pdMat-class               package:lme4               R Documentation

_C_l_a_s_s _p_d_M_a_t, _p_o_s_i_t_i_v_e-_d_e_f_i_n_i_t_e _m_a_t_r_i_c_e_s

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

     A virtual class of parameterized positive-definite symmetric
     matrices.  This class describes the slots and methods that actual
     classes of positive-definite matrices are expected to incorporate.
      Some classes that inherit from `pdMat' have additional slots and
     methods.

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

     Objects of class `pdMat' are not constructed directly; only
     objects from classes that inherit from `pdMat' are constructed.

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

     `_f_o_r_m': Object of class `"formula"', holds the formula for the
          object 

     `_N_a_m_e_s': Object of class `"character"' holding the names of the
          rows (and columns) of the positive-definite symmetric matrix
          represented by the object.

     `_p_a_r_a_m': `"numeric"' - the parameter vector.

     `_N_c_o_l': `"integer"' - the number of columns (and rows) in the
          matrix.

     `_f_a_c_t_o_r': `"matrix"' - a square-root factor of the matrix.

     `_l_o_g_D_e_t': `"numeric"' - the logarithm of the determinant of the
          factor.

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

     _c_o_e_r_c_e `signature(from = "pdMat", to = "pdfactor")': extract a
          square-root factor of the matrix represented by the object. 
          This factor has a `logDet' slot giving the logarithm of its
          determinant.  In the case of `pdLogChol' these are both
          scalars and the `logDet' attribute is the logarithm of the
          absolute value of the factor.

     _c_o_r_M_a_t_r_i_x `signature(object = "pdMat")': Extract the correlation
          matrix corresponding to the positive-definite matrix
          represented by the object.  This method is present for back
          compatibility only.  The preferred way of extracting the
          correlation matrix is to coerce the object to the
          `"corrmatrix"' class.

     _d_i_m `signature(x = "pdMat")': the dimensions of the
          positive-definite matrix represented by the object. 

     _f_o_r_m_u_l_a `signature(x = "pdMat")': extract the formula 

     _i_s_I_n_i_t_i_a_l_i_z_e_d `signature(object = "pdMat")': `TRUE' if the object
          has been initialized, otherwise `FALSE'.

     _l_o_g_D_e_t `signature(object = "pdMat", covariate = "missing")': the
          logarithm of the determinant of the factor of the
          positive-definite matrix represented by the object. 

     _n_a_m_e_s `signature(x = "pdMat")': extract a vector of names, which
          are both the column names and the row names of the
          positive-definite matrix represented by the object.

     _n_a_m_e_s<- `signature(x = "pdMat")': assign the names,

     _p_d_F_a_c_t_o_r `signature(object = "pdMat")': Extract the square root
          factor of positive-definite symmetric matrix represented by
          the object.  This method is present for back compatibility
          only.  The preferred way of extracting the factor is to
          coerce the object to the `"pdfactor"' class.

     _p_d_M_a_t_r_i_x `signature(object = "pdMat")': Extract the
          positive-definite symmetric matrix represented by the object.
          This method is present for back compatibility only.  The
          preferred way of extracting the positive-definite symmetric
          matrix is to coerce the object to the `"pdmatrix"' class.

     _s_h_o_w `signature(x = "pdMat")': show the object.

     _s_o_l_v_e `signature(a = "pdMat", b = "missing")': Create an object of
          the same class representing the inverse of the
          positive-definite matrix.

     _s_u_m_m_a_r_y `signature(object = "pdMat")':

_N_o_t_e:

     `pdMat' objects are primarily used to represent the
     variance-covariance matrix or the precision matrix of
     random-effects terms in mixed-effects models.  Frequently they are
     constructed from a formula only in the call to the mixed-effects
     modelling function then assigned a value as part of the
     initialization of the model.

_R_e_f_e_r_e_n_c_e_s:

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

     `pdCompSymm-class', `pdDiag-class', `pdLogChol-class',
     `pdIdent-class'

