ELYP (version 0.7-1)

CoxFindL3: Find the Wilks Confidence Interval Upper Bound from the Given Empirical Likelihood Ratio Function

Description

This program uses simple search to find the Lower 95% Wilks confidence limits based on the log likelihood function supplied.

Usage

CoxFindL3(BetaMLE, StepSize, Hfun, Efun, y, d, Z, level=3.84)

Arguments

BetaMLE

a vector containing the two NPMLEs: beta1 hat and beta2 hat.

StepSize

a vector of length 3. Approx. length of the 3 conf. intervals: beta1, beta2 and lambda.

Hfun

a function that 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.

y

a vector of censored survival time.

d

a vector of 0 and 1

Z

covariates of the Cox model.

level

confidence level

Value

• A list with the following components:
• Lower
  the lower confidence bound.
• maxParameterNloglik
  Final values of the 4 parameters, and the log likelihood.

Details

Basically we repeatedly testing the value of the parameter, until we find those which the -2 log likelihood value is equal to 3.84 (or other level, if set differently).

References

Zhou, M. (2002). Computing censored empirical likelihood ratio by EM algorithm. JCGS

Examples

## 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)