mids                  package:mice                  R Documentation

_M_u_l_t_i_p_l_y _I_m_p_u_t_e_d _D_a_t_a _S_e_t

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

     An object containing a multiply imputed data set. The "mids"
     object is generated by the mice and mice.mids functions. The
     "mids" class of objects has methods for the following generic
     functions: 'print', 'summary', 'plot'

_U_s_a_g_e:

     ## S3 method for class 'mids':
     print(x, ...)
     ## S3 method for class 'mids':
     summary(object, ...)
     ## S3 method for class 'mids':
     plot(x, y, ...)

_A_r_g_u_m_e_n_t_s:

       x: A mids object.

  object: A mids object.

       y: Not used.

     ...: Not used.

_V_a_l_u_e:

    call: The call that created the object.

    data: A copy of the incomplete data set.

       m: The number of imputations.

    nmis: An array containing the number of missing observations per
          column.

     imp: A list of nvar components with the generated multiple
          imputations.  Each part of the list is a 'nmis[j]' by m
          matrix of imputed values for  variable j.

imputationMethod: A vector of strings of length(nvar) specifying the
          elementary  imputation method per column.

predictorMatrix: A square matrix of size 'ncol(data)' containing 0/1
          data specifying  the predictor set.

visitSequence: The sequence in which columns are visited.

    seed: The seed value of the solution.

iteration: Last Gibbs sampling iteration number.

lastSeedValue: The most recent seed value.

chainMean: A list of m components. Each component is a
          'length(visitSequence)' by maxit matrix containing the mean
          of the generated multiple  imputations. The array can be used
          for monitoring convergence. Note that observed data are not
          present in this mean.

chainCov: A list with similar structure of itermean, containing the
          covariances  of the imputed values.

     pad: A list containing various settings of the padded imputation
          model,  i.e. the imputation model after creating dummy
          variables. Normally,  this array is only useful for error
          checking.

_A_u_t_h_o_r(_s):

     Stef van Buuren, Karin Oudshoorn, 2000

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

     Van Buuren, S. & Oudshoorn, C.G.M. (2000). Multivariate Imputation
     by Chained Equations:  MICE V1.0 User's manual. Report
     PG/VGZ/00.038, TNO Prevention and Health, Leiden.

