mxComputeConfidenceInterval {OpenMx}

Find likelihood-based confidence intervals

Description

There are various equivalent ways to pose the optimization problems required to estimate confidence intervals. Most accurate solutions are achieved when the problem is posed using non-linear constraints. However, the available optimizers (CSOLNP, SLSQP, and NPSOL) often have difficulty with non-linear constraints.

Usage

mxComputeConfidenceInterval(
  plan,
  ...,
  freeSet = NA_character_,
  verbose = 0L,
  engine = NULL,
  fitfunction = "fitfunction",
  tolerance = NA_real_,
  constraintType = "none"
)

Arguments

plan

compute plan to optimize the model

...

Not used. Forces remaining arguments to be specified by name.

freeSet

names of matrices containing free variables

verbose

integer. Level of run-time diagnostic output. Set to zero to disable

engine

fitfunction

the name of the deviance function

tolerance

constraintType

one of c('ineq', 'none')

References

Neale, M. C. & Miller M. B. (1997). The use of likelihood based confidence intervals in genetic models. Behavior Genetics, 27(2), 113-120.

Pek, J. & Wu, H. (2015). Profile likelihood-based confidence intervals and regions for structural equation models. Psychometrika, 80(4), 1123-1145.

Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior genetics, 42(6), 886-898.