emplikH.disc2 {emplik}

Two sample empirical likelihood ratio for discrete hazards with right censored, left truncated data, one parameter.

Description

Use empirical likelihood ratio and Wilks theorem to test the null hypothesis that

\int{f_1(t) I_{[dH_1 <1]} \log(1-dH_1(t))} - \int{f_2(t) I_{[dH_2 <1]} \log(1-dH_2(t))} = \theta

where H_*(t) is the (unknown) discrete cumulative hazard function; f_*(t) can be any predictable functions of t. \theta is the parameter. The given value of \theta in these computation is the value to be tested. The data can be right censored and left truncated.

When the given constants \theta is too far away from the NPMLE, there will be no hazard function satisfy this constraint and the -2 Log empirical likelihood ratio will be infinite. In this case the computation will stop.

Usage

emplikH.disc2(x1, d1, y1= -Inf, x2, d2, y2 = -Inf,
              theta, fun1, fun2, tola = 1e-6, maxi, mini)

Arguments

x1

a vector, the observed survival times, sample 1.

d1

a vector, the censoring indicators, 1-uncensor; 0-censor.

y1

optional vector, the left truncation times.

x2

a vector, the observed survival times, sample 2.

d2

a vector, the censoring indicators, 1-uncensor; 0-censor.

y2

optional vector, the left truncation times.

fun1

a predictable function used to calculate the weighted discrete hazard in H_0. fun1(x) must be able to take a vector input x.

fun2

similar to fun1, but for sample 2.

tola

an optional positive real number, the tolerance of iteration error in solve the non-linear equation needed in constrained maximization.

theta

a given real number. for Ho constraint.

maxi

upper bound for lambda, usually positive.

mini

lower bound for lambda, usually negative.

Details

The log likelihood been maximized is the ‘binomial’ empirical likelihood:

\sum D_{1i} \log w_i + (R_{1i}-D_{1i}) \log [1-w_i] + \sum D_{2j} \log v_j + (R_{2j}-D_{2j}) \log [1-v_j]

where w_i = \Delta H_1(t_i) is the jump of the cumulative hazard function at t_i, D_{1i} is the number of failures observed at t_i, R_{1i} is the number of subjects at risk at time t_i.

For discrete distributions, the jump size of the cumulative hazard at the last jump is always 1. We have to exclude this jump from the summation in the constraint calculation since \log( 1- dH(\cdot)) do not make sense.

The constants theta must be inside the so called feasible region for the computation to continue. This is similar to the requirement that in ELR testing the value of the mean, the value must be inside the convex hull of the observations. It is always true that the NPMLE values are feasible. So when the computation stops, try move the theta closer to the NPMLE. When the computation stops, the -2LLR should have value infinite.

Value

A list with the following components:

times

the location of the hazard jumps.

wts

the jump size of hazard function at those locations.

lambda

the final value of the Lagrange multiplier.

"-2LLR"

The -2Log Likelihood ratio.

Pval

P-value

niters

number of iterations used

Author(s)

Mai Zhou

References

Zhou and Fang (2001). “Empirical likelihood ratio for 2 sample problems for censored data”. Tech Report, Univ. of Kentucky, Dept of Statistics

Examples

if(require("boot", quietly = TRUE)) {
####library(boot)
data(channing)
ymale <- channing[1:97,2]
dmale <- channing[1:97,5]
xmale <- channing[1:97,3]
yfemale <- channing[98:462,2]
dfemale <- channing[98:462,5]
xfemale <- channing[98:462,3]
fun1 <- function(x) { as.numeric(x <= 960) }
emplikH.disc2(x1=xfemale, d1=dfemale, y1=yfemale, 
 x2=xmale, d2=dmale, y2=ymale, theta=0.2, fun1=fun1, fun2=fun1, maxi=4, mini=-10)
######################################################
### You should get "-2LLR" = 1.511239 and a lot more other outputs.
########################################################
emplikH.disc2(x1=xfemale, d1=dfemale, y1=yfemale, 
 x2=xmale, d2=dmale, y2=ymale, theta=0.25, fun1=fun1, fun2=fun1, maxi=4, mini=-5)
########################################################
### This time you get "-2LLR" = 1.150098 etc. etc.
##############################################################
}