Package 'emplik'

Empirical likelihood for mean functional/hazard functional with possibly censored data.

Description

Empirical likelihood ratio tests and confidence intervals for means/hazards from possibly censored and/or truncated data. In particular, empirical likelihood for the Kaplan-Meier/Nelson-Aalen estimators. Now does AFT regression too.

Details

For non-censored data and mean parameters, use el.test( ).

For censored data and mean parameters, use el.cen.EM2( ).

For censored data and hazard parameters, use emplikH1.test( ) [Poisson type likelihood]; use emplikH.disc( ) [binomial type likelihood].

For constructing confidence intervals from likelihood ratio, use findUL( ).

Author(s)

Mai Zhou. (el.test( ) is adapted from Owen's splus code elm. Yifan Yang for some C code.)

Maintainer: Mai Zhou <[email protected]>

References

Zhou, M. (2016). Empirical Likelihood Method in Survival Analysis. (Chapman and Hall/CRC Biostatistics Series) CRC press 2016

See Also

Another R package kmc for possible faster results when testing mean functional with right censored data.

---

The Buckley-James censored regression estimator

Description

Compute the Buckley-James estimator in the regression model

$y_i = \beta x_i + \epsilon_i$

with right censored $y_i$. Iteration method.

Usage

BJnoint(x, y, delta, beta0 = NA, maxiter=30, error = 0.00001)

Arguments

x	a matrix or vector containing the covariate, one row per observation.
y	a numeric vector of length N, censored responses.
delta	a vector of length N, delta=0/1 for censored/uncensored.
beta0	an optional vector for starting value of iteration.
maxiter	an optional integer to control iterations.
error	an optional positive value to control iterations.

Details

This function compute the Buckley-James estimator when your model do not have an intercept term. Of course, if you include a column of 1's in the x matrix, it is also OK with this function and it is equivalent to having an intercept term. If your model do have an intercept term, then you could also use the function bj( ) in the Design library. It should be more refined than BJnoint in the stopping rule for the iterations. However, the variance estimator bj( ) provided is not consistent.

This function is included here mainly to produce the estimator value that may provide some useful information with the function bjtest( ). For example you may want to test a beta value near the Buckley-James estimator.

Value

A list with the following components:

beta	the Buckley-James estimator.
iteration	number of iterations performed.

Author(s)

Mai Zhou.

References

Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.

Zhou, M. (2016). Empirical Likelihood Method in Survival Analysis. CRC Publishing.

Examples

x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
## Suppose now we wish to test Ho: 2mu(1)-mu(2)=0, then
y <- 2*x[,1]-x[,2]
xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)

---

Test the Buckley-James estimator by Empirical Likelihood

Description

Use the empirical likelihood ratio and Wilks theorem to test if the regression coefficient is equal to beta.

The log empirical likelihood been maximized is

$\sum_{d=1} \log \Delta F(e_i) + \sum_{d=0} \log [1-F(e_i)];$

where $e_i$ are the residuals.

Usage

bjtest(y, d, x, beta)

Arguments

y	a vector of length N, containing the censored responses.
d	a vector (length N) of either 1's or 0's. d=1 means y is uncensored; d=0 means y is right censored.
x	a matrix of size N by q.
beta	a vector of length q. The value of the regression coefficient to be tested in the model $y_i = \beta x_i + \epsilon_i$

Details

The above likelihood should be understood as the likelihood of the error term, so in the regression model the error epsilon should be iid.

This version can handle the model where beta is a vector (of length q).

The estimation equations used when maximize the empirical likelihood is

$0 = \sum d_i \Delta F(e_i) (x \cdot m[,i])/(n w_i)$

which was described in detail in the reference below.

Value

A list with the following components:

"-2LLR"	the -2 loglikelihood ratio; have approximate chisq distribution under $H_o$.
logel2	the log empirical likelihood, under estimating equation.
logel	the log empirical likelihood of the Kaplan-Meier of e's.
prob	the probabilities that max the empirical likelihood under estimating equation.

Author(s)

Mai Zhou.

References

Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.

Zhou, M. and Li, G. (2008). Empirical likelihood analysis of the Buckley-James estimator. Journal of Multivariate Analysis 99, 649-664.

Zhou, M. (2016) Empirical Likelihood Method in Survival Analysis. CRC Press.

Examples

xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)

---

Test the Buckley-James estimator by Empirical Likelihood, 1-dim only

Description

Use the empirical likelihood ratio and Wilks theorem to test if the regression coefficient is equal to beta. For 1-dim beta only.

The log empirical likelihood been maximized is

$\sum_{d=1} \log \Delta F(e_i) + \sum_{d=0} \log [1-F(e_i)] .$

Usage

bjtest1d(y, d, x, beta)

Arguments

y	a vector of length N, containing the censored responses.
d	a vector of either 1's or 0's. d=1 means y is uncensored. d=0 means y is right censored.
x	a vector of length N, covariate.
beta	a number. the regression coefficient to be tested in the model y = x beta + epsilon

Details

In the above likelihood, $e_i = y_i - x * beta$ is the residuals.

Similar to bjtest( ), but only for 1-dim beta.

Value

A list with the following components:

"-2LLR"	the -2 loglikelihood ratio; have approximate chi square distribution under $H_o$.
logel2	the log empirical likelihood, under estimating equation.
logel	the log empirical likelihood of the Kaplan-Meier of e's.
prob	the probabilities that max the empirical likelihood under estimating equation constraint.

Author(s)

Mai Zhou.

References

Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.

Owen, A. (1990). Empirical likelihood ratio confidence regions. Ann. Statist. 18 90-120.

Zhou, M. and Li, G. (2008). Empirical likelihood analysis of the Buckley-James estimator. Journal of Multivariate Analysis. 649-664.

Examples

xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)

---

Alternative test of the Buckley-James estimator by Empirical Likelihood

Description

Use the empirical likelihood ratio (alternative form) and Wilks theorem to test if the regression coefficient is equal to beta, based on the estimating equations.

The log empirical likelihood been maximized is

$\log EL = \sum_{j=1}^n \log p_j ; ~ \sum p_j =1$

where the probability $p_j$ is for the jth martingale differences of the estimating equations.

Usage

bjtestII(y, d, x, beta)

Arguments

y	a vector of length N, containing the censored responses.
d	a vector of length N. Either 1's or 0's. d=1 means y is uncensored; d=0 means y is right censored.
x	a matrix of size N by q.
beta	a vector of length q. The value of the regression coefficient to be tested in the model $Y_i = \beta x_i + \epsilon_i$

Details

The above likelihood should be understood as the likelihood of the martingale difference terms. For the definition of the Buckley-James martingale or the related estimating equation, please see the (2016) book in the reference list below.

The estimation equations used when maximize the empirical likelihood is

$0 = \sum d_i \Delta F(e_i) (x \cdot m[,i])/(n w_i)$

where $e_i$ is the residuals, other details are described in the reference book of 2015 below.

The final test is carried out by el.test. So the output is similar to the output of el.test.

