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

Description Usage Arguments Details Value Author(s) References Examples

View source: R/BJfindL2.R

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

1
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

1
## See the Rd file of BJfindU2 for example.

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

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