xmuValues: Set values in RAM model, matrix, or path
For models to be estimated, it is essential that path values start at credible values.
xmuValues takes on that task for you.
xmuValues(obj = NA, sd = NA, n = 1, onlyTouchZeros = FALSE)
obj |
The RAM or matrix |
sd |
Optional Standard Deviation for start values |
n |
Optional Mean for start values |
onlyTouchZeros |
Don't alter parameters that have starts (useful to speed |
xmuValues can set start values for the free parameters in both RAM and Matrix mxModel()s.
It can also take an mxMatrix as input.
It tries to be smart in guessing starts from the values in your data and the model type.
note: If you give xmuValues a numeric input, it will use obj as the mean, and return a list of length n, with sd = sd.
mxModel() with updated start values
Core functions:
Other Advanced Model Building Functions:
umxAlgebra(),
umxFixAll(),
umxJiggle(),
umxRun(),
umxThresholdMatrix(),
umxUnexplainedCausalNexus(),
umx,
xmuLabel()
require(umx)
data(demoOneFactor)
latents = c("G")
manifests = names(demoOneFactor)
# ====================================================================
# = Make an OpenMx model (which will lack start values and labels..) =
# ====================================================================
m1 = mxModel("One Factor", type = "RAM",
manifestVars = manifests, latentVars = latents,
mxPath(from = latents , to = manifests),
mxPath(from = manifests, arrows = 2),
mxPath(from = latents , arrows = 2, free = FALSE, values = 1.0),
mxData(cov(demoOneFactor), type = "cov", numObs=500)
)
mxEval(S, m1) # default variances are jiggled away from near-zero
# Add start values to the model
m1 = xmuValues(m1)
mxEval(S, m1) # plausible variances
umx_print(mxEval(S,m1), 3, zero.print = ".") # plausible variances
xmuValues(14, sd = 1, n = 10) # Return vector of length 10, with mean 14 and sd 1Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.