First hybrid MC success by drastically shrinking epsilon.
I also tried scaling the components by an estimated newton step
size, but it did not  work at all.  That code is commented out.
increase epsilon x 5, to .001.  Seems to help some and hurt others.
Visually, these effective sizes appear to pessimistic, but here's what
they were from the previous commit
> effectiveSize(MCTRACE[,1:9])
      var1       var2       var3       var4       var5       var6       var7
  1.626498 100.000000   2.467846 100.000000   1.610230   5.755671   8.587148
      var8       var9
  5.952469   1.245618
and this one
> date(); system.time(imp <- mice(sp2a, m=1, maxit=1, method=ini$method, pred=ini$pred, seed=1776)); date();
[1] "Thu Jul  4 10:32:11 2013"

 iter imp variable
  1   1  pAnyUAVI  HIVTest
   user  system elapsed
 70.768   0.000  71.092
[1] "Thu Jul  4 10:33:22 2013"

> effectiveSize(MCTRACE[,1:9])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 2.116776 11.265308  3.331315 45.607589  2.450199  4.170663  9.462086  3.301020
      tau
 5.606814
Use weights of 1/step size.  Promising.
All my effective sample sizes are now similar, though low:
> effectiveSize(MCTRACE[,1:9])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 1.914887  2.449829  3.392589  9.366586  1.594346  5.862298  8.362028  6.329107
      tau
 1.198916
epsilon x 10 to .001
seems generally a bit better; note the nParters effective size now
exceeds the 100 samples.  Could be negative correlation between steps.
Graphically, doesn't look like convergence.
epsilon x 10 to .01.  better
Effective sample sizes for last commit and then this:
> effectiveSize(MCTRACE[,1:9])
   intrcpt        age    rac_gay  nPartners   ethnic.1   ethnic.2      HIV.1
  1.488847   6.984004   2.082781 227.770823   5.098273  11.910514  13.039992
     HIV.2        tau
  3.723603   1.438963

> effectiveSize(MCTRACE[,1:9])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 6.907481  9.295316  8.314970 21.579489 26.153745 52.516591 26.626610  5.798788
      tau
 7.152319

Inspection of graph shows significant flat parts, presumably meaning
a lot of the candidates are getting rejected.  Suggests step size
is a bit too big.
expsilon/2, now .005 marginally worse
NOTE this and previous messages give the minimum epsilon; the max
is 4 times higher.
 effectiveSize(MCTRACE[,1:9])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 5.873172 19.628601  7.834025 37.514275 34.602116 13.636538 16.285749  7.428086
      tau
 7.401233
Note some of these values are an improvement over before.
epsilon .01 only.  worse
This is without a range, roughly mid-point of previous one.
> effectiveSize(MCTRACE[,1:11])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 2.439692  9.411273  3.407702 71.372604 14.448664 50.737925  9.281562  6.758827
      tau    theta1    theta2
 3.249881 14.571320 10.736279
30 steps instead of 20.  still epsilon=.01 only.  a bit worse.
Also slower (i5 times)
> date(); system.time(imp <- mice(sp2a, m=1, maxit=1, method=ini$method, pred=ini$pred, seed=1776)); date();
[1] "Thu Jul  4 11:41:53 2013"

 iter imp variable
  1   1  pAnyUAVI  HIVTest
   user  system elapsed
218.874   1.397 221.689
> effectiveSize(MCTRACE[,1:11])
  intrcpt       age   rac_gay nPartners  ethnic.1  ethnic.2     HIV.1     HIV.2
 3.801479 11.691201  2.959668 20.418189 15.720205 48.242976 17.298159 10.527796
      tau    theta1    theta2
 3.449258 17.959407 10.426316
revert to 26892e1540e70ece81d93331cb2e341a140d83dc, apparent best so far.
epsilon [.01, .04], 20 steps
x10 samples, now 1,000.  Ugh.
More than linear increase in time, less than linear increase in effective
sample size.
> date(); system.time(imp <- mice(sp2a, m=1, maxit=1, method=ini$method, pred=ini$pred, seed=1776)); date();
[1] "Thu Jul  4 13:24:10 2013"

 iter imp variable
  1   1  pAnyUAVI  HIVTest
    user   system  elapsed
4631.773    3.421 4656.120
[1] "Thu Jul  4 14:41:46 2013"
> effectiveSize(MCTRACE[,1:11])
   intrcpt        age    rac_gay  nPartners   ethnic.1   ethnic.2      HIV.1
 36.908095  41.659467  32.603437  27.867659  50.051680  46.014307 116.234722
     HIV.2        tau     theta1     theta2
 49.180792   3.187656  58.572746   9.898327

Possibly related to massive tracing matrices.