[R-sig-ME] Unacceptibly high autocorrelation in MCMCglmm

[Stuart Luppescu]

Hello, I'm running this ordered category outcome model:

glme5.very.len <- MCMCglmm(very.len.summative.o ~ 1 ,
                   prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1,
nu=0), G2=list(V=1, nu=0), G3=list(V=1, nu=0), G4=list(V=1, nu=0) )),
                   random = ~emplid + deptid + grade.f + subject.f ,
                   family = "ordinal",
                   nitt=300000,
                   data = summative.ratings.prin.yr1.full)

I ran it first with nitt=100000 but had very high autocorrelations and
non-sensical variance components and fixed effects, so I increased nitt
to 200000 and then to 300000 but got no change. Here's the summary
output:

 summary(glme5.very.len)

 Iterations = 3001:299991
 Thinning interval  = 10
 Sample size  = 29700 

 DIC: -13239.32 

 G-structure:  ~emplid

       post.mean  l-95% CI u-95% CI eff.samp
emplid     405.3 1.493e-11     1106    7.909

               ~deptid

       post.mean  l-95% CI u-95% CI eff.samp
deptid     131.8 1.118e-16    475.2    42.65

               ~grade.f

        post.mean  l-95% CI u-95% CI eff.samp
grade.f    0.9143 1.405e-17    1.575    15784

               ~subject.f

          post.mean  l-95% CI u-95% CI eff.samp
subject.f     1.633 1.951e-17    2.748    10101

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units         1        1        1        0

 Location effects: very.len.summative.o ~ 1 

            post.mean l-95% CI u-95% CI eff.samp  pMCMC    
(Intercept)    29.007    2.091   54.969    2.381 <3e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

 Cutpoints: 
                                     post.mean l-95% CI u-95% CI eff.samp
cutpoint.traitvery.len.summative.o.1     14.06   0.8102    27.38    9.382
cutpoint.traitvery.len.summative.o.2     40.34   2.9611    76.04    2.694

Here are some of the autocorrs:

 autocorr(glme5.very.len$VCV)
, , emplid

           emplid    deptid    grade.f  subject.f units
Lag 0   1.0000000 0.5860851 0.04668197 0.06081864   NaN
Lag 10  0.9514313 0.6132116 0.04345287 0.05652945   NaN
Lag 50  0.9459831 0.6259477 0.04881253 0.06093640   NaN
Lag 100 0.9433509 0.6282599 0.04492884 0.06037288   NaN
Lag 500 0.9267886 0.6373151 0.03873992 0.05371885   NaN

, , deptid

           emplid    deptid    grade.f  subject.f units
Lag 0   0.5860851 1.0000000 0.03070680 0.03453008   NaN
Lag 10  0.6137187 0.7579551 0.03233992 0.04139315   NaN
Lag 50  0.6255810 0.7169468 0.02903334 0.03960446   NaN
Lag 100 0.6269979 0.7029498 0.03244468 0.04857241   NaN
Lag 500 0.6322900 0.6650247 0.04049514 0.04306019   NaN

Is there a problem in my data or in the model?

Thank you.

-- 
Stuart Luppescu -=- slu .at. ccsr.uchicago.edu        
University of Chicago -=- CCSR 
才文と智奈美の父 -=-    Kernel 3.2.1-gentoo-r2                
Benjamin Lloyd-Hughes: Has anyone had any joy
 getting the rgdal package to compile under
<windows? Roger Bivand: The closest anyone has got 
 so far is Hisaji Ono, who used MSYS
 (http://www.mingw.org/) to build PROJ.4 and GDAL
 (GDAL depends on PROJ.4, PROJ.4 needs a PATH to
 metadata files for projection and transformation),
 and then hand-pasted the paths to the GDAL headers
 and library into src/Makevars, running Rcmd