Value

A list with the following components:

"-2LLR"	the -2 loglikelihood ratio; have approximate chisq distribution under $H_o$.
logel2	the log empirical likelihood, under estimating equation.
logel	the log empirical likelihood of the Kaplan-Meier of e's.
prob	the probabilities that max the empirical likelihood under estimating equation.

Author(s)

Mai Zhou.

References

Zhou, M. (2016) Empirical Likelihood Methods in Survival Analysis. CRC Press.

Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.

Zhou, M. and Li, G. (2008). Empirical likelihood analysis of the Buckley-James estimator. Journal of Multivariate Analysis, 99, 649–664.

Zhu, H. (2007) Smoothed Empirical Likelihood for Quantiles and Some Variations/Extension of Empirical Likelihood for Buckley-James Estimator, Ph.D. dissertation, University of Kentucky.

Examples

data(myeloma)
bjtestII(y=myeloma[,1], d=myeloma[,2], x=cbind(1, myeloma[,3]), beta=c(37, -3.4))

---

Empirical likelihood ratio for mean with right, left or doubly censored data, by EM algorithm

Description

This program uses EM algorithm to compute the maximized (wrt $p_i$) empirical log likelihood function for right, left or doubly censored data with the MEAN constraint:

$\sum_{d_i=1} p_i f(x_i) = \int f(t) dF(t) = \mu .$

Where $p_i = \Delta F(x_i)$ is a probability, $d_i$ is the censoring indicator, 1(uncensored), 0(right censored), 2(left censored). It also returns those $p_i$.

The empirical log likelihood been maximized is

$\sum_{d_i=1} \log \Delta F(x_i) + \sum_{d_i=0} \log [1-F(x_i)] + \sum_{d_i=2} \log F(x_i) .$

Usage

el.cen.EM(x,d,wt=rep(1,length(d)),fun=function(t){t},mu,maxit=50,error=1e-9,...)

Arguments

x	a vector containing the observed survival times.
d	a vector containing the censoring indicators, 1-uncensored; 0-right censored; 2-left censored.
wt	a weight vector (case weight). positive. same length as d
fun	a left continuous (weight) function used to calculate the mean as in $H_0$. fun(t) must be able to take a vector input t. Default to the identity function $f(t)=t$.
mu	a real number used in the constraint, the mean value of $f(X)$.
maxit	an optional integer, used to control maximum number of iterations.
error	an optional positive real number specifying the tolerance of iteration error. This is the bound of the $L_1$ norm of the difference of two successive weights.
...	additional arguments, if any, to pass to fun.

Details

This implementation is all in R and have several for-loops in it. A faster version would use C to do the for-loop part. But this version seems faster enough and is easier to port to Splus.

We return the log likelihood all the time. Sometimes, (for right censored and no censor case) we also return the -2 log likelihood ratio. In other cases, you have to plot a curve with many values of the parameter, mu, to find out where is the place the log likelihood becomes maximum. And from there you can get -2 log likelihood ratio between the maximum location and your current parameter in Ho.

In order to get a proper distribution as NPMLE, we automatically change the $d$ for the largest observation to 1 (even if it is right censored), similar for the left censored, smallest observation. $\mu$ is a given constant. When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop. The -2 Log empirical likelihood ratio should be infinite.

The constant mu must be inside $( \min f(x_i) , \max f(x_i) )$ for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation stops, try move the mu closer to the NPMLE —

$\sum_{d_i=1} p_i^0 f(x_i)$

$p_i^0$ taken to be the jumps of the NPMLE of CDF. Or use a different fun.

Difference to the function el.cen.EM2: here duplicate (input) observations are collapsed (with weight 2, 3, ... etc.) but those will stay separate by default in the el.cen.EM2. This will lead to a different loglik value. But the -2LLR value should be same in either version.

Value

A list with the following components:

loglik	the maximized empirical log likelihood under the constraint. This may be different from the result of el.cen.EM2 because here the tied observations are collapes into 1 with weight. (while el.cen.EM2 do not). However, the -2LLR should be the same.
times	locations of CDF that have positive mass. tied obs. are collapesd
prob	the jump size of CDF at those locations.
"-2LLR"	If available, it is Minus two times the Empirical Log Likelihood Ratio. Should be approximately chi-square distributed under Ho.
Pval	The P-value of the test, using chi-square approximation.
lam	The Lagrange multiplier. Added 5/2007.

Author(s)

Mai Zhou

References

Zhou, M. (2005). Empirical likelihood ratio with arbitrary censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics, 643-656.

Murphy, S. and van der Vaart (1997) Semiparametric likelihood ratio inference. Ann. Statist. 25, 1471-1509.

Examples

## example with tied observations
x <- c(1, 1.5, 2, 3, 4, 5, 6, 5, 4, 1, 2, 4.5)
d <- c(1,   1, 0, 1, 0, 1, 1, 1, 1, 0, 0,   1)
el.cen.EM(x,d,mu=3.5)
## we should get "-2LLR" = 1.2466....
myfun5 <- function(x, theta, eps) {
u <- (x-theta)*sqrt(5)/eps 
INDE <- (u < sqrt(5)) & (u > -sqrt(5)) 
u[u >= sqrt(5)] <- 0 
u[u <= -sqrt(5)] <- 1 
y <- 0.5 - (u - (u)^3/15)*3/(4*sqrt(5)) 
u[ INDE ] <- y[ INDE ] 
return(u)
}
el.cen.EM(x, d, fun=myfun5, mu=0.5, theta=3.5, eps=0.1)
## example of using wt in the input. Since the x-vector contain
## two 5 (both d=1), and two 2(both d=0), we can also do
xx <- c(1, 1.5, 2, 3, 4, 5, 6, 4, 1, 4.5)
dd <- c(1,   1, 0, 1, 0, 1, 1, 1, 0,   1)
wt <- c(1,   1, 2, 1, 1, 2, 1, 1, 1,   1)
el.cen.EM(x=xx, d=dd, wt=wt, mu=3.5)
## this should be the same as the first example.

---

Empirical likelihood ratio test for a vector of means with right, left or doubly censored data, by EM algorithm

Description

This function is similar to el.cen.EM(), but for multiple constraints. In the input there is a vector of observations $x = (x_1, \cdots , x_n)$ and a function fun. The function fun should return the (n by k) matrix

$( f_1(x), f_2(x), \cdots, f_k (x) ) .$

Also, the ordering of the observations, when consider censoring or redistributing-to-the-right, is according to the value of x, not fun(x). So the probability distribution is for values x. This program uses EM algorithm to maximize (wrt $p_i$) empirical log likelihood function for right, left or doubly censored data with the MEAN constraint:

$j = 1,2, \cdots ,k ~~~~ \sum_{d_i=1} p_i f_j(x_i) = \int f_j(t) dF(t) = \mu_j ~.$

Where $p_i = \Delta F(x_i)$ is a probability, $d_i$ is the censoring indicator, 1(uncensored), 0(right censored), 2(left censored). It also returns those $p_i$. The log likelihood function is defined as

$\sum_{d_i=1} \log \Delta F(x_i) + \sum_{d_i=2} \log F(x_i) + \sum_{d_i=0} \log [ 1-F(x_i)] ~.$

Usage

el.cen.EM2(x,d,xc=1:length(x),fun,mu,maxit=50,error=1e-9,...)

