EBest                 package:spdep                 R Documentation

_G_l_o_b_a_l _E_m_p_i_r_i_c_a_l _B_a_y_e_s _e_s_t_i_m_a_t_o_r

_D_e_s_c_r_i_p_t_i_o_n:

     The function computes global empirical Bayes estimates for rates
     "shrunk" to the overall mean.

_U_s_a_g_e:

     EBest(n, x, family="poisson")

_A_r_g_u_m_e_n_t_s:

       n: a numeric vector of counts of cases

       x: a numeric vector of populations at risk

  family: either "poisson" for rare conditions or "binomial" for
          non-rare conditions

_D_e_t_a_i_l_s:

     Details of the implementation for the "poisson" family are to be
     found in Marshall, p. 284-5, and Bailey and Gatrell p. 303-306 and
     exercise 8.2, pp. 328-330. For the "binomial" family, see Martuzzi
     and Elliott (implementation by Olaf Berke).

_V_a_l_u_e:

     A data frame with two columns: 

     raw: a numerical vector of raw (crude) rates

   estmm: a numerical vector of empirical Bayes estimates

       a: global method of moments phi value

       m: global method of moments gamma value

_A_u_t_h_o_r(_s):

     Roger Bivand Roger.Bivand@nhh.no and Olaf Berke, Population
     Medicine, OVC, University of Guelph, CANADA

_R_e_f_e_r_e_n_c_e_s:

     Marshall R M (1991) Mapping disease and mortality rates using
     Empirical Bayes Estimators, Applied Statistics, 40, 283-294;
     Bailey T, Gatrell A (1995) Interactive Spatial Data Analysis,
     Harlow: Longman, pp. 303-306, Martuzzi M, Elliott P (1996)
     Empirical Bayes estimation of small area prevalence of non-rare
     conditions, Statistics in Medicine 15, 1867-1873.

_S_e_e _A_l_s_o:

     'EBlocal', 'probmap', 'EBImoran.mc'

_E_x_a_m_p_l_e_s:

     example(auckland)
     res <- EBest(auckland$M77_85, 9*auckland$Und5_81)
     attr(res, "parameters")
     cols <- grey(6:2/7)
     brks <- c(-Inf,2,2.5,3,3.5,Inf)
     plot(auckland, col=cols[findInterval(res$estmm*1000, brks, all.inside=TRUE)])
     legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
     title(main="Global moment estimator of infant mortality per 1000 per year")
     data(huddersfield)
     res <- EBest(huddersfield$cases, huddersfield$total, family="binomial")
     round(res[,1:2],4)*100

