Description Usage Arguments Details Value Author(s) References Examples
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]
1 |
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. |
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).
A list with the following components:
Lower |
the lower confidence bound. |
minParameterNloglik |
Final values of the 4 parameters, and the log likelihood. |
Mai Zhou
Zhou, M. (2002). Computing censored empirical likelihood ratio by EM algorithm. JCGS
1 2 3 4 5 6 7 8 | ## 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)
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