| umxCI_boot {umx} | R Documentation |
umxCI_boot
Description
Compute boot-strapped Confidence Intervals for parameters in an mxModel()
The function creates a sampling distribution for parameters by repeatedly drawing samples
with replacement from your data and then computing the statistic for each redrawn sample.
Usage
umxCI_boot(
model,
rawData = NULL,
type = c("par.expected", "par.observed", "empirical"),
std = TRUE,
rep = 1000,
conf = 95,
dat = FALSE,
digits = 3
)
Arguments
model |
is an optimized mxModel |
rawData |
is the raw data matrix used to estimate model |
type |
is the kind of bootstrap you want to run. "par.expected" and "par.observed" use parametric Monte Carlo bootstrapping based on your expected and observed covariance matrices, respectively. "empirical" uses empirical bootstrapping based on rawData. |
std |
specifies whether you want CIs for unstandardized or standardized parameters (default: std = TRUE) |
rep |
is the number of bootstrap samples to compute (default = 1000). |
conf |
is the confidence value (default = 95) |
dat |
specifies whether you want to store the bootstrapped data in the output (useful for multiple analyses, such as mediation analysis) |
digits |
rounding precision |
Value
expected covariance matrix
References
-
https://openmx.ssri.psu.edu/thread/2598 Original written by https://openmx.ssri.psu.edu/users/bwiernik
See Also
Other Reporting functions:
RMSEA.MxModel(),
RMSEA.summary.mxmodel(),
RMSEA(),
extractAIC.MxModel(),
loadings.MxModel(),
loadings(),
residuals.MxModel(),
tmx_show.MxMatrix(),
tmx_show(),
umxCI(),
umxConfint(),
umxExpCov(),
umxExpMeans(),
umxFitIndices(),
umxRotate()
Examples
## Not run:
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)
m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
umxPath("G", to = manifests),
umxPath(var = manifests),
umxPath(var = "G", fixedAt = 1.0)
)
umxCI_boot(m1, type = "par.expected")
## End(Not run)