1) Usage
This function accepts a model as input, and returns a model as output where all the RAM objective functions have been transformed to either ML or FIML objective functions, where the necessary algebras have been auto-generated in order to calculate the expected covariance and means matrices.
omxRAMtoML(model)
2) Arguments
model - a MxModel object.
3) Examples
# Read libraries and set options.
require(OpenMx)
# ----------------------------------
# Read the data and print descriptive statistics.
data(factorExample1)
# ----------------------------------
# Build an OpenMx single factor FIML model with fixed variance
indicators - names(factorExample1)
latents - c("F1")
loadingLabels - paste("b_", indicators, sep="")
uniqueLabels - paste("U_", indicators, sep="")
meanLabels - paste("M_", indicators, sep="")
factorVarLabels - paste("Var_", latents, sep="")
oneFactorRaw1 - mxModel("Single Factor FIML Model with Fixed Variance",
type="RAM",
manifestVars=indicators,
latentVars=latents,
mxPath(from=latents, to=indicators,
arrows=1, connect="all.pairs",
free=TRUE, values=.2,
labels=loadingLabels),
mxPath(from=indicators,
arrows=2,
free=TRUE, values=.8,
labels=uniqueLabels),
mxPath(from=latents,
arrows=2,
free=FALSE, values=1,
labels=factorVarLabels),
mxPath(from="one", to=indicators,
arrows=1, free=TRUE, values=.1,
labels=meanLabels),
mxData(observed=factorExample1, type="raw")
)
oneFactorRawML - omxRAMtoML(oneFactorRaw1)
oneFactorRawMLOut - mxRun(oneFactorRawML, suppressWarnings=TRUE)
# See the results...
summary(oneFactorRawMLOut)
data:
$`Single Factor FIML Model with Fixed Variance.data`
x1 x2 x3 x4
Min. :-2.99780 Min. :-1.579400 Min. :-2.13250 Min. :-3.00650
1st Qu.:-0.62555 1st Qu.:-0.365850 1st Qu.:-0.26977 1st Qu.:-0.69588
Median :-0.03170 Median : 0.007300 Median : 0.05055 Median :-0.04330
Mean :-0.01161 Mean :-0.006821 Mean : 0.02396 Mean :-0.03135
3rd Qu.: 0.59815 3rd Qu.: 0.333675 3rd Qu.: 0.33495 3rd Qu.: 0.68142
Max. : 2.54270 Max. : 1.800600 Max. : 1.26530 Max. : 2.88340
x5 x6 x7 x8
Min. :-3.20380 Min. :-3.54670 Min. :-4.15680 Min. :-2.05160
1st Qu.:-0.71252 1st Qu.:-0.98603 1st Qu.:-1.07967 1st Qu.:-0.64263
Median :-0.02015 Median :-0.07750 Median :-0.14610 Median :-0.05310
Mean :-0.04548 Mean :-0.09178 Mean :-0.06732 Mean :-0.03902
3rd Qu.: 0.62877 3rd Qu.: 0.77910 3rd Qu.: 0.91097 3rd Qu.: 0.58552
Max. : 2.85080 Max. : 3.26040 Max. : 3.74800 Max. : 2.63280
x9
Min. :-3.68950
1st Qu.:-0.83327
Median :-0.04285
Mean :-0.05999
3rd Qu.: 0.72447
Max. : 3.47750
free parameters:
name matrix row col Estimate Std.Error lbound ubound
1 b_x1 A x1 F1 0.68395558 0.03517218
2 b_x2 A x2 F1 0.32481984 0.02238500
3 b_x3 A x3 F1 0.10886694 0.02076627
4 b_x4 A x4 F1 0.47440890 0.04457067
5 b_x5 A x5 F1 0.60180412 0.04221052
6 b_x6 A x6 F1 1.12063877 0.04569668
7 b_x7 A x7 F1 1.25933139 0.04883099
8 b_x8 A x8 F1 0.64739267 0.03057637
9 b_x9 A x9 F1 0.71872734 0.04926900
10 U_x1 S x1 x1 0.35279611 0.02484526
11 U_x2 S x2 x2 0.17619283 0.01193414
12 U_x3 S x3 x3 0.19353556 0.01230270
13 U_x4 S x4 x4 0.79987497 0.05201061
14 U_x5 S x5 x5 0.63305704 0.04272612
15 U_x6 S x6 x6 0.36762720 0.03207912
16 U_x7 S x7 x7 0.34023767 0.03483551
17 U_x8 S x8 x8 0.23403773 0.01730076
18 U_x9 S x9 x9 0.85441146 0.05777368
19 M_x1 M 1 x1 -0.01161303 0.04050289
20 M_x2 M 1 x2 -0.00682285 0.02373273
21 M_x3 M 1 x3 0.02396104 0.02026669
22 M_x4 M 1 x4 -0.03135672 0.04527295
23 M_x5 M 1 x5 -0.04548168 0.04460893
24 M_x6 M 1 x6 -0.09178376 0.05696533
25 M_x7 M 1 x7 -0.06732317 0.06204879
26 M_x8 M 1 x8 -0.03902037 0.03613431
27 M_x9 M 1 x9 -0.05999675 0.05235709
observed statistics: 4500
estimated parameters: 27
degrees of freedom: 4473
-2 log likelihood: 9706.388
saturated -2 log likelihood: NA
number of observations: 500
chi-square: NA
p: NA
Information Criteria:
df Penalty Parameters Penalty Sample-Size Adjusted
AIC 760.3878 9760.388 NA
BIC -18091.5542 9874.182 9788.483
CFI: NA
TLI: NA
RMSEA: NA
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Comments
utilization of created matrices
mraw is my model as RAM.
When I use mrawML$covariance@name and mrawML$covariance@name as expected covariance and expected means, respectively, I get the error at the end of this post:
> mrawML <- omxRAMtoML(mraw)
> FIMLobjective <- mxFIMLObjective(covariance=mrawML$covariance@name,means=mrawML$means@name,dimnames=Namen)
>
> mrawMLwithFIML <- mxModel('Modell SES FIML', mraw, FIMLobjective)
>
> FitrawFIML <- mxRun(mrawMLwithFIML)
Running Modell SES FIML
Error: Unknown expected covariance name 'covariance' detected in the objective function of model 'Modell SES FIML'
>
Thanks in advance for any help!
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In the future, I recommend
mrawMLwithFIML <- mxModel(mrawML, mraw, FIMLobjective)mrawMLwithFIML <- mxRename(mrawMLwithFIML, 'Modell SES FIML')
In playing around with this example, I found that omxRAMtoML() has a bug and incorrectly throws an error if the input model has data with type='cov'. This will be corrected in OpenMx 1.2.5.
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