el.test.dfp {el.convex}R Documentation

Empirical likelihood ratio test for the means using DFP method for optimization

Description

Compute the empirical likelihood ratio with the mean vector fixed at mu. The log empirical likelihood been maximized el.test.dfp uses DFP method

Usage

el.test.dfp(x, mu, lam, maxit = 100, tol = 1e-07)

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 H0
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
tol an optional real value for convergence test

Details

If mu is in the interior of the convex hull of the observations x, then wts should sum to 1

Value

-2LLR the -2 loglikelihood ratio; approximate chisq distribution under H0
Pval the observed P-value by chi-square approximation
lambda the final value of Lagrange multiplier
nits number of iteration performed
wts weights on the observations
mu the means that are achieved

Warning

el.convex has not been thoroughly tested. Please report bugs.

Author(s)

Dan Yang, Dylan Small

References

A Study of Methods for Computing Empirical Likelihood Numerical recipes in C

Examples

x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
el.test.dfp(x, mu=c(1,2))

[Package el.convex version 1.0 Index]