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- _N_o_n_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
-
- abcnon(x, tt, epsilon=0.001,
- alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975))
-
- _A_r_g_u_m_e_n_t_s:
-
- x : the data. Must be either a vector, or a
- matrix whose rows are the observations
-
- tt : function defining the parameter in the resam-
- pling form tt(p,x), where p is the vector of
- proportions and x is the data
-
- epsilon : optional argument specifying step size for
- finite difference calculations
-
- alpha : optional argument specifying confidence lev-
- els desired
-
- _V_a_l_u_e_s:
-
- list with following components
-
- limits : The estimated confidence points, from the ABC and
- standard normal methods
-
- stats : list consisting of t0=observed value of tt,
- sighat=infinitesimal jackknife estimate
- of standard error of tt, bhat= estimated bias
-
- constants : list consisting of a=acceleration constant,
- z0=bias adjustment, cq=curvature component
-
- tt.inf : approximate influence components of tt
-
- pp : matrix whose rows are the resampling points in the
- least favourable family . The abc confidence
- points are the function tt evaluated at these
- points
-
- _R_e_f_e_r_e_n_c_e_s:
-
- Efron, B, and DiCiccio, T. (1992) More accurate confi-
- dence 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.
-
-