| findU4 {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. Caution: it take about 3 min. to run on a data set of 90 obs. [GastricCancer]
Usage
findU4(NPmle, ConfInt, LogLikfn2, Pfun, dataMat, level=3.84)
Arguments
NPmle |
a vector containing the three NPMLEs: beta1 hat, beta2 hat and alpha hat. |
ConfInt |
a vector of length 4. Approximate length of the 4 confidence intervals: beta1, beta2, alpha and lambda. They are the initial search step. |
LogLikfn2 |
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 |
The significance level. Default to 3.84. |
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:
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.
# findU3(NPmle=c(1.816674, -1.002082), ConfInt=c(1.2, 0.5, 10),
# LogLikfn=myLLfun, Pfun=Pfun, dataMat=GastricCancer)