findL4 {ELYP} R Documentation

## Find the Wilks Confidence Interval Lower 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. Caution: it takes about 1 min. to run on a data set of 90 obs. [GastricCancer]

### Usage

findL4(NPmle, ConfInt, LogLikfn2, Pfun, dataMat, level=3.84)


### Arguments

 NPmle a vector containing the three NPMLEs: beta1 hat, beta2 hat and alpha hat. from a Y-P model. ConfInt a vector of length 4. Approx. length of the 4 conf. intervals: beta1, beta2, alpha and lambda. LogLikfn2 a function that compute the empirical likelihood of the Y-P model. given the parameters beta1, beta2, alpha, and lam. 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 Lower Value. dataMat a matrix. level The significance level. Default to 3.84; corresponds to a 95 percent confidence interval.

### 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:

 Lower the lower confidence bound. minParameterNloglik 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]