amelia.disperse(output, m = 5, dims = 1, p2s = 0, frontend = FALSE, ...)amelia.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.
compare.density, disperse, and
tscsPlot.