Active forum topics
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enWelcome to the OpenMx SEM modeling forum.
https://openmx.ssri.psu.edu/thread/19-welcome-openmx-sem-modeling-forum
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>This forum is designed for discussions about how to create and fit SEM models with OpenMx. This forum is designed to be about the nuts and bolts of how to use OpenMx rather than a general purpose SEM modeling forum. For questions about the models themselves, rather than how to implement them in OpenMx, please see the OpenSEM forums where models are split by many different types.</p>
</div>
</div></div></div>Fri, 31 Jul 2009 18:58:29 +0000Steve19 at https://openmx.ssri.psu.eduConstraining Lambda Loadings
https://openmx.ssri.psu.edu/thread/127
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>Hi All,<br />
I am wondering if it is possible to place constraints on lambda loadings (i.e., constraining the loadings of a two indicator construct to be equal, or constraining the average of a construct's loadings to equal 1 to set the scale by the effects coding method). I've been working primarily with the path specification approach, and I can't see an obvious way that this could be implemented, I've seen mention of equality constraints on the forums, but I can't find any mention of such constraints or suggestions in the documentation. Any insight would be greatly appreciated.</p>
</div>
</div></div></div>Sat, 29 Aug 2009 01:22:49 +0000klang127 at https://openmx.ssri.psu.eduInflated degrees of freedom
https://openmx.ssri.psu.edu/thread/132
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>When I run the following model, the program is telling me that I have 7405 degrees of freedom when there should only be around 370. I'm just wondering if anyone can offer any insight into what could be causing this discrepancy? Here's the code I'm running:</p>
<p>MissDataSim2 <- read.table("F:/Kyle/Research/OpenMx/Data/Kyle.MissDataSim2.txt", header = TRUE)</p>
<p>library(OpenMx)</p>
<p>MissDataModel3<-mxModel("Missing Data Simulation Structural Model with Phantom Constructs",<br />
type="RAM",<br />
mxData(MissDataSim2,<br />
type="raw"<br />
),</p>
<pre><code>manifestVars=c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10",
"b1","b2","b3","b4","b5","b6","b7","b8","b9","b10",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10"),
latentVars=c("FA","FB","FC","PA","PB","PC"),
# residual variances
mxPath(from=c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10",
"b1","b2","b3","b4","b5","b6","b7","b8","b9","b10",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10"),
arrows=2,
free=TRUE,
values=c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1),
labels=c("e1","e2","e3","e4","e5","e6","e7","e8","e9","e10",
"e11","e12","e13","e14","e15","e16","e17","e18","e19","e20",
"e21","e22","e23","e24","e25","e26","e27","e28","e29","e30")
),
# latent variances and covariance
mxPath(from=c("FA","FB","FC"),
arrows=2,
all=2,
free=c(FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE),
values=c(0,0,0,
0,0,0,
0,0,0),
labels=c("varFA","covAB","covAC",
"covBA","varFB","covBC",
"covCA","covCB","varFC")
),
# phantom variances and covariance
mxPath(from=c("PA","PB","PC"),
arrows=2,
all=2,
free=c(FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE,
FALSE,FALSE,FALSE),
values=c(1,0,0,
0,1,0,
0,0,1),
labels=c("varPA","covPAB","covPAC",
"covPBA","varPB","covPBC",
"covPCA","covPCB","varPC")
),
# factor loadings for a variables
mxPath(from="FA",
to=c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10"),
arrows=1,
free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE),
values=c(1,1,1,1,1,1,1,1,1,1),
labels=c("l1","l2","l3","l4","l5","l6","l7","l8","l9","l10")
),
#Effects Coding
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l1", name = "la"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l2", name = "lb"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l3", name = "lc"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l4", name = "ld"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l5", name = "le"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l6", name = "lf"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l7", name = "lg"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l8", name = "lh"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l9", name = "li"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l10", name = "lj"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = FALSE, values = 10, labels = "con", name = "cona"),
