abcpar {bootstrap} | R Documentation |
abcpar(x, tt, S, etahat, mu, n=rep(1,length(x)),lambda=0.001, alpha=c(0.025, 0.05, 0.1, 0.16))
x |
vector of data |
tt |
function of expectation parameter mu defining the parameter of interest |
S |
maximum likelihood estimate of the covariance matrix of x |
etahat |
maximum likelihood estimate of the natural parameter eta |
mu |
function giving expectation of x in terms of eta |
n |
optional argument containing denominators for binomial (vector of
length length(x) ) |
lambda |
optional argument specifying step size for finite difference calculation |
alpha |
optional argument specifying confidence levels desired |
list with the following components
call |
the call to abcpar |
limits |
The nominal confidence level, ABC point, quadratic ABC point, and standard normal point. |
stats |
list consisting of observed value of tt , estimated standard error and estimated bias |
constants |
list consisting of a =acceleration constant,
z0 =bias adjustment, cq =curvature component |
Efron, B, and DiCiccio, T. (1992) More accurate confidence intervals in exponential families. Bimometrika 79, pages 231-245.
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.
# binomial # x is a p-vector of successes, n is a p-vector of # number of trials S <- matrix(0,nrow=p,ncol=p) S[row(S)==col(S)] <- x*(1-x/n) mu <- function(eta,n){n/(1+exp(eta))} etahat <- log(x/(n-x)) #suppose p=2 and we are interested in mu2-mu1 tt <- function(mu){mu[2]-mu[1]} x <- c(2,4); n <- c(12,12) a <- abcpar(x, tt, S, etahat,n)