disperse               package:Amelia               R Documentation

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

     A visual diagnostic of EM convergence from multiple overdispersed
     starting values for an output from 'amelia'.

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

     disperse(output, m = 5, dims = 1, p2s = 0, frontend = FALSE, ...)

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

  output: output from the function 'amelia'.

       m: the number of EM chains to run from overdispersed starting
          values.

    dims: the number of principle components of the parameters to
          display and assess convergence on (up to 2).

     p2s: an integer that controls printing to screen. 0 (default)
          indicates no printing, 1 indicates normal screen output and 2
          indicates diagnostic output.

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 tracks the convergence of 'm' EM chains which start
     from various overdispersed starting values. This plot should give
     some indication of the sensitivity of the EM algorithm to the
     choice of starting values in the imputation model in 'output'. If
     all of the lines converge to the same point, then we can be
     confident that starting values are not affecting the EM algorithm.

     As the parameter space of the imputation model is of a
     high-dimension, this plot tracks how the first (and second if
     'dims' is 2) principle component(s) change over the iterations of
     the EM algorithm. Thus, the plot is a lower dimensional summary of
     the convergence and is subject to all the drawbacks inherent in
     said summaries.

     For 'dims==1', the function plots a horizontal line at the
     position where the first EM chain converges. Thus, we are checking
     that the other chains converge close to that horizontal line. For
     'dims==2', the function draws a convex hull around the point of
     convergence for the first EM chain. The hull is scaled to be
     within the tolerance of the EM algorithm. Thus, we should check
     that the other chains end up in this hull.

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

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