mxAlgebra(name="sa",
la+lb+lc+ld+le+lf+lg+lh+li+lj
),
mxConstraint("sa","=","cona"),
#factor loadings for b variables
mxPath(from="FB",
to=c("b1","b2","b3","b4","b5","b6","b7","b8","b9","b10"),
arrows=1,
free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE),
values=c(1,1,1,1,1,1,1,1,1,1),
labels=c("l11","l12","l13","l14","l15","l16","l17","l18","l19","l20")
),
#Effects Coding
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l11", name = "lba"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l12", name = "lbb"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l13", name = "lbc"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l14", name = "lbd"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l15", name = "lbe"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l16", name = "lbf"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l17", name = "lbg"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l18", name = "lbh"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l19", name = "lbi"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l20", name = "lbj"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = FALSE, values = 10, labels = "cons", name = "conb"),
mxAlgebra(name="sb",
lba+lbb+lbc+lbd+lbe+lbf+lbg+lbh+lbi+lbj
),
mxConstraint("sb","=","conb"),
#factor loadings for b variables
mxPath(from="FC",
to=c("c1","c2","c3","c4","c5","c6","c7","c8","c9","c10"),
arrows=1,
free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE),
values=c(1,1,1,1,1,1,1,1,1,1),
labels=c("l21","l22","l23","l24","l25","l26","l27","l28","l29","l30")
),
#Effects Coding
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l21", name = "lca"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l22", name = "lcb"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l23", name = "lcc"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l24", name = "lcd"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l25", name = "lce"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l26", name = "lcf"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l27", name = "lcg"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l28", name = "lch"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l29", name = "lci"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = TRUE, values = 1, labels = "l30", name = "lcj"),
mxMatrix(type = "Full", nrow = 1, ncol = 1,
free = FALSE, values = 10, labels = "const", name = "conc"),
mxAlgebra(name="sc",
lca+lcb+lcc+lcd+lce+lcf+lcg+lch+lci+lcj
),
mxConstraint("sc","=","conc"),
#loadings of phantom constructs
mxPath(from="PA",
to="FA",
arrows=1,
free=TRUE,
values=1,
labels="b1"
),
mxPath(from="PB",
to="FB",
arrows=1,
free=TRUE,
values=1,
labels="b2"
),
mxPath(from="PC",
to="FC",
arrows=1,
free=TRUE,
values=1,
labels="b3"
),
mxPath(from="PA",
to="PB",
arrows=1,
free=TRUE,
values=1,
labels="bp1"
),
mxPath(from="PB",
to="PC",
arrows=1,
free=TRUE,
values=1,
labels="bp2"
),
mxPath(from="one",
to=c("a1","a2","a3","a4","a5","a6","a7","a8","a9","a10",
"b1","b2","b3","b4","b5","b6","b7","b8","b9","b10",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10",
"FA","FB","FC","PA","PB","PC"),
arrows=1,
free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,
TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,
TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,
FALSE,FALSE,FALSE,FALSE,FALSE,FALSE),
values=c(1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,
1,1,1,1,1,1,1,1,1,1,
0,0,0,0,0,0),
labels=c("meana1","meana2","meana3","meana4","meana5",
"meana6","meana7","meana8","meana9","meana10",
"meanb1","meanb2","meanb3","meanb4","meanb5",
"meanb6","meanb7","meanb8","meanb9","meanb10",
"meanc1","meanc2","meanc3","meanc4","meanc5",
"meanc6","meanc7","meanc8","meanc9","meanc10",
NA,NA,NA,NA,NA,NA)
)
</code></pre><p>)</p>
<p>MissDataPhantom2Fit <- mxRun(MissDataModel3)</p>
<p>MissDataPhantom2Fit@output</p>
<p>summary(MissDataPhantom2Fit)</p>
<p>And here's the pertinent output that results:</p>
<p>Observed statistics: 7500<br />
Estimated parameters: 95<br />
Degrees of freedom: 7405<br />
-2 log likelihood: 20470.93<br />
Saturated -2 log likelihood:<br />
Chi-Square:<br />
p:<br />
AIC (Mx): 5660.932<br />
BIC (Mx): -10207.74<br />
adjusted BIC:<br />
RMSEA: 0</p>
</div>
</div></div></div>Thu, 03 Sep 2009 01:21:53 +0000klang132 at https://openmx.ssri.psu.eduPower calculations for structural equation models
