findU32 {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

findU32(NPmle, ConfInt, LogLikfn, Pfun, dataMat, level=3.84)


### 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 step size. LogLikfn a function that computes the loglikelihood. 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. dataMat a matrix. level Significance level. Default to 3.84 (95 percent).

### Details

This is a sister function to findU3( ). A bit faster. Use about half the time compared to findU3().

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 0.5 min to run.
# findU32(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]