Arguments

x	a vector containing the observed survival times.
d	a vector containing the censoring indicators, 1-uncensored; 0-right censored; 2-left censored.
xc	an optional vector of collapsing control values. If xc[i] xc[j] have different values then (x[i], d[i]), (x[j], d[j]) will not merge into one observation with weight two, even if they are identical. Default is not to merge.
fun	a left continuous (weight) function that returns a matrix. The columns (=k) of the matrix is used to calculate the means and will be tested in $H_0$. fun(t) must be able to take a vector input t.
mu	a vector of length k. Used in the constraint, as the mean of $f(X)$.
maxit	an optional integer, used to control maximum number of iterations.
error	an optional positive real number specifying the tolerance of iteration error. This is the bound of the $L_1$ norm of the difference of two successive weights.
...	additional inputs to pass to fun().

Details

This implementation is all in R and have several for-loops in it. A faster version would use C to do the for-loop part. (but this version is easier to port to Splus, and seems faster enough).

We return the log likelihood all the time. Sometimes, (for right censored and no censor case) we also return the -2 log likelihood ratio. In other cases, you have to plot a curve with many values of the parameter, mu, to find out where the log likelihood becomes maximum. And from there you can get -2 log likelihood ratio between the maximum location and your current parameter in Ho.

In order to get a proper distribution as NPMLE, we automatically change the $d$ for the largest observation to 1 (even if it is right censored), similar for the left censored, smallest observation. $\mu$ is a given constant vector. When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop. The -2 Log empirical likelihood ratio should be infinite.

The constant vector mu must be inside $( \min f(x_i) , \max f(x_i) )$ for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation stops, try move the mu closer to the NPMLE —

$\hat \mu _j = \sum_{d_i=1} p_i^0 f_j(x_i)$

where $p_i^0$ taken to be the jumps of the NPMLE of CDF. Or use a different fun.

Difference to the function el.cen.EM: due to the introduction of input xc here in this function, the output loglik may be different compared to the function el.cen.EM due to not collapsing of duplicated input survival values. The -2LLR should be the same from both functions.

Value

A list with the following components:

loglik	the maximized empirical log likelihood under the constraints.
times	locations of CDF that have positive mass.
prob	the jump size of CDF at those locations.
"-2LLR"	If available, it is Minus two times the Empirical Log Likelihood Ratio. Should be approx. chi-square distributed under Ho.
Pval	If available, the P-value of the test, using chi-square approximation.
lam	the Lagrange multiplier in the final EM step. (the M-step)

Author(s)

Mai Zhou

References

Zhou, M. (2005). Empirical likelihood ratio with arbitrary censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics, 643-656.

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)
### first we estimate beta, the MLE
lm.wfit(x=cbind(rep(1,12),x), y=y, w=WKM(x=y, d=d)$jump[rank(y)])$coef
## you should get 1.392885 and 2.845658
## then define myfun7 with the MLE value
myfun7 <- function(y, xmat) {
temp1 <- y - ( 1.392885 +  2.845658 * xmat)
return( cbind( temp1, xmat*temp1) )
}
## now test 
el.cen.EM2(y,d, fun=myfun7, mu=c(0,0), xmat=x)
## we should get, Pval = 1 , as the MLE should.
## for other values of (a, b) inside myfun7, you get other Pval
##
rqfun1 <- function(y, xmat, beta, tau = 0.5) {
temp1 <- tau - (1-myfun55(y-beta*xmat))
return(xmat * temp1)
}
myfun55 <- function(x, eps=0.001){
u <- x*sqrt(5)/eps
INDE <- (u < sqrt(5)) & (u > -sqrt(5))
u[u >= sqrt(5)] <- 0
u[u <= -sqrt(5)] <- 1
y <- 0.5 - (u - (u)^3/15)*3/(4*sqrt(5))
u[ INDE ] <- y[ INDE ]
return(u)
}
## myfun55 is a smoothed indicator fn. 
## eps should be between (1/sqrt(n), 1/n^0.75) [Chen and Hall]
el.cen.EM2(x=y,d=d,xc=1:12,fun=rqfun1,mu=0,xmat=x,beta=3.08,tau=0.44769875)
## default tau=0.5 
el.cen.EM2(x=y,d=d,xc=1:12,fun=rqfun1,mu=0,xmat=x,beta=3.0799107404)
###################################################
### next 2 examples are testing the mean/median residual time
###################################################
mygfun <- function(s, age, muage) {as.numeric(s >= age)*(s-(age+muage))}
mygfun2 <- function(s, age, Mdage) 
          {as.numeric(s <= (age+Mdage)) - 0.5*as.numeric(s <= age)}
## Not run: 
library(survival) 
time <- cancer$time
status <- cancer$status-1
###for mean residual time 
el.cen.EM2(x=time, d=status, fun=mygfun, mu=0, age=365.25, muage=234)$Pval
el.cen.EM2(x=time, d=status, fun=mygfun, mu=0, age=365.25, muage=323)$Pval
### for median resudual time
el.cen.EM2(x=time, d=status, fun=mygfun2, mu=0.5, age=365.25, Mdage=184)$Pval
el.cen.EM2(x=time, d=status, fun=mygfun2, mu=0.5, age=365.25, Mdage=321)$Pval

## End(Not run)
## Not run: 
#### For right censor only data (Kaplan-Meier) we can use this function to get a faster computation
#### by calling the kmc 0.2-2 package.
el.cen.R <- function (x, d, xc = 1:length(x), fun, mu, error = 1e-09, ...)
{
xvec <- as.vector(x)
d <- as.vector(d)
mu <- as.vector(mu)
xc <- as.vector(xc)
n <- length(d)
if (length(xvec) != n)
stop("length of d and x must agree")
if (length(xc) != n)
stop("length of xc and d must agree")
if (n <= 2 * length(mu) + 1)
stop("Need more observations")
if (any((d != 0) & (d != 1) ))
stop("d must be 0(right-censored) or 1(uncensored)")
if (!is.numeric(xvec))
stop("x must be numeric")
if (!is.numeric(mu))
stop("mu must be numeric")

funx <- as.matrix(fun(xvec, ...))
pp <- ncol(funx)
if (length(mu) != pp)
stop("length of mu and ncol of fun(x) must agree")
temp <- Wdataclean5(z = xvec, d, zc = xc, xmat = funx)
x <- temp$value
d <- temp$dd
w <- temp$weight
funx <- temp$xxmat
d[length(d)] <- 1
xd1 <- x[d == 1]
if (length(xd1) <= 1)
stop("need more distinct uncensored obs.")
funxd1 <- funx[d == 1, ]
xd0 <- x[d == 0]
wd1 <- w[d == 1]
wd0 <- w[d == 0]
m <- length(xd0)

pnew <- NA
num <- NA
if (m > 0) {
gfun <- function(x) { return( fun(x, ...) - mu ) }
temp <- kmc.solve(x=x, d=d, g=list(gfun))
logel <- temp$loglik.h0
logel00 <- temp$loglik.null
lam <- - temp$lambda
}
if (m == 0) {
num <- 0
temp6 <- el.test.wt2(x = funxd1, wt = wd1, mu)
pnew <- temp6$prob
lam <- temp6$lambda
logel <- sum(wd1 * log(pnew))
logel00 <- sum(wd1 * log(wd1/sum(wd1)))
}
tval <- 2 * (logel00 - logel)
list(loglik = logel, times = xd1, prob = pnew, lam = lam,
iters = num, `-2LLR` = tval, Pval = 1 - pchisq(tval,
df = length(mu)))
}


