mxModel {OpenMx} | R Documentation |

This function creates a new MxModel object.

mxModel(model = NA, ..., manifestVars = NA, latentVars = NA, remove = FALSE, independent = NA, type = NA, name = NA)

`model` |
This argument is either an MxModel object or a string. If 'model' is an MxModel object, then all elements of that model are placed in the resulting MxModel object. If 'model' is a string, then a new model is created with the string as its name. If 'model' is either unspecified or 'model' is a named entity, data source, or MxPath object, then a new model is created. |

`...` |
An arbitrary number of mxMatrix, mxPath, mxData, and other functions such as mxConstraints and mxCI. These will all be added or removed from the model as specified in the 'model' argument, based on the 'remove' argument. |

`manifestVars` |
For RAM-type models, A list of manifest variables to be included in the model. |

`latentVars` |
For RAM-type models, A list of latent variables to be included in the model. |

`remove` |
logical. If TRUE, elements listed in this statement are removed from the original model. If FALSE, elements listed in this statement are added to the original model. |

`independent` |
logical. If TRUE then the model is evaluated independently of other models. |

`type` |
character vector. The model type to assign to this model. Defaults to options("mxDefaultType"). See below for valid types |

`name` |
An optional character vector indicating the name of the object. |

The mxModel function is used to create MxModel objects. Objects created by this function may be new, or may be modified versions of existing MxModel objects. By default a new MxModel object will be created: To create a modified version of an existing MxModel object, include this model in the 'model' argument.

Other named-entities may be added as arguments to the mxModel function, which are then added to or removed from the model specified in the ‘model’ argument. Other functions you can use to add objects to the model to this way are mxCI, mxAlgebra, mxBounds, mxConstraint, mxData, and mxMatrix objects, as well as objective functions. You can also include MxModel objects as sub-models of the output model, and may be estimated separately or jointly depending on shared parameters and the ‘independent’ flag discussed below. Only one MxData object and one objective function may be included per model, but there are no restrictions on the number of other named-entities included in an mxModel statement.

All other arguments must be named (i.e. ‘latentVars = names’), or they will be interpreted as elements of the ellipsis list. The ‘manifestVars’ and ‘latentVars’ arguments specify the names of the manifest and latent variables, respectively, for use with the mxPath function. The ‘remove’ argument may be used when mxModel is used to create a modified version of an existing MxMatrix object. When ‘remove’ is set to TRUE, the listed objects are removed from the model specified in the ‘model’ argument. When ‘remove’ is set to FALSE, the listed objects are added to the model specified in the ‘model’ argument.

Model independence may be specified with the ‘independent’ argument. If a model is independent (‘independent = TRUE’), then the parameters of this model are not shared with any other model. An independent model may be estimated with no dependency on any other model. If a model is not independent (‘independent = FALSE’), then this model shares parameters with one or more other models such that these models must be jointly estimated. These dependent models must be entered as arguments in another model, so that they are simultaneously optimized.

The model type is determined by a character vector supplied to the ‘type’ argument. The type of a model is a dynamic property, ie. it is allowed to change during the lifetype of the model. To see a list of available types, use the mxTypes command. When a new model is created and no type is specified, the type specified by `options("mxDefaultType")`

is used.

To be estimated, MxModel objects must include objective functions as arguments (mxAlgebraObjective, mxFIMLObjective, mxMLObjective or mxRAMObjective) and executed using the mxRun function. When MxData objects are included in models, the 'type' argument of these objects may require or exclude certain objective functions, or set an objective function as default.

Named entities in MxModel objects may be viewed and referenced by name using double brackets (`model[["matrixname"]]`

). Slots may be referenced with the @ symbol (`model@data`

). See the documentation for Classes and the examples in this document for more information.

Returns a new MxModel object. MxModel objects must include an objective function to be used as arguments in mxRun functions.

The OpenMx User's guide can be found at http://openmx.psyc.virginia.edu/documentation.

See mxCI for information about adding Confidence Interval calculations to a model. See mxPath for information about adding paths to RAM-type models. See mxMatrix for information about adding matrices to models. See mxData for specifying the data a model is to be evaluated against. See MxModel for the S4 class created by mxMatrix. Many advanced options can be set via mxOption (such as calculating the Hessian). More information about the OpenMx package may be found here.

# Create an empy model, and place it in an object. model <- mxModel() # Create a model named 'firstdraft' with one matrix model <- mxModel('firstdraft', mxMatrix('Full', nrow = 3, ncol = 3, name = "A")) # Add other matrices to model 'firstdraft', and rename that model 'finaldraft' model <- mxModel(model, mxMatrix('Symm', nrow = 3, ncol = 3, name = "S"), mxMatrix('Iden', nrow = 3, name = "F"), name= "finaldraft") # Add data to the model from an existing data frame in object 'data' data <- data.frame() model <- mxModel(model, mxData(data, type='raw')) #View the matrix named "A" in MxModel object 'model' model[["A"]] #View the data associated with MxModel object 'model' model$data # An example Using OpenMx's Path Syntax data(HS.fake.data) #load the data Spatial <- c("visual","cubes","paper") # the manifest variables loading on each proposed latent variable Verbal <- c("general","paragrap","sentence") Math <- c("numeric","series","arithmet") latents <- c("vis","math","text") manifests <- c(Spatial,Math,Verbal) model <- mxModel("Holzinger and Swineford (1939)", type="RAM", manifestVars = manifests, # list the measured variables (boxes) latentVars = latents, # list the latent variables (circles) # factor loadings from latents to manifests mxPath(from="vis", to=Spatial),# factor loadings mxPath(from="math", to=Math), # factor loadings mxPath(from="text", to=Verbal), # factor loadings # Allow latent variables to covary mxPath(from="vis" , to="math", arrows=2, free=TRUE), mxPath(from="vis" , to="text", arrows=2, free=TRUE), mxPath(from="math", to="text", arrows=2, free=TRUE), # Allow latent variables to have variance (first fixed @ 1) mxPath(from=latents, arrows=2, free=c(FALSE,TRUE,TRUE), values=1.0), # Manifest have residual variance mxPath(from=manifests, arrows=2), # the data to be analysed mxData(cov(HS.fake.data[,manifests]), type="cov", numObs=301)) fit <- mxRun(model) # run the model summary(fit) # examine the output: Fits statistics and (unstandardized) path loadings

[Package *OpenMx* version 1.2.0-1931 Index]