ABO                   package:VGAM                   R Documentation

_T_h_e _A_B_O _B_l_o_o_d _G_r_o_u_p _S_y_s_t_e_m

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

     Estimates the two independent parameters of the  the ABO blood
     group system.

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

     ABO(link = "logit", ir = NULL, ip = NULL)

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

    link: Link function applied to 'p' and 'q'. See 'Links' for more
          choices.

  ir, ip: Optional initial value for 'r' and 'p'. A 'NULL' value means
          values are computed internally. 

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

     The parameters 'p' and 'q' are probabilities, so that 'r=1-p-q' is
     the third probability. The probabilities 'p' and 'r' correspond to
     A and O respectively, so that 'q' is the probability for B. It is
     easier to make use of initial values for 'r' than for 'q'.

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

     An object of class '"vglmff"' (see 'vglmff-class'). The object is
     used by modelling functions such as 'vglm' and 'vgam'.

_N_o_t_e:

     The input can be a 4-column matrix of counts, where the columns 
     are A, B, AB, O (in order). Alternatively, the input can be a
     4-column matrix of  proportions (so each row adds to 1) and the
     'weights' argument is used to specify the total number of counts
     for each row.

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

     T. W. Yee

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

     Lange, K. (2002) _Mathematical and Statistical Methods for Genetic
     Analysis_, 2nd ed. New York: Springer-Verlag.

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

     'AA.Aa.aa', 'AB.Ab.aB.ab', 'AB.Ab.aB.ab2', 'G1G2G3', 'MNSs'.

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

     y = cbind(A=725, B=258, AB=72, O=1073) # Order matters, not the name
     fit = vglm(y ~ 1, ABO(link=logit), trace=TRUE, cri="coef")
     fit = vglm(y ~ 1, ABO(link=probit), trace=TRUE, cri="coef")
     fit = vglm(y ~ 1, ABO(link=cloglog), trace=TRUE, cri="coef")
     fit = vglm(y ~ 1, ABO(link=identity), trace=TRUE, cri="coef")
     coef(fit, matrix=TRUE)
     Coef(fit) # Estimated p and q
     rbind(y, sum(y)*fitted(fit))
     sqrt(diag(vcov(fit)))