## End(Not run)

---

Empirical likelihood ratio for 1 mean constraint with right censored data

Description

This program uses a fast recursive formula to compute the maximized (wrt $p_i$) empirical log likelihood ratio for right censored data with one MEAN constraint:

$\sum_{d_i=1} p_i f(x_i) = \int f(t) dF(t) = \mu .$

Where $p_i = \Delta F(x_i)$ is a probability, $d_i$ is the censoring indicator, 1(uncensored), 0(right censored). It also returns those $p_i$.

The empirical log likelihood been maximized is

$\sum_{d_i=1} \log \Delta F(x_i) + \sum_{d_i=0} \log [1-F(x_i)] .$

Usage

el.cen.kmc1d(x, d, fun, mu, tol = .Machine$double.eps^0.5, step=0.001, ...)

Arguments

x	a vector containing the observed survival times.
d	a vector containing the censoring indicators, 1-uncensored; 0-right censored.
fun	a left continuous (weight) function used to calculate the mean as in $H_0$. fun(t) must be able to take a vector input t.
mu	a real number used in the constraint, the mean value of $f(X)$.
tol	a small positive number, for the uniroot error tol.
step	a small positive number, for use in the uniroot function (as interval) to find lambda root. Sometimes uniroot will find the wrong root or no root, resulting a negative "-2LLR" or NA. Change the step to a different value often can fix this (but not always). Another sign of wrong root is that the sum of probabilities not sum to one, or has negative probability values.
...	additional arguments, if any, to pass to fun.

Details

This function is similar to the function in package kmc, but much simpler, i.e. all implemented in R and only for one mean constraint. This implementation have two for-loops in R. A faster version would use C to do the for-loop part. But this version seems fast enough and is easier to port to other languages.

We return the log likelihood all the time. Sometimes, (for right censored case) we also return the -2 log likelihood ratio. In other cases, you have to plot a curve with many values of the parameter, mu, to find out where is the place the log likelihood becomes maximum. And from there you can get -2 log likelihood ratio between the maximum location and your current parameter in Ho.

The input step is used in uniroot function to find a root of lambda. Sometimes a step value may lead to no root or result in a wrong root. You may try several values for the step to see. If the probabilities returned do not sum to one, then the lambda root is a wrong root. We want the root closest to zero.

In order to get a proper distribution as NPMLE, we automatically change the $d$ for the largest observation to 1 (even if it is right censored). $\mu$ is a given constant. When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop or return something ridiculas, (as negative -2LLR). The -2 Log empirical likelihood ratio may be +infinite.

The constant mu must be inside $( \min f(x_i) , \max f(x_i) )$ (with uncensored $x_i$) for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation stops, try move the mu closer to the NPMLE —

$\sum_{d_i=1} p_i^0 f(x_i)$

$p_i^0$ taken to be the jumps of the NPMLE of CDF. Or use a different fun.

Value

A list with the following components:

loglik	the maximized empirical log likelihood under the constraint. Note, here the tied observations are not collapsed into one obs. with weight 2 (as in el.cen.EM), so the value may differ from those that do collapse the tied obs. In any case, the -2LLR should not differ (whether collaps or not).
times	locations of CDF that have positive mass.
prob	the jump size of CDF at those locations.
"-2LLR"	If available, it is minus two times the empirical Log Likelihood Ratio. Should be approximately chi-square distributed under Ho. If you got NA or negative value, then something is wrong, most likely the uniroot has found the wrong root. Suggest: use el.cen.EM2() which uses EM algorithm. It is more stable but slower.
Pval	The P-value of the test, using chi-square approximation.
lam	The Lagrange multiplier.

Author(s)

Mai Zhou

References

Zhou, M. and Yang, Y. (2015). A recursive formula for the Kaplan-Meier estimator with mean constraints and its application to empirical likelihood. Computational Statistics Vol. 30, Issue 4 pp. 1097-1109.

Zhou, M. (2005). Empirical likelihood ratio with arbitrary censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics, 14(3), 643-656.

Examples

x <- c(1, 1.5, 2, 3, 4.2, 5, 6.1, 5.3, 4.5, 0.9, 2.1, 4.3)
d <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1)
ff <- function(x) {
    x - 3.7
}
el.cen.kmc1d(x=x, d=d, fun=ff, mu=0)
#######################################

---

Empirical likelihood ratio for mean with right censored data, by QP.

Description

This program computes the maximized (wrt $p_i$) empirical log likelihood function for right censored data with the MEAN constraint:

$\sum_i [ d_i p_i g(x_i) ] = \int g(t) dF(t) = \mu$

where $p_i = \Delta F(x_i)$ is a probability, $d_i$ is the censoring indicator. The $d$ for the largest observation is always taken to be 1. It then computes the -2 log empirical likelihood ratio which should be approximately chi-square distributed if the constraint is true. Here $F(t)$ is the (unknown) CDF; $g(t)$ can be any given left continuous function in $t$. $\mu$ is a given constant. The data must contain some right censored observations. If there is no censoring or the only censoring is the largest observation, the code will stop and we should use el.test( ) which is for uncensored data.

The log empirical likelihood been maximized is

$\sum_{d_i=1} \log \Delta F(x_i) + \sum_{d_i=0} \log [ 1-F(x_i) ].$

Usage

el.cen.test(x,d,fun=function(x){x},mu,error=1e-8,maxit=15)

Arguments

x	a vector containing the observed survival times.
d	a vector containing the censoring indicators, 1-uncensor; 0-censor.
fun	a left continuous (weight) function used to calculate the mean as in $H_0$. fun(t) must be able to take a vector input t. Default to the identity function $f(t)=t$.
mu	a real number used in the constraint, sum to this value.
error	an optional positive real number specifying the tolerance of iteration error in the QP. This is the bound of the $L_1$ norm of the difference of two successive weights.
maxit	an optional integer, used to control maximum number of iterations.

Details

When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop. The -2 Log empirical likelihood ratio should be infinite.

The constant mu must be inside $( \min f(x_i) , \max f(x_i) )$ for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation cannot continue, try move the mu closer to the NPMLE, or use a different fun.

This function depends on Wdataclean2(), WKM() and solve3.QP()

This function uses sequential Quadratic Programming to find the maximum. Unlike other functions in this package, it can be slow for larger sample sizes. It took about one minute for a sample of size 2000 with 20% censoring on a 1GHz, 256MB PC, about 19 seconds on a 3 GHz 512MB PC.

