reStruct-class             package:lme4             R Documentation

_C_l_a_s_s "_r_e_S_t_r_u_c_t"

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

     The random-effects model structure in a linear mixed-effects model
     or a generalized linear mixed-effects model or a nonlinear
     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("reStruct",
     ...)'.

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

     `_r_a_n_d_o_m': A `"list"' of `lmeLevel' objects giving the levels of
          random effects in the model, the fixed-effects, and the
          response or the working residual.

     `_f_i_x_e_d': The `"formula"' of the response and fixed-effects terms.

     `_o_f_f_s_e_t': `"numeric"': the offset in the model, if present.

     `_d_i_r_t_y_D_e_c_o_m_p_o_s_e_d': `"logical"': if `TRUE' the decomposed matrix
          must be recalculated.

     `_u_s_e_W_e_i_g_h_t_e_d': `"logical"': if `TRUE' calculate and use the
          weighted model matrix to form the decompositions; otherwise
          use the original model matrix.

     `_d_i_r_t_y_S_t_o_r_e_d': `"logical"': if `TRUE' the matrix of stored
          decompositions must be recalculated.

     `_d_i_r_t_y_B_b_e_t_a_s': `"logical"': if `TRUE' the BLUP's and the
          conditional estimates of the fixed-effects must be
          recalculated.

     `_l_o_g_L_i_k': `"numeric"': the log-likelihood at the current parameter
          values.

     `_R_E_M_L': `"logical"': if `TRUE' the parameters will be estimated
          according to the REML criterion

     `_a_n_a_l_y_t_i_c_H_e_s_s_i_a_n': `"logical"': if `TRUE' the `hessianArray' slot
          of the lmeLevel objects are calculated during optimization.

     `_r_e_v_e_r_s_e_O_r_d_e_r': `"integer"': the permutation of the rows that
          provides the original order.

     `_o_r_i_g_O_r_d_e_r': `"integer"': the inverse of the `reverseOrder'
          permutation.

     `_o_r_i_g_i_n_a_l': `"matrix"': the original model matrix, including the
          original response in the last column.

     `_w_e_i_g_h_t_e_d': `"matrix"': the model matrix and responses after
          applying weights.

     `_s_t_o_r_e_d': `"matrix"': a model matrix of intermediate decomposition
          results needed for evaluating the BLUPs and the EM or ECME
          iterations.

     `_d_e_c_o_m_p_o_s_e_d': `"matrix"': the model matrix after predecomposition.
           This generally has many few rows than `original'.

     `_b_b_e_t_a_s': `"numeric"': the BLUPs and the conditional estimates of
          the fixed-effects parameters at the current values of the
          relative precision matrices.

     `_d_o_n_t_C_o_p_y': `"logical"': if `TRUE' it indicates that this object
          has just been created and is only assigned to one name.  In
          these circumstances changes are made directly on the object
          without copying. This is dangerous.  You probably don't want
          to modify this setting.

     `_a_s_s_i_g_n._X': Object of class `"ANY"': the `assign' attribute from
          the model matrix for the fixed effects.

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

     _E_M_s_t_e_p_s<- `signature(x = "reStruct", value = "list")': perform the
          EM iterations

     _L_M_E_g_r_a_d_i_e_n_t `signature(x = "reStruct", A = "missing", nlev =
          "missing")': evaluate the gradient of the linear
          mixed-effects profiled log-likelihood.

     _L_M_E_o_p_t_i_m_i_z_e<- `signature(x = "reStruct", value = "list")':
          Optimize with `optim' or `nlm'.

     _V_a_r_C_o_r_r `signature(x = "reStruct")': Extract the variances and
          covariances of the random effects.

     _c_o_e_f `signature(object = "reStruct")': return the current
          parameters in the object.

     _c_o_e_f<- `signature(object = "reStruct", value = "numeric")': assign
          the parameters to the object.

     _f_i_t_t_e_d `signature(object = "reStruct")': extract the fitted
          values.

     _f_i_x_e_f `signature(object = "reStruct")': extract the fixed effects.

     _f_i_x_e_f<- `signature(object = "reStruct", value = "numeric")':
          assign the fixed effects.

     _g_e_t_F_i_x_D_F `signature(object = "reStruct")': extract the denominator
          degrees of freedom for the fixed-effects terms.

     _g_e_t_G_r_o_u_p_s `signature(object = "reStruct", form = "missing", level
          = "ANY", data = "missing", sep = "missing")': return the
          grouping factors for the random effects

     _g_e_t_R_e_s_p_o_n_s_e `signature(object = "reStruct")': extract the
          response.

     _l_o_g_L_i_k `signature(object = "reStruct")': return the log-likelihood
          at the current parameter values. 

     _m_o_d_e_l._m_a_t_r_i_x `signature(object = "reStruct")': return the model
          matrix for the object.

     _m_o_d_e_l._m_a_t_r_i_x<- `signature(x = "reStruct", value = "matrix")':
          assign the model matrix to the object.

     _r_a_n_e_f `signature(object = "reStruct")': extract the random effects
          predictors.

     _r_e_s_p_o_n_s_e<- `signature(x = "reStruct", value = "numeric")': set the
          working response (used in GLMM models).

     _s_u_m_m_a_r_y `signature(object = "reStruct")': summarize the object.

     _w_e_i_g_h_t_e_d<- `signature(x = "reStruct", value = "matrix")': update
          the weights.

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

