fitYP4: Compute Alpha and Baseline Hazard for the Given Data, Given...

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

Description

This function finds the NPMLE of alpha and baseline, for the given beta1 and beta2. and then compute the empirical likelihood.

Usage

fitYP4(Y, d, Z, beta1=1, beta2=-1, maxiter=60)

Arguments

Y	a vector containing the observed survival times.
d	a vector containing the censoring indicators, 1-uncensored; 0-right censored.
Z	a vector of ...
beta1	a scalar. short term
beta2	a scalar. long term
maxiter	an integer.

Details

Difference to the function fitYP3: there is no constraint on the baseline. So, there is no lam input.

On the other hand, it try to find the NPMLE of alpha, via cox model iteration. So, it will output alpha hat.

Value

A list with the following components (may be I should also return the baseline Surv?):

EmpLik	this is actually the log empirical likelihood value.
BaselineH	The baseline hazard estimate.
alpha	The regression coefficient estimate, that is proportional hazard.

Author(s)

Mai Zhou

References

Zhou, M. (2002). Computing censored empirical likelihood ratio by EM algorithm. Tech Report, Univ. of Kentucky, Dept of Statistics

Examples

## censored regression with one right censored observation.
## we check the estimation equation, with the MLE inside myfun7. 
y <- c(3, 5.3, 6.4, 9.1, 14.1, 15.4, 18.1, 15.3, 14, 5.8, 7.3, 14.4)
x <- c(1, 1.5, 2,   3,   4,    5,    6,    5,    4,  1,   2,   4.5)
d <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0)

Example output

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