Hello all,
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).