Versions 0.9.0 vs. 0.5.2

I am fitting some variance known models (akin to meta-analysis). To handle models with possible covariates, I use a RAM model. The results based on 0.5.2 are comparable to those based on other statistical packages. After upgraded to 0.9.0, the results look odd.

The followings are an example. The -2LL of the 0.5.2 and 0.9.0 versions are 27.79916 and 29.02565, respectively. Any suggestions are highly appreciated. Thanks.

Regards,
Mike

yi <- c(-0.264,-0.230,0.166,0.173,0.225,0.291,0.309,0.435,0.476,0.617,0.651,0.718,0.740,0.745,0.758,0.922,0.938,0.962,1.522,1.844)
vi <- c(0.086,0.106,0.055,0.084,0.071,0.078,0.051,0.093,0.149,0.095,0.110,0.054,0.081,0.084,0.087,0.103,0.113,0.083,0.100,0.141)
my.df <- cbind(yi,vi)
test <- mxModel("test", mxData(observed=my.df, type="raw"),
mxMatrix("Zero", ncol=1, nrow=1, free=FALSE, values=0, name="A"),
mxMatrix("Full", ncol=1, nrow=1, free=FALSE, values=0, labels="data.vi", name="V"),
mxMatrix("Full", ncol=1, nrow=1, free=TRUE, values=0.1, lbound=0.0000001, name="Tau"),
mxMatrix("Iden", ncol=1, nrow=1, name="F", dimnames=list(c("yi"), c("yi"))),
mxMatrix("Full", ncol=1, nrow=1, free=T, values=0, name="M"),
mxAlgebra(V+Tau, name="S"),
mxRAMObjective("A", "S", "F", "M")
)
summary(mxRun(test))

######################## Version 0.9.0
## Running test
## data:
## $test.data
## yi vi
## Min. :-0.2640 Min. :0.05100
## 1st Qu.: 0.2745 1st Qu.:0.08025
## Median : 0.6340 Median :0.08650
## Mean : 0.5999 Mean :0.09120
## 3rd Qu.: 0.7990 3rd Qu.:0.10375
## Max. : 1.8440 Max. :0.14900

## The final iterate satisfies the optimality conditions to the accuracy requested, but the sequence of iterates has not yet converged. NPSOL was terminated because no further improvement could be made in the merit function (Mx status GREEN).

## free parameters:
## name matrix row col Estimate Std.Error
## 1 Tau 1 1 0.1000 NaN
## 2 M 1 1 0.5999 9.115587e-10

## observed statistics: 20
## estimated parameters: 2
## degrees of freedom: 18
## -2 log likelihood: 29.02565
## saturated -2 log likelihood: NA
## number of observations: 20
## chi-square: NA
## p: NA
## AIC (Mx): -6.974355
## BIC (Mx): -12.44877
## adjusted BIC:
## RMSEA: NA
## timestamp: 2010-09-11 09:48:48
## frontend time: 0.3790069 secs
## backend time: 0.003436089 secs
## independent submodels time: 0.0001730919 secs
## wall clock time: 0.3826160 secs
## cpu time: 0.3826160 secs
## openmx version number: 0.9.0-1417

## Warning message:
## In model 'test' NPSOL returned a non-zero status code 1. The final iterate satisfies the optimality conditions to the accuracy requested, but the sequence of iterates has not yet converged. NPSOL was terminated because no further improvement could be made in the merit function (Mx status GREEN).

######################## Version 0.5.2
## Running test
## data:
## $test.data
## yi vi
## Min. :-0.2640 Min. :0.05100
## 1st Qu.: 0.2745 1st Qu.:0.08025
## Median : 0.6340 Median :0.08650
## Mean : 0.5999 Mean :0.09120
## 3rd Qu.: 0.7990 3rd Qu.:0.10375
## Max. : 1.8440 Max. :0.14900

## The final iterate satisfies the optimality conditions to the accuracy requested, but the sequence of iterates has not yet converged. NPSOL was terminated because no further improvement could be made in the merit function (Mx status GREEN).

## free parameters:
## name matrix row col Estimate Std.Error
## 1 Tau 1 1 0.1315197 0.07353606
## 2 M 1 1 0.5790348 0.10510040

## observed statistics: 20
## estimated parameters: 2
## degrees of freedom: 18
## -2 log likelihood: 27.79916
## saturated -2 log likelihood: NA
## number of observations: 20
## chi-square: NA
## p: NA
## AIC (Mx): -8.200837
## BIC (Mx): -13.06201
## adjusted BIC:
## RMSEA: NA
## timestamp: 2010-09-11 09:53:24
## frontend time: 0.3790886 secs
## backend time: 0.01191211 secs
## independent submodels time: 0.0001611710 secs
## wall clock time: 0.3911619 secs
## cpu time: 0.3911619 secs
## openmx version number: 0.5.2-1376

## Warning message:
## In model 'test' NPSOL returned a non-zero status code 1. The final iterate satisfies the optimality conditions to the accuracy requested, but the sequence of iterates has not yet converged. NPSOL was terminated because no further improvement could be made in the merit function (Mx status GREEN).