mxSE {OpenMx} | R Documentation |
This function allows you to obtain standard errors for arbitrary expressions, named entities, and algebras.
mxSE(x, model, details = FALSE, cov, ...)
x |
the parameter to get SEs on (reference or expression) |
model |
the |
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 |
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.
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.
- https://en.wikipedia.org/wiki/Standard_error
- mxCI
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)