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omxRAMtoML

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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)

Arguments

model a MxModel object.

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, all=TRUE, 
           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)
 
	<em># See the results...</em>
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 

Please add material here as you learn... If you have questions not answers, then add those here: That's how a wiki works.

Comments

sbremer's picture

utilization of created matrices

How can one use the matrices created by omxRAMtoML with the mxFIMLObjective?
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!

mspiegel's picture

In the future, I recommend

In the future, I recommend posting your questions to the forums. New forum posts are noticed much faster than new comments to a wiki page. With regards to the issue at hand, you'll need to include the original model as the 1st argument to the mxModel() function in order to modify the existing model. As currently specified, you are constructing a new model with the objective function and data but it is missing all of the other matrices and algebras. Use the function mxRename() to give the model a new name, it will correct any internal references from the old name to the new name. I would write it as follows:

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.