overimpute              package:Amelia              R Documentation

_O_v_e_r_i_m_p_u_t_a_t_i_o_n _d_i_a_g_n_o_s_t_i_c _p_l_o_t

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

     Treats each observed value as missing and imputes from the
     imputation model from 'amelia' output.

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

     overimpute(output, var, legend = TRUE, xlab, ylab, main,
                frontend = FALSE, ...)

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

  output: output from the function 'amelia'.

     var: column number or variable name of the variable to overimpute.

  legend: a logical value indicating if a legend should be plotted.

    xlab: the label for the x-axis. The default is "Observed Values."

    ylab: the label for the y-axis. The default is "Imputed Values."

    main: main title of the plot. The default is to smartly title the
          plot using the variable name.

frontend: a logical value used internally for the Amelia GUI.

     ...: further graphical parameters for the plot.

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

     This function temporarily treats each observed value in 'var' as
     missing and imputes that value based on the imputation model of
     'output'. The dots are the mean imputation and the vertical lines
     are the 90% percent confidence intervals for imputations of each
     observed value. The diagonal line is the y=x line. If all of the
     imputations were perfect, then our points would all fall on the
     line. A good imputation model would have about 90% of the
     confidence intervals containing the truth; that is, about 90% of
     the vertical lines should cross the diagonal.

     The color of the vertical lines displays the fraction of missing
     observations in the pattern of missingness for that observation.
     The legend codes this information. Obviously, the imputations will
     be much tighter if there are more observed covariates to use to
     impute that observation.

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

     Other imputation diagnostics are 'compare.density', 'disperse',
     and 'tscsPlot'.

