findU3 {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 upper 95% Wilks confidence limits based on the log likelihood function supplied.

### Usage

findU3(NPmle, ConfInt, LogLikfn, Pfun, level=3.84, dataMat)


### Arguments

 NPmle a vector containing the two NPMLEs: beta1 hat and beta2 hat. ConfInt a vector of length 3. Approximate length of the 3 confidence intervals: beta1, beta2 and lambda. They are used as initial search steps. LogLikfn a function that takes input of beta1, beta2 lam, dataMat, and output the log likelihood value. Pfun 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 Upper Value. level confidence level, default to 3.84 for 95 percent. dataMat a matrix. for the loglik computation.

### 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).

### Value

A list with the following components:

 Upper the upper 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 10 sec. to run on an i7-4690 CPU.
# 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]