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- _P_a_r_a_m_e_t_r_i_c _A_B_C _c_o_n_f_i_d_e_n_c_e _l_i_m_i_t_s
-
- abcpar(x, tt, S, etahat, mu, n=rep(1,length(x)),lambda=0.001,
- alpha=c(0.025, 0.05, 0.1, 0.16))
-
- _A_r_g_u_m_e_n_t_s:
-
- x: vector of data
-
- tt: function of expectation parameter mu defining
- the parameter of interest
-
- S: maximum likelihood estimate of the covari-
- ance 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 lev-
- els desired
-
- _V_a_l_u_e_s:
-
- 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
-
- _R_e_f_e_r_e_n_c_e_s:
-
- Efron, B, and DiCiccio, T. (1992) More accurate confi-
- dence 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.
-
- _E_x_a_m_p_l_e_s:
-
- # 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)
-
-