Value

A list with the following components:

"-2LLR"	The -2Log Likelihood ratio.
xtimes	the location of the CDF jumps.
weights	the jump size of CDF at those locations.
Pval	P-value
error	the $L_1$ norm between the last two wts.
iteration	number of iterations carried out

Author(s)

Mai Zhou, Kun Chen

References

Pan, X. and Zhou, M. (1999). Using 1-parameter sub-family of distributions in empirical likelihood ratio with censored data. J. Statist. Plann. Inference. 75, 379-392.

Chen, K. and Zhou, M. (2000). Computing censored empirical likelihood ratio using Quadratic Programming. Tech Report, Univ. of Kentucky, Dept of Statistics

Zhou, M. and Chen, K. (2007). Computation of the empirical likelihood ratio from censored data. Journal of Statistical Computing and Simulation, 77, 1033-1042.

Examples

el.cen.test(rexp(100), c(rep(0,25),rep(1,75)), mu=1.5)
## second example with tied observations
x <- c(1, 1.5, 2, 3, 4, 5, 6, 5, 4, 1, 2, 4.5)
d <- c(1,   1, 0, 1, 0, 1, 1, 1, 1, 0, 0,   1)
el.cen.test(x,d,mu=3.5)
# we should get  "-2LLR" = 1.246634  etc.

---

Empirical likelihood ratio for mean with left truncated and right censored data, by EM algorithm

Description

This program uses EM algorithm to compute the maximized (wrt $p_i$) empirical log likelihood function for left truncated and right censored data with the MEAN constraint:

$\sum_{d_i=1} p_i f(x_i) = \int f(t) dF(t) = \mu ~.$

Where $p_i = \Delta F(x_i)$ is a probability, $d_i$ is the censoring indicator, 1(uncensored), 0(right censored). The $d$ for the largest observation $x$, is always (automatically) changed to 1. $\mu$ is a given constant. This function also returns those $p_i$.

The log empirical likelihood function been maximized is

$\sum_{d_i=1} \log \frac{ \Delta F(x_i)}{1-F(y_i)} + \sum_{d_i=0} \log \frac{1-F(x_i)}{1-F(y_i)}.$

Usage

el.ltrc.EM(y,x,d,fun=function(t){t},mu,maxit=30,error=1e-9)

Arguments

y	an optional vector containing the observed left truncation times.
x	a vector containing the censored survival times.
d	a vector containing the censoring indicators, 1-uncensored; 0-right censored.
fun	a continuous (weight) function used to calculate the mean as in $H_0$. fun(t) must be able to take a vector input t. Default to the identity function $f(t)=t$.
mu	a real number used in the constraint, mean value of $f(X)$.
error	an optional positive real number specifying the tolerance of iteration error. This is the bound of the $L_1$ norm of the difference of two successive weights.
maxit	an optional integer, used to control maximum number of iterations.

Details

We return the -2 log likelihood ratio, and the constrained NPMLE of CDF. The un-constrained NPMLE should be WJT or Lynden-Bell estimator.

When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop. The -2 Log empirical likelihood ratio should be infinite.

The constant mu must be inside $( \min f(x_i) , \max f(x_i) )$ for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation stops, try move the mu closer to the NPMLE —

$\sum_{d_i=1} p_i^0 f(x_i)$

$p_i^0$ taken to be the jumps of the NPMLE of CDF. Or use a different fun.

This implementation is all in R and have several for-loops in it. A faster version would use C to do the for-loop part. (but this version is easier to port to Splus, and seems faster enough).

Value

A list with the following components:

times	locations of CDF that have positive mass.
prob	the probability of the constrained NPMLE of CDF at those locations.
"-2LLR"	It is Minus two times the Empirical Log Likelihood Ratio. Should be approximate chi-square distributed under Ho.

Author(s)

Mai Zhou

References

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

Tsai, W. Y., Jewell, N. P., and Wang, M. C. (1987). A note on product-limit estimator under right censoring and left truncation. Biometrika, 74, 883-886.

Turnbbull, B. (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data. JRSS B, 290-295.

Zhou, M. (2005). Empirical likelihood ratio with arbitrarily censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics 14, 643-656.

Examples

## example with tied observations
y <- c(0, 0, 0.5, 0, 1, 2, 2, 0, 0, 0, 0, 0 )
x <- c(1, 1.5, 2, 3, 4, 5, 6, 5, 4, 1, 2, 4.5)
d <- c(1,   1, 0, 1, 0, 1, 1, 1, 1, 0, 0,   1)
el.ltrc.EM(y,x,d,mu=3.5)
ypsy <- c(51, 58, 55, 28, 25, 48, 47, 25, 31, 30, 33, 43, 45, 35, 36)
xpsy <- c(52, 59, 57, 50, 57, 59, 61, 61, 62, 67, 68, 69, 69, 65, 76)
dpsy <- c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1 )
el.ltrc.EM(ypsy,xpsy,dpsy,mu=64)

---

Empirical likelihood ratio test for the means, uncensored data

Description

Compute the empirical likelihood ratio with the mean vector fixed at mu.

The log empirical likelihood been maximized is

$\sum_{i=1}^n \log \Delta F(x_i).$

Usage

el.test(x, mu, lam, maxit=25, gradtol=1e-7, 
                 svdtol = 1e-9, itertrace=FALSE)

Arguments

x	a matrix or vector containing the data, one row per observation.
mu	a numeric vector (of length = ncol(x)) to be tested as the mean vector of x above, as $H_0$.
lam	an optional vector of length = length(mu), the starting value of Lagrange multipliers, will use $0$ if missing.
maxit	an optional integer to control iteration when solve constrained maximization.
gradtol	an optional real value for convergence test.
svdtol	an optional real value to detect singularity while solve equations.
itertrace	a logical value. If the iteration history needs to be printed out.

Details

If mu is in the interior of the convex hull of the observations x, then wts should sum to n. If mu is outside the convex hull then wts should sum to nearly zero, and -2LLR will be a large positive number. It should be infinity, but for inferential purposes a very large number is essentially equivalent. If mu is on the boundary of the convex hull then wts should sum to nearly k where k is the number of observations within that face of the convex hull which contains mu.

When mu is interior to the convex hull, it is typical for the algorithm to converge quadratically to the solution, perhaps after a few iterations of searching to get near the solution. When mu is outside or near the boundary of the convex hull, then the solution involves a lambda of infinite norm. The algorithm tends to nearly double lambda at each iteration and the gradient size then decreases roughly by half at each iteration.

The goal in writing the algorithm was to have it “fail gracefully" when mu is not inside the convex hull. The user can either leave -2LLR “large and positive" or can replace it by infinity when the weights do not sum to nearly n.

Value

A list with the following components:

-2LLR	the -2 loglikelihood ratio; approximate chisq distribution under $H_o$.
Pval	the observed P-value by chi-square approximation.
lambda	the final value of Lagrange multiplier.
grad	the gradient at the maximum.
hess	the Hessian matrix.
wts	weights on the observations
nits	number of iteration performed

Author(s)

Original Splus code by Art Owen. Adapted to R by Mai Zhou.

