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", earg=list(), ipA = NULL, ipO = NULL)

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

    link: Link function applied to 'pA' and 'pB'. See 'Links' for more
          choices.

    earg: List. Extra argument applied to each of the links. See 'earg'
          in 'Links' for general information.

ipA, ipO: Optional initial value for 'pA' and 'pO'. A 'NULL' value
          means values are computed internally. 

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

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

_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=identity), trace=TRUE, cri="coef")
     coef(fit, matrix=TRUE)
     Coef(fit) # Estimated pA and pB
     rbind(y, sum(y)*fitted(fit))
     sqrt(diag(vcov(fit)))

