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

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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