glimML-class               package:aod               R Documentation

_R_e_p_r_e_s_e_n_t_a_t_i_o_n _o_f _M_o_d_e_l_s _o_f _F_o_r_m_a_l _C_l_a_s_s "_g_l_i_m_M_L"

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

     Representation of models of formal class "glimML" fitted by
     maximum-likelihood method.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("glimML", ...)'
     or,  more commonly, via the functions 'betabin' or 'negbin'.

_S_l_o_t_s:

     '_C_A_L_L' The call of the function.

     '_l_i_n_k' The link function used to transform the mean: "logit",
          "cloglog" or "log".

     '_m_e_t_h_o_d' The type of fitted model: "BB" for beta-binomial and "NB"
          for negative-binomial models.

     '_f_o_r_m_u_l_a' The formula used to model the mean.

     '_r_a_n_d_o_m' The formula used to model the overdispersion parameter
          phi.

     '_d_a_t_a' Data set to which model was fitted. Different from the
          original data in case of missing value(s).

     '_p_a_r_a_m' The vector of the ML estimated parameters b and phi.

     '_v_a_r_p_a_r_a_m' The variance-covariance matrix of the ML estimated
          parameters b and phi.

     '_l_o_g_L' The log-likelihood of the fitted model.

     '_l_o_g_L._m_a_x' The log-likelihood of the maximal model (data).

     '_d_e_v' The deviance of the model, i.e., '- 2 * (logL - logL.max)'.

     '_d_f._r_e_s_i_d_u_a_l' The residual degrees of freedom of the fitted model.

     '_n_b_p_a_r' The number of *estimated* parameters, i.e., nbpar = total
          number of parameters - number  of fixed parameters. See
          argument 'fixpar' in 'betabin' or 'negbin'.

     '_i_t_e_r_a_t_i_o_n_s' The number of iterations performed in 'optim'.

     '_c_o_d_e' An integer (returned by 'optim') indicating why the
          optimization process terminated.

          _1 Relative gradient is close to 0, current iterate is
               probably solution.

          _2 Successive iterates within tolerance, current iterate is
               probably solution.

          _3 Last global step failed to locate a point lower than
               estimate. Either estimate is an approximate  local
               minimum of the function or 'steptol' is too small.

          _4 Iteration limit exceeded.

          _5 Maximum step size 'stepmax' exceeded 5 consecutive times.
               Either the function is unbounded below,  becomes
               asymptotic to a finite value from above in some
               direction or 'stepmax' is too small.


     '_p_a_r_a_m._i_n_i' The initial values provided to the ML algorithm.

     '_n_a._a_c_t_i_o_n' A function defining the action taken when missing
          values are encountered.

