el.test.bfgs {el.convex} | R Documentation |
Compute the empirical likelihood ratio with the mean vector fixed at mu. The log empirical likelihood been maximized. el.test.bfgs uses BFGS method.
el.test.bfgs(x, mu, lam, maxit = 100, tol = 1e-07)
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 |
If mu is in the interior of the convex hull of the observations x, then wts should sum to 1.
-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 |
el.convex has not been thoroughly tested. Please report bugs.
Dan Yang, Dylan Small
A Study of Methods for Computing Empirical Likelihood Numerical recipes in C
x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50) el.test.newton(x, mu=c(1,2)) el.test.bfgs(x, mu=c(1,2))