merMCMC-class              package:lme4              R Documentation

_M_i_x_e_d-_m_o_d_e_l _M_a_r_k_o_v _c_h_a_i_n _M_o_n_t_e _C_a_r_l_o _r_e_s_u_l_t_s

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

     Objects of class '"merMCMC"' are Markov chain Monte Carlo samples
     from the distribution of the parameters of a fitted mixed-effects
     model.

_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("merMCMC", ...)'
     or, more commonly, via the 'mer' method for the generic 'mcmcsamp'
     function.

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


     '_G_p': a copy of the 'Gp' slot of the original 'mer' object

     '_S_T': matrix of samples from the parameters determining the 'ST'
          slot of the 'mer' object

     '_c_a_l_l': Matched call from the original 'mer' object

     '_d_e_v_i_a_n_c_e': vector of samples of the (ML) deviance

     '_d_i_m_s': a copy of the 'dims' slot of the original 'mer' object

     '_f_i_x_e_f': matrix of samples of the fixed-effects parameters

     '_n_c': Integer vector of length 'dims["nf"]'.  The number of
          columns of random effects in each term.

     '_r_a_n_e_f': matrix of samples of the random effects.  This matrix has
          zero columns unless 'saveb = TRUE' is specified in the call
          to 'mcmcsamp'.  Consider the size of this matrix, which could
          be very large, before setting 'saveb = TRUE'.

     '_s_i_g_m_a': vector of samples of the common scale parameter or
          'numeric(0)' if 'dims["useSc"]' is 'FALSE'.

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


     _H_P_D_i_n_t_e_r_v_a_l 'signature(object = "merMCMC")': use the chain to
          calculate Highest Posterior Density (HPD) intervals of a
          given empirical probability content for the model parameters.
           See 'HPDinterval'.

     _V_a_r_C_o_r_r 'signature(x = "merMCMC")': transform the 'ST' and 'sigma'
          slots to some combination of variances,  covariances,
          standard deviations and correlations.  See 'VarCorr' for
          details.

     _a_s._d_a_t_a._f_r_a_m_e 'signature(x = "merMCMC")': returns the
          fixef-effects and variance-covariance parameters from the
          chain in the form of a data frame.  The 'type' argument for
          the 'VarCorr' method can be passed to this method to select
          the type of variance-covariance parameters returned.

     _a_s._m_a_t_r_i_x 'signature(x = "merMCMC")': Same as the 'as.data.frame'
          method described above but returning a matrix.

     _c_o_e_r_c_e 'signature(from = "merMCMC", to = "data.frame")': Same as
          the 'as.data.frame' method.

     _d_e_n_s_i_t_y_p_l_o_t 'signature(object = "merMCMC")': plot empirical
          densities for the parameters from the chain.  See also
          'densityplot'.

     _q_q_m_a_t_h 'signature(object = "merMCMC")': plot quantile-quantile
          plots for the parameters from the sample in the chain. See
          also 'qqmath'.

     _x_y_p_l_o_t 'signature(object = "merMCMC")': plot traces of the
          parameter samples in the chain.

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

     'mcmcsamp' produces these objects, 'lmer', 'glmer' and 'nlmer'
     produce the 'mer' objects.

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

     showClass("merMCMC")

