CoxFindL3 {ELYP} R Documentation

## 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% (or other)Wilks confidence limits based on the log likelihood function (CoxEL) 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. 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

### Details

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).

### Value

A list with the following components:

 Lower the lower confidence bound. maxParameterNloglik Final values of the 4 parameters, and the log likelihood.

Mai Zhou

### 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)



[Package ELYP version 0.7-5 Index]