Expected covariance matrix is non-positive-definite
Robert Long
Joined: 02/22/2013
Hi all
I'm new to OpenMx. Actually I'm trying to convert some SEMs written in Stata into R for a module that I am helping to deliver, and for better or worse, we have chosen OpenMx as the R package to use.
It is a very simple path analysis. In Stata the code is just
ssd init BMI SO RT SE
ssd set correlations ///
1 \ ///
0.43 1 \ ///
0.16 0.53 1 \ ///
0.02 -0.18 -0.25 1
ssd set sd 4.46 7.09 5.68 5.97
ssd set observations 202
sem ///
( SE <- RT ) ///
( RT <- SO ) ///
( SO <- BMI ) ///
, standardized nocapslatent
Using OpenMX I have tried to replicate this with
require(OpenMx)
require(MBESS)
selVars <- c("BMI","SO","RT","SE")
cormat <- matrix(c(1,0.43,0.16,0.02,
0.43,1,0.53,-0.18,
0.16,0.53,1,-0.25,
0.02,-0.18,-0.25, 1
),
nrow=4)
sds <- c(4.46, 7.09, 5.68, 5.97)
covmat <- round(cor2cov(cormat,sds),2)
rownames(covmat) <- selVars
colnames(covmat) <- selVars
m.tigg <- mxModel("mtigg",
manifestVars= selVars,
mxPath(
from=c("BMI", "SO"),
arrows=1,
free=T,
values=1,
lbound=.01,
labels=c("p1",p2")
),
mxPath(
from=c("SO", "RT"),
arrows=1,
free=T,
values=1,
lbound=.01,
labels=c("p2","p3")
),
mxPath(
from=c("RT", "SE"),
arrows=1,
free=T,
values=1,
lbound=.01,
labels=c("p3","p4")
),
mxData(
observed=covmat,
type="cov",
numObs=202
),
type="RAM"
)
f.tigg <- mxRun(m.tigg)
However it complains that "Expected covariance matrix is non-positive-definite."
I would really appreciate any guidance about what I'm doing wrong as I have a *lot* of these models to convert, and very little time to do so....
FWIW, the output I get in Stata (which also agrees with MPlus) is
------------------------------------------------------------------------------
| OIM
Standardized | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural |
SE <- |
RT | -.25 .0659595 -3.79 0.000 -.3792782 -.1207218
-----------+----------------------------------------------------------------
RT <- |
SO | .53 .0504741 10.50 0.000 .4310726 .6289274
-----------+----------------------------------------------------------------
SO <- |
BMI | .43 .0546349 7.87 0.000 .3229175 .5370825
-------------+----------------------------------------------------------------
Variance |
e.SE | .9375 .0329797 .8750389 1.00442
e.RT | .7191 .0535025 .6215243 .8319945
e.SO | .8151 .0469861 .7280208 .9125948
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(3) = 4.11, Prob > chi2 = 0.2495
Ultimately, this is what I am trying to replicate.
Thanks a lot
Robert Long
Welcome to OpenMx!
Welcome to OpenMx!
I can attest to their being some "cost of entry", but there's some of that with any SEM program.
Here is the model I think you meant to specify.
require(OpenMx)
require(MBESS)
selVars <- c("BMI","SO","RT","SE")
cormat <- matrix(c(1,0.43,0.16,0.02,
0.43,1,0.53,-0.18,
0.16,0.53,1,-0.25,
0.02,-0.18,-0.25, 1
),
nrow=4)
sds <- c(4.46, 7.09, 5.68, 5.97)
covmat <- round(cor2cov(cormat,sds),2)
rownames(covmat) <- selVars
colnames(covmat) <- selVars
m.tigg <- mxModel("mtigg",
manifestVars= selVars,
type="RAM",
mxPath(
from=selVars[1:3],
to=selVars[2:4],
arrows=1,
free=T,
values=1,
labels=c("p1","p2","p3")
),
mxPath(
from=selVars,
arrows=2,
values=.9,
labels=paste("var", selVars, sep=""),
free=TRUE
),
mxData(
observed=round(cov2cor(covmat), 3),
type="cov",
numObs=202
)
)
f.tigg <- mxRun(m.tigg)
summary(f.tigg)
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In reply to Welcome to OpenMx! by mhunter
Dear mhunter Thank you so
Dear mhunter
Thank you so much for this ! The output agrees nicely with Stata/MPlus now. I do apreciate that there is going to be a learning curve with this - as with all packages. At this stage I am just confused about this part of the model:
from=selVars[1:3],
to=selVars[2:4],
I am a bit confused by this, as there is only one exogenous variable: the paths just go from 1->2->3->4
Thanks again
RL
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In reply to Dear mhunter Thank you so by Robert Long
In OpenMx, paths go FROM one
In OpenMx, paths go FROM one variable TO another. The "from" and the "to" arguments can be given vectors where the default behavior is to match elementwise. I could have specified several paths: (1) 1->2, (2) 2->3, and (3) 3->4. Instead, I specified one path:
from to
1 2
2 3
3 4
The square brackets just select some of the elements from the vector selVars.
Hope this helps! Let us know if we can clear anything else up.
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In reply to In OpenMx, paths go FROM one by mhunter
Thanks again mhunter, that
Thanks again mhunter, that makes perfect sense !
When I looked at the examples in the tutorial I thought mxData was working with a covariance matrix, and I had not got far enought through the docs to see that a correlation matrix is also possible. However, I see you have used the correlation matrix - yet the type is still "cov", and changing from "cov" to "cor" doesn't seem to change anything. Am I missing something ?
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In reply to Thanks again mhunter, that by Robert Long
I think it should only change
I think it should only change the degrees of freedom in this case.
Glad to help!
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