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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)
model | a MxModel object. |
# 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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