mxFitFunctionMultigroup {OpenMx} | R Documentation |

`mxFitFunctionMultigroup`

creates a fit function consisting of the sum of the fit statistics
from a list of submodels provided. Thus, it aggregates fit statistics from multiple submodels.

This total provides the optimization target for fitting a multi-group model.

In addition to being more compact and readable, using `mxFitFunctionMultigroup`

has
additional side effects which are valuable for multi-group modeling.

First, it aggregates analytic derivative calculations.

Second, it allows `mxRefModels`

to compute saturated models for raw data, as this function can learn which are the constituent submodels.

Third, and finally, it allows `mxCheckIdentification`

to evaluate the local identification of the multigroup model.

```
mxFitFunctionMultigroup(groups, ..., verbose = 0L)
```

`groups` |
vector of submodel names (strings) |

`...` |
Not used. Forces subsequent arguments to be specified by name. |

`verbose` |
the level of debugging output |

Conceptually, `mxFitFunctionMultigroup`

is equivalent to summing the subModel objectives in an mxAlgebra,
and using an mxFitFunctionAlgebra to optimize the model based on this summed likelihood.

e.g. this 1-line call to mxFitFunctionMultigroup:

`mxFitFunctionMultigroup(c("model1", "model2"))`

is equivalent to the following pair of statements:

`mxAlgebra(name = "myAlgebra", model1.objective + model2.objective)`

`mxFitFunctionAlgebra("myAlgebra")`

*Note*: If needed, you can refer to the algebra generated by `mxFitFunctionMultigroup`

as:

`modelName.fitfunction`

Where "modelName" is the name of the container or supermodel.

Other fit functions:
`mxFitFunctionML`

,
`mxFitFunctionWLS`

, `mxFitFunctionAlgebra`

,
`mxFitFunctionGREML`

, `mxFitFunctionR`

,
`mxFitFunctionRow`

More information about the OpenMx package may be found here.

```
#------------------------------------------------
# Brief non-running example
require("OpenMx")
mxFitFunctionMultigroup(c("model1", "model2")) # names of sub-models to be jointly optimised
# ===========================================
# = Longer, fully featured, running example =
# ===========================================
# Create and fit a model using mxMatrix, mxExpectationRAM, mxFitFunctionML,
# and mxFitFunctionMultigroup.
# The model is multiple group regression.
# Only the residual variances are allowed to differ across groups.
library(OpenMx)
# Simulate some data
# Group 1
N1 = 100
x = rnorm(N1, mean= 0, sd= 1)
y = 0.5*x + rnorm(N1, mean= 0, sd= 1)
ds1 <- data.frame(x, y)
dsNames <- names(ds1)
# Group 2: y has greater variance; x & y slightly lower correlation...
N2= 150
x= rnorm(N2, mean= 0, sd= 1)
y= 0.5*x + rnorm(N2, mean= 0, sd= sqrt(1.5))
ds2 <- data.frame(x, y)
# Define the matrices (A matrix implementation of 2 RAM models)
I <- mxMatrix(name="I", type="Iden", nrow=2, ncol=2)
M <- mxMatrix(name = "M", type = "Full", nrow = 1, ncol = 2, values=0,
free=TRUE, labels=c("Mean_x", "Mean_y"))
# A matrix containing a path "b" of x on y
A <- mxMatrix(name = "A", type = "Full", nrow = 2, ncol = 2, values=c(0,1,0,0),
free=c(FALSE,TRUE,FALSE,FALSE), labels=c(NA, "b", NA, NA))
S1 <- mxMatrix(name = "S", type = "Diag", nrow = 2, ncol = 2, values=1,
free=TRUE, labels=c("Var_x", "Resid_y_group1"))
S2 <- mxMatrix(name = "S", type = "Diag", nrow = 2, ncol = 2, values=1,
free=TRUE, labels=c("Var_x", "Resid_y_group2"))
# Define the expectation
expect <- mxExpectationRAM('A', 'S', 'I', 'M', dimnames= dsNames)
# Choose a fit function
fitFunction <- mxFitFunctionML(rowDiagnostics=TRUE)
# Also return row likelihoods (the fit function value is still 1x1)
# Multiple-group fit function sums the model likelihoods
# from its component models
mgFitFun <- mxFitFunctionMultigroup(c('g1model', 'g2model'))
# Define model 1 and model 2
m1 = mxModel(model="g1model",
M, S1, A, I, expect, fitFunction,
mxData(cov(ds1), type="cov", numObs=N1, means=colMeans(ds1))
)
m2 = mxModel(model="g2model",
M, S2, A, I, expect, fitFunction,
mxData(cov(ds2), type="cov", numObs=N2, means=colMeans(ds2))
)
mg <- mxModel(model='multipleGroup', m1, m2, mgFitFun)
# note!: Paths with the same name in both submodels are
# constrained to the same value across models. i.e.,
# b has only 1 value, as does Var_x. But Resid_y can take distinct
# values in the two groups.
# Fit the model and print a summary
mg <- mxRun(mg)
summary(mg)
# Examine fit function results
# Fit in -2lnL units)
mxEval(fitfunction, mg)
# Fit function results for each submodel:
mxEval(g1model.fitfunction, mg)
mxEval(g2model.fitfunction, mg)
mg2 = omxSetParameters(mg,
labels = c("Resid_y_group1", "Resid_y_group2"),
newlabels = "Resid_y", name = "equated")
mg2 = omxAssignFirstParameters(mg2)
mg2 = mxRun(mg2)
mxCompare(mg, mg2)
# ouch... that was a significant loss in fit: the residuals definately are larger in group2!
```

[Package *OpenMx* version 2.21.11 Index]