findU32 {ELYP}

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.

Author(s)

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)