Parametric ABC Confidence Limits
See Efron and Tibshirani (1993) for details on this function.
abcpar(y, tt, S, etahat, mu, n=rep(1,length(y)),lambda=0.001,
alpha=c(0.025, 0.05, 0.1, 0.16))y |
vector of data |
tt |
function of expectation parameter |
S |
maximum likelihood estimate of the covariance matrix of |
etahat |
maximum likelihood estimate of the natural parameter eta |
mu |
function giving expectation of |
n |
optional argument containing denominators for binomial (vector of
length |
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 |
constants |
list consisting of |
,
asym.05 |
asymmetry 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
## Not run:
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)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.