References

Owen, A. (1990). Empirical likelihood ratio confidence regions. Ann. Statist. 18, 90-120.

Examples

x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
el.test(x, mu=c(1,2))
## Suppose now we wish to test Ho: 2mu(1)-mu(2)=0, then
y <- 2*x[,1]-x[,2]
el.test(y, mu=0)
xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)
el.test(xx, mu=15)  #### -2LLR = 1.805702

---

Weighted Empirical Likelihood ratio for mean, uncensored data

Description

This program is similar to el.test( ) except it takes weights, and is for one dimensional mu.

The mean constraint considered is:

$\sum_{i=1}^n p_i x_i = \mu .$

where $p_i = \Delta F(x_i)$ is a probability. Plus the probability constraint: $\sum p_i =1$.

The weighted log empirical likelihood been maximized is

$\sum_{i=1}^n w_i \log p_i.$

Usage

el.test.wt(x, wt, mu, usingC=TRUE)

Arguments

x	a vector containing the observations.
wt	a vector containing the weights.
mu	a real number used in the constraint, weighted mean value of $f(X)$.
usingC	TRUE: use C function, which may be benifit when sample size is large; FALSE: use pure R function.

Details

This function used to be an internal function. It becomes external because others may find it useful elsewhere.

The constant mu must be inside $( \min x_i , \max x_i )$ for the computation to continue.

Value

A list with the following components:

x	the observations.
wt	the vector of weights.
prob	The probabilities that maximized the weighted empirical likelihood under mean constraint.

Author(s)

Mai Zhou, Y.F. Yang for C part.

References

Owen, A. (1990). Empirical likelihood ratio confidence regions. Ann. Statist. 18, 90-120.

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

Examples

## example with tied observations
x <- c(1, 1.5, 2, 3, 4, 5, 6, 5, 4, 1, 2, 4.5)
d <- c(1,   1, 0, 1, 0, 1, 1, 1, 1, 0, 0,   1)
el.cen.EM(x,d,mu=3.5)
## we should get "-2LLR" = 1.2466....
myfun5 <- function(x, theta, eps) {
u <- (x-theta)*sqrt(5)/eps 
INDE <- (u < sqrt(5)) & (u > -sqrt(5)) 
u[u >= sqrt(5)] <- 0 
u[u <= -sqrt(5)] <- 1 
y <- 0.5 - (u - (u)^3/15)*3/(4*sqrt(5)) 
u[ INDE ] <- y[ INDE ] 
return(u)
}
el.cen.EM(x, d, fun=myfun5, mu=0.5, theta=3.5, eps=0.1)

---

Weighted Empirical Likelihood ratio for mean(s), uncensored data

Description

This program is similar to el.test( ) except it takes weights.

The mean constraints are:

$\sum_{i=1}^n p_i x_i = \mu .$

Where $p_i = \Delta F(x_i)$ is a probability. Plus the probability constraint: $\sum p_i =1$.

The weighted log empirical likelihood been maximized is

$\sum_{i=1}^n w_i \log p_i.$

Usage

el.test.wt2(x, wt, mu, maxit = 25, gradtol = 1e-07, Hessian = FALSE, 
    svdtol = 1e-09, itertrace = FALSE)

Arguments

x	a matrix (of size nxp) or vector containing the observations.
wt	a vector of length n, containing the weights. If weights are all 1, this is very simila to el.test. wt have to be positive.
mu	a vector of length p, used in the constraint. weighted mean value of $f(X)$.
maxit	an integer, the maximum number of iteration.
gradtol	a positive real number, the tolerance for a solution
Hessian	logical. if the Hessian needs to be computed?
svdtol	tolerance in perform SVD of the Hessian matrix.
itertrace	TRUE/FALSE, if the intermediate steps needs to be printed.

Details

This function used to be an internal function. It becomes external because others may find it useful.

It is similar to the function el.test( ) with the following differences:

(1) The output lambda in el.test.wts, when divided by n (the sample size or sum of all the weights) should be equal to the output lambda in el.test.

(2) The Newton step of iteration in el.test.wts is different from those in el.test. (even when all the weights are one).

Value

A list with the following components:

lambda	the Lagrange multiplier. Solution.
wt	the vector of weights.
grad	The gradian at the final solution.
nits	number of iterations performed.
prob	The probabilities that maximized the weighted empirical likelihood under mean constraint.

Author(s)

Mai Zhou

References

Owen, A. (1990). Empirical likelihood ratio confidence regions. Ann. Statist. 18, 90-120.

Zhou, M. (2005). Empirical likelihood ratio with arbitrary censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics, 14, 643-656.

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

Examples

## example with tied observations
x <- c(1, 1.5, 2, 3, 4, 5, 6, 5, 4, 1, 2, 4.5)
d <- c(1,   1, 0, 1, 0, 1, 1, 1, 1, 0, 0,   1)
el.cen.EM(x,d,mu=3.5)
## we should get "-2LLR" = 1.2466....
myfun5 <- function(x, theta, eps) {
u <- (x-theta)*sqrt(5)/eps 
INDE <- (u < sqrt(5)) & (u > -sqrt(5)) 
u[u >= sqrt(5)] <- 0 
u[u <= -sqrt(5)] <- 1 
y <- 0.5 - (u - (u)^3/15)*3/(4*sqrt(5)) 
u[ INDE ] <- y[ INDE ] 
return(u)
}
el.cen.EM(x, d, fun=myfun5, mu=0.5, theta=3.5, eps=0.1)

---

Empirical likelihood ratio for mean with left truncated data

Description

This program uses EM algorithm to compute the maximized (wrt $p_i$) empirical log likelihood function for left truncated data with the MEAN constraint:

$\sum p_i f(x_i) = \int f(t) dF(t) = \mu ~.$

Where $p_i = \Delta F(x_i)$ is a probability. $\mu$ is a given constant. It also returns those $p_i$ and the $p_i$ without constraint, the Lynden-Bell estimator.

The log likelihood been maximized is

$\sum_{i=1}^n \log \frac{\Delta F(x_i)}{1-F(y_i)} .$

Usage

el.trun.test(y,x,fun=function(t){t},mu,maxit=20,error=1e-9)

Arguments

y	a vector containing the left truncation times.
x	a vector containing the survival times. truncation means x>y.
fun	a continuous (weight) function used to calculate the mean as in $H_0$. fun(t) must be able to take a vector input t. Default to the identity function $f(t)=t$.
mu	a real number used in the constraint, mean value of $f(X)$.
error	an optional positive real number specifying the tolerance of iteration error. This is the bound of the $L_1$ norm of the difference of two successive weights.
maxit	an optional integer, used to control maximum number of iterations.

Details

This implementation is all in R and have several for-loops in it. A faster version would use C to do the for-loop part. But it seems faster enough and is easier to port to Splus.

When the given constants $\mu$ is too far away from the NPMLE, there will be no distribution satisfy the constraint. In this case the computation will stop. The -2 Log empirical likelihood ratio should be infinite.

