small/NaN standard errors

Hi all,

I'm fitting a univariate threshold model on binary data and it appears to work well (see below for the script), except that in the summary I get either very small or NaN standard errors, like this:

 name     matrix row col   Estimate     Std.Error

1 ab11 ACE.aboys 1 1 0.5690466 2.121996e-314
2 cb11 ACE.cboys 1 1 0.7157107 6.365987e-314
3 eb11 ACE.eboys 1 1 0.4049003 NaN
4 ag11 ACE.agirls 1 1 0.2620615 NaN
5 cg11 ACE.cgirls 1 1 0.8921872 NaN
6 eg11 ACE.egirls 1 1 0.3678666 1.060998e-313
7 threall ACE.Tall 1 1 -0.5358325 NaN
8 thredzm ACE.Tdzm 1 1 -0.7374305 NaN

Another thing is that when I run the submodel which equates a, c and e across sex I get the following output in OpenMx:

 name    matrix row col   Estimate Std.Error

1 ab11 ACE.aboys 1 1 0.4833659 NaN
2 cb11 ACE.cboys 1 1 0.7886090 NaN
3 eb11 ACE.eboys 1 1 0.3800700 NaN
4 threall ACE.Tall 1 1 -0.5369859 NaN
5 thredzm ACE.Tdzm 1 1 -0.7300285 NaN

Observed statistics: 2807
Estimated parameters: 5
Degrees of freedom: 2802
-2 log likelihood: 2961.374

Whereas when I run the same model in classic Mx it says:

X 1 1 1 95.0 0.4816 0.2191 0.6461 0 1 1 1

Y 1 1 1 95.0 0.7899 0.6788 0.8761 0 1 0 1

Z 1 1 1 95.0 0.3796 0.3077 0.4583 0 1 0 0

B 1 1 1 95.0 -0.5125 -0.5789 -0.4464 0 0 0 0

R 1 1 1 95.0 -0.6879 -0.7839 -0.5934 0 0 0 1

-2 times log-likelihood of data >>> 2954.333
Degrees of freedom >>>>>>>>>>>>>>>> 2802

So there is some difference in both the parameter estimates (mostly the thresholds) and in the -2LL, should this worry me? And I'm curious about the OpenMx standard errors, why they are so small or NaN.

Thanks again!

Lot

ACE model specification (OpenMx):

