CoxFindL3 {ELYP} | R Documentation |
This program uses simple search to find the Lower 95% (or other)Wilks confidence limits based on the log likelihood function (CoxEL) supplied.
CoxFindL3(BetaMLE, StepSize, Hfun, Efun, y, d, Z, level=3.84)
BetaMLE |
a vector containing the two NPMLEs: beta1 hat and beta2 hat. |
StepSize |
a vector of length 3. Approximate length of the 3 confidence intervals: beta1, beta2 and lambda. |
Hfun |
a function that used to defines the baseline feature, mu. |
Efun |
a function that takes the input of 3 parameter values (beta1, beta2 and Mulam) and returns a parameter that we wish to find the confidence Interval Lower Value. The input variables must be called beta and theta. beta: in the form of a 2-d vector (i.e., the beta1, beta2) and theta: (=Mulam) |
y |
a vector of censored survival time. |
d |
a vector of 0 and 1, censoring indicator |
Z |
covariates of the Cox model. |
level |
confidence level |
Basically we repeatedly testing the value of the parameter (defined by Efun), try to go to lower and lower values of the parameter until we find those which the -2 log likelihood value is equal to 3.84 (or other level, if set differently).
A list with the following components:
Lower |
the lower confidence bound. |
maxParameterNloglik |
Final values of the 4 parameters, and the log likelihood. |
Mai Zhou
Zhou, M. (2002). Computing censored empirical likelihood ratio by EM algorithm. JCGS
## Here Mulam is the value of int g(t) d H(t) = Mulam
## For example g(t) = I[ t <= 2.0 ]; look inside myLLfun().
data(GastricCancer)
# The following will take about 0.5 min to run.
# findU3(NPmle=c(1.816674, -1.002082), ConfInt=c(1.2, 0.5, 10),
# LogLikfn=myLLfun, Pfun=Pfun, dataMat=GastricCancer)