mle2-class               package:bbmle               R Documentation

_C_l_a_s_s "_m_l_e_2". _R_e_s_u_l_t _o_f _M_a_x_i_m_u_m _L_i_k_e_l_i_h_o_o_d _E_s_t_i_m_a_t_i_o_n.

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

     This class encapsulates results of a generic maximum likelihood
     procedure.

_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("mle2", ...)',
     but most often as the result of a call to 'mle2'.

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


     '_c_a_l_l': (language) The call to 'mle2'.

     '_c_a_l_l._o_r_i_g': (language) The call to 'mle2', saved in its original
          form (i.e. without data arguments evaluated).

     '_c_o_e_f': (numeric) Vector of estimated parameters.

     '_d_a_t_a': (data frame or list) Data with which to evaluate the
          negative log-likelihood function

     '_f_u_l_l_c_o_e_f': (numeric) Fixed and estimated parameters.

     '_v_c_o_v': (numeric matrix) Approximate variance-covariance matrix,
          based on the second derivative matrix at the MLE.

     '_m_i_n': (numeric) Minimum value of objective function = minimum
          negative log-likelihood.

     '_d_e_t_a_i_l_s': (list) Return value from 'optim'.

     '_m_i_n_u_s_l_o_g_l': (function) The negative log-likelihood function.

     '_o_p_t_i_m_i_z_e_r': (character) The optimizing function used.

     '_m_e_t_h_o_d': (character) The optimization method used.

     '_f_o_r_m_u_l_a': (character) If a formula was specified, a character
          vector giving the formula and parameter specifications.

_M_e_t_h_o_d_s:


     _c_o_n_f_i_n_t 'signature(object = "mle2")': Confidence intervals from
          likelihood profiles.

     _s_h_o_w 'signature(object = "mle2")': Display object briefly.

     _s_h_o_w 'signature(object = "summary.mle2")': Display object briefly.

     _s_u_m_m_a_r_y 'signature(object = "mle2")': Generate object summary.

     _u_p_d_a_t_e 'signature(object = "mle2")':  Update fit.

     _v_c_o_v 'signature(object = "mle2")': Extract variance-covariance
          matrix.

     _p_l_o_t 'signature(object="profile.mle2,missing")': Plot profile. 

