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))} = θ
*

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

When the given constants *θ* 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.

1 2 | ```
emplikH.disc2(x1, d1, y1= -Inf, x2, d2, y2 = -Inf,
theta, fun1, fun2, tola = 1e-6, maxi, mini)
``` |

`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 |

`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. |

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

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

where *w_i = Δ 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.

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 |

Mai Zhou

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
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.
##############################################################
}
``` |

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