R: Compute standard errors in OpenMx

mxSE {OpenMx} | R Documentation

## Compute standard errors in OpenMx

### Description

This function allows you to obtain standard errors for arbitrary expressions, named entities, and algebras.

### Usage

    mxSE(x, model, details = FALSE, cov, ...)

### Arguments

`x` | the parameter to get SEs on (reference or expression)
`model` | the `mxModel` to use.
`details` | logical. Whether to provide further details, e.g. the full sampling covariance matrix of x.
`cov` | optional matrix of covariances among the free parameters. If missing, the inverse Hessian from the fitted model is used.
`...` | further named arguments passed to `mxEval`

### Details

x can be the name of an algebra, a bracket address, named entity or arbitrary expression. It is a frontend-only file that works much like mxEval. When the `details` argument is TRUE, the full sampling covariance matrix of `x` is also returned as part of a list. The square root of the diagonals of this sampling covariance matrix are the standard errors.

When supplying the `cov` argument, take care that the free parameter covariance matrix is given, not the information matrix. These two are inverses of one another.

### Value

SE value(s) returned as a matrix when `details` is FALSE. When `details` is TRUE, a list of the SE value(s) and the full sampling covariance matrix.

### References

- https://en.wikipedia.org/wiki/Standard_error

### See Also

- `mxCI`

### Examples

    library(OpenMx)
    data(demoOneFactor)
    # ===============================
    # = Make and run a 1-factor CFA =
    # ===============================

    latents  = c("G") # the latent factor
    manifests = names(demoOneFactor) # manifest variables to be modeled
    # ===========================
    # = Make and run the model! =
    # ===========================
    m1 <- mxModel("One Factor", type = "RAM",
    	manifestVars = manifests, latentVars = latents,
    	mxPath(from = latents, to = manifests, labels=paste0('lambda', 1:5)),
    	mxPath(from = manifests, arrows = 2),
    	mxPath(from = latents, arrows = 2, free = FALSE, values = 1),
    	mxData(cov(demoOneFactor), type = "cov", numObs = 500)
    )
    m1 = mxRun(m1)
    mxSE(lambda5, model = m1)
    mxSE(lambda1^2, model = m1)

[Package OpenMx version 2.9.6 Index]