https://openmx.ssri.psu.edu/thread/255
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>So a question was on SEMNET the other day, as to whether there were any free SEM packages that do power calculations. In essence, OpenMx does, given a bit of help from the R package pwr. Basically, one would fit the true model to the data, fix one or more parameters to predetermined values (usually to zero) and refit the model. Suppose that the difference in the -2lnL fit functions (as might be obtained from two mxRun commands</p>
<p>sat<-mxRun(saturated)<br />
and<br />
sub <-mxRun(submodel)</p>
<p>then the difference in the fit of these two models would be:</p>
<p>deltachi<-mxEval(objective,sub) - mxEval(objective,sat)</p>
<p>and the difference in the degrees of freedom would be (sorry this is crude but df is not healthy in OpenMx yet)</p>
<p>deltadf<-length(sat@output$estimate) - length(sub@output$estimate)</p>
<p>To compute the power to reject the null hypothesis at significance level alpha=.05, we could use:</p>
<p>require(stats)<br />
alpha<-.05<br />
1-pchisq(qchisq(1-alpha,deltadf),deltadf,deltachi)</p>
<p>This probably belongs in the documentation somewhere, for now it is just a convenient aide-memoire pour moi.</p>
</div>
</div></div></div>Sat, 31 Oct 2009 20:33:28 +0000neale255 at https://openmx.ssri.psu.eduComparing sem() and mxRun() output
https://openmx.ssri.psu.edu/thread/298
<div class="field field-name-upload field-type-file field-label-hidden"><div class="field-items"><div class="field-item even"><table class="sticky-enabled">
<thead><tr><th>Attachment</th><th>Size</th> </tr></thead>
<tbody>
<tr class="odd"><td><span class="file"><img class="file-icon" alt="Binary Data" title="application/octet-stream" src="/modules/file/icons/application-octet-stream.png" /> <a href="https://openmx.ssri.psu.edu/sites/default/files/from%20sem%20to%20openmx.R" type="application/octet-stream; length=3365" title="from sem to openmx.R">from sem to openmx.R</a></span></td><td>3.29 KB</td> </tr>
</tbody>
</table>
</div></div></div><div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>Hi</p>
<p>Are the algorithms different in the sem package and OpenMx package for solving a sem model? Or the starting values? The question is actually whether one is expected to get the same results from the different packages? (as an aside: how can I obtain the value of the evaluated objective function? after runnnig mxRun?)</p>
<p>I'm asking as I tried to replicate example "Wheaton et al. alienation data " (see ?sem), I think I succeeded in having the same model but coef differ slighty or much...</p>
<p>run attached example and compare:<br />
summary(Run)<br />
name matrix row col Estimate Std.Error<br />
1 A SEI SES 5.2730210 4.0774084<br />
2 A Alienation67 SES -0.6321773 0.5495341<br />
3 A Alienation71 SES -0.2325439 0.5333972<br />
4 A Alienation71 Alienation67 0.6105953 0.4505951<br />
5 the1 S Anomia67 Anomia67 4.1128138 2.3309929<br />
6 the2 S Powerless67 Powerless67 3.1626654 1.6110255<br />
7 S Anomia67 Anomia71 1.5514337 2.2886042<br />
8 S Education Education 2.8734482 4.7788626<br />
9 S SEI SEI 262.9797477 173.9318663<br />
10 S SES SES 6.7365517 6.1315362<br />
11 S Alienation67 Alienation67 5.7430329 3.9999990<br />
12 S Alienation71 Alienation71 4.5054334 3.2001184</p>
<p>summary(sem.wh.1)<br />
Parameter Estimates<br />
Estimate Std Error z value Pr(>|z|)<br />
lamb 5.36880 0.433982 12.3710 0.0000e+00 SEI <--- SES<br />
gam1 -0.62994 0.056128 -11.2233 0.0000e+00 Alienation67 <--- SES<br />
beta 0.59312 0.046820 12.6680 0.0000e+00 Alienation71 <--- Alienation67<br />
gam2 -0.24086 0.055202 -4.3632 1.2817e-05 Alienation71 <--- SES<br />
the1 3.60787 0.200589 17.9864 0.0000e+00 Anomia67 <--> Anomia67<br />
the2 3.59494 0.165234 21.7567 0.0000e+00 Powerless67 <--> Powerless67<br />
the3 2.99366 0.498972 5.9996 1.9774e-09 Education <--> Education<br />
the4 259.57583 18.321121 14.1681 0.0000e+00 SEI <--> SEI<br />
the5 0.90579 0.121710 7.4422 9.9032e-14 Anomia71 <--> Anomia67<br />
psi1 5.67050 0.422906 13.4084 0.0000e+00 Alienation67 <--> Alienation67<br />
psi2 4.51481 0.334993 13.4773 0.0000e+00 Alienation71 <--> Alienation71<br />
phi 6.61632 0.639506 10.3460 0.0000e+00 SES <--> SES</p>
<p>Thanks a lot!!</p>
</div>
</div></div></div>Wed, 09 Dec 2009 09:12:16 +0000Matifou298 at https://openmx.ssri.psu.eduSummary of sem: can I remove the summary of variables?
