choynowski               package:spdep               R Documentation

_C_h_o_y_n_o_w_s_k_i _p_r_o_b_a_b_i_l_i_t_y _m_a_p _v_a_l_u_e_s

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

     Calculates Choynowski probability map values.

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

     choynowski(n, x, row.names=NULL, tol = .Machine$double.eps^0.5)

_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

row.names: row names passed through to output data frame

     tol: accumulate values for observed counts >= expected until value
          less than tol

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

     A data frame with columns: 

    pmap: Poisson probability map values: probablility of getting a
          more ``extreme'' count than actually observed, one-tailed
          with less than expected and more than expected folded
          together

    type: logical: TRUE if observed count less than expected

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

     Roger Bivand Roger.Bivand@nhh.no

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

     Choynowski, M (1959) Maps based on probabilities, Journal of the
     American Statistical Association, 54, 385-388; Cressie, N, Read,
     TRC (1985), Do sudden infant deaths come in clusters? Statistics
     and Decisions, Supplement Issue 2, 333-349; Bailey T, Gatrell A
     (1995) Interactive Spatial Data Analysis, Harlow: Longman, pp.
     300-303.

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

     'probmap'

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

     example(auckland)
     res <- choynowski(auckland$M77_85, 9*auckland$Und5_81)
     res1 <- probmap(auckland$M77_85, 9*auckland$Und5_81)
     table(abs(res$pmap - res1$pmap) < 0.00001, res$type)
     lt005 <- (res$pmap < 0.05) & (res$type)
     ge005 <- (res$pmap < 0.05) & (!res$type)
     cols <- rep("white", length(lt005))
     cols[lt005] <- grey(2/7)
     cols[ge005] <- grey(5/7)
     plot(auckland, col=cols) 
     legend("bottomleft", fill=grey(c(2,5)/7), legend=c("low", "high"), bty="n")

