lme-class                package:lme4                R Documentation

_C_l_a_s_s "_l_m_e"

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

     A fitted linear mixed-effects model.

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

     Many methods for the '"lme"' class simply recall the generic on
     the '"rep"' component of the first argument.  Thus the methods
     actually are applied to an object of the '"ssclme"' class.

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

     Objects are usually created by calls to the constructor function
     'lme'.  They also can be created by calls of the form 'new("lme",
     ...)'.

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

     '_c_a_l_l': A copy of the function call that created the object.

     '_f_a_c_s': A 'list' of (possibly reordered) grouping factors
          associated with the random effects.

     '_x': If the optional argument 'x' to 'lme' is 'TRUE', a 'list' of
          model matrices associated with the random effects, and the
          fixed effects with the response appended. Otherwise, an empty
          list.

     '_m_o_d_e_l': The model frame (of class '"data.frame"') for the model
          or, if the optional argument 'model' to 'lme' is 'FALSE', an
          empty frame.

     '_R_E_M_L': A '"logical"' indicator of the model having been fit
          according to the REML criterion.

     '_r_e_p': An '"ssclme"' object representing the fitted model.

     '_f_i_t_t_e_d': A '"numeric"' vector of fitted values.

     '_r_e_s_i_d_u_a_l_s': A '"numeric"' vector of raw residuals.

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

     _V_a_r_C_o_r_r 'signature(x = "lme")': Extract the variances, standard
          deviations, and correlations of the random effects.

     _a_n_o_v_a 'signature(object = "lme")': Perform an analysis of
          variance.

     _c_o_e_f 'signature(object = "lme")': Extract the parameters that
          determine the relative precision matrices. The optional
          argument 'unconst' determines if the constrained or
          unconstrained parameterization is used.

     _d_e_v_i_a_n_c_e 'signature(object = "lme")': Extract the deviance as a
          numeric scalar.  The optional argument 'REML' determines if
          the REML or ML criterion is used.

     _f_i_t_t_e_d 'signature(object = "lme")': Extract the fitted values as a
          numeric vector.

     _f_i_x_e_f 'signature(object = "lme")': Extract the fixed effects
          coefficients as a named numeric vector 

     _f_o_r_m_u_l_a 'signature(x = "lme")': Extract the formula of the
          response and the fixed effects.

     _l_o_g_L_i_k 'signature(object = "lme")': Extract the log-likelihood
          corresponding to the ML or REML criterion.

     _p_l_o_t 'signature(x = "lme")':

     _r_a_n_e_f 'signature(object = "lme")': Extact the random effects as a
          named list of numeric matrices.

     _r_e_s_i_d_u_a_l_s 'signature(object = "lme")': Extract the residuals as a
          numeric vector.

     _s_h_o_w 'signature(object = "lme")': Print a concise description of
          the object.

     _s_u_m_m_a_r_y 'signature(object = "lme")': Create a summary object of
          class '"summary.lme"'.

     _u_p_d_a_t_e 'signature(object = "lme")': Create an updated fitted
          model.

     _v_c_o_v 'signature(object = "lme")': Extract the variances and
          covariances of the fixed-effects parameter estimates.

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

     'lme', 'ssclme-class'

