umx (version 4.9.0)

umxCI_boot: 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

• umxExpMeans(), umxExpCov()

Other Reporting functions: RMSEA.MxModel(), RMSEA.summary.mxmodel(), RMSEA(), extractAIC.MxModel(), loadings(), residuals.MxModel(), 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")
# }