umxCI_boot: umxCI_boot

In tbates/umx: Structural Equation Modeling and Twin Modeling in R

umxCI_boot

Description

Compute boot-strapped Confidence Intervals for parameters in an OpenMx::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

See Also

• umxExpMeans(), umxExpCov()

Other Reporting functions: RMSEA(), RMSEA.MxModel(), RMSEA.summary.mxmodel(), extractAIC.MxModel(), loadings(), loadings.MxModel(), residuals.MxModel(), tmx_show(), tmx_show.MxMatrix(), 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)

tbates/umx documentation built on Dec. 14, 2024, 11:28 a.m.