https://github.com/cran/emplik
Tip revision: e2f16b0adc0f6df124c7ee126345f66d8d2c7961 authored by Mai Zhou on 09 October 2011, 00:00:00 UTC
version 0.9-7
version 0.9-7
Tip revision: e2f16b0
emplikH2.test.R
###############################################
############# emplikH2() ######################
###############################################
emplikH2.test <- function(x, d, y= -Inf, K, fun,
tola = .Machine$double.eps^.5,...)
{
if(!is.numeric(x)) stop("x must be numeric values -- observed times")
n <- length(x)
if( n <= 2 ) stop("Need more observations than two")
if( length(d) != n ) stop("length of x and d must agree")
if(any((d!=0)&(d!=1))) stop("d must be 0/1's for censor/not-censor")
#temp <- summary(survfit(Surv(x,d),se.fit=F,type="fleming",conf.type="none"))
#
newdata <- Wdataclean2(x,d)
temp <- DnR(newdata$value, newdata$dd, newdata$weight, y=y)
Dtime <- temp$times # only uncensored time? Yes.
risk <- temp$n.risk
jump <- (temp$n.event)/risk
funtime <- fun(Dtime,...)
funh <- sqrt(n) * funtime/risk # that is Zi/sqrt(n)
funtimeTjump <- funtime * jump
if(jump[length(jump)] >= 1) funh[length(jump)] <- 0 #for inthaz and weights
inthaz <- function(x, ftj, fh, Konst){ sum(ftj/(1 + x * fh)) - Konst }
diff <- inthaz(0, funtimeTjump, funh, K)
if( diff == 0 ) { lam <- 0 } else {
step <- 0.2/sqrt(n)
### if(abs(diff) > 99*log(n)*step ) ##why 99*log(n)? no reason, you
### stop("given theta value is too far away from theta0") # need something.
mini<-0
maxi<-0
if(diff > 0) {
maxi <- step
while(inthaz(maxi, funtimeTjump, funh, K) > 0 && maxi < 99*log(n)*step)
maxi <- maxi+step
}
else {
mini <- -step
while(inthaz(mini, funtimeTjump, funh, K) < 0 && mini > - 99*log(n)*step)
mini <- mini - step
}
if(inthaz(mini,funtimeTjump,funh,K)*inthaz(maxi,funtimeTjump,funh,K)>0)
stop("given theta is too far away from theta0")
temp2 <- uniroot(inthaz,c(mini,maxi), tol = tola,
ftj=funtimeTjump, fh=funh, Konst=K)
lam <- temp2$root
}
onepluslamh<- 1 + lam * funh # this is 1 + lam Zi in Ref.
weights <- jump/onepluslamh #need to change last jump to 1? NO. see above
loglik <- 2*(sum(log(onepluslamh)) - sum((onepluslamh-1)/onepluslamh) )
#?is that right? YES see (3.2) in Ref. above. This is ALR, or Poisson LR.
#last <- length(jump) #to compute loglik2, we need to drop last jump
#if (jump[last] == 1) {
# risk1 <- risk[-last]
# jump1 <- jump[-last]
# weights1 <- weights[-last]
# } else {
# risk1 <- risk
# jump1 <- jump
# weights1 <- weights
# }
#
#loglik2 <- 2*( sum(log(onepluslamh)) +
# sum( (risk1 -1)*log((1-jump1)/(1- weights1) ) ) )
# this version of LR seems to give negative value sometime???
list( "-2LLR"=loglik, ### drop this output "-2logemLRv2"=loglik2,
lambda=lam/sqrt(n), times=Dtime, wts=weights,
nits=temp2$nf, message=temp2$message )
}
# what should be the fun() and K if I want to perform a (1-sample)
# log-rank test?
# fun3 <- function(t1, z1) { psum( t( outer(z1, t1, FUN=">=") ) ) }
# this is similar to the function in LogRank2() function. Need psum/2/3.
# And K = int R(t) dH(t) = sum( H(z1) ) For example if H() is
# exponential(0.3) then H(t) = 0.3*t, i.e. K <- sum(0.3* z1)
# so finally a call may look like
#
# Assume z1 and d1 are the inputs:
# emlik2(z1, d1, sum(0.3* z1),
# fun3 <- function(t1,z){psum(t(outer(z,t1,FUN=">=") ) ) }, z=z1)
#
# Now use z1<-c(1,2,3,4,5) and d1<-c(1,1,0,1,1) we get
# emplik2(z1, d1, sum(0.25* z1),
# fun3 <- function(t1,z){psum(t(outer(z,t1,FUN=">=") ) ) }, z=z1)
#
# with outputs that include this (and more)
# $ "-2logemLR":
# [1] 0.02204689
# This tests if the (censored) obs. z1 is from exp(0.25)