Nonparametric ABC Confidence Limits
See Efron and Tibshirani (1993) for details on this function.
abcnon(x, tt, epsilon=0.001,
alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975))x |
the data. Must be either a vector, or a matrix whose rows are the observations |
tt |
function defining the parameter in the resampling form
|
epsilon |
optional argument specifying step size for finite difference calculations |
alpha |
optional argument specifying confidence levels desired |
list with following components
limits |
The estimated confidence points, from the ABC and standard normal methods |
stats |
list consisting of |
constants |
list consisting of |
tt.inf |
approximate influence components of |
pp |
matrix whose rows are the resampling points in the least
favourable family. The abc confidence points are the function |
call |
The deparsed call |
Efron, B, and DiCiccio, T. (1992) More accurate confidence intervals in exponential families. Biometrika 79, pages 231-245.
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.
# compute abc intervals for the mean
x <- rnorm(10)
theta <- function(p,x) {sum(p*x)/sum(p)}
results <- abcnon(x, theta)
# compute abc intervals for the correlation
x <- matrix(rnorm(20),ncol=2)
theta <- function(p, x)
{
x1m <- sum(p * x[, 1])/sum(p)
x2m <- sum(p * x[, 2])/sum(p)
num <- sum(p * (x[, 1] - x1m) * (x[, 2] - x2m))
den <- sqrt(sum(p * (x[, 1] - x1m)^2) *
sum(p * (x[, 2] - x2m)^2))
return(num/den)
}
results <- abcnon(x, theta)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.