https://openmx.ssri.psu.edu/thread/302
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>Hi</p>
<p>I'm using openMx in a sweave file, where I show the output of a sem model. I wonder if it is possible to show only the bottom of what is printed by summary, i.e., only the coefficients table and the index, not the individual summary, whihc is pretty long?</p>
<p>Is this possible?</p>
<p>As second question: how do I see the code for summary? I'm not quite comfortable with S4 modelling, in S3 I would have done:<br />
getAnywhere(summary.MxRAMModel)</p>
<p>How do I do with S4?</p>
<p>Thanks a lot!</p>
</div>
</div></div></div>Tue, 22 Dec 2009 10:29:42 +0000Matifou302 at https://openmx.ssri.psu.edumxPath problem
https://openmx.ssri.psu.edu/thread/309
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>This is from BivariateSaturated_PathCov.R in the documentation</p>
<p>> bivSatModel1 <- mxModel("bivSat1",<br />
+ manifestVars= selVars,<br />
+ mxPath(<br />
+ from=c("X", "Y"),<br />
+ arrows=2,<br />
+ free=T,<br />
+ values=1,<br />
+ lbound=.01,<br />
+ labels=c("varX","varY")<br />
+ ))<br />
Error: The model type of model 'bivSat1' does not recognize paths.</p>
<p>any help?<br />
greg</p>
</div>
</div></div></div>Thu, 31 Dec 2009 16:11:01 +0000carey309 at https://openmx.ssri.psu.eduAdvice, please, for two factor model setup
https://openmx.ssri.psu.edu/thread/365
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>Hello,</p>
<p>I am delighted that OpenMx is now available for SEM and I am working my way through the tutorials. However, one of my students has a slightly different two factor model setup to that shown in the tutorial and i am trying to modify the 2 factor example accordingly, but I am getting a bit stuck with the setup ...</p>
<p>In particular, I am trying to modify the two factor model given on this page https://openmx.ssri.psu.edu/docs/OpenMx/latest/FactorAnalysis_Path.html so that, in terms of the RAM diagram, i do not have a 2 headed arrow to and from F1 to F2, but instead just have a single headed arrow from F1 to F2.</p>
<p>Also I would like to obtain a measure of the covariance (or correlation) of F1 and F2 to show how strongly they are related.</p>
<p>To achieve this, I assume I have to modify this section of mxPath:</p>
<p># latent variances and covariance<br />
mxPath(<br />
from=c("F1","F2"),<br />
arrows=2,<br />
all=TRUE,<br />
free=TRUE,<br />
values=c(1, .5,.5, 1),<br />
labels=c("varF1","cov","cov","varF2")</p>
<p>to something like:</p>
<p># latent variances and covariance<br />
mxPath(<br />
from=c("F1"),<br />
to=c("F2"),<br />
arrows=1,<br />
all=TRUE,<br />
free=TRUE,<br />
values=c(1),<br />
labels=c("F1onF2")</p>
<p>however, whilst the model runs fine with this spec i am getting a bit lost with the output. In particular, how can I see how strongly F1 and F2 are related?</p>
<p>Any advice?</p>
<p>Thanks,</p>
<p>Mark.</p>
</div>
</div></div></div>Tue, 26 Jan 2010 23:57:07 +0000mtranmer365 at https://openmx.ssri.psu.edupath diagram
https://openmx.ssri.psu.edu/thread/380
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>when i run the command omxGraphvizm, can i have it load the estimates of the paths as well as the paths themselves??</p>
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</div></div></div>Thu, 04 Feb 2010 05:20:05 +0000joecool250380 at https://openmx.ssri.psu.eduQuestion about fitting saturated models with FIML
https://openmx.ssri.psu.edu/thread/455
<div class="field field-name-taxonomy-forums field-type-taxonomy-term-reference field-label-above"><div class="field-label">Forums: </div><div class="field-items"><div class="field-item even"><a href="/forums/openmx-help/openmx-structural-equation-modeling">OpenMx Structural Equation Modeling</a></div></div></div><div class="field field-name-body field-type-text-with-summary field-label-hidden"><div class="field-items"><div class="field-item even"><div class="tex2jax">
<p>From other posts on this forum, I mostly understand why fit indexes are not calculated when using FIML. It sounds like the solution is to fit a saturated model and then use this to calculate these indexes. However, when I do this, the saturated model takes an extremely long time to run, and I am wondering (a) whether I am doing something wrong and (b) if there is any way around this.</p>
<p>I had initially tested a saturated model for a dataset with 24 observed variables (three observed variables at each of eight waves) and about 600 observations. After an hour it was still going, so I stopped it to see how long it would take for a smaller model. My model is identical to the bivariate saturated matrix model presented in the OpenMx document (adjusted for the larger number of variables), and it does run. A saturated model with six variables took about six seconds to run. A model with eight variables took 20 seconds to run, and a model with 16 variables took about 10 minutes to run. As I said, I stopped the 24-variable model after an hour.</p>
<p>Is this to be expected, and is there anything I can do to speed things up? My ultimate goal is to test this same model across many different variables from a few different data sets (with up to 25 waves and tens of thousands of observations). OpenMx and R have the advantage of being able to automate all of this, but I think would be close to impossible if the saturated model consistently takes many hours (or longer) to run. So any advice would be appreciated.</p>
<p>Thanks,</p>
<p>Rich</p>
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</div></div></div>Mon, 22 Mar 2010 18:10:46 +0000rlucas455 at https://openmx.ssri.psu.edu