MNSs                  package:VGAM                  R Documentation

_T_h_e _M_N_S_s _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 three independent parameters of the  the MNSs blood
     group system.

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

     MNSs(link = "logit", earg=list(), imS = NULL, ims = NULL, inS = NULL)

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

    link: Link function applied to the three parameters. See 'Links'
          for more choices.

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

imS, ims, inS: Optional initial value for 'mS', 'ms' and 'nS'
          respectively.  A 'NULL' means they are computed internally.

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

     There are three independent parameters: 'm_S', 'm_s', 'n_S', say,
     so that  'n_s = 1 - m_S - m_s - n_S'.   We let the eta vector
     (transposed) be '(g(m_S), g(m_s), g(n_S))' where 'g' is the link
     function.

_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 6-column matrix of counts, where the columns
     are MS, Ms, MNS, MNs, NS, Ns (in order). Alternatively, the input
     can be a 6-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:

     Elandt-Johnson, R. C. (1971) _Probability Models and Statistical
     Methods in Genetics_, New York: Wiley.

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

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

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

     # Order matters only:
     y = cbind(MS=295, Ms=107, MNS=379, MNs=322, NS=102, Ns=214) 
     fit = vglm(y ~ 1, MNSs("logit", .25, .28, .08), trace=TRUE)
     fit = vglm(y ~ 1, MNSs(link=logit), trace=TRUE, cri="coef")
     Coef(fit) 
     rbind(y, sum(y)*fitted(fit))
     diag(vcov(fit))^0.5