The constant mu must be inside $( \min f(x_i) , \max f(x_i) )$ for the computation to continue. It is always true that the NPMLE values are feasible. So when the computation stops, try move the mu closer to the NPMLE —

$\sum_{d_i=1} p_i^0 f(x_i)$

$p_i^0$ taken to be the jumps of the NPMLE of CDF. Or use a different fun.

Value

A list with the following components:

"-2LLR"	the maximized empirical log likelihood ratio under the constraint.
NPMLE	jumps of NPMLE of CDF at ordered x.
NPMLEmu	same jumps but for constrained NPMLE.

Author(s)

Mai Zhou

References

Zhou, M. (2005). Empirical likelihood ratio with arbitrary censored/truncated data by EM algorithm. Journal of Computational and Graphical Statistics, 14, 643-656.

Li, G. (1995). Nonparametric likelihood ratio estimation of probabilities for truncated data. JASA 90, 997-1003.

Turnbull (1976). The empirical distribution function with arbitrarily grouped, censored and truncated data. JRSS B 38, 290-295.

Examples

## example with tied observations
vet <- c(30, 384, 4, 54, 13, 123, 97, 153, 59, 117, 16, 151, 22, 56, 21, 18,
         139, 20, 31, 52, 287, 18, 51, 122, 27, 54, 7, 63, 392, 10)
vetstart <- c(0,60,0,0,0,33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
el.trun.test(vetstart, vet, mu=80, maxit=15)

---

Empirical likelihood ratio for discrete hazard with right censored, left truncated data

Description

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

$\sum_i[f(x_i, \theta) \log(1- dH(x_i))] = K$

where $H(t)$ is the (unknown) discrete cumulative hazard function; $f(t,\theta)$ can be any predictable function of $t$. $\theta$ is the parameter of the function and K is a given constant. The data can be right censored and left truncated.

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

Usage

emplikH.disc(x, d, y= -Inf, K, fun, tola=.Machine$double.eps^.25, theta)

Arguments

x	a vector, the observed survival times.
d	a vector, the censoring indicators, 1-uncensor; 0-censor.
y	optional vector, the left truncation times.
K	a real number used in the constraint, sum to this value.
fun	a left continuous (weight) function used to calculate the weighted discrete hazard in $H_0$. fun(x, theta) must be able to take a vector input x, and a parameter theta.
tola	an optional positive real number specifying the tolerance of iteration error in solve the non-linear equation needed in constrained maximization.
theta	a given real number used as the parameter of the function $f$.

Details

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

$\sum D_i \log w_i + (R_i-D_i) \log [1-w_i]$

where $w_i = \Delta H(t_i)$ is the jump of the cumulative hazard function, $D_i$ is the number of failures observed at $t_i$, $R_i$ 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 since $\log( 1- dH(\cdot))$ do not make sense.

The constants theta and K must be inside the so called feasible region for the computation to continue. This is similar to the requirement that in 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 and K closer to the NPMLE. When the computation stops, the -2LLR should have value infinite.

In case you do not need the theta in the definition of the function $f$, you still need to formally define your fun function with a theta input, just to match the arguments.

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 discrete -2Log Likelihood ratio.
Pval	P-value
niters	number of iterations used

Author(s)

Mai Zhou

References

Fang, H. (2000). Binomial Empirical Likelihood Ratio Method in Survival Analysis. Ph.D. Thesis, Univ. of Kentucky, Dept of Statistics.

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

Zhou, M. and Fang, H. (2006). A comparison of Poisson and binomial empirical likelihood. Tech Report, Univ. of Kentucky, Dept of Statistics

Examples

fun4 <- function(x, theta) { as.numeric(x <= theta) }
x <- c(1, 2, 3, 4, 5, 6, 5, 4, 3, 4, 1, 2.4, 4.5)
d <- c(1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1)
# test if -H(4) = -0.7 
emplikH.disc(x=x,d=d,K=-0.7,fun=fun4,theta=4)
# we should get "-2LLR" 0.1446316  etc....
y <- c(-2,-2, -2, 1.5, -1)
emplikH.disc(x=x,d=d,y=y,K=-0.7,fun=fun4,theta=4)

---

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.
##############################################################
}

---

Empirical likelihood for hazard with right censored, left truncated data

Description

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

$\int f(t) dH(t) = \theta$

with right censored, left truncated data. Where $H(t)$ is the unknown cumulative hazard function; $f(t)$ can be any given function and $\theta$ a given constant. In fact, $f(t)$ can even be data dependent, just have to be ‘predictable’.

Usage

emplikH1.test(x, d, y= -Inf, theta, fun, tola=.Machine$double.eps^.5)

Arguments

x	a vector of the censored survival times.
d	a vector of the censoring indicators, 1-uncensor; 0-censor.
y	a vector of the observed left truncation times.
theta	a real number used in the $H_0$ to set the hazard to this value.
fun	a left continuous (weight) function used to calculate the weighted hazard in $H_0$. fun must be able to take a vector input. See example below.
tola	an optional positive real number specifying the tolerance of iteration error in solve the non-linear equation needed in constrained maximization.

Details

This function is designed for the case where the true distributions are all continuous. So there should be no tie in the data.

The log empirical likelihood used here is the ‘Poisson’ version empirical likelihood:

$\sum_{i=1}^n \delta_i \log (dH(x_i)) - [ H(x_i) - H(y_i) ] ~.$

If there are ties in the data that are resulted from rounding, you may break the tie by adding a different tiny number to the tied observation(s). If those are true ties (thus the true distribution is discrete) we recommend use emplikdisc.test().

The constant theta must be inside the so called feasible region for the computation to continue. This is similar to the requirement that in 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 complains that there is no hazard function satisfy the constraint, you should try to move the theta value closer to the NPMLE. When the computation stops prematurely, 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 Lagrange multiplier.
"-2LLR"	the -2Log Likelihood ratio.
Pval	P-value
niters	number of iterations used

Author(s)

Mai Zhou

References

Pan, X. and Zhou, M. (2002), “Empirical likelihood in terms of hazard for censored data”. Journal of Multivariate Analysis 80, 166-188.

Examples

fun <- function(x) { as.numeric(x <= 6.5) }
emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=2, fun=fun) 
fun2 <- function(x) {exp(-x)}  
emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=0.2, fun=fun2)

---

Return binomial empirical likelihood ratio for the given lambda, with right censored data

Description

Compute the binomial empirical likelihood ratio for the given tilt parameter lambda. Most useful for construct Wilks confidence intervals. The null hypothesis or constraint is defined by the parameter $\theta$, where

$\int fung(t) d \log (1-H(t)) = \theta$

Where $H(t)$ is the unknown cumulative hazard function; $fung(t)$ can be any given function. In the future, the function $fung$ may replaced by the vector of $fung(x)$, since this is more flexible.

Input data can be right censored. If no censoring, set d=rep(1, length(x)).

