp2$n.risk
index2 <- (jump2 < 1)
k2 <- k2[index2]
eve2 <- temp2$n.event[index2]
tm2 <- temp2$times[index2]
rsk2 <- temp2$n.risk[index2]
jmp2 <- jump2[index2]
funtime2 <- as.matrix(fun2(tm2))
if( ncol(funtime2) != q ) stop("check the output dim of fun2")
## funh2 <- funtime2/rsk2
##################################################################
# funtime12 are matrix of n12 x q. rsk12, eve12 are vectors of length n1/n2.
############################################################################
Kcent <- sum(log(1-jmp1)%*%funtime1) - sum(log(1-jmp2)%*%funtime2)
if( itertrace ) print(c("Kcenter=", Kcent))
##################################################################
TINY <- sqrt( .Machine$double.xmin )
if(tola < TINY) tola <- TINY
lam <- rep(0,q)
N <- n1+n2 ## this is used in llog function. May be we should make it larger?
#
# Preset the weights for combining Newton and gradient
# steps at each of 16 inner iterations, starting with
# the Newton step and progressing towards shorter vectors
# in the gradient direction. Most commonly only the Newton
# step is actually taken, though occasional step reductions
# do occur.
#
nwts <- c( 3^-c(0:3), rep(0,12) )
gwts <- 2^( -c(0:(length(nwts)-1)))
gwts <- (gwts^2 - nwts^2)^.5
gwts[12:16] <- gwts[12:16] * 10^-c(1:5)
#
# Iterate, finding the Newton and gradient steps, and
# choosing a step that reduces the objective if possible.
#
nits <- 0
gsize <- tola + 1
while( nits < maxit && gsize > tola ){
grad <- gradf2(lam, funtime1,eve1,rsk1,funtime2,eve2,rsk2,K=theta,n=N)
gsize <- mean( abs(grad) )
## HESS <- hessf2(lam, funtime1, eve1, rsk1, funtime2, eve2, rsk2, n=N)
##hessf2 <- function(lam, funt1, evt1, rsk1, funt2, evt2, rsk2, n)
arg1 <- as.vector(rsk1 + funtime1 %*% lam)
arg2 <- as.vector(rsk2 - funtime2 %*% lam)
ww1 <- as.vector(-llogpp(arg1, 1/N))^.5
ww2 <- as.vector(-llogpp(arg2, 1/N))^.5
tt1 <- sqrt(eve1/(1-eve1/arg1))*ww1 ##
tt2 <- sqrt(eve2/(1-eve2/arg2))*ww2 ## shall we change to max(TINY,tt2)?
HESS <- t(funtime1 * tt1)%*%(funtime1 * tt1) +
t(funtime2 * tt2)%*%(funtime2 * tt2)
# -1
# The Newton step is -(hess'hess) grad,
# where the matrix hess is a sqrt of the Hessian.
# We shall just compute hess'hess = HESS.
#
##### nstep <- as.vector( - solve(HESS) %*% grad )
################# this may be better #############
nstep <- as.vector( - solve(HESS, grad) )
gstep <- grad
if( sum(nstep^2) < sum(gstep^2) )
gstep <- gstep*(sum(nstep^2)^.5/sum(gstep^2)^.5)
ninner <- 0
for( i in 1:length(nwts) ){
lamtemp <- lam+nwts[i]*nstep+gwts[i]*gstep
ngrad <- gradf2(lamtemp,funtime1,eve1,rsk1,funtime2,eve2,rsk2,K=theta,n=N)
ngsize <- mean( abs(ngrad) )
if( ngsize < gsize ){
lam <- lamtemp
ninner <- i
break
}
}
nits <- nits+1
if( ninner==0 )nits <- maxit
if( itertrace )
print( c(lam, gsize, ninner) )
}
##################################################################
arg1 <- as.vector(rsk1 + funtime1 %*% lam)
arg2 <- as.vector(rsk2 - funtime2 %*% lam)
onePlam <- arg1/rsk1 ##########1+lam*funh1
weights1 <- eve1/arg1 ##########jmp1/onePlam
oneMlam <- arg2/rsk2 ##########1-lam*funh2
weights2 <- eve2/arg2 ##########jmp2/oneMlam
loglik1 <- sum(eve1*llog(onePlam, 1/N)) +
sum((-k1)*llog((1-jmp1)/(1-weights1), 1/N))
loglik2 <- sum(eve2*llog(oneMlam, 1/N)) +
sum((-k2)*llog((1-jmp2)/(1-weights2), 1/N))
#### Use the llog() instead of log() to avoid infinite, NA, etc.
loglikR <- 2*(loglik1+loglik2)
#MZ <- gradf2(lam, funtime1, eve1, rsk1, funtime2, eve2, rsk2, K=theta, n=N)
#print(MZ)
list("-2LLR" = loglikR, lambda = lam, "-2LLR(sample1)"=2*loglik1,
times1 = tm1, times2 = tm2 )
}
####################
gradf2 <- function(lam, funt1, evt1, rsk1, funt2, evt2, rsk2, K, n) {
arg1 <- as.vector(rsk1 + funt1 %*% lam)
arg2 <- as.vector(rsk2 - funt2 %*% lam)
VV <- llog(1-(evt1/arg1),1/n)%*%funt1-llog(1-(evt2/arg2),1/n)%*%funt2-K
return( as.vector( VV ))
}
############################################################################
# In the above function, lam, K are vectors of length q.
# funt12 are matrix of n12 x q. rsk12, evt12 are vectors of length n1/n2.
############################################################################
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