univACEOrdModel <- mxModel("univACEOrd",
mxModel("ACE",
# Matrices aboys, cboys, and eboys to store a, c, and e path coefficients for boys
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="ab11", name="aboys" ),
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="cb11", name="cboys" ),
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="eb11", name="eboys" ),
# Matrices Aboys, Cboys, and Eboys compute variance components for boys
mxAlgebra( expression=aboys %% t(aboys), name="Aboys" ),
mxAlgebra( expression=cboys %% t(cboys), name="Cboys" ),
mxAlgebra( expression=eboys %% t(eboys), name="Eboys" ),
# Matrices agirls, cgirls, and egirls to store a, c, and e path coefficients for girls
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="ag11", name="agirls" ),
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="cg11", name="cgirls" ),
mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="eg11", name="egirls" ),
# Matrices Agirls, Cgirls, and Egirls compute variance components for girls
mxAlgebra( expression=agirls %% t(agirls), name="Agirls" ),
mxAlgebra( expression=cgirls %% t(cgirls), name="Cgirls" ),
mxAlgebra( expression=egirls %% t(egirls), name="Egirls" ),
# Algebra to compute total variance for boys and girls and standard deviations (diagonal only)
mxAlgebra( expression=Aboys+Cboys+Eboys, name="Vboys" ),
mxAlgebra( expression=Agirls+Cgirls+Egirls, name="Vgirls" ),
mxMatrix( type="Iden", nrow=nv, ncol=nv, name="I"),
mxAlgebra( expression=solve(sqrt(IVboys)), name="sdboys"),
mxAlgebra( expression=solve(sqrt(IVgirls)), name="sdgirls"),
# Constraint on variance of ordinal variables
mxConstraint( alg1="Vboys", "=", alg2="I", name="Varboys"), # varianties sommeren tot 1 (identiteitsmatrix)
mxConstraint( alg1="Vgirls", "=", alg2="I", name="Vargirls"),
# Matrix & Algebra for expected means vector
mxMatrix( type="Zero", nrow=1, ncol=nv, name="M" ),
mxAlgebra( expression= cbind(M,M), name="expMean" ),
# threshold voor de hele groep:
mxMatrix( type="Full", nrow=1, ncol=nv, free=TRUE, values=-.6, label="threall", name="Tall" ),
mxAlgebra( expression= cbind(Tall,Tall), dimnames=list('th1',selVars), name="expThreall" ),
# threshold voor de dz jongens:
mxMatrix( type="Full", nrow=1, ncol=nv, free=TRUE, values=-.8, label="thredzm", name="Tdzm" ),
mxAlgebra( expression= cbind(Tdzm,Tdzm), dimnames=list('th1',selVars), name="expThredzm" ),
# Algebra for expected variance/covariance matrix in mzm
mxAlgebra( expression= rbind ( cbind(Aboys+Cboys+Eboys , Aboys+Cboys),
cbind(Aboys+Cboys , Aboys+Cboys+Eboys)), name="expCovmzm" ),
# Algebra for expected variance/covariance matrix in dzm, note use of 0.5, converted to 11 matrix
mxAlgebra( expression= rbind ( cbind(Aboys+Cboys+Eboys , 0.5%x%Aboys+Cboys),
cbind(0.5%x%Aboys+Cboys , Aboys+Cboys+Eboys)), name="expCovdzm" ),
# Algebra for expected variance/covariance matrix in mzf
mxAlgebra( expression= rbind ( cbind(Agirls+Cgirls+Egirls , Agirls+Cgirls),
cbind(Agirls+Cgirls , Agirls+Cgirls+Egirls)), name="expCovmzf" ),
# Algebra for expected variance/covariance matrix in dzf
mxAlgebra( expression= rbind ( cbind(Agirls+Cgirls+Egirls , .5%x%Agirls+Cgirls),
cbind(.5%x%Agirls+Cgirls , Agirls+Cgirls+Egirls)), name="expCovdzf" ),
# Algebra for expected variance/covariance matrix in dosmf
mxAlgebra( expression= rbind ( cbind(Aboys+Cboys+Eboys , .5%x%(aboys%%t(agirls))+ cboys%%t(cgirls)),
cbind(.5%x%(agirls%%t(aboys))+ cgirls%*%t(cboys) , Agirls+Cgirls+Egirls)), name="expCovdosmf" )
),
mxModel("mzm",
mxData( observed=mzmdata, type="raw" ),
mxFIMLObjective( covariance="ACE.expCovmzm", means="ACE.expMean", dimnames=selVars, thresholds="ACE.expThreall" )
),
mxModel("dzm",
mxData( observed=dzmdata, type="raw" ),
mxFIMLObjective( covariance="ACE.expCovdzm", means="ACE.expMean", dimnames=selVars, thresholds="ACE.expThredzm" )
),
mxModel("mzf",
mxData( observed=mzfdata, type="raw" ),
mxFIMLObjective( covariance="ACE.expCovmzf", means="ACE.expMean", dimnames=selVars, thresholds="ACE.expThreall" )
),
mxModel("dzf",
mxData( observed=dzfdata, type="raw" ),
mxFIMLObjective( covariance="ACE.expCovdzf", means="ACE.expMean", dimnames=selVars, thresholds="ACE.expThreall" )
),
mxModel("dosmf",
mxData( observed=dosmfdata, type="raw" ),
mxFIMLObjective( covariance="ACE.expCovdosmf", means="ACE.expMean", dimnames=selVars, thresholds="ACE.expThreall" )
),
mxAlgebra( expression=mzm.objective + dzm.objective + mzf.objective + dzf.objective + dosmf.objective, name="min2sumll" ),
mxAlgebraObjective("min2sumll")
)

univACEOrdFit <- mxRun(univACEOrdModel)
univACEOrdSumm <- summary(univACEOrdFit)
univACEOrdSumm
LL_ACE <- mxEval(objective, univACEOrdFit);LL_ACE

And the submodel:

ACEequalModel = univACEOrdModel

ACEequalModel$ACE$agirls <- mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="ab11", name="agirls" )
ACEequalModel$ACE$cgirls <- mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="cb11", name="cgirls" )
ACEequalModel$ACE$egirls <- mxMatrix( type="Full", nrow=nv, ncol=nv, free=TRUE, values=.6, label="eb11", name="egirls" )

ACEequalFit = mxRun(ACEequalModel)
ACEequalSumm = summary(ACEequalFit)
ACEequalSumm
LL_ACEeq = mxEval(objective,ACEequalFit)
LL_ACEeq