Usage

emplikH1B(lambda, x, d, fung, CIforTheta=FALSE)

Arguments

lambda	a scalar. Can be positive or negative. The amount of tiling.
x	a vector of the censored survival times.
d	a vector of the censoring indicators, 1-uncensor; 0-right censor.
fung	a left continuous (weight) function used to calculate the weighted hazard in the parameter $\theta$. fung must be able to take a vector input. See example below.
CIforTheta	an optional logical value. Default to FALSE. If set to TRUE, will return the integrated hazard value for the given lambda.

Details

This function is used to calculate lambda confidence interval (Wilks type) for $\theta$.

This function is designed for the case where the true distribution should be discrete. Ties in the data are OK.

The log empirical likelihood used here is the ‘binomial’ version empirical likelihood:

$\sum_{i=1}^n \delta_i \log (dH(x_i)) + (R_i - \delta_i)\log [1- dH(x_i) ] .$

Value

A list with the following components:

times	the location of the hazard jumps.
jumps	the jump size of hazard function at those locations.
lambda	the input lambda.
"-2LLR"	the -2Log Likelihood ratio.
IntHaz	The theta defined above, for the given lambda.

Author(s)

Mai Zhou

References

Pan, X. and Zhou, M. (2002), “Empirical likelihood in terms of hazard for censored data”. Journal of Multivariate Analysis 80, 166-188.

Examples

## fun <- function(x) { as.numeric(x <= 6.5) }
## emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=2, fun=fun) 
## fun2 <- function(x) {exp(-x)}  
## emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=0.2, fun=fun2)

---

Return Poisson Empirical likelihood ratio for the given lambda, with right censored data

Description

Compute the Poisson empirical likelihood ratio for the given tilt parameter lambda. Most useful for the construction of Wilks confidence intervals. The null hypothesis or constraint is defined by the parameter $\theta$, where

$\int fung(t) dH(t) = \theta$

Where $H(t)$ is the unknown cumulative hazard function; $fung(t)$ can be any given function.

In the future, the function $fung$ may replaced by the vector of $fung(x)$, since this is more flexible.

Input data can be right censored. If no censoring, set d=rep(1, length(x)).

Usage

emplikH1P(lambda, x, d, fung, CIforTheta=FALSE)

Arguments

lambda	a scalar. Can be positive or negative. The amount of tiling.
x	a vector of the censored survival times.
d	a vector of the censoring indicators, 1-uncensor; 0-right censor.
fung	a left continuous (weight) function used to calculate the weighted hazard in the parameter $\theta$. fung must be able to take a vector input. See example below.
CIforTheta	an optional logical value. Default to FALSE. If set to TRUE, will return the integrated hazard value for the given lambda.

Details

This function is for calculate lambda confidence intervals for $\theta$.

This function is designed for the case where the true distribution should be continuous. So there should be no tie in the data.

The log empirical likelihood used here is the ‘Poisson’ version empirical likelihood:

$\sum_{i=1}^n \delta_i \log (dH(x_i)) - [ H(x_i) ] ~.$

If there are ties in the data that are resulted from rounding, you may want to break the tie by adding a different tiny number to the tied observation(s). For example: 2, 2, 2, change to 2.00001, 2.00002, 2.00003. If those are true ties (thus the true distribution must be discrete) we recommend to use emplikH1B instead.

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 Lagrange multiplier.
"-2LLR"	the -2Log Empirical Likelihood ratio, Poisson version.
MeanHaz	The theta defined above, the hazard integral, if CIforTheta =TRUE.

Author(s)

Mai Zhou

References

Pan, X. and Zhou, M. (2002), “Empirical likelihood in terms of hazard for censored data”. Journal of Multivariate Analysis 80, 166-188.

Examples

## fun <- function(x) { as.numeric(x <= 6.5) }
## emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=2, fun=fun) 
## fun2 <- function(x) {exp(-x)}  
## emplikH1.test( x=c(1,2,3,4,5), d=c(1,1,0,1,1), theta=0.2, fun=fun2)

---

Empirical likelihood for weighted hazard with right censored, left truncated data

Description

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

$\int f(t, ... ) dH(t) = K$

with right censored, left truncated data, where $H(t)$ is the (unknown) cumulative hazard function; $f(t, ... )$ can be any given left continuous function in $t$; (of course the integral must be finite).

Usage

emplikH2.test(x, d, y= -Inf, K, fun, tola=.Machine$double.eps^.5,...)

Arguments

x	a vector containing the censored survival times.
d	a vector of the censoring indicators, 1-uncensor; 0-censor.
y	a vector containing the left truncation times. If left as default value, -Inf, it means no truncation.
K	a real number used in the constraint, i.e. to set the weighted integral of hazard to this value.
fun	a left continuous (in t) weight function used to calculate the weighted hazard in $H_0$. fun(t, ... ) must be able to take a vector input t.
tola	an optional positive real number specifying the tolerance of iteration error in solve the non-linear equation needed in constrained maximization.
...	additional parameter(s), if any, passing along to fun. This allows an implicit function of fun.

Details

This version works for implicit function f(t, ...).

This function is designed for continuous distributions. Thus we do not expect tie in the observation x. If you believe the true underlying distribution is continuous but the sample observations have tie due to rounding, then you might want to add a small number to the observations to break tie.

The likelihood used here is the ‘Poisson’ version of the empirical likelihood

$\prod_{i=1}^n ( dH(x_i) )^{\delta_i} \exp [-H(x_i)+H(y_i)] .$

For discrete distributions we recommend use emplikdisc.test().

Please note here the largest observed time is NOT automatically defined to be uncensored. In the el.cen.EM( ), it is (to make F a proper distribution always).

The constant K must be inside the so called feasible region for the computation to continue. This is similar to the requirement that when testing the value of the mean, the value must be inside the convex hull of the observations for the computation to continue. It is always true that the NPMLE value is feasible. So when the computation cannot continue, that means there is no hazard function dominated by the Nelson-Aalen estimator satisfy the constraint. You may try to move the theta and K closer to the NPMLE. When the computation cannot continue, 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 Lagrange multiplier.
"-2LLR"	the -2Log Likelihood ratio.
Pval	P-value
niters	number of iterations used

Author(s)

Mai Zhou

References

Pan, XR and Zhou, M. (2002), “Empirical likelihood in terms of cumulative hazard for censored data”. Journal of Multivariate Analysis 80, 166-188.

See Also

emplikHs.test2

Examples

z1<-c(1,2,3,4,5)
d1<-c(1,1,0,1,1)
fun4 <- function(x, theta) { as.numeric(x <= theta) }
emplikH2.test(x=z1,d=d1, K=0.5, fun=fun4, theta=3.5)
#Next, test if H(3.5) = log(2) .
emplikH2.test(x=z1,d=d1, K=log(2), fun=fun4, theta=3.5)
#Next, try one sample log rank test
indi <- function(x,y){ as.numeric(x >= y) }
fun3 <- function(t,z){rowsum(outer(z,t,FUN="indi"),group=rep(1,length(z)))} 
emplikH2.test(x=z1, d=d1, K=sum(0.25*z1), fun=fun3, z=z1) 
##this is testing if the data is from an exp(0.25) population.

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