BJfindL2: Find the Wilks Confidence Interval Lower Bound for Betafun...

In ELYP: Empirical Likelihood Analysis for the Cox Model and Yang-Prentice (2005) Model

Description

This function uses simple search to find the lower level (default 95%) 1 parameter Wilks confidence limits based on the Buckley-James empirical likelihood test function for two dimensional beta's. Betafun determines the 1 parameter we are finding the lower bound.

Usage

BJfindL2(NPmle, ConfInt, LLRfn, Betafun, dataMat, level=3.84)

Arguments

NPmle	a 2-d vector: the NPMLEs: beta1 hat and beta2 hat.
ConfInt	a vector of length 2. Approx. length of the 2 conf. intervals for beta1 and beta2.
LLRfn	a function that returns -2LLR value.
Betafun	a function that takes the input of 2 parameter values (beta1,beta2) and returns a parameter that we wish to find the confidence Interval lower Value.
dataMat	matrix of covariates
level	confidence level. Use chi-square(df=1), but calibration possible.

Details

Basically we repeatedly testing the value of the 2 parameters, finding the -2LLR values, until we find those Betafun which the -2 log likelihood Ratio 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 2 parameters, and the log likelihood.

Author(s)

Mai Zhou

References

Zhou, M. and Li, G. (2006). Computing censored empirical likelihood ratio by EM algorithm. JCGS

Examples

## See the Rd file of BJfindU2 for example.

ELYP documentation built on May 2, 2019, 